RECENT PROGRESS IN CFD FOR NAVAL … progress in cfd for naval architecture and ocean engineering...

Proceedings of the 11th

International Conference on

Hydrodynamics (ICHD 2014)

October 19 – 24, 2014

Singapore

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Copyright © 2014 by ICHD

RECENT PROGRESS IN CFD FOR NAVAL ARCHITECTURE AND OCEAN

ENGINEERING (KEYNOTE SPEAKER)

FREDERICK STERN, ZHAOYUAN WANG, JIANMING YANG, HAMID SADAT-HOSSEINI, MAYSAM

MOUSAVIRAAD, SHANTI BHUSHAN*, MATTEO DIEZ†, SUNG-HWAN YOON, PING-CHEN WU, SEONG MO

YEON, TIMUR DOGAN, DONG-HWAN KIM, SILVIA VOLPI, MICHAEL CONGER, THAD MICHAEL, TAO XING‡,

ROBERT S. THODAL§ AND JOACHIM L. GRENESTEDT**

IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, IA, USA

An overview is provided of CFDShip-Iowa modeling, numerical methods and HPC, including both current V4.5 & V5.5 and

next generation V6. Examples for naval architecture highlight capability and needs. High fidelity V6 simulations for ocean

engineering and fundamental physics describe increased resolution for analysis of physics of fluids. Uncertainty quantification

research is overviewed as first step towards development stochastic optimization.

1. Introduction

CFD capabilities continue to advance at ever-faster speed and ever-more-impressive accomplishments, as

recently reviewed for ship hydrodynamics by Stern et al. (2013). None-the-less CFD is slow in its adaptation by

industry since most users are at universities and R&D laboratories (ITTC, 2011). However, slowly-but-surely

CFD is transforming engineering design as the build-and-test design spiral approach transforms to the SBD

approach offering innovative out-of-the-box 21st century design concepts with improved safety, energy and

economy. First generation SBD capability has focused more on functionality than high fidelity and exascale

computing requiring significant advancements to achieve the next generation SBD capability for fully resolved,

fully coupled, sharp-interface, multi-scale, multi-phase, multi-disciplinary turbulent ship flow including fluid

structure interactions and utilizing billions of grid points.

Herein, recent progress in CFD for naval architecture and ocean engineering is overviewed based

specifically on CFDShip-Iowa URANS/DES toolbox, as an example of the current state-of-the-art. The

emphasis is on the latest research since Stern et al. (2013). For a more complete list of references with regard to

the development and applications of CFDShip-Iowa URANS/DES toolbox within the field of computational

ship hydrodynamics, the readers are referred to Stern et al. (2013). Iowa science and technology paradigm for

the development of the SBD capability is described. An overview is provided of CFDShip-Iowa modeling,

numerical methods and HPC, including both current V4.5 & V5.5 and next generation V6. Examples for naval

architecture highlight capability and needs. High fidelity V6 simulations for ocean engineering and fundamental

physics describe increased resolution for analysis of physics of fluids. Uncertainty quantification research is

overviewed as first step towards development stochastic optimization. Recent progress deterministic and

stochastic optimization research is not reviewed herein since recently provided by Campana (2013).

2. Paradigm for Development SBD for Ship Hydrodynamics

Rapid advancements in simulation technology are revolutionizing engineering practice, as SBD and ultimately

virtual reality are replacing current reliance on experimental observations and analytical methods. It is expected

that a major shift in how scientific method forms its basis of conceptual truth, a shift from reliance on

observations, based on experiments, to reliance on logic, based on simulations supported by experiments. SBD

* Currently at Center for Advanced Vehicular Systems, Mississippi State University, Starkville, MS 39759 † Also affiliated CNR-INSEAN, Rome, Italy ‡ Currently at Department of Mechanical Engineering, University of Idaho § Lehigh University, Bethlehem, PA, 18015 ** Lehigh University, Bethlehem, PA, 18015

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covers a broad range from computerized systems based methods to solutions of physics based initial boundary

value problems (IBVP). Present interest is in solutions of physics based IBVP for ship hydrodynamics. SBD for

ship hydrodynamics merges traditional fields of resistance and propulsion, seakeeping, maneuvering, open-

ocean and littoral environmental effects, and offers new opportunities for future ships to meet challenges of the

21st century. Development SBD involves new paradigm for hydrodynamics research in which CFD,

experimental fluid dynamics (EFD), and uncertainty analysis (UA) are conducted simultaneously for benchmark

geometries and conditions using an integrated approach along with optimization methods, all of which serve as

internal engine guaranteeing simulation fidelity. International collaborations with other research institutions and

organizations include participation in ITTC and NATO AVT working groups and naval engineering educational

consortium (NEEC), organizing international CFD workshops and current NICOP projects. Those activities are

mutual-beneficial and magnifying individual institute capabilities, which has been foundational in the

unprecedented achievements of computational ship hydrodynamics.

3. CFDShip-Iowa URANS/DES/LES SBD Toolbox

CFDShip-Iowa is general-purpose CFD simulation software developed at the University of Iowa’s IIHR—

Hydroscience & Engineering for support of student thesis and project research as well as transition to Navy

laboratories, industry, and other universities. CFDShip-Iowa has been a leading ship hydrodynamics CFD code

for over 20 years, which has been verified and validated for many applications in ship flows. The current

versions include CFDShip-Iowa V4.5, V6.1, and V6.2, with V5.5 and V6.3 under development.

3.1. V4.5 & 5.5 modeling, numerical methods, HPC

CFDShip-Iowa V4.5 is an incompressible URANS/DES solver designed for ship hydrodynamics (Huang et al.,

2008). The equations are solved in either absolute or relative inertial non-orthogonal curvilinear coordinate

system for arbitrary moving but non-deforming control volumes. Turbulence models include blended k–/k–

based isotropic and ASM (Algebraic Stress Model) based anisotropic RANS, and DES approaches with near-

wall models or wall functions. A single-phase level-set method is used for free-surface capturing. Captive, semi-

captive, and full 6DOF capabilities for multi-objects with parent/child hierarchy are available. The fully

discretized propeller or body-force propeller model can be employed for propulsion. The water-jet propulsion

can be included using actual water-jet with detailed simulation of the duct flow or water-jet model with the

reaction forces and moments. Incompressibility is enforced by a strong pressure/velocity coupling, achieved

using either PISO or projection algorithms. The fluid flow equations are solved in an earth-fixed inertial

reference system, while the rigid body equations are solved in the ship system. Other modeling capabilities

include semi-coupled two phase air/water modeling, environmental waves and winds, bubbly flow, and fluid-

structure interaction.

Numerical methods include finite difference discretization on body-fitted curvilinear grids, with high order

upwind schemes for the convection terms and second-order centered for the viscous terms. The temporal terms

are discretized using a second-order backward difference Euler scheme. Since the solver is designed for high-

Reynolds number flows, the transport and re-initialization equations are weakly elliptical and thus pentadiagonal

line solvers in an alternate-direction-implicit (ADI) scheme are used. A MPI-based domain decomposition

approach is used, where each decomposed block is mapped to one processor. The resulting algebraic equation is

solved with the PETSc toolkit using block Jacobi incomplete factorization (ILU) pre-conditioners and bi-

conjugate gradients stabilized (BCGSL). All equations of motion are solved in a sequential form and iterated to

achieve convergence within each time step.

Extension of CFDShip-Iowa Version 4.5 to Version 5.5 with a fully coupled two-phase flow solver using

the Volume-of-Fluid (VOF) method is in progress. The approach includes implementing the highly accurate

geometric VOF interface tracking method developed for V6, developing fully-coupled two-phase flow solver,

implementing cavitation and mixture models for air/water/vapor three-phase interaction, and developing

capabilities for the necessary applications. The numerical methods, HPC, and SBD functional areas are similar

to Version 4.5.

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3.2. V6.1, 6.2 & 6.3 modeling, numerical methods, HPC

The next-generation high-fidelity SBD tools, CFDShip-Iowa V6, are already under development for milestone

achievement in increased capability focusing on orders of magnitude improvements in accuracy, robustness, and

exascale HPC capability.

In Version 6.1, Cartesian grids are used with immersed boundary methods for complicated geometries

(Yang and Stern, 2009), and the level set based ghost fluid method is used for sharp interface treatment and fully

two-phase coupling with the VOF method for interface tracking. Extension to orthogonal curvilinear grids was

made in V6.2 (Wang et al., 2012a) with enhanced technologies for the interface modeling (Wang et al., 2012b,

c) and similar numerical methods and HPC capabilities as V6.1.

A finite-difference method is used to discretize the governing equations on a non-uniform staggered grid, in

which the velocity components are defined at the cell face centers. All other variables are defined at the cell

centers. Time advancement is based on the semi-implicit four-step fractional step method. The diagonal

diffusion terms are advanced with the second-order Crank–Nicholson method and the other terms by the second-

order explicit Adams–Bashforth method. The pressure Poisson equation is solved to enforce the continuity

equation. The convective terms are discretized using the fifth-order WENO scheme. The other terms are

discretized by the second-order central difference scheme. The pressure Poisson equation is solved using a semi-

coarsening multi-grid solver from the HYPRE library.

The code is parallelized via a domain decomposition (in three directions) technique using the MPI library.

All inter-processor communications for ghost cell information exchange are in non-blocking mode. Parallel I/O

using MPI2 have been implemented such that all processors read from and write to one single file

simultaneously (Yang et al., 2008). In order to speed up the computations and improve the accuracy and

efficiency for very large grid simulations (billions of grid points), some enhanced technologies have been

implemented such as semi-Lagrangian advection schemes and optimized memory usage. The water/air interface

is extracted as PLY polygon file format for post-processing. A multi-block grid capability has been recently

incorporated into CFDShip-Iowa Version 6.2.

Development of the general curvilinear grid solver, V6.3, is in progress, which is built on the success of

V6.1 and V6.2 to achieve all functionalities of V4.5 and beyond. CFDShip-Iowa V6.3 is aimed at the high-

fidelity, high-resolution simulations of fully coupled, multi-scale, multi-phase, turbulent ship flows with fluid-

structure interactions utilizing billions of grid points. The approaches include finite volume method, multi-

block, body-fitted, general non-orthogonal curvilinear structured grids, overset background Cartesian grids, and

highly modularized, developer-friendly code structure written in Modern Fortran (2008) and MPI.

The second-order finite volume method with accurate geometric approximations for non-smooth, non-

orthogonal structured grids is used for the discretization. A generic transport equation is solved for momentum

components and scalars with central difference and high-order upwind schemes used for face-centered value

reconstruction. Exact projection method is implemented for machine-accuracy mass conservation where central

difference and high-order upwind schemes for contra-variant volume flux reconstruction at cell face centers.

Scalable MPI communication using new MPI-3 features will be implemented and MPI sub-array data type is

extensively utilized for scalable MPI communication and I/O in V6.3.

4. Naval Architecture

4.1. Resistance and seakeeping, captive and free running maneuvering, free running course keeping,

and intact and damaged stability

Resistance and seakeeping predictions are included in Gothenburg 2010 (G2010) and upcoming Tokyo 2015

(T2015) workshops. Prediction of resistance is the oldest application of CFD in ship hydrodynamics and its

accuracy has been significantly improved since Gothenburg 1980 (G1980), the first CFD workshop held in

1980. In G2010, 89 submissions of resistance prediction are documented, which is the largest number in the

workshop series (Larsson et al., 2014). More than 90% of the simulations were conducted using grids smaller

than 10M points. The resistance prediction simulations were carried out for a wide range of applications and

conditions. Other than resistance, sinkage and trim, local flow fields such as boundary layer and wake, and wave

patterns were also predicted by many simulations. Different geometries including tankers, container ships, and

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surface combatants were studied at a range of very small to large Froude numbers (Fr). The simulations showed

average error of 3.3% for resistance for both low and high Fr while sinkage and trim showed less errors for high

Fr. The average error for sinkage/trim at low and high Fr was 9.7% /11% and 35%/55%, respectively. For

seakeeping, several seakeeping test cases were included in G2010 with numerous contributions for each case.

CFD computations of seakeeping have been rapidly increasing since Tokyo 2005 CFD workshop (T2005) in

which there was only one forward-speed diffraction case with no motions. The applications for seakeeping

predictions included a wide range of wave conditions, Froude numbers, and motion conditions. Similar to the

resistance test cases, different geometries including tankers, container ships, and surface combatants were

studied. Grid sizes ranging from 0.4 M to 71 M points were used with a clear trend toward increasing accuracy

with grid size. The CFD predictions are assessed separately for 1st order vs. 2nd order terms. The mean value of

resistance and the amplitude of motions were considered 1st order terms whereas the amplitude of resistance and

mean value of motions were considered 2nd

order terms. The simulations showed large average error for the

second order terms (44%D) while the average error was less than 15% for the first order terms.

Captive and free running maneuvering simulations are included in the SIMMAN 2008 workshop (Stern et

al., 2011) and upcoming SIMMAN 2014. The applications for captive predictions included PMM-type forced

motions such as static rudder, static drift, pure sway, pure yaw, and yaw & drift conditions for different

geometries. For SIMMAN 2008, 16 submissions were received for the forced motion simulations, comprising

different CFD-based methods such as RANS, URANS, and DES. Grid sizes ranging from 2.1 M to 250 M

points were used. It was concluded that finer grids were needed especially for the rudder and appendages and in

regions of large vortices, as well as more advanced turbulence and propeller models for improvements in the

CFD predictions of static and dynamic PMM maneuvers. Overall, the average error for captive maneuvering

simulation was 13.6%D. The largest error values were generally observed for pure yaw and static rudder

simulations. For linear derivatives, the average error was much larger for yaw moment (40%D) than sway force

(15%D). For nonlinear derivatives, the average error value was about 40%D. Free running maneuvering

simulations were reported for limited cases in the SIMMAN 2008 workshop. The maneuvering simulation

included standard maneuver test cases such as turning circle and zigzag. The results showed 6%D error for

trajectories for turning maneuver prediction while larger errors (13%D) were obtained for zigzag maneuver. The

grid sizes were from 0.4M to 14.9 M points for these simulations. For most SIMMAN 2008 computations, the

propulsion was implemented as an axisymmetric body force distributed in the propeller disk. The body force

was specified in a non-iterative manner in which the ship wake on the body force was neglected. Recently, Wu

et al. (2013) used Yamasaki propeller model coupled with the RANS code to give a model that interactively

determines propeller-hull interaction without requiring detailed modeling of the propeller geometry. Yamasaki

model is based on a potential theory formulation, in which the propeller is represented by bound vortex sheets

on the propeller disk and free vortices shed from them downstream of the propeller. Wu et al. (2013) showed the

Yamasaki propeller model could predict successfully the asymmetric wake field. In addition, the propeller rpm

was predicted with less than 0.5% error for Yamasaki compared to 12% for non-iterative axisymmetric body

force. Free running simulations are also conducted with more advanced propulsion system such as water-jet.

Sadat-Hosseini et al. (2013) performed maneuvering simulations for a catamaran and validated the results

against the experimental data (See Fig. 1). The simulations were conducted either for bare hull with integral

force models for water-jet or with actual water-jet with body force impeller defined by pump curves. Turning

maneuver simulations showed average error of 9-22.6%D for CFD simulations with minimum error for the

actual water-jet simulation. Zigzag maneuvers showed larger errors. In addition, the extremely large overshoot

angles in zigzag showed the deficiency of water-jet propulsion system for maneuvering. Since CFD is

computationally expensive for maneuvering in comparison to system based (SB) methods, some studies have

focused on improving the SB mathematical model by using CFD with system identification methods. Araki et

al. (2013) employed CFD free running outputs to improve a 4DOF mathematical model developed for

maneuvering in calm water and following waves. The CFD predictions were first validated against the

experimental data from different facilities including IIHR wave basin (Sanada et al., 2013&2014). For calm

water, it was shown that the average system based prediction error drops from 16% to 8% using the

maneuvering coefficients and rudder forces estimated from CFD free running instead of those from captive

experiments. For waves, Araki et al. (2013) showed that the mathematical model with wave loads estimated

from CFD outputs provide better prediction for maneuvering in moderate following and quartering waves,

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compared to the original mathematical model with the wave loads computed from slender body theory.

However, the improved mathematical model was too stable in severe waves and unable to predict the

instabilities such as periodic motion or broaching. For upcoming SIMMAN2014 workshop, Sadat-Hosseini et

al. (2014a) conducted simulations for free running maneuvers of KVLLCC2 in calm water using body-force

propeller model and actual propeller (see Fig. 2). The grid size was 6.8M-8.4M for different cases. The

computational cost was 3-5 times higher for the simulations with the actual propeller. The results for turning

maneuver showed E=6.6%D using propeller model and much less error (E=2.2%D) using actual propeller.

Similarly, zigzag simulations showed better prediction using actual propeller. Sadat-Hosseini and Stern (2014)

performed maneuvering simulations for 5415M test cases of SIMMAN 2014 using twin counter-rotating

propellers based on body-force propeller model with total grid size of 6.7M points (see Fig. 3). The results

showed about E=12%D for turning and zigzag 2020 while larger errors were shown for zigzag1010. In addition,

Sadat-Hosseini and Stern (2014) conducted system-based simulations for 5415M maneuvering in calm water.

The maneuvering coefficients were found from system identification using CFD outputs. To estimate the

coefficients, parallel processing technique was used in which CFD free running data for several turning and

zigzag maneuvers were first combined and then used to estimate one set of maneuvering coefficients. The

system based predictions showed an average error of 5.30, 12.64 and 4.67%D for trajectories for turning 35,

zigzag 1010 and 2020, respectively.

Among free running maneuvering simulations, there are very limited studies on local flow. Recently, Sadat-

Hosseini et al. (2014b) studied DES predictions of the local flow including transom wave field and vortex

structures in turning maneuver. Similar study was previously conducted only for straight-ahead condition

(Bhushan et al., 2012). The mean and unsteadiness of transom wave field were predicted with 9% and 11%D

error while the trajectories were predicted with <3%D. The asymmetry of mean wave field was significantly

under predicted due to surprisingly large asymmetry of EFD data. The unsteadiness spectra at few points in the

transom wave field showed f-1.5

scaling. The resolved turbulence kinetic energy was 86% in the transom region.

The simulations showed Karman-like instability at transom, horseshoe vortices at the juncture of strut-hull and

strut-shaft, and shear layer instability at the strut-hull intersection. Figure 4 shows the predicted transom wave

field and vortex structures. Compared to straight-ahead condition, the Karman-like frequencies were 3% higher

while others were 8-35% lower for turning. In addition, the predicted frequency for Karman-like, horseshoe and

shear layer vortex shedding in turning showed 2.4%, 3.7-7.7% and 8.6% asymmetry, respectively.

There are few simulations conducted to investigate free running course keeping and instability. Stern and

Toxopeus (2013) and Sadat-Hosseini et al. (2014c) performed course keeping simulations in calm water, regular

and irregular waves for the fully appended 5415M ship hull, in collaboration with NATO AVT 216 Evaluation

of Prediction Methods for Ship Maneuvering and Control. The results were validated against the experiments

not only for the ship motions but also for the loads on the appendages. The results showed good prediction for

the trajectories and loads on the appendages (<10%D) even for very complex geometries with dynamic

stabilizer and rudders (see Fig. 5). Comparing the irregular wave results with the results computed from regular

wave simulations at several discrete wavelength conditions showed that the ship has similar motion in both

regular and irregular waves with same wavelength condition. The course keeping simulations focusing on intact

instability are summarized in Stern et al. (2013), showing good prediction for different instabilities including

parametric roll, broaching and capsize, surf-riding, and periodic motion. For damaged stability, Sadat-Hosseini

et al. (2014d) showed good prediction for both ship motions and water heights inside the compartment for

damaged ship in calm water and waves.

Overall, free running simulations have been increasing in past few years and it is expected that the future

challenges and method development efforts for modelling, numerical methods and HPC will focus on free

running rather than captive simulations. In addition, more research will focus on improving the SB mathematical

model by using CFD since CFD is computationally expensive in comparison to SB methods.

4.2. Turbulence

Prediction of turbulent viscous flow for ship hulls is of central importance and focused topic at CFD Workshops

since G1980 to most recent G2010. Verification and validation of CFD predictions have been performed for

tanker KVLCC2, container KCS and surface combatant 5415 hull forms at straight ahead conditions. In in

recent workshop extensive local-flow analysis was performed for KVLCC2 (bluff body) and 5415 (slender

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body) focusing on the effect of turbulence modeling. URANS with anisotropic turbulence model perform better

than isotropic model. For KVLCC2, URANS under predicted axial velocity and vortical strength by 10% and

over predicted turbulent structures by 35%, when compared with the experimental data. DES predicted unsteady

flow with up to 95% resolved turbulence. DES mean flow predictions were quantitatively comparable to that of

URANS, but were over predictive for both velocity and vortical and turbulent structures. DES showed grid

induced separation inside the boundary layer and modeled stress depletion, and the former was resolved by

using delayed DES approach, whereas the latter issue was unresolved. For 5415, URANS provided reasonably

good agreement with the experimental data, but under predicted the vorticity magnitude and boundary layer

bulge, and over predicted turbulent structures at nominal wake plane. In DES, the resolved TKE levels were less

than 3%, thus the results were unacceptable. Nonetheless, provided for the first time plausible description of the

overall vortex structures, and helped in understanding the sparse experimental data. Overall firm conclusions

were not possible since grid and turbulence modeling errors could not be separated and sparseness of

experimental data, especially for turbulence variables and onset and progression of 5415 vortices.

NATO AVT-183: Reliable Prediction of Separated Flow Onset and Progression for Air and Sea Vehicles

research effort for the sea facet focused on procurement of detailed experimental data using PIV techniques

(Yoon et al., 2014; Maksoud et al., 2014; Broglia et al., 2014), and evaluation and validation of CFD predictions

using different codes by NATO members. The study focused on three ship hulls: KVLCC2 at static drift = 30

deg; 5415 with bilge keels at straight ahead and = 20 deg; and Delft Catamaran at static drift conditions. Note

that for 5415 cases, EFD data were procured for both planar sections and volumes surrounding the primary

vortices. This allowed evaluation of Q-criteria along the vortex, which enabled validation for the vortex core

predictions for the first time. Validation of 5415 case has been largely completed, and discussed below.

CFDShip-Iowa simulations for the 5415 cases were performed using anisotropic URANS and DES models

using finest adaptive grids to date, to reduce grid errors. In both the cases, URANS results do not improve when

the grid is refined beyond 50M points. The best URANS predictions showed excessive decay of the vortices as

shown in Fig. 6, and resulted large errors for the progression of the vortices as shown in Fig. 7(a), when

compared with experimental data. Considering that the results did improve with grid refinement, the large errors

in URANS predictions were attributed to modeling errors. DES predictions for the straight-ahead case showed

very low resolved turbulence levels, similar to G2010. For the static drift case, DES predictions improved with

grid resolution. On the finest 84M grid, the resolved turbulence levels were >95%, and the flow predictions

compared better with experimental data than those obtained using URANS. However, as shown in Fig. 7(b),

they predicted stronger vortex strength at onset and weaker vortex strength downstream. Note that the large

errors could be partly due to grid resolution issues. CFD submissions using other codes were mostly using

URANS, and one submission for the straight ahead case was using DES. The URANS results from other codes

were very similar to that of CFDShip-Iowa for the static drift case. However for the straight ahead case, solvers

predicted different decay rate of the vortices for similar size grids. The differences could be due to differences in

numerical methods, grid topologies or turbulence model implementation, which needs to be investigated. The

DES submission for the straight-ahead case, showed significantly high vortex strengths than experiments,

similar to CFDShip-Iowa prediction, affirming the limitations of DES models.

Vortex onset and separation in the straight ahead case was identified due to open-type cross flow

separation, wherein the vortex separates from the surface due to the presence of adverse axial pressure gradient

along converging streamline, and is identified from the peak of div(w). The vortex separation patterns for the

static drift case included both open-, closed- and open-closed type separations. The separation pattern and

topology were consistent with those available in the literature. The closed-type separation satisfied the

topological rules expected for a close-separation formed over an isolated body or body intersecting a wall or

free-surface.

Overall, turbulence modeling is a roadblock for improved prediction of viscous flow for ship hulls, as

URANS is too dissipative and DES has limitations for both slender and bluff bodies. For the slender body at

straight ahead condition, DES fails to trigger resolved turbulence. For slender bodies at static drift and bluff

bodies at both straight ahead and static drift conditions, DES predicts sufficient resolved turbulence levels and

the predictions are better than that of URANS, but shows large comparison errors for the progression of the

vortices probably due to modeled stress issue. LES are ideal for accurate CFD predictions, as they have less

dependence on modeling; however, they are prohibitively expensive due to grid resolution requirements in the

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boundary layer. Hybrid RANS/LES models provide a reasonable alternative, wherein URANS is used in the

boundary layer and LES in the wake. However, more advance hybrid RANS/LES models should be investigated

to address the DES modeling issues. The research should particularly focus on investigation of: turbulence

trigger models to enable transition from RANS to LES for slender body simulations; blended RANS-LES

models as they include explicit LES modeling that are more rigorously validated than the LES mode of single

parameter models, such as DES; and physics based RANS-LES blending rather than grid based blending to

address modeled stress depletion issues.

4.3. Ship-Ship Interaction

CFD computations of ship-ship interaction have been reported in the recent International Conference on Ship

Maneuvering in Shallow and Confined Water. Mousaviraad et al. (2014a) used CFDShip-Iowa to study Hope

and Bobo in replenishment condition in calm water and waves, and in overtaking maneuver in waves. The

average error against EFD was 21%D for calm water, and 10%D for replenishment in waves. The sheltering

effect was significant for oblique waves, with 105% difference between mid-mid and mid-bow configuration.

The separation distance effect was more important for head waves than oblique waves, being 43% and 23%

respectively. During the overtaking, the interaction effect decreases motions and increases sway forces, roll and

yaw moments, being more significant for the smaller vessel. Sadat-Hosseini et al. (2011) and Wu et al. (2013)

investigated the interaction between two different tankers Aframax and KVLCC2 using CFDShip-Iowa. The

ships were free to heave and pitch advancing in shallow water with same speed and fixed separation distance.

Both ships were appended with rudder and operating propellers, which were modelled by axisymmetric body

force propeller model with same RPM as experiments. Overall, the simulations showed large errors for

predicted forces, moments and motions compared with the experimental data. Later, it was found the

longitudinal positions of the two ships in the experiments were not reported correctly. Therefore, the simulations

were repeated with the revised conditions but the errors were still large and thus more studies should be

conducted to evaluate the experimental setup. In addition, the accuracy of the axisymmetric body-force

propeller model for propulsion in shallow water should be investigated.

4.4. Advanced hull forms and fluid-structure interaction: ACV/SES, WAM-V, planing hulls

CFD studies of advanced hull forms impose significant challenges due to complex and multi-disciplinary

modeling requirements, very high speeds introducing different physics than conventional ships, and difficulties

in validation studies due to limitations in model testing and limited measurements in sea trials. Modeling

requirements are different for specific hulls, e.g. fluid-structure interactions (FSI) including multi-body

dynamics (MBD) for suspension systems and finite element (FE) modeling for flexible hulls.

ACV/SES capabilities are implemented in CFDShip-Iowa including cushion models, seal models, air-flow

over the above water seals and superstructure, decoupled cushion cavity flow, waterjet propulsion with side

forces and yaw moments induced by nozzle rotations and reverse buckets, and air-fan propulsion model.

Validation simulations are carried out for a combined SES/ACV ship (T-Craft) for captive tests in deep and

shallow water. Free-running simulations of T-Craft in turning and zigzag maneuvers in deep and shallow water

and in calm water and waves are also carried out. Recent analyses showed that the resistance and moment due to

cushion pressure distribution inside the cavity is significant for seakeeping cases while not considered in the

initial simulations. The improved results will be published for captive validation studies and free-running

demonstration simulations.

The Wave Adaptive Modular Vessel (WAM-V) is an ultra-light flexible catamaran that conforms to the

surface of the water through a collective suspension and is modularly designed enabling a wide variety of

applications. The springs, shock absorbers, and ball joints articulate the vessel such that the hulls can move

semi-independently and along with the inflatable pontoons adapt to the water surface/waves to mitigate

structural stresses and reduce drag. WAM-V capabilities are implemented in CFDShip-Iowa including: LS_IBM

(level-set immersed boundary) method for treatment of the gap between pontoon and hinged pod, a two-body

dynamics model for hinged pod motions, and a jet force model moving with hinged pod for free-running

simulations. Captive calm water verification and validation studies are carried out with average error of 5.7%D

(Mousaviraad et al., 2013a). Validation against full-scale sea trial data and coupling with MBD modeling are

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carried out in collaboration with Prof. Mehdi Ahmadian of Virginia Tech University. Free-running validation

studies are carried out against sea trial data in calm water and seas (Conger et al., 2014). For simulations in

waves, statistical analysis of the sea trial data in waves is conducted to provide an estimate of the dominant

encounter frequency. CFD regular head waves simulations (Fig. 8) are carried out at the dominant encounter

frequency and with a wave height over wavelength of H/λ=1/64, the typical value for sea state 3. The results are

compared with sea trial data in Fig. 9 and show that although the EFD data have large peaks, their standard

deviation (SD) values converge to values very close to CFD. The CFD regular head wave results are then used

by Virginia Tech University as inputs to run a 2-post shaker rig testing and a SIMULINK virtual shaker rig

modeling and the results for the payload suspension motions are shown in Fig. 10 with very good agreement.

MBD modeling for the suspended payload is carried out for a 2DOF cylinder drop as a first step to WAM-V

suspended payload modeling. The SIMULINK MBD code is coupled with CFDShip-Iowa in 1-way weak

coupling and 2-way strong coupling approaches and the results are shown in Fig. 11. The 2-Way coupling

results show significant improvement over 1-Way results: for the un-sprung mass displacement the initial slope

after the pontoon hits the water free surface is more accurately predicted, the double hump at the first peak is

predicted, and the frequency of occurrence is maintained correctly through the displacement curve; for the

sprung mass displacement, 2-Way results follow the EFD displacement curves both in magnitude and

frequency, especially for the first second, while after 1 second the sprung mass displacement is slightly over-

predicted. Overall the results are validated with acceptable agreement. Future work will couple the CFDShip-

Iowa and the MBD model for WAM-V, and perform validation studies against measurements of the

motions/accelerations of the suspended payload during the full-scale sea trials.

Planing hull capabilities including hydrodynamic performance and structural loads and slamming are

implemented and validated for calm water, regular waves, and irregular waves for the Fridsma geometry

(Mousaviraad et al., 2013b; Mousaviraad et al., 2014b). CFD and EFD studies are carried out to validate the

hydrodynamic forces, moments, hull pressures, accelerations, motions, and the multiphase free-surface flow

field generated by the USNA planing craft at high-speed (Fr=1.8 - 2.1) in calm water and regular and irregular

waves (Fu et al., 2014). The work is conducted by collaborations with Carolyn Judge of United States Naval

Academy (USNA). CFDShip-Iowa simulations for calm and regular waves were carried out blind, before EFD

data was available. Calm water spray root at Fr=1.83 is compared in Fig. 12 with underwater surface photo from

EFD indicating very close agreement. CFDShip-Iowa V5.5 simulations with volume of fluid free surface solver

showed negligible effects on resistance and motions, while the extension of the jet spray flow was resolved

better than V4.5 level-set solver. Regular wave results for USNA experiments (Run 43 and 44) and CFD

simulations using CFDShip-Iowa V4.5 and NFA solvers are compared in Fig. 13 for motions and slamming

pressure. The phase of the heave and pitch is well predicted, while the amplitude of the numerical simulations is

greater than measured experimentally. Pitch motions at twice the lowest frequency are not evident in

simulations performed using either CFDShip-Iowa or NFA. Single point pressure measurements show good

agreement for slam duration while the re-entering pressure amplitudes are under-predicted for both codes. A

smaller time step may be needed to capture the peak pressure. The emerging peak pressures are missed in NFA

simulations while captured in CFDShip with close agreement. Irregular waves simulations are validated with

good agreement in terms of expected values and standard deviations of motions, accelerations, and slamming

pressures. Slamming statistical studies are carried out for both experimental data and simulation results and

validation results are shown in Fig. 14 for slamming pressure. Extreme slamming events are studies both for

EFD and CFD by examining the standard score for re-entering pressure ( = ( ) ⁄ ). For EFD, 4 slam

events with >2 and 6 events with 1< <2 are detected. These events are found to correlate with ship motions,

namely the vertical velocity of the ship bow at the time of impact. CFD studies are carried out to provide further

insight by correlating the extreme slam events with relative bow/wave motions as well as history of previous

zero crossing waves. The CFD extreme events are grouped in 3 categories: >1.5 (3 events), 1< <1.5 (4

events), and 0< <1 (14 events). For each slam event, wavelength over ship length (λ/L) and wave height over

wavelength (H/λ) values for the immediate wave, as well as averaged values for the last 2, 3, 4, and 5 waves are

calculated. In group 1, slam pressures correlate 100% with smaller λ/L and larger H/λ for the last 3 waves. For

groups 2 and 3, strongest correlations are for larger H/λ averaged over the last 2 and 3 waves, respectively.

Considering all the slams in all 3 groups, strongest correlation is found for smaller λ/L from the last 3 waves and

9


larger H/λ from the last 2 waves. Type-2 slams characterized by containing only one pressure peak (re-entering

pressure) with smaller peak values and shorter duration are identified both in EFD and CFD.

Fluid-structure interactions (FSI) studies (Volpi et al., 2015) are carried out for the Numerette planing hull

(slamming load test facility at Lehigh University) to provide a better understanding of slamming using

benchmark full-scale validation EFD data. The studies are conducted in collaboration with Dr. Joachim

Grenestedt of Lehigh University. Initially rigid body CFD simulations are conducted for both bare hull and

appended hull with sterndrive unit and body-force propeller model excluding the superstructure. The predicted

motions and loads are used for one way coupling with FE model for composite bottom panels to evaluate

displacement, strain, and stress. CFDShip-Iowa is used for CFD simulations and the commercial FE code

ANSYS is used as structural solver. Studies are carried out in calm water (Fr=0.7) and different regular head

waves conditions at Fr=0.7, 2.24 and 2.9. CFD/FE results show good prediction for displacement, strain, and

stress distribution for both starboard and bottom panels. Fig. 15 shows the panel force and displacement for a

regular head wave simulation with Sea-State 3 most probable wave conditions at Fr=0.7. Fig. 16 shows EFD

and CFD-FE strain for a regular head wave simulation with sea state 3 most probable wave condition at Fr=2.9.

Two-way coupling will be implemented by first using modal analysis with added mass modeling, and then fully

coupled CFD-FEA. FSI V&V studies are also planned for slamming loads on Athena semi-planing hull.

5. Ocean Engineering

Simulations of 3D unsteady separation (vortex shedding) around offshore structures and wave run-up induced

by ocean waves are still challenging for ocean engineering applications. Recently, the capabilities of state-of-

the-art CFD codes for vortex shedding and wave run-up are investigated in ITTC ocean engineering workshop

held in Nantes, France October 17-18, 2013. The capabilities of CFDShip-Iowa V4.5 and V6.2 for these

applications are reported in Yeon et al. (2013) and Yoon et al. (2013). The studies focused on the flow around

single/multiple cylinder(s), a typical geometry for both applications.

5.1. Single- and two-phase vortex shedding

In Koo et al. (2014) the two-phase turbulent flow past an interface-piercing circular cylinder was studied

using large-eddy simulation with a Lagrangian dynamic subgrid-scale model. It was shown that the air-water

interface makes the separation point more delayed for all regimes of Re and the air-water interface structures are

remarkably changed with different Froude numbers. However, the deep flow did not display the correct single-

phase flow behavior due to the deficient grid resolution and non-conservative convection scheme, among other

issues, employed with CFDShip-Iowa V6.2. Yeon et al. (2013) conducted a detailed study of the single-phase

vortex shedding around a circular cylinder for ITTC ocean engineering workshop test cases. The simulations are

conducted using CFDShip-Iowa V6.2, covering sub- to super-critical Re. A careful verification and validation

study were carried out. The effects of aspect ratio/span length, conservative vs. non-conservative convection

schemes, and grid resolution were investigated. The mean velocity, mean pressure, Reynolds stresses, and TKE

distribution were obtained and discussed. The snapshot POD method was employed to analyze flow structures

in the boundary layer, shear layer and wake. Fig. 17 shows coherent flow structures visualized with iso-surface

of Q criterion colored by the non-dimensional eddy viscosity. The wake width and amplitude of the shedding is

large for the sub-critical Reynolds number (Re) and become smaller as Re increases. Energy spectra of the

streamwise velocity in the shear layers are also shown in Fig. 17. At lower wavenumbers the energy spectra give

scaling exponents close to the Kolmogorov slope, which verifies that the large-eddy simulations properly

modeled the turbulence and preserved the correct energy decay behavior, although the ranges of wavenumbers

with the -5/3 spectral slope become narrower as Re increases. On the other hand, for larger wave numbers, the

rates of energy decay are faster than Kolmogorov’s decay law and the scaling slopes become much steeper. A

main cause of this rapid decay is the numerical dissipation from the upwind convection schemes used in the

simulations. The Kolmogorov wavenumbers estimated from the local velocity fluctuations are smaller than the

grid cut-off wavenumbers. This indicates the grid resolution is adequate in the shear layers, where the

turbulence intensity is usually lower than that in the wake. Fig. 18 shows comparisons for CD, CLRMS

, LS/TS,

and –Cpb. The drag crisis is well predicted, although more cases in the critical and post-critical regime are

desirable. The LES CLRMS

is close to the most reliable data for sub-, critical and super-critical Re. The angle of

10


separation is close to the experiments for sub- and critical Re, but substantially under predicted for super-critical

Re. The base suction pressure shows good agreement with the experiments for sub- and super-critical Re, but is

under predicted for critical Re. The largest difference is for critical Re, where the drag drops sharply with small

changes in Re resulting in large changes in CD between facilities and likely simulations. The grid resolution,

convection scheme, and the effect of upstream disturbance ubiquitous in experiments, but missing in the

simulations, are most likely responsible for the under-predicted separation angle for the critical Re.

5.2. Wave run-up

CFD simulations of wave run-up around single/multiple truncated vertical cylinder(s) for ITTC ocean

engineering workshop tests cases were conducted by Yoon et al. (2013). The simulations were conducted in

regular head waves for various wave conditions including /D=4.7 and /D=21.9 with H/λ=1/30, 1/16 and 1/10.

Sensitivity studies are conducted for the effects of grid distribution, domain size and turbulence model.

Validation studies focused on averaged wave height at crest/trough and 0th

, 1st and 2

nd harmonics for wave

elevation and horizontal force. CFD predictions were assessed separately for 1st and 2

nd order variables. The

averaged wave height at crest/trough and the 1st harmonics were considered as the 1

st order variables, whereas

the 0th

and 2nd

harmonics were considered as the 2nd

order variables. In addition, the wave field pattern around

cylinder(s), vortex shedding, and interaction among cylinders were analyzed. The grid sensitivity for the 1st and

2nd order variables was 3.17% and 77.74%, respectively, both less than the facility bias estimated from the

provided experimental data from two facilities. Nonetheless, the 2nd order variable sensitivity was large

indicting the need for finer grids to resolve 2nd order terms. The domain size sensitivity was also very small,

1.14% and 2.34% for 1st and 2nd order variables. The turbulence model sensitivity was conducted using

URANS and DES and the sensitivity for 1st and 2nd order variables was 1.64% and 6.55%, respectively,

suggesting that the URANS turbulence model is sufficient for the validation studies. The validation studies

showed 10% error for wave crest/trough, 7% error for the 1st harmonic of wave elevation; and 70% error for the

2nd

order variables including the 0th

and 2nd

harmonics of wave elevation. The horizontal forces also showed 9%

error for the 1st harmonic amplitude while larger errors are predicted for the mean and 2

nd harmonics. The

detailed study of the wave field showed that the mean wave field elevations are similar to the free surface

elevations for a cylinder in a steady flow due to the large wave induced current (up to 15% of the orbital

velocity for the steepest wave). The results showed larger effects of the wave steepness on the wave mean and

2nd

amplitude than on the 1st harmonic amplitude. The wave steepness effect was also more prominent for

/D=4.7 than /D=21.9. The studies were also conducted on the total wave field to evaluate the diffracted wave

pattern. The nonlinearities in the incident wave caused difficulties extracting the diffraction wave from the total

wave field. However, the total wave field could show the diffraction wave at upstream which was more

dominant for /D=4.7 and steeper waves. Fig. 19 and 20 show the wave field for single and four cylinder cases.

The studies on vortex structures showed more vortex shedding for longer wave conditions as its longer wave

period provides enough time to develop vortices around the cylinder. For both wavelength conditions, the vortex

shedding is more at instants the wave crest is located near the cylinder as the flow field velocity is larger. Lastly,

the comparison of four and single cylinder cases shows that the interaction of cylinders increases wave trough

for 4-10% while the wave crest increases about 9-25%. The largest interaction effect is found for the shoulder

side of the cylinders.

6. Fundamental Physics

6.1. IBM for idealized and practical geometries

Immersed boundary methods are simple and efficient approaches for many problems with complex geometries

and moving boundaries, thanks to the relaxation of the requirement of generating boundary-fitting grids in

numerical simulations. CFDShip-Iowa V6.1 is a Cartesian grid solver utilizing a direct forcing immersed

boundary method. The research focus is on efficient strong coupling schemes for fluid-structure interactions and

the extension to wave-body interaction problems in naval architecture and ocean engineering. In Yang & Stern

(2012) an efficient strong coupling scheme for 6DOF motion prediction was developed. The predictor-corrector

loop in each time step includes the adjustments of the structure displacements and velocities, but the fluid flow

11


solver was excluded. Then in Yang & Stern (2013) an efficient and robust immersed boundary setup procedure

was developed for further accelerating the strongly coupled simulations of fluid-structure interactions. This

approach can be a viable choice for particulate flows as shown in Yang & Stern (2014). Currently a non-

iterative strong coupled scheme has been developed. Fig. 21 shows the flow charts of three different strong

coupling schemes in one time step for fluid-structure interaction problems. Compared with the scheme in Yang

et al. (2008) with a complete iterative loop including multiple Poisson solves and the scheme in Yang & Stern

(2012) with one Poisson solve but multiple local reconstruction steps, the present scheme utilizes an

intermediate step with a non-inertial reference frame (NIRF) attached to a solid body and no iterative loop is

involved. The improved efficiency and reduced algorithm complexity is evident. The next step of development

will be combination of this new scheme with an efficient two-phase flow solver for ultra-scale simulations of

6DOF motions in naval architecture and ocean engineering. It should be pointed out that the development of

wall models in immersed boundary methods is necessary if high Reynolds number flows are the target

application and a reasonable approximation of the turbulent boundary layers is required.

6.2. Bubble, droplet, and spray in breaking waves

Air entrainment, bubbles, droplets, jets, and spray in breaking waves are of great importance to ship

hydrodynamics. Previous experimental and computational studies are mainly focused on the global structures of

the wave breaking. With the development of the CFD technology, detailed studies of the small scale structures,

such as water droplets and air bubbles, in the two-phase region become possible. In the study by Wang et al.

(2012d), wave breakings around a wedge-shaped bow and over a submerged bump are simulated using very

large grids (1.0~2.2 billion grid points). This study is the first attempt to directly simulate the unsteady and

energetic wave breaking flows to the scale of micrometers. In Wang et al. (2014), even large grids (up to 11.8

billion) are used in order to resolve the bubbles/droplets in breaking Stokes waves at the scale of several

micrometers. Fig. 22a shows the wave profile at time t = 1.76 when the splash-ups are being generated after the

wave plunging. The 3D interface instability in the spanwise direction is clearly demonstrated in Fig.22b. The

study of the flow over a bump in a shallow water flume by Gui et al. (2014a,b) showed that the Görtler type

centrifugal instability is the most relevant mechanism for the free surface instabilities. Fig. 22c and d show the

applications of the Görtler inviscid instability and Rayleigh instability theories in the stream-wise central plane,

respectively. In the wave breaking region, Görtler stability criterion is violated in most locations and Rayleigh

stability criterion is broken only in small regions. These results support the idea that breaking wave instabilities

are mainly due to Gortler type centrifugal instability. Fig. 22e shows the formation of bubbles/droplets in the

process of wave breaking. Power-law scaling for the bubble size distribution was obtained with two different

slopes separated by a Hinze radius of 1.2 mm as shown in Fig. 22f. The simulation results are in good agreement

with the experimental findings.

6.3. Cavitation

Cavitation degrades the performance of lifting surfaces found on ships, such as propeller blades and rudders and

may cause erosion. Past computational models have generally been homogenous mixture models, which average

the effects of many bubbles, or discrete bubble models, which model only a limited number of bubbles. In

Michael et al. (2014) a new sharp interface cavitation model was described within the framework of CFDShip-

Iowa V6.2. The interface is advected using a volume of fluid method with the addition of an additional velocity

due to phase change. The phase change component of the interface velocity is modeled using a simplification of

the Rayleigh-Plesset equation computed through a volume source term included semi-implicitly in the pressure

Poisson equation. A marching cubes method is used to compute the interface area in each computational cell

and for the determination of the phase at the cell and face centers. Fig. 23a shows a time series of cavitation on

a 2D NACA66 hydrofoil at a 6° angle of attack. The details of the shedding process can be seen. Fig. 23b

shows the bubble growth, merging, and advection process in the simulation of the same foil in 3D shortly after

cavitation inception. This type of high fidelity simulation offers the opportunity for deeper insight into the

physics of cavitating flows.

12


7. Uncertainty Quantification

Initial research focused on development and application of deterministic verification and validation (V&V)

methodologies and procedures for high-fidelity CFD simulations. Initial studies for validation methodologies

(Coleman and Stern, 1997) were subsequently extended to verification procedures for deterministic uncertainties

stemming from iterative, grid and time-step convergence (Stern et al., 2006; Xing and Stern, 2010). V&V

methodologies and examples were presented at the AVT-147 Symposium on Computational Uncertainty (Stern,

2007).Recently, the research focus moved to stochastic uncertainty quantification (UQ) methods as an essential

part of stochastic design optimization for real ocean environment and operations, such as robust design

optimization (RDO) and reliability based design optimization (RBDO). UQ research was undertaken within

NATO AVT 191 “Application of Sensitivity Analysis and UQ to Military Vehicle Design”. The objective was

the development and validation of efficient UQ methods for application to realistic ship hydrodynamic

problems. Non-intrusive UQ methods were addressed with high-fidelity physics-based CFD solvers. Evaluation

metrics for efficient UQ methods were developed, based on deterministic and stochastic convergence criteria

and validation versus numerical benchmark (Mousaviraad et al., 2013c; Diez et al., 2013), and efficiency of

overall UQ procedure by assessing the number of CFD simulations required to achieved prescribed error

thresholds (Volpi et al., 2014). Numerical benchmarks were provided by statistically convergent MC simulation

with direct use of CFD computations. UQ methods included metamodel-based Monte Carlo (MC) simulation,

quadrature formulas, and polynomial chaos methods. Applications covered unit studies and advanced industrial

problems. Specifically, a unit problem for a NACA 0012 hydrofoil with variable Reynolds number was

presented and assessed in Mousaviraad et al. (2013c). The high-speed Delft catamaran (DC) advancing in calm

water with variable Froude number and geometry was presented and studied in Diez et al. (2013). DC in

stochastic irregular and regular head waves (see Fig. 24) with variable speed and geometry was assessed in He

et al. (2013a). A combination of UQ problems for the DC was selected from Diez et al. (2013) and He et al.

(2013a) and used for further investigation in He et al. (2013b), focusing on the polynomial chaos method, and

Volpi et al. (2014), focusing on dynamic metamodels.

In conclusion, stochastic UQ methods were found mature for application to realistic stochastic optimization

problems. Based on the evaluation metrics, MC with dynamic metamodels was found the most promising

method overall. The high computational efficiency of dynamic metamodels, by auto-tuning and adaptive

sampling, makes the approach also recommended for stochastic optimization. Metamodel-based UQ has been

applied to stochastic design optimization of DC in real ocean environment and operations, as shown in Diez et

al. (2013) and Tahara et al. (2014). Future extensions include the application of metamodel-based UQ and

optimization to multi-disciplinary analysis and optimization (MDA, MDO) of FSI problems.

8. Future Research

The oncoming exascale HPC era is to change our approaches to grand scientific and engineering challenges and

to transform modeling and simulation into a specified discipline of predictive science. Current mainstream

RANS solvers for ship hydrodynamics are expected to continue performing well for even larger grids of up to a

few billions of points. However, there will be a threshold that further increase of grid size cannot improve the

results anymore because of the inherently limited RANS/DES turbulence models and the widely-used lower-

order discretization schemes. High-fidelity, first-principles-based simulations with unprecedented resolution can

reveal vast unknown temporal-spatial correlations in multi-scale and multi-physics phenomena that are beyond

today’s computing and experimental capabilities. With comprehensive verification and validation procedures,

assisted by targeted physical experiments, and rigorous uncertainty quantification, they are to revolutionize ship

hydrodynamics research and, along with optimization techniques, the ship design process for greatly reduced

design cycles and cost and much improved operation safety and economy. Therefore, the next-generation, high-

fidelity ship hydrodynamics solvers have to be developed aiming at the oncoming exascale computing

platforms, and addressing modeling issues, discretization schemes, and HPC memory and scalability restraints

at the same time.

13


Acknowledgments

This work was supported by research Grants from the Office of Naval Research (ONR), with Dr. Patrick Purtell,

Dr. Ki-Han Kim, Dr. Thomas Fu, Ms. Kelly Cooper; Dr. Roshdy Barsoum, and Dr. Robert Brizzolara as the

program managers. The fluid-structure interaction studies are performed in collaboration with Dr. Joachim

Grenestedt of Lehigh University. The WAM-V studies are conducted in collaboration with Dr. Mehdi

Ahmadian of Virginia Tech University. The planning hull studies are conducted in collaboration with Dr.

Carolyn Judge of USNA. The simulations were performed at the Department of Defense (DoD) Supercomputing

Resource Centers (DSRCs) through the High Performance Computing Modernization Program (HPCMP).

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14


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33. Sadat-Hosseini, H., Stern F., “System based and CFD simulations of 5415M maneuvering”, In preparation

for SIMMAN2014 workshop, 2014.

34. Sanada, Y., Elshiekh, H., Toda, Y., Stern, F., “Effects of Waves on Course Keeping and Maneuvering for

Surface Combatant ONR Tumblehome,” 30th Symposium on Naval Hydrodynamics, 2014.

35. Sanada, Y., Tanimoto, K., Takagi, K., Gui, L., Toda, Y., Stern, F., “Trajectories for ONR Tumblehome

maneuvering in calm water, ” Ocean Engineering 72, 45-65, 2013.

36. Stern, F., “Quantitative V&V of CFD Solutions and Certification of CFD Codes with Examples for Ship

Hydrodynamics,” Symposium on Computational Uncertainty, AVT-147, December 2007, Athens, Greece.

37. Stern, F., Agdrup, K., Kim, S.Y., Hochbaum, A.C., Rhee, K.P., Quadvlieg, F., Perdon, P., Hino, T., Broglia,

R., and Gorski, J., “Experience from SIMMAN 2008—The First Workshop on Verification and Validation

of Ship Maneuvering Simulation Methods”, Journal of Ship Research, Vol. 55, No. 2, pp. 135-147, 2011.

38. Stern, F., Toxopeus, S., “Chapter 1 – Experimental and Computational Studies of Course Keeping in Waves

for Naval Surface Combatant”, NATO AVT-161 report, 2013.

39. Stern, F., Wilson, R., and Shao, J., 2006. Quantitative approach to V&V of CFD simulations and

certification of CFD codes, Intl. J. for Numerical Methods in Fluids, Vol. 50, pp. 1335-1355.

40. Stern, F., Yang, J., Wang, Z., Sadat-Hosseini, H., Mousaviraad, M., Bhushan, S., Xing, T., “Computational

Ship Hydrodynamics: Nowadays and Way Forward”, 29th Symposium on Naval Hydrodynamics

Gothenburg, 2012.

15


41. Stern, F., Yang, J., Wang, Z., Sadat-Hosseini, H., Mousaviraad, M., Bhushan, S., Xing, T., “Computational

ship hydrodynamics: nowadays and way forward,” International Ship Building Progress, Invited paper, Vol.

60, No.1-4, pp. 3–105, 2013.

42. Tahara, Y., Diez, M., Volpi, S., Chen, X., Campana, E.F., Stern, F., “CFD-based multiobjective stochastic

optimization of a waterjet propelled high speed ship”, 30th Symposium on Naval Hydrodynamics, Hobart,

Tasmania, Australia, 2-7 November 2014.

43. Volpi, S., Diez, M., Gaul, N.J., Song, H., Iemma, U., Choi, K.K., Campana, E.F., Stern, F., “Development

and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty

quantification,” Structural Multidisciplinary Optimization, 2014.

44. Volpi, S., Sadat-Hosseini, H., Kim, D.H., Diez, M., Stern, F., Thodal, R., and Grenestedt, J.G.,

“Validation high-fidelity CFD/FE FSI for full-scale high-speed planing hull with composite bottom panels

slamming”, Abstract submitted International Conference on Coupled Problems in Science and Engineering,

San Servoro Island, Venice, Italy, 2015.

45. Wang, Z., Suh, J., Yang, J., and Stern, F., “Sharp Interface LES of Breaking Waves by an Interface Piercing

Body in Orthogonal Curvilinear Coordinates,” 50th AIAA Paper, January 2012a.

46. Wang, Z., Yang, J., Stern, F., “A new volume-of-fluid method with a constructed distance function on

general structured grids,” Journal of Computa-tional Physics, Vol. 231, Issue 9, 2012b, pp. 3703-3722.

47. Wang, Z., Yang, J., Stern, F., “A simple and conservative operator-splitting semi-Lagrangian volume-of-

fluid advection scheme,” Journal of Computational Physics, Vol. 231, Issue 15, 2012c, pp. 4981-4992.

48. Wang, Z., Yang, J., Stern, F., “High-Fidelity Simulations of Bubble, Droplet, and Spray Formation in

Breaking Waves”, HPC Insights, 2012d, Fall Issue.

49. Wang, Z., Yang, J., Stern, F., “High-Fidelity Simulations of Bubble, Droplet, and Spray Formation in

Breaking Waves,” 30th Symposium on Naval Hydrodynamics, Hobart, Tasmania, Australia, 2-7 November

2014.

50. Wu, P.C, Sadat-Hosseini H., Toda, Y., Stern, F., “URANS Studies of Ship-Ship Interactions in Shallow

Water”, 3rd

Intl Conf. on Ship Manoeuvring in Shallow and Confined Water, Belgium, 2013.

51. Xing, T. and Stern, F., “Factors of safety for Richardson extrapolation”, J. of Fluids Engineering, Vol. 132,

2010.

52. Yang, J., Bhushan, S., Suh, J., Wang, Z., Koo, B., Sakamoto, N., Xing, T., Stern, F., Large-eddy simulation

of ship flows with wall-layer models on Cartesian grids, Proc. 27th Symposium on Naval Hydrodynamics,

2008, Seoul, Korea.

53. Yang, J. and Stern, F., A sharp interface direct forcing immersed boundary approach for fully resolved

simulations of particulate flows, ASME Journal of Fluids Engineering, 136(4), 040904 (10 pages), 2014.

54. Yang, J. and Stern, F., Robust and efficient setup procedure for complex triangulations in immersed

boundary simulations, ASME Journal of Fluids Engineering, 135(10), 101107(11 pages), 2013.

55. Yang, J. and Stern, F., A simple and efficient direct forcing immersed boundary framework for fluid-

structure interactions, Journal of Computational Physics, Vol. 231 (15), pp. 5029-5061, 2012.

56. Yang, J. and Stern, F., Sharp interface immersed-boundary/level-set method for wave-body interactions,

Journal of Computational Physics, Vol. 228 (17), pp. 6590-6616, 2009.

57. Yang, J., Preidikman, S., Balaras, E., A strongly-coupled, embedded-boundary method for fluid-structure

interactions of elastically mounted rigid bodies, Journal of Fluids and Structures, 24, pp. 167-182, 2008.

58. Yeon, S., Yang, J. and Stern, F., 2013. Large eddy simulation of drag crisis in turbulent flow past a circular

cylinder. ITTC workshop on wave run-up and vortex shedding, Nantes France, 17-18 October 2013.

59. Yoon, H., Gui, L., Bhushan, S. and Stern F. “Tomographic PIV Measurements For Surface Combatant 5415

Straight Ahead and Static Drift 10 and 20 Degree Conditions.” IIHR Report, 2014.

60. Yoon, S.H., Kim, D.H., Sadat-Hosseini, H., Yang, J. and Stern, F., 2013. High-fidelity CFD simulation of

wave run-up around vertical cylinders in monochromatic waves. ITTC workshop on wave run-up and

vortex shedding, Nantes, France, 17-18 October 2013.

16


Figure 1: Turning maneuver simulation with water-jet propulsion (Sadat-Hosseini et al., 2013)

Figure 2: The grid topology and propeller vortices for KVLCC2 free running simulations with fully discretized propeller (Sadat-Hosseini et

al., 2014a)

(a)

(b) (c)

Figure 3: 5415M maneuvering simulations in calm water: (a) vortex structures for maximum drift angel during zigzag 1010; (b) free surface

profile for turning/pull-out; (c) CFD and SB trajectories compared with the experimental data (Sadat-Hosseini and Stern, 2014)

x (m)

y(m

)

0 10

0

5

10

15

EFD

CFD

SB_SI

inlet

17


Figure 4: The predicted transom free surface and vortex structures for turning maneuver simulation (Sadat-Hosseini et al., 2014b)

Figure 5: Course keeping simulation in irregular oblique waves (Sadat-Hosseini et al., 2014c)

(a)

(b)

Figure 6: Overall vortical structures predicted by CFDShip-Iowa URANS (Middle) and DES (Right) predictions on adapted 84M grid for

5415 with bilge keels at (a) straight ahead and (b) = 20 deg conditions.

EFD URANS DES

18


(a)

(b)

Figure 7: Variation of QPeak (LEFT) and TKE (RIGHT) at primary vortex cores predicted by best CFDShip-Iowa simulations are compared

with EFD data. (a) URANS predictions on 84M grid for sonar dome vortex (SDV) and fore body keel vortex (FBKV) cores is compared for

straight ahead case. (b) DES predictions on 84M grid for sonar dome tip vortex (SDTV) and bilge keel tip vortex (BKTV).

Figure 8: WAM-V CFDShip-Iowa regular head wave results in most probable conditions of SS3

1

10

100

1000

0 0.2 0.4 0.6 0.8 1

Q

x/L

Variation QPeak

SDV: EFD

SDV: 84M URANS

FBKV: EFD

FBKV: 84M URANS

5.0E-04

2.5E-03

4.5E-03

6.5E-03

0 0.2 0.4 0.6 0.8 1

Vo

rtex

Co

re T

KE

x/L

Variation of TKECore

SDV: EFD

SDV: 84M URANS

FBKV: EFD

FBKV: 84M URANS

100

1000

10000

100000

0 0.2 0.4 0.6 0.8 1

Q

x/L

Variation of QPeak

SDTV: EFD

SDTV: 84M, DES

BKTV: EFD

BKTV: 84M, DES

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 0.2 0.4 0.6 0.8 1

Co

re T

KE

x/L

Variation of TKEcore

SDTV: EFD

SDTV: 84M, DES

BKTV: EFD

BKTV: 84M, DES

19


Figure 9: WAM-V hydrodynamic modeling for CFD simulations in regular head waves compared with EFD sea trials in random seas

Figure 10: WAM-V 2-post testing (left) and 6-post suspension simulation (middle) using CFD results as inputs. Comparison of payload

accelerations is shown (right) for 6-post simulation (yellow) and 2-post test data (purple)

Figure 11: CFD-MBD 2DOF 1-Way and 2-Way coupling results for cylinder drop compared with EFD data for pontoon (top) and sprung

mass (bottom) motions

20


Figure 12: Underwater surface photo from EFD for USNA planing model at Fr=1.83 (Top) and comparison with CFDShip-Iowa predictions

Figure 13: USNA planing hull regular wave simulations using CFDShip-Iowa and NFA compared with experiments for: (a) Heave, (b) Pitch

and (c) Pressure at probe P13

21


Figure 14: Irregular wave slamming pressures for USNA planing hull: (a) EFD slamming events aligned by peak pressure; (b) CFDShip-

Iowa validation showing expected value (EV) and +/- standard deviation (SD) bars for re-entering and emerging pressures and duration

Figure 15: CFD-FEA simulation results for FSI studies showing force and displacement distribution for the bay 4, port panel on the full-

scale Numerette planing vessel in regular head waves corresponding to sea state 3 most probable wave condition at Fr=0.7

Figure 16: Average, min and max of EFD (Experimental Fluid Dynamics) strain with its expected value (EV) and standard deviation (STD)

at peak compared with CFD/FE predicted strain for sea state 3 most probable wave condition and Fr=2.9

22


Figure 17: Vortical structures with Q-criterion (left) and energy spectra of the streamwise velocity in the shear layer (right): sub-critical

(Re=1.26x105) (top), critical (Re=2.52x105) (center) and super-critical (Re=7.57x105) (bottom).

23


Figure 18: Drag coefficient, RMS lift coefficient, separation angle, and base pressure vs. Re.

Figure 19: EFD and CFD comparison of mean wave field for single cylinder cases

λ/D=4.7,

H/λ=1/30

λ/D=4.7,

H/λ=1/16 λ/D=4.7,

H/λ=1/10

λ/D=21.9,

H/λ=1/30 λ/D=21.9,

H/λ=1/16 λ/D=21.9,

H/λ=1/10

24


Figure 20: Mean and 1st harmonic amplitude of the wave field for single cylinder (λ/D=4.7, H/λ=1/10) and four cylinder (λ/D=4.7, H/λ=1/30)

cases

Figure 21: Flow charts of three different strong coupling schemes in one time step for fluid-structure

h0

0.18

0.15

0.12

0.09

0.06

0.03

-0.01

-0.04

-0.07

-0.10

Mean

1st

Mean

1st

25


(a) (b)

(c) (d)

(e) (f)

Figure 22: Stokes wave breaking. (a) perspective view; (b) detailed 3D interface structures; (c) Görtler inviscid instabilities; (d)

Rayleigh/Taylor instabilities (red: stable; blue: unstable); (e) side view showing bubbles and droplets; and (f) bubble size distribution.

26


(a) (b)

Figure 23: (a) Cavitating NACA66 hydrofoil with a 6° angle of attack; 2D solution showing cavity growth and shedding. (b) Close up

views of a sharp interface simulation of a cavitating NACA66 hydrofoil with a 6° angle of attack showing bubble growth, merging, and

advection shortly after inception at the leading edge.

(a)

(b) (c)

Figure 24: Comparison of time history distributions from irregular wave (benchmark) and regular wave UQ for the Delft Catamaran in head waves, at sea state 6 and Fr=0.5. Empirical and Normal density functions are shown for (a) force X, (b) heave z/L, (c) pitch θ (He et al.,

2013a).

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

-25 -20 -15 -10 -5 0 5

PD

F

X 103

benchmark (empirical)

UQ (empirical)

benchmark (Normal)

UQ (Normal)

0

5

10

15

20

25

30

35

-0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06

PD

F

z/L


UQ (empirical)

benchmark (Normal)

UQ (Normal)

0

2

4

6

8

10

12

-0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25

PD

F

q [rad]


UQ (empirical)

benchmark (Normal)

UQ (Normal)

RECENT PROGRESS IN CFD FOR NAVAL … progress in cfd for naval architecture and ocean engineering...

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