Structural, Thermal, and Optical Performance (STOP ...

48th International Conference on Environmental Systems ICES-2018-062 8-12 July 2018, Albuquerque, New Mexico

Copyright © 2018 Jet Propulsion Laboratory/California Institute of Technology

Structural, Thermal, and Optical Performance (STOP)

Modeling and Analysis for the Surface Water and Ocean

Topography Mission

Louis A. Tse1, Zensheu Chang2, Ruwan P. Somawardhana3, Eric Slimko4

Jet Propulsion Laboratory/California Institute of Technology, Pasadena, CA, 91109

The Surface Water and Ocean Topography (SWOT) mission objectives include high-resolution measurement

of continental water levels and the topography of the ocean surface, which aids climate modeling and

predictions. The primary instrument is the Ka-band Radar Interferometer (KaRIn), which has stringent

requirements on overall phase error in order to achieve its design performance. We conducted extensive

structural, thermal, and optical performance (STOP) modeling in order to analyze the design viability to

meet performance requirements. In this paper, we present the modeling and results of our study. These

predictions will be used to project results of on-orbit performance. We also share lessons learned regarding

STOP design and analysis flow.

Nomenclature

CCHP = constant conductance heat pipe

CNES = French National Space Studies Center (Centre National D’Etudes Spatiales)

CTE = coefficient of thermal expansion

DAA = deployable antenna assembly

FEA = finite element analysis

FSS = feed support structure

KaRIn = Ka-band Radar Interferometer Instrument

km = kilometer

LHP = loop heat pipe

MLI = multi-layer insulation

NASA = National Aeronautics and Space Administration

PSD = power spectrum density

STOP = structural-thermal-optical

SWOT = Surface Water Ocean Topography

I. Introduction

he Surface Water Ocean Topography (SWOT) is a joint partnership between NASA, Centre National d-Etudes

Spatiales (CNES) and Canadian Space Agency, to conduct a comprehensive global survey of Earth’s surface

water and ocean topography, planned for launch in 2021. The principal objective of SWOT is to collect precise

measurements of surface water hydrology, observe details of ocean surface topography and circulation, and measure

how water bodies change over time.

The SWOT mission is composed of six payloads that include an altimeter, microwave radiometer, global positioning

systems, a laser retroreflector, and an interferometer. The primary instrument is the Ka-band Radar Interferometer

(KaRIn) which will make large swath measurements to measure the surface elevations of water bodies; one with

horizontal polarity (H-pol) and another with vertical polarity (V-pol). The precision measurements needed to meet the

primary science objectives impose challenging requirements for the engineering team to accommodate for the KaRIn

instrument design. One of the main thermal challenges is to maintain precise temporal stability requirements

1 Thermal Engineer, Spacecraft Thermal Engineering, MS 125-123, 4800 Oak Grove Dr, Pasadena, CA 91109 2 Mechanical Engineer, Electroactive Technologies, MS 67-119, 4800 Oak Grove Dr, Pasadena, CA 91109 3 Lead Thermal Engineer, Instrument And Payload Thermal Engineering, MS 125-123, 4800 Oak Grove Dr, Pasadena,

CA 91109 4 Lead Mechanical Engineer, Payload & Small Spacecraft Mechanical Engineering, MS 321-356, 4800 Oak Grove

Dr, Pasadena, CA 91109

T

International Conference on Environmental Systems

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(<0.05°C/min) in a low earth orbit while managing large electronic dissipation (>1,000 W) that configurationally

requires co-location.

Figure 1: SWOT mission architecture [1].

II. KaRIn Overview and Performance Metrics

KaRIn is a bistatic synthetic aperture radar (SAR) system that utilizes near-nadir swaths on deployable antennas

on both sides of the satellite track, as shown in Figure 1. The deployable antenna assembly (DAA) is formed by two

5 m long and 0.3 m wide deployable antennae on opposite ends of a 10 m boom. Reflectarray technology is installed

on the ends of the booms, which consist of flat panels with etched elements on its surface which creates the necessary

phase change to emulate a parabolic reflector. The chain of these subsystems collectively serves as the principal

instrument on the mission, and each subsystem has several stringent design requirements in order to maintain earth

observation measurement performance. These requirements are dependent on the interdependent behavior of the

electronics, structural, and thermal subsystems. From a thermal standpoint, one key requirement is thermal stability,

which leads to reduced signal noise of the electronics subsystem. The fidelity of the ocean topography measurement

drives the defined thermal stability requirements over different timescales. The KaRIn instrument provides

fundamental ocean topography measurements at wavelengths shorter than 1,000 km, which corresponds to a time

window of 2.6 minutes during nominal science orbit. To account for land passes during the science orbit, the KaRIn

requirement also extends to spatial scales longer than 12,500 km to meet the error budget, which corresponds to a 31.6

minute time window [2], [3].


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The thermal subsystem is designed with four zones in order meet acute space constraints and high electronics

dissipative heat (greater than 1,000 W). As shown in Figure 2, each zone utilizes a thermal pallet with three to five

embedded constant conductance heat pipes (CCHPs) and one loop heat pipe (LHP) with variable conductance.

Figure 2: Diagram of one of the four thermal pallets for the KaRIn instrument, which utilizes a combination

of CCHPs and LHPs to transport heat from electronics boxes.

The benefits of incorporating LHPs into the thermal architecture include the ability to transport a large quantity of

heat with limited use of survival power, and simplicity in flight system integration and ground testing. One of the

challenges of LHPs is depending on its boundary conditions, high and low frequency oscillations have been reported

and can be related by heat source fluctuations, improper radiator sizing, and varying heat sink temperatures [2].

III. STOP Model Overview

The performance budget for SWOT governs that the opto-mechanical stability of the KaRIn radio frequency chain

must be maintained in order to reduce signal noise, which requires an iterative and collaborative analysis spanning

three models. The thermal, structural and optical design establish worst-case performance requirements independently.

The optical design governs distortion limits to the structural design, which then informs deformation limits in the form

of temperature gradients to the thermal design. Firstly, the basic process flow is shown in Figure 3. The modeling

tasks occur sequentially due to the required data exchange for each step. The STOP model development is conducted

using Siemens NX and is described in detail, along with verification of the results. The results of the thermal and

structural mapping are shown from the STOP analysis, along with a description of trade study results.


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Figure 3: STOP analysis process flow.

For the scope of this paper, pointing error at the observatory level is reported. There are several contributors to

instability, including environmental load fluctuations (solar flux, albedo, Earth IR), effective emissivity of multi-layer

insulation (MLI), LHP conductance to radiator, and geometric positioning of reflective components such as solar

arrays. The orbit parameters for SWOT during science mode are as follows: 77.6° orbit inclination, altitude is 905 km,

orbital period is 103 minutes.

A. Mesh Comparison The meshes for the thermal and structural models at the payload level are shown in Figure 4. Detailed views of the

thermal model mesh and CAD geometry for critical components are subsequently shown in the following figures,

such as the V-pol and H-pol feeds, metering structure, reflectarray panels, and boom tube assembly, respectively. The

fidelity of the thermal model, which has approximately 70,000 elements, is appropriate for the advanced stage of the

project. It contains sufficient detail in the optically sensitive components to represent critical thermo-structural effects

during trade studies. Thermal mapping to the structural model was accomplished in Siemens NX. Structural

deformations are mapped to the optical model to assess optical performance.

(a)


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(b)

Figure 4: (a) Thermal model mesh, and (b) structural model mesh.

CAD model Thermal model Structural model

Figure 5: Thermal math model mesh compared with CAD model design of the feeds.


Figure 6: Thermal math model mesh compared with CAD model design of the metering structure.


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Figure 7: Thermal math model mesh compared with CAD model design of the reflectarray panels.


Figure 8: Thermal math model mesh compared with CAD model design of the boom tube assembly.

The beta angle is defined as the angle between the solar vector and its projection onto the orbit plane. The nominal

on-orbit scenario that has been shown to be the worst case for thermal stability is for a beta angle of 55°, which

maximizes the fluctuation in lighting environment, such as maximum solar flux with longest time in eclipse, as well

as situations of shadowing or reflected incident energy from one component of the spacecraft to another.

B. Thermal Analysis Transient temperature plots for the components that require STOP analysis are shown in the following section.

Overlaid on the plots is the eclipse duration (blue band). Temperature contours are also shown, for the orbital position

at the end of the orbit.

(a) (b)


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(c) (d)

Figure 9: Transient temperature plot of the feeds: (a) V-pol +Y, (b) H-pol +Y, (c) V-pol -Y, and (d) H-pol -Y.

Blue band denotes eclipse.

Because of the chosen orbit and spacecraft attitude during science mode, the +Y Feeds are facing away from the sun;

as a result, they undergo a smaller temperature difference than the -Y Feeds. Additionally, in the nominal case, the

+Y Feeds are transmitting constantly during the orbit; the V-pol Feed dissipates 7.7 W and the H-pol dissipates 5.9 W

uniformly (and the -Y Feeds are off and thus, have zero dissipation).

Figure 10: Transient temperature plot of the metering structure. Blue band denotes eclipse.

The metering structure is thermally well-isolated from the environment, and does not exhibit large temperature swings.

Typically, it observes a hot spot at the location of the transmitting feeds. Conversely, it experiences a cold spot where

the star tracker is installed and thermally coupled to the end of the box beam on the +Y side, which has a constant

view to space in the nominal orbit attitude.


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(a) (b)

Figure 11: Transient temperature plot of the reflectarray panels for (a) +Y side and (b) -Y side. Blue band

denotes eclipse.

The +Y and -Y reflectarrays exhibit a large difference in temperature and transient behavior, for several reasons.

Firstly, the +Y panels experience some transient temperature peaks due to solar energy specularly reflected from the

spacecraft. Secondly, the -Y panels are significantly lower temperature because it is not sun-lit for a substantial portion

of the orbit, and when it is, many of its panels are shadowed by the spacecraft.

(a) (b)

Figure 12: Transient temperature plot of the boom tube assembly for (a) +Y side and (b) -Y side. Blue band

denotes eclipse.

Inversely to the behavior of the reflectarrays, the -Y boom tube assembly is constantly sun-lit, while the +Y boom

tube assembly is shadowed by the solar arrays and eclipse. However, the boom tube assembly is covered by MLI and

as a result, does not experience as wide a temperature swing as the reflectarrays.

C. Thermal Mapping

Temperature distribution on the payload is derived through thermal analysis. The temperatures at each node in the

thermal model are subsequently mapped to each node in the structural model using Siemens NX. Generally, this is


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achieved via proximity: the temperature of a given thermal model node is mapped to the structural node closest in

proximity. An additional approach provides more precise selection of mapping, by explicitly selecting a group of

nodes in the thermal model that will explicitly be mapped to a group of nodes in the structural model (e.g. selecting a

specific feed in the thermal model, and selecting the same feed in the structural model). To ensure the quality of the

mapping, the temperature contours and ranges were compared between the thermal and the structural models at a few

time steps. Several Matlab scripts were also created to check the temperature of every time step.

Thermal model Structural model

Figure 13: Temperatures mapped from the thermal model, to the structural model, at the end of the orbit.

D. Structural Analysis

The design of the payload meets requirements that include meeting minimum fundamental frequencies in lateral and

axial directions, surviving maximum launch loads, as well as requirements for jitter, sinusoidal vibration, and more.

From an optical point of view, the design shall meet the optical performance requirements after being exposed to the

on-orbit thermal environments. Nastran models were constructed to perform finite element analysis (FEA) of the

payload. The commercially available FEMAP software tool was used as the pre- and post-processor. The models were

verified by NASA standard procedures, including checks on element geometry, grounding, maximum diagonal ratio,

Nastran Epsilon, unit gravity constraint loads, free-free modal, common coefficient of thermal expansion (CTE), etc.

After several iterations of design changes and finite element analysis, the STOP-ready version of the telescope FEA

model weighs 880 kg, and comprises about 280,000 nodes and 270,000 elements.

The modal analysis of the payload with proper boundary conditions prescribed at the Spacecraft-Payload interface

predicted the frequency of the fundamental lateral mode to be 25.6 Hz. The structural model was sent to the thermal

team to perform temperature mapping from the thermal model to the structural model. Thermoelastic analyses were

then performed using the structural FEM (

Figure 4b) and the temperature data mapped from the thermal model.

Displacements due to temperature change were calculated for the reflectarrays, feeds, and instruments on the Nadir

deck. Matlab scripts were created to calculate the Power Spectrum Density (PSD) of the baseline dilation, roll, and

phase of the KaRIn antennae. Pointing errors including elevation error, azimuth error, and relative azimuth errors

were also calculated. The results are shown from Figure 14-Figure 15.


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(a) (b)

(c)

Figure 14: a) Smoothed Baseline PSD, b) Smoothed Roll PSD, , and c) Smoothed Phase PSD, for beta angle =

55°.

(a) (b)


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(c)

Figure 15: Pointing error for KaRIn at observatory level, beta angle = 55°.

As shown in Figure 14-Figure 15, each component meets the error allocation budget with appreciable margin in the

worst case for thermal stability. The confirmation of the magnitude of thermal stability due to orbital illumination and

eclipse gives confidence that the overall performance requirements are met by a robust structural, thermal, and optical

design at the observatory level. Additionally, the STOP analysis results, and more importantly the various resulting

errors, determines if there exists a strong correlation between a specific component and the overall performance.

Assessment of environmental loads as a function of orbital location such as Earth IR and albedo, varying MLI effective

emissivity, and optical property degradation are currently ongoing. Future studies include more refined modeling

approach of the LHP reservoir and evaporator for each thermal pallet.

IV. Lessons Learned

During the STOP analysis process, there have been many lessons learned; some issues were encountered during the

temperature mapping process that may be useful to share. The issues are summarized, with subsequent

recommendations from the authors, to highlight process improvements.

Separate radiation enclosures in NX: Calculating radiation conductances (or radks) is a computationally expensive

calculation in the thermal model. This is one of the reasons that the thermal and structural models are often separate:

the structural model may be too large to simply use the same structural FEM directly for thermal analysis.

Additionally, another reason is that many components that require high fidelity in the structural model (e.g. integral

components on the load path) were simplified or not represented in the thermal model and vice versa for components

pertinent to thermal analysis (e.g. temperature-sensitive electronics, multi-layer insulation). This can be seen more

clearly in Figure 4, particularly for the antennae on the Nadir module. The authors recommend defining separate

Radiation Enclosures if permitted, to lessen the number of radks that are calculated. For instance, a separate Radiation

Enclosure is defined solely for the components internal to the Nadir module (and similarly for the KaRIn module and

Reflectarrays).

Targeted mapping: NX uses node proximity as the default mode to match nodes between the two models. Using

the nearest node method is often used for large system models, due to time constraints, though can lead to artificial

thermal gradients. However, it is recommended to utilize Targeted Mapping Zones, which entails extensive manual

mapping to limits specific thermal nodes that can be mapped to corresponding structural nodes. As such, the drawback

of this is the significant time and effort to create these relational groups that correspond between the two models,

which must be balanced with higher accuracy.

Data or software management tool: Another productivity bottleneck during analysis flow is the data exchange

between the different models and analysts. Often, the thermal, structural, and optical data has to be converted into a

suitable format for analysis for each discipline. Data manipulation is often conducted by one-use codes or by hand. A

subsequent improvement from this effort would have been to develop an automated data management tool for the

specific set of analysis tools used, to establish consistent inputs and outputs. An even more advanced solution would

be to develop an automated software management tool, which would not only improve analysis flow, but design flow

as well. STOP analysis inherently is a multi-disciplinary analysis which utilizes different software packages for each

discipline, and has model translation hurdles that necessitates streamlined communication and data transfer methods


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between contributors. Some efforts have developed integrated analysis tool packages, such as OptiOpt and IMPipeline

[4], [5].

V. Conclusion

This paper presents an overview of the integrated STOP modeling process used to predict on-orbit thermoelastic

stability for the SWOT mission at the observatory level. The STOP analysis involves running analyses using three

different models (thermal, structural, and optical) and mapping simulation results to transfer nodal data between

models, ultimately to predict thermal distortion and measurement performance. Results are shown for the bounding

on-orbit case for maximum thermal instability and PSD results. The STOP analysis results determined that the current

mission architecture design meets system requirements for on-orbit thermal stability and PSD requirements.

Additionally, lessons learned are shared for significant improvements in accurate and automated STOP analysis flow

between independent thermal, structural, and optical models that are built using industry-standard software. Specific

recommendations to NX include defining separate radiation enclosures to reduce radk runtime, and using the Targeted

Mapping Zone feature to limit nearest-node mapping errors. Broader suggestions for streamlined STOP analysis

include adoption of top-level data or software management tools that enable tighter integration between thermal,

structural, and optical models. Recognizing methods to preserve manageable computational runtime without

sacrificing accuracy is paramount to an effective STOP design and analysis campaign.

Acknowledgments

The work described in this paper was performed at the Jet Propulsion Laboratory of the California Institute of

Technology, under contract with the National Aeronautics and Space Administration. The authors would like to thank

Howard Tseng (Jet Propulsion Laboratory), and Chris Pye and Jean Frederic Ruel (MAYA Heat Transfer

Technologies) for their support in thermal analysis and mapping.

References

[1] H. Fang, E. Sunada, J. Chaubell, D. Esteban-Fernandez, M. Thomson, and F. Nicaise, “Thermal Deformation

and RF Performance Analyses for the SWOT Large Deployable Ka-Band Reflectarray,” in 51st

AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference 18th

AIAA/ASME/AHS Adaptive Structures Conference 12th, 2010, p. 2502.

[2] R. Somawardhana, “Thermal Stability Testing of Two-Phase Thermal Control Hardware for the Surface Water

Ocean Topography Mission,” 2016.

[3] R. P. Somawardhana, “Surface Water Ocean Topography Ka-band Radar Interferometer Payload Thermal

Design Challenges,” 2014.

[4] B. Cullimore, T. Panczak, J. Baumann, V. Genberg, and M. Kahan, “Integrated analysis of

thermal/structural/optical systems,” SAE Technical Paper, 2002.

[5] N. Saini, K. Anderson, Z. Chang, G. Gutt, and B. Nemati, “IMPipeline: an integrated STOP modeling pipeline

for the WFIRST coronagraph (Conference Presentation),” in Techniques and Instrumentation for Detection

of Exoplanets VIII, 2017, vol. 10400, p. 1040008.

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