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HUMOS - An FE Model for Advanced Safety and Comfort Assessments Kambiz Kayvantash Director, Cranfield Impact Centre Cranfield, Bedfordshire, MK43 0AL k.kayvantash@Cranfield.ac.uk Samuel Bidal Senior Software Developer, Altair Development France Les Collines de Sophia 1900, Route des Cretes, Sophia Antipolis 06903, Francesbidal@altair.com Franck Delcroix Program Manager, Altair Development France Centre d'affaires 2 rue de la, Renaissance, Antony 92160, France fdelcroix@altair.com Abstract Biomechanics modelling is becoming increasingly accepted as a tool for enhance assessment of vehicle safety, in particular in the field of injury assessment and virtual testing. Firstly, a generic RADIOSS model for safety applications (HUMOS2) is presented and applications are demonstrated. Important tools associated with the scaling, and positioning of the model is also described. Secondly, an innovative model for scaling of human organs (individualization) is presented. The method which employs optimization techniques, identifies critical (optimal) anatomical control points which allow for a best scaled model of the HUMOS2 representing an individual. Finally, some remaining challenges for future human models are discussed and solution paths are described. Keywords: RADIOSS, HyperStudy 1.0 Introduction The idea of virtual testing has been investigated for some years now. There is more and more acceptance of the feasibility of virtual testing (CARS21, EUDirective 2007/46/CE) and many automotive OEM’s consider that they are either already capable or not far from applying “self-approval” methodologies using virtual testing (in particular in the USA). The potential of using computer models of human body instead of those of mechanical devices (dummies) representing human bodies has also been investigated during the last ten years. One of the most successful attempts in order to develop a complete FE model of a human body has been the EU-funded project HUMOS and HUMOS II – Contract G3RDCT- 2002-00803). These two research projects have set the foundations via creating a prototype of a human body model and have succeeding in attracting and initiating many researchers in this field, thus contributing to further investigations which resulted in other EU-funded projects such as ADVANCE and APROSYSSP5/ SP7. The aim of the above mentioned research has always been to push forward the usage of simulation technologies into unexplored domains of passive safety and human comfort, both of major importance in automotive design. This paper summarizes a series of questions still relevant to biomechanics modelling research and answers to some of them which are also related to virtual testing applications.

2.0 Human Modelling Issues Since its development during early 2000, the HUMOS model has pioneered global human body modelling techniques and has directly (or indirectly) generated massive amount of information and know-how both in human modelling techniques and injury assessment criteria and methodologies. In recent years, attempts have been made, based on stochastic methods, to create corridors of human responses from the HUMOS model. This has proved to be particularly promising and valuable for virtual testing of vehicles. There is a direct relation between biomechanics and that of the uncertainty of data represented by “stochastic” modeling techniques. The most immediate relation is the question of representation of a whole population by a single individual model as well as the numerous unknowns of even a single human model (based at best on cadaver data). Behavioral issues may even further complicate this task due to the differences between a cadaver and a living person (reflex, active muscles, physiology, psychology, behavior, etc.). Additionally, we need to respond to a series of fundamental questions raised often when confronted with limitations and choices of modeling. The answer to these questions defines the roadmap which need be adopted for a scientific work which may claim to have technological applications. 3.0 Representativeness The first question which comes to our minds is that of the relevance of the model to the practical usage for which it is intended. Do our models represent the real world (average) population or are they a special case? The answer is yes, as long as they are selected according to precise anatomical criteria, that their usage and modelling limitations are known and if they are tuned to the population averages (percentiles) which they need to represent. Certain details remain of major importance and contribute to dispersion of models: The answer to this question needs to take into account the following issues:

• Differences in cadavers cf. living • Homogeneity, age, sex • Data acquisition tools (methods) • Choice of anatomical sections, • Scans, MRI, etc. • 3-D visualization tools (real time)

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• Mesh size (optimality), • Material models, material data • Scaling • Geometry (Morphing, Kriging) - Inverse or external scaling • Properties (Kriging) • Injury thresholds

4.0 Definition of Scalars How can we measure the « goodness » of a simulation based on incomplete initial conditions and its closeness to the reality (which has probably never happened or at least not the way we think it did)? This needs to be established via the completion of various validation studies, evaluating the response of the model in simple (segment level) as well as full body level movements. Once simple kinematic movements are validated, further assessments on the global (impact) response of the model are also needed. In order to complete the assessment and define relevant metrics for the evaluation we could use:

• Mechanical responses (computed vs. observed data) • Kinematics (images, movies) • Injury (indices, medical observations) • Cadaver corridors • Dummy corridors • Reliability indices (computed) • Modelling criteria (mesh, data, etc.)

The above mentioned criteria are part of the HUMOS model data base and integrated into the ADVISER tool which is designed for automated safety assessments and virtual testing purposes.

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5.0 Pre-Crash Conditioning The general physiological conditions of the human subject just before the application of loading are of prime importance since they may result in different initial residual stress distribution within the body. In addition, out-of position configurations are of particular relevance to biomechanical studies in safety related issues. These condition the relative movements of anatomic organs as well as generating variations in the application of loads and contacts with the environment. . An appropriate treatment of the pre-crash configuration update requires various adapted tools and validated methodologies. The most important issues are:

• How can we model the self activated muscle effects without relating them to an external signal or sensor

• How should we take into account of the change of position prior to impact loading • Global geometrical shapes or local and relative position of organs (including model

posture and structural deformation) • Mechanical « pre-stresses », pressurization • Reflex modelling

6.0 Post-Injury How can we model the initiation and the propagation of rupture in human organs, causing injury or bleeding (often related to fatality even without visual external signs?). There is still a long way to be able to do real injury predictive models due to the lack of proper injury initiation and propagation models as well as corresponding data. However, there have been important advances in this domain lately (APROSYS-SP5 project) and some solutions are envisaged. One of the most important improvements currently undertaken is the inclusion of fluid/structure interactions for the better simulation of injury, pressurisation and bleeding mechanism. Another modelling technique for initiation and propagation of cracks in bone is also envisaged (XFEM – Extended Finite Elements). In summary the following challenges remain to be overcome:

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• Rupture (ligaments) • Damage (soft tissues) • Fracture (bones) • Propagation in failure zone • Bleeding

7.0 Repeatability Can we repeat the same simulation success of one model applied to one set of boundary conditions to another situation (or is there a strong dependence on input choices)? The answer to this question may indeed be obtained by applying stochastic modelling techniques to human models. The concept is based on the introduction of uncertainties (data with controlled range and form of variation). Stochastic modelling assumes that since the data itself has a spread and uncertainty, and since the modelling choices are not unique, the interpretation of the results of a deterministic model needs to be done with care. Conclusions are only relevant (or valid) if they are presented in terms of the probabilities of a certain outcome and not a certitude. Many solutions have been proposed by various researchers, but the majority of them propose essentially the same methodology which is performing as much as possible computation runs, while modifying the data and the modelling choices and obtaining the distribution of the results. The related issues which need yet further investigation are listed below:

• Sensitivity (sources of dispersion, model data, loading, etc.) • Stochastic modeling methods and tools and data analysis (corridors, dispersion

models, minimum dimension of population (i.e., min. no. of runs), etc. • Reliability index – How sure are we of what we predict? (see section on virtual

testing) • Robustness of reconstructions – How dependent is our result on model data and

definitions (see section on virtual testing)

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8.0 Future Trends and Current Activities HUMOS model was developped for 5%, 50% and 95% population sizes. A standing version of the 50% model was also developed using calculation and subsequent re-meshing. Currently many modeling activities are underway and new models are appearing. One the most promising activities is based on the development of a complete and detailed physiological model of human (INRETS/LBA-Marseille, Cranfield University/Cranfield Impact Centre – see www.simbio-m.com). Simultaneously efforts are being made in order to develop child, elderly and pregnant female models of human.

9.0 What is Virtual Testing? So far we have presented some of the most challenging issues for modelling of human bodies, to be integrated into structural models of safety (including dummies). We shall now introduce a few ideas developed recently on the topic of virtual testing itself (APROSYS-SP7). In order to understand the underlying features of certification and validation procedure based on virtual testing approach we needs to recall that before all any “Physical Testing (PT)” is a “Virtual Testing (VT)” using “similitude” and “simplified” physical modeling as a © Altair Engineering 2009 HUMOS – An FE Model for Advanced Safety & Comfort Assessments 8-6

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tool for simulated analysis. Nearly all PT procedures apply also to VT, the only difference being the nature of the model used The principal task of any mechanical analysis lies within the identification and integration of the loading, structural behaviour, material response and the assembly processes. In this respect, any simulation based (physical or virtual) is based on the following assumptions:

• Simplified physical testing loading conditions • Idealized prototype (structure and materials) • Controlled (closed) environment (assembly process)

In addition, as in PT, although not necessarily unique, any procedure for VT needs to be:

• Predictive (extrapolate to non tested situations) • Robust (insensitive to “small” errors of data or model choice) • Repeatable (site independence) • Efficient (effort vs. precision) • Cost-effective (cheaper than PT and feasible) • Traceable (automated)

10.0 Complementarities of VT to PT In reality the analysis cycle is never unique and is influenced by the variations in the choice of loading, structure, material and process model. The consequence is that the model result is also non-unique and is only an indicator of the response of the real structure. It is this non-uniqueness which is often criticized as being the major obstacle for adopting dedicated VT procedure for extending the range or simplifying the processes of PT. The truth is that it is this particular uncertainty aspect of the testing itself which is less realistically reproduced in PT. Often we are forwarded with experimental results showing the goodness of the experiment due to the small dispersion observed. This is however only one side of the coin indicating also that we are not allowing for or limiting the experience for a limited number of variations in the 4 elements mentioned above (loading, structure, material, processes). The other side of the coin is indicating that we are not really capturing the arbitrary nature of the impact configuration itself. Where as PT is ideal for reproducing as closely as possible the same test result, VT is ideally tooled in order to identify the role of the uncontrolled (known and unknown) testing parameters and variables. As for major differences, PT is clearly “deterministic” and seeks essentially an answers to the question of “What happens if …?“ (cf. a “laboratory reference condition”) whereas VT is fundamentally “probabilistic” and responds to the question “What is the chance of…”? As an example, of one can think of the case where the question is posed in the sense of “when will the structure fail” or “what is the threshold value” and perform an observation (PT) where the tested object (e.g. structure of a high risk vehicle such as a satellite launcher) is submitted to a single (or a few tests) in order to obtain an indicator of the strength of the structure. A useful design formulation of this problem could be to minimize the probability of the failure (assuming that it should/may not happen). Tests are actually made in order to distance the design as far as possible from the failure zone.

Principle Components of Analysis in Mechanical Simulation The Observations made via VT are related to the “complementary part” of the observation where an indicator is sought after in order to establish the percentage of fail to no fail scenarios giving an estimate of the chance of a tested object (e.g., car), to perform as designed for. This may also be formulated in terms of providing the complementary part of PT tests (virtually) in a statistical manner. This information may easily be used in order to maximize the chance of the structure not failing. Simulations are typically based to make the design to come as close as possible to the failure zone (minimization of costs), but with a sufficient safety factor established in order to obtain end use satisfaction. Indeed in some situations combinations may be the most efficient way to proceed, and this has been the solution which has received the most interest during the APROSYS-Virtual Testing project. This allows obtaining the best “mean” around which a cloud of “dispersion” or potential outcomes may be constructed and used throughout the design. Cost-benefit studies show that there may be a gain in efficiency for VT+PT if adapted procedures and infrastructure exist provided that the information concerning the cost of technician vs. engineer, space vs. software/hardware and maintenance vs. training exist. 11.0 VT Critical Issues 11.1 Component vs. Full Vehicle Testing Undoubtedly analysis and simulation is simpler to perform and to trust at a component level than the full-vehicle validation. “Virtual prototyping” is the term commonly used for estimating the limit loading and displacement values for a component designed under given specifications. However, virtual testing refers to a validation (homologation) procedure which tests the end product, assembled only in a simulated life configuration, and in interaction with other “optimally designed” components. It is this interaction which needs yet to be more understood, in particular with respect to those elements of the “real life” usage which are not fully represented or understood such as environmental, manufacturing and exact loading conditions. The size (extent) of the model under study is often a key solution to successful VT. 11.2 Design Variables vs. Model Parameters

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Distinction between variables and parameters is not always straight forward and often requires insight and experience or pre-specified rules for the selection. It is also important to note that what is considered to be a model parameter in one analysis (e.g. material properties) may be considered as design variables for another one. The choice is very

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similar to the one made during optimization strategies. Can model changes (parameters) be overshadowed by design variables and vice versa and would these two interact and influence each other in an uncontrolled or unexpected manner? The answer is yes. As an example, in an analysis where the velocity of the impact is considered as a design variable, the strain rate material properties representing features of the model (parameters). Inversely, if a given structure under a given velocity of impact (model parameters) is to be optimized for its material (design variable) the choice of the strain rate dependencies becomes very important. 11.3 Real Life Validations and “Representative” Number of Simulations Whereas a PT examines a batch of manufactured vehicles, the VT covers a wider range of vehicles and “boundary conditions” often not yet available for real testing. This implies the non-uniqueness of the tested structure (vehicle). Consequently real life VT simulations need be multiple and this depends on the availability of computing resources (hardware and software). The choice of the number of runs (computations) being representative of a given scenario is not known a-priori and often engineering judgment or a-posteriori “convergence” analysis may be considered. This is not an absolute drawback but is linked to the HW/SW issue and needs to be addressed with the providers. Additionally remote distance simulation solutions and on-demand computing solutions represent yet a black hole in the computing community in spite of the available technologies. This is more related to the licensing issues than anything else. 11.4 The Final Validation The final “proof” of a VT approach is in its feasibility, usefulness and reliability. Even if the method performs successfully from a mathematical or theoretical point of view (to be evaluated by OEM and homologation institutes) it will only be adapted if the OEMs find it organizationally feasible and cost-effective. On the other hand the homologation instances may only approve of the methods if they are within their reach (feasibility, certification and transfer of know-how) and equally reliable and site independent (robustness). A final physical validation may be performed via performing at least one test on the final product and positioning it within the simulated corridors. Alternatively if additional PT budget is available, dispersion clouds of the real testing may also be obtained, especially for component testing. This results in the creation of similarity clouds (same probabilities) or identical clouds (with same “average” outcomes) and either behaviours or most probable outcomes may be extrapolated. It is important to distinguish and eliminate dispersion caused by model imperfections (model parameters) from those related to the design variables of physical uncertainties. This is a complex task and needs adequate experience in modelling, not always available these days due to “black-box” or “push-button” approaches in modern computing community. 12.0 Virtual Testing Methods The two most attractive and industrially robust method s for performing virtual testing investigations are summarized hereafter. A summary table is also presented and both methods are compared. 12.1 The Response Surface Method This method is essentially based on a spatial extrapolation of validated results, obtained at an initial set of qualified control points. It assumes that if, at the regulated control points, the model predicts closely enough the danger zones and is comparable to known physical results, and if the corresponding threshold criteria values are also closely matched (i.e. both x and f(x)), it then has the capability of identifying (via extrapolation) other vehicle specific critical points and may thus contribute to their removal via introduction and verification of structural modifications.

The effect of the modifications may be verified on the model, assuming it of having only a local influence. In this way VT contributes to the improvement of safety measures with reduced cost of testing, since no exhaustive physical verification of all critical points is undertaken. A final physical validation is performed. The underlying mathematical principle is identical to any other “interpolation-extrapolation” strategy. 12.2 The 3R method (Rating, Reliability, Robustness) This method is based on the observation the whatever the predictive capabilities of simulation tools may be, the validity of the prediction needs yet to be demonstrated either by “clustering” large number of simulations with the objective of predicting the same phenomena but with different starting points, or via an adapted experimental validation. Note tat the experimental approach is not aimed at validating the vehicle but the numerical prediction of the response of the vehicle subject to a given impact loading situation. The 3R method assumes that if the model represents adequately a set of control points and their function values for a similar scenario, and if the obtained values at the control points are robust or insensitive enough to uncertainties (of choice of basis set and function parameters), then the function may be used in order to obtain coefficients of reliability indicating it’s degree of representativeness. Additionally if reductions of the model dispersion may be obtained by means of adjustments to the “basis”, then the choice of basis is adequate meaning that it may also be used as a measure of the influence of the modifications to the vehicle. These improvements may then be represented by local modifications to the original model and results may be exploited. A final physical test is used to validate the overall extrapolated information.

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Qualitative Comparison of Response Surface vs. 3R solutions for Virtual Testing 12.3 Rules and Options Both methods follow similar rules for the evaluation/homologation to be considered as feasible:

• A model must be made available for expertise if needed • A model must comply with specifications (types and limits of usage) and should pre-

identify accepted variables, parameters, computing environments, etc. For example: o Variables : space, velocity, mass o Parameters : modelling options, discretisation, material data o Computing : OS, Code (Version, platform), number of CPU’s, etc.

• Model need be provided with users’ manual • Model should run at evaluation site without any modifications to the original package • Model may be run in house if resources available • Manual or automated post-processing

The following two scenarios summarise the procedures for the “Response Surface” and the “Rating, Reliability, Robustness” methods of Virtual testing proposed by the APROSYS-SP7 partnership. It should be noted that other similar (or even completely different) approaches may also be devised. The below described methods have essentially been developed with respect to the extended pedestrian safety scenarios. This emphasises the need for further investigation into other existing or new safety evaluation scenarios including those of integrated safety, combining the detection/prevention as well as the protection phase of the various vehicle safety scenarios.

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13.0 References [1] Altair HyperWorks v9, Altair Engineering, 2009

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