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Parametric Design/Analysis with MSC/PATRAN- A New Capability

James G. Crose, Douglas A. Marx, Mark Kranz, Paul Olson and Carl BallThe MacNeal-Schwendler Corporation

2975 Redhill AvenueCosta Mesa, California 92626

(714) 444-5050greg.crose@macsch.com

This paper describes a new capability of MSC/PATRAN to provide for automatedparametric analysis in support of complex design processes. Computational resources areavailable today that can efficiently permit at least an order of magnitude more analysis support tothe design process than was available only a few years ago. MSC has been participating inDARPA’s RaDEO (Rapid Design Exploration and Optimization) project with Ford MotorCompany and the Rocketdyne and St. Louis divisions of Boeing Aircraft Company. That projecthas developed a new computer program to facilitate robust design processes that involve ordersof magnitude more analytical simulation than is typically applied in design. The Robust DesignComputational System (RDCS) computer program provides for automation of design processessuch as parametric design scanning, application of Taguchi concepts, optimization andprobabilistic analysis. It (RDCS) depends on the automation of multi-disciplinary parametricmath models that simulate the behavior of the object being designed. It is this requirement forautomation that is addressed in the paper.

MSC is supporting RDCS by creating a powerful parameterization capability with theMSC/PATRAN pre and post-processor. The present result of this effort is a modification ofMSC/PATRAN that permits the use of named variables to replace the usual fixed numericalvalues of the modeling parameters. These variable names are captured in the session file alongwith a default value. In addition, the values of these parameters different than the prescribeddefault can be provided by an external file that can be produced by another code such as theRDCS code referred to above. The goal of the MSC/PATRAN parameterization project is tomake it possible for the user to use names and default values for variables (parameters) in everyentry point on every form that can be accessed for modeling purposes. This goal has now beenmet for a large fraction of all the MSC/PATRAN forms. Similarly, we have provided for thedefinition and output of named response parameters such as maxima and minima of stress, strain,displacements and complex functions of results ( e.g.,Von Mises stresses or other measures offailure). These responses are directed to an output file for use by other codes such as the RDCScode referred to above.

Since a MSC/PATRAN session file can be re-run in a batch mode, including running theanalysis preference, the parameterized version of this file can also be executed in batch mode.This makes it possible to simulate the response of an unlimited number of design variations andcapture the responses as a function of the parametric variables and do so in batch mode withoutuser intervention.

This paper presents a description of this new capability that can be added toMSC/PATRAN and shows numerous examples of its use. Also, the coupling with RDCS isdemonstrated.

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Introduction

Detailed Finite Element Analysis (FEA) is mostly used in the mode of designcertification or for trouble shooting. Although desirable as an aid to conceptual design and atother stages in the design process, the cost and time to accomplish it are frequently prohibitive.The present paper addresses one step in the evolution of design analysis that is necessary toposition complex analytical simulation further towards the beginning stages of a productsdevelopment. The payoff for this can be tremendous. The fact that FEA is frequently used in anafter the fact trouble shooting mode suggests that it could have been used earlier to prevent thetrouble! The cost of correcting a design flaw was reflected in a study done by the Germanautomotive industry (Reference 1) with the following result:

• Suppose that a vehicle design has a serious flaw.• When this flaw is detected at the conceptual design stage the cost for fixing it will be,

for example, one unit.• If this flaw is detected during the detailed design and analysis phase the cost will be

10 units.• If the problem occurs when building prototypes, 100 units will be required.• Finally, if the flaw is detected during production, the cost will increase to 1000 units.

It appears that processes that improve the quality and lower the cost of complexanalytical simulation has great value to industry. Detailed FEA can play an important role insuch processes.

There are three major developments required to achieve the short time and low cost ofimplementing detailed FEA at earlier stages of design:

• Computational efficiency.• Design scan software.• Automated parametric math models.

Improvements in computational efficiency are taking place quite well without ourintervention. New developments in CPU speed, lower cost of memory and mass storage andspeedy networks provide the base upon which we can accomplish the other necessarydevelopments.

The Robust Design Computational System (RDCS) is one example of design scansoftware. It is also being reported on at this conference. It and the developments reported herehave been partially funded by DARPA as part of their multi-year Rapid Design Exploration andOptimization (RaDEO) Program that is nearing its end. RDCS is capable of producing orders ofmagnitude more design explorations via automated parametric math models. These math modelscan have any number of components called functional models. Each functional model representsone part of a products evaluation or performance simulation. They can be independent ordependent on other functional models. RDCS permits the construction of a master math modelwith all the dependencies accounted for and can execute that math model over a network ofmany computers and achieve parallelism to the extent permissible by the computer network and

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functional model dependencies. Basically, RDCS iterates through a large number of designinstances. Each design instance is a realization of the effect of setting the values of all thevariable design parameters as input to the math model. Upon completion of the math modelexecution, output response parameters are collected from the functional models and associatedwith the input parameter values. By so doing, any one of the available design tools in RDCS canbe applied systematically. For example, design space can be explored and defined by samplingparameter values within defined limits, Taguchi methods can be applied to address robustness,sensitivity coefficients can be defined for each of the parameters, optimization can beaccomplished iteratively, and probabilistic methods can be applied to define probability offailure. All of these functions can be applied through a simple and easy to understand graphicaluser interface. However, the entire process depends on automation and parameterization of themath model. It is this requirement that dictated the developments discussed in this paper. It wasdesired to provide a means for automating and parameterizing finite element based productsimulations.

MSC/NASTRAN is one example of a finite element code that can be a desirable part ofany comprehensive math model representing a product’s function and for evaluating itssurvivability in its intended environments. The overall capabilities of MSC/PATRAN as a preand post processor to MSC/NASTRAN and many other finite element codes, makes it an idealvehicle for achieving automation of parametric FEA simulations for use with RDCS and otherdesign scan managers. Making the key design parameters variable quantities that are thenimplemented in the modeling process does this. The model, its material constituency, itsboundary conditions and its loading can all be variable, thereby representing an unlimitednumber of design alternatives. This can be done for static and dynamic structural analysis,thermal analysis, aerodynamic analysis and any of the many other computational processesserved by MSC/PATRAN. The important contribution is the parameterization of a completeanalytical simulation, not just geometric descriptions as are available in many CAD programs.

Problem Definition

It was desired to be able to use named variables as substitutes for quantified values asentries into any form presented to the modeler while using MSC/PATRAN. This would enablehim to describe a range of models, each one defined by setting the values for all the variables. Itwas also desired to be able to re-model and re-analyse in batch mode (non-interactive) forsubsequent variable definitions. The session file was adopted as the medium for re-modeling.Also, there must be a default value for each variable in order that the modeling process can beaccomplished in the first place. Finally, there must be a way to define current values of designvariables without user interaction. It was decided that an external file could provide these valuesand could be created by another program such as RDCS. The MSC/PATRAN code had a featurecalled “global variables” that had much of the functional capability desired. Unfortunately, manyof the sub-forms lacked the ability to take full advantage of “global variables.” The problem ofmaking the variable name and its default value and a reference to an external file persist in thesession file for re-running required considerable programming effort to accomplish. This hasnow been accomplished for the majority of the graphical user interface. We have also developeda new adaptive form for the GUI that is used for the purpose of defining input variables and theirdefault values as well as manipulation of results to define meaningful results parameters for

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output to an external results file. It is now possible to conduct re-modeling, analysis processingand results definition entirely in batch mode by submitting the session file for execution andproviding an input file with the new input parameter values.

Description of the MSC/PATRAN Parameterization Feature

A new toolbox implemented as an adaptive form accessed from the main menu bar andcalled “Parametric Modeling” has been developed. Figure 1 shows the toolbox/form in itsvariables definition mode. This form is used to define a variable name and its default value to beused during the modeling process. Variable types can be integer, real, or string and arrays ofthose types. This particular view indicates that several variables have already been defined. Theyare height, width, enf_disp (a vector), mesh_density, pressure and thickness. A new variable canbe established by simply typing in a name, setting its type and giving it a current value. Anoptional description can also be added. New variables can also be defined using other variablesand any of the extensive collection of available functions in MSC/PATRAN. The context of theirusage must of course be consistent. Variable names can then be used in any otherMSC/PATRAN form and are denoted by enclosing the name in the back tick symbol, as was thecase for global variables. Upon completion of a modeling step, it can be seen in the session filethat the variable name is provided along with its default value and a reference to an external filewhere subsequent different values can be set. When re-played, MSC/PATRAN will look for thatfile, read its contents to determine if there is a new value for the variable and if so, use that valueinstead of the default. Note that the modeling process must be accomplished in a sequentialfashion. Later re-definition of a parameter value will not retroactively result in the imaginedchanges. Therefore, proper use of a variable depends on the sequence of modeling operations.

Figure 2 shows the “Parametric Modeling Toolbox” in the mode of creating responsevariables. This view of the form is available after an analysis has been completed with thedefault value of all the input parameters. The purpose of the form in this context is to searchthrough and manipulate the results of analysis to define meaningful metrics for export to anexternal file. Examples of such metrics might be maximum Von Mises stress in one previouslydefined group or material region or maximum strain in the fiber direction of a particularcomposite material, or any number of other criteria that can be developed based on the resultscoming back from the analysis code. Figure 2 shows that the user has already captured themaximum displacement, and maximum Von Mises stress from the results. Note that the valuesdetermined are also shown. The user has just completed finding the maximum Von Mises stressat a node from the stress tensor at that node with that result coming from the static subcase.

Output variable types include nodal or element scalar, vector or tensor quantities. Resultsmay be sorted by maximum, minimum, absolute maximum or absolute minimum value. Sortingmay be based on element or node ID, all entities in a graphics window, all in a user definedgroup, material ID or property ID. Results can be sorted to designated ply for compositematerials. Mass properties can also be output with this form. The form changes based onselections made and only those parts of the overall analysis results applicable to those choicesare allowed as further choices. Thus, the user is made aware of all the factors affecting hisspecification of outputs.

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Application and demonstrations of the parametric capability

The parameterization modification of MSC/PATRAN has been applied to a number ofexample problems. These include design of a composite tubular strut, modeling of a tire anddesign of a composite wing structure. The following sections present a summary of theseexamples.

Cure Cycle Shrinkage Stresses in a Composite Strut

This problem consists of a square cross-section hollow composite strut designed for usein a space structure as illustrated in Figure 3. The strut consists of nine graphite epoxy plies. Thelongitudinal plies are high modulus and the +/- 45 plies and the 90 plies are low modulus. Theyare laid up at 0,90,0,-45,0,+45,0,90,0. The problem was studied previously by Stanton and Mack(Reference 2) in 1986. Due to the geometry and ply orientation distribution, the strut twists anddevelops residual stress due to cure cycle shrinkage. Since the weight critical design requiresmaximum zero degree reinforcement while also providing strength and stiffness in the torsionand hoop modes, the imbalance due to +/- 45 plies must be present. However, the resulting twistand residual stresses were unacceptable as reported by Stanton and Mack. We wondered if theremight be a better design possible by perturbing the orientation of the 90 and 45 plies.MSC/PATRAN was used to model this structure for MSC/NASTRAN solution with the anglesto the reinforcements as variable parameters. The parameterized session file was prepared as anRDCS math model. A design scan was completed with the RDCS code. RDCS produced thedesign surfaces shown in Figures 4 and 5. The constraint limits of the “90” plies from 80 to 100and the “+/- 45” plies from +45 to +60 and -45 to –60 were chosen to represent the minimumrequired strength/stiffness in hoop and torsion to satisfy design requirements. It is seen fromthese figures that a lay-up of 0,95,0,60,0,-60,0,85,0 produces a design that greatly mitigates theamount of twist and residual stress while maintaining the minimum weight design and satisfyingthe torsional and hoop stiffness/strength constraints. This type of design investigation can beaccomplished quite quickly with a parameterized model used with the RDCS code. In one day,we were able to make a substantial improvement in the design. In the original study, severalman-months were expended on testing and computation. The ability to perturb design parametersin a good simulation up front in a design process has been shown to be extremely valuable.

Rubber Tire Model

A project was conducted to parameterize the design of a passenger car tire. The resultingmodel is shown in Figure 6. The 40,000 degree of freedom model of a 30 degree sector consistsof parametric descriptions of the tread, two radial ply composites, two bias steel belt composite,wire bead, rim cushion, bead wrap, filler, inner liner and chafer. The mesh strategy was alsoparameterized to preserve high quality modeling while making large changes in the geometry ofthe constituent materials and the overall tire size. The loading was also parameterized to allowfor variable inflation pressure, vehicle speed, and vehicle weight. A loaded patch shape wasassumed for the purpose of demonstration. Figure 7 shows some of the more extreme possiblepermutations of the base line design. Each model is based on exactly the same session file, butwith changes in the variable parameter values. The passenger tire model was also used as a math

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model in RDCS where sensitivity studies and design scans were completed. Figure 8 shows theresult of a sensitivity study where the sensitivity of the analytical results to the designparameters: velocity; inflation pressure; outer ply thickness; buttress radius and; tread moduluswere obtained from use of the RDCS code. Figure 9 shows the results of a design scan on vehiclespeed as it effects maximum Von Mises stress. Figure 10 is the same type of design scan but forinflation pressure instead of vehicle speed. Once a parametric model and associated simulationanalysis are available, the RDCS code can accomplish orders of magnitude more design studiesof a given object than would be possible without parametric models.

Composite Aircraft Wing Study

An aircraft wing in the shape and size of the F22 aircraft was modeled in MSC/PATRANfor static analysis by MSC/NASTRAN. The base line model is shown in Figure 11. The modelconsists of a composite skin, spars, ribs, leading edge flaps, trailing edge flap, aileron and wingtip. 59 variable input parameters were used which give external control to the overall geometry,meshing, loading, composite construction and material properties. It has 14,000 degrees offreedom in its base line configuration. The flexibility of this model is illustrated in Figure 12,where it can be seen that a single MSC/PATRAN session file can be manipulated with the inputparameters to produce a complete engineering model of wings shaped and sized as an S37forward sweep wing, a B2 wing with 28,000 degrees of freedom and even a propellerconfiguration with twist. The wing is loaded with pressure using linearized supersonic shockanalysis, which allows the loading to be variable with velocity, altitude and angle of attack.

Conclusions

MSC is providing a parameterization capability for use with MSC/PATRAN which willdramatically improve the ability to create automated parametric math models for use with designscan management software such as RDCS. The linkage with such software packages is verysimple. It only requires the preparation of an input file providing the parameter values for eachdesign instance, a script for executing MSC/PATRAN and ability to process the parameteroutput file that MSC/PATRAN produces. Once a parametric session file is available, the analysispreference associated with it runs under control of MSC/PATRAN and the user does not have tointervene in the execution or post-processing as all that is controlled via the parameterizedsession file.

The parameterization capability in MSC/PATRAN enables the use of preferences such asMSC/NASTRAN, ABAQUS and other solvers in a parametric mode without having to furtheraddress solver parameterization.

The parametric session file can be used productively by someone not schooled in how tooperate MSC/PATRAN or the associated solver. Communication can be entirely via designterminology that relates specifically to the design problem at hand. The session file can beincorporated within a design process, which includes a design scan manager such as RDCS ormanipulated one case at a time by editing the parametric input file. That file can include an easilyunderstood, parametric description of the design. After execution of MSC/PATRAN, theparametric output file can then be accessed to discover the results of the changes to the input file.

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AcknowledgmentsThis work was performed pursuant to a cooperative agreement with the Rocketdyne

Division of Boeing Corporation and the Ford Motor Corporation with partial funding from theDefense Advanced Research Projects Agency managed by Wright-Patterson Air Force Base. It ispart of the Robust Design Computational System (RDCS) program which is part of DARPA’sRapid Design and Optimization (RaDEO) Program. The work was also supported by MSCinternal funding.

References

1. Sippel, H., E. L. Stanton, and J. G. Crose, “The Usage of Numerical Optimization in theDevelopment Process,” Presented at the 1998 International FEM Congress in Baden-Baden,Germany, The MacNeal-Schwendler Corporation.

2. Stanton, E. L. and T. E. Mack “A Case Study of Cure Cycle Shrinkage Deformations,”ASME J. of Engineering for Industry, Vol. 109, February 1987.

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Figure 1. Variable Creation

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Figure 2. Response Variable Creation

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Figure 3. Composite Space Structure Strut

Figure 4. Twist due to cure shrinkage

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Figure 5. Residual stress due to cure shrinkage

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Figure 6. Rubber tire model

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Figure 7. Permutation of rubber tire designs

Default Passenger Truck Tire

Racing Tire Motorcycle

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Figure 8. Sensitivity analysis results

Figure 9. Design scan results for velocity

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Figure 10. Design scan results for inflation pressure

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Figure 11. F22 style wing model

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Figure 12. Permutations of wing designs

F22 S37

B2??

~ 14,000 DOF

~ 28,000 DOF