教程5.13g加密所属

1、Section 20Test-Analysis CorrelationSection 20Test-Analysis CorrelationINTRODUCTION TO TEST-ANALYSIS CORRELATION. 20 - 3INTEGRATED TEST-ANALYSIS PROCEDURE. 20 - 5PRE-TEST PLANNING. 20 - 6POST-TEST EVALUATION 20 - 9SUMMARY 20 - 18MODEL REFINEMENT. 20 - 19REFERENCES 20 - 27Introduction to Test-Analysis

2、 CorrelationMSC.Nastran results and test data may not match due to modeling and testing uncertainties.Common saying: “No one believes the analytical results (except for the modeler), and everyone believes the test data (except for the experimentalist).”Sources of modeling uncertainties:Physics of th

3、e problem being simulatedBoundary conditionsMaterial propertiesJoint flexibility“As-built” versus “as-designed”DampingIntroduction to Test-Analysis Correlation (Cont.)Goals of test-analysis correlation:Assess the degree of correlation between MSC.Nastran results and test data.Refine the MSC.Nastran

4、model to match test data.Person doing the correlation must understand the test data, the MSC.Nastran results, and the uncertainties in both.Integrated Test-Analysis ProcedureFour phases of an integrated test-analysis procedure:1.Pre-test planning (test simulation)2.Data acquisition. (Capture raw dat

5、a, such as, accelerations.)3.Data reduction and analysis. (Process raw data into quantities of interest, such as, mode shapes.)4.Post-test evaluation. (Assess goodness of fit between test data and MSC.Nastran results; refine the MSC.Nastran model to match the test data.)The analyst is involved in ph

6、ases 1 and 4, and the experimentalist is involved in phases 2 and 3.Pre-Test PlanningCreate a baseline MSC.Nastran model to determine optimal excitation and measurement locations. There are two methods for doing this:Simulation and inspectionCross-orthogonality checkSimulation and inspection. Use MS

7、C.Nastran to simulate the test, and choose input and output locations that give maximum response.Cross-orthogonality check:From a proposed set of measurement locations create an A-set, use Guyan reduction, and compute the mode shapes normalizing to a unit modal mass. Call this set of vectors (as in

8、“test” modes). Output the modes that span the frequency range of interest and the A-set mass matrix.Then, remove the A-set, repeat the modal calculation computing responses for the A-set DOFs for the full model, and output these modes (Fa, as in “analysis” modes).Pre-Test Planning (Cont.)In a third

9、MSC.Nastran run, read both sets of results and compute:If the proposed measurement locations (A-set) are adequate, then the resulting matrix has 1 on the diagonal and 0 as off-diagonal terms. If the off-diagonal terms are not 0, the proposed measurement locations are not adequate and a new set must

10、be formulated. (In actuality, off-diagonal terms less than 0.05 are acceptable.)DMAP alter for pre-test planning, premaca.vxx is on the MSC.Nastran delivery.Pre-Test Planning (Cont.)Modal effective mass1 and modal kinetic energy2 calculations can also be made in MSC.Nastran to ensure that the test s

11、pecimen is well understood before testing.Test and analytical locations need to align fully (location and coordinate direction) to facilitate pre-test planning and post-test evaluation. RBARs, MPCs, and alternate output coordinate systems can be used to “line up” the locations.Once the excitation an

12、d measurement locations are verified, then the test should be simulated to ensure that the test specimen is not overstressed during testing.1.For the i-th mode, effective mass = where M = mass matrix and Dm = rigid-body vector.2.For the i-th mode, kinetic energy = . This shows the energy distributio

13、n within a mode.Post-Test EvaluationCompare MSC.Nastran results to test data by inspection or cross-orthogonality checks.InspectiongraphicsXY plots:Plot test data and MSC.Nastran results together and assess the degree of correlation in magnitude and frequency content.Make sure that the curves plotte

14、d together represent the same spatial location and direction.Structure plots:Plot test and analytical mode shapes together and assess the degree of correlation. (Plots can also be animated.)Inspection can also be applied to non-graphical data such as, comparison of measured and computed resonant fre

15、quencies.Post-Test Evaluation (Cont.)Cross-orthogonality checkQuantitative check on the degree of test-analysis correlation is the cross-orthogonality check defined bywhereFt=test modes Maa=A-set mass matrix Fa=analysis modes computed for the A-setOff-diagonal terms should be less than 10% of the di

16、agonal terms in order to have a reliable match between test and analysis.Modes may be “switched” which is reflected by large off-diagonal terms in COR, the correlation matrix.If there are discrepancies between test data and analytical results, it may be desirable to refine the MSC.Nastran model to g

17、et a better match.Post-Test Evaluation (Cont.)COR example:Consider the 2-D beam model shown below. Assume that accelerometers are placed on the beam at every other grid point. The translational accelerations are measured in the x and y directions.Post-Test Evaluation (Cont.)Test Frequencies and Mode

18、 ShapesPost-Test Evaluation (Cont.)MSC.Nastran Frequencies and Mode ShapesTest and MSC.Nastran frequencies are close.ID PRETEST, DYNOTESSOL 103TIME 15COMPILE MODERS, SOUIN=MSCSOU, NOLIST, NOREFALTER mxx.*phix$MATPRN MXX,/$MATPCH MXX,PHIX,/ $CENDTITLE = MSC.Nastran MSC/XLSUBTITLE = MODES CASE CONTROL

19、LABEL = DEFAULT SUBCASE STRUCTUREDISP = ALL SPC=1METHOD=100BEGIN BULKPARAM AUTOSPC YESGRDSET, , , , , , ,345BAROR, , , , , 0., 1., 0.GRID 1 0.0 0.0 0.0GRID 2 1. 0.0 0.0GRID 3 2. 0.0 0.0GRID 4 3. 0.0 0.0GRID 5 4. 0.0 0.0GRID 6 5. 0.0 0.0GRID 7 6. 0.0 0.0GRID 8 7. 0.0 0.0GRID 9 8. 0.0 0.0GRID 10 9. 0.

20、0 0.0GRID 11 10. 0.0 0.0CBAR 1 1 1 2 CBAR 2 1 2 3 CBAR 3 1 3 4 CBAR 4 1 4 5 CBAR 5 1 5 6 CBAR 6 1 6 7 CBAR 7 1 7 8 CBAR 8 1 8 9 CBAR 9 1 9 10 CBAR 10 1 10 11 $ REDUCE TO TEST DOFASET1, 12, 2, 4, 6, 8, 10SPC 1 1 123456 0.0 PBAR 1 1 .01 .016 .016 MAT1 1 3.+7 .3 7.7 EIGRL 100 0.0 10000. 6 ENDDATAPost-T

21、est Evaluation (Cont.)Note:The locations of the A-set points are in the same geometric location as the test specimen.Post-Test Evaluation (Cont.)ID COR, DYN.NOTESTIME 30SOL 100COMPILE USERDMAP, SOUIN=MSCSOU, NOLIST, NOREFALTER 2 $ DMAP TO COMPUTE CROSS-ORTHOGONALITY$ INPUTS FROM MSC.Nastran RUN: MXX

22、 (A-SET MASS)$ PHIX (A-SET MODE SHAPES)$ (PREVIOUS M/N RUN USED MATPCH TO PUNCH DMI ENTRIES)$ INPUT FROM TEST: PHITEST (A-SET MODE SHAPES)$ OUTPUTS: UNITCHK (UNIT MASS CHECK)$ COR (CROSS-ORTHOGONALITY MATRIX)$ READ DMI INPUTDMIIN DMI,DMINDX/PHIX,PHITEST,MXX,/ $ VERIFY INPUT MATRICESMATPRN PHIX,PHITE

23、ST,MXX,/ $ MULTIPLY PHIX(TRANS)*MXX = PHITMASSMPYAD PHIX,MXX,/ PHITMASS /1/$ MULTIPLY PHITMASS*PHIX = UNITCHKMPYAD PHITMASS,PHIX,/ UNITCHK / $ PRINT TITLE AND UNITCHKMATPRN UNITCHK,/ $MESSAGE / CHECK ON UNIT MASS/ $ MULTIPLY PHITMASS*PHITEST = CORMPYAD PHITMASS,PHITEST,/ COR / $ PRINT TITLE AND CORM

24、ATPRN COR,/ $MESSAGE / CROSS-ORTHOGONALITY MATRIX/ $ENDALTERCENDTITLE = CROSS-ORTHOGONALITY CHECKBEGIN BULKDMI,PHITEST,0,2,1,0,10,6DMI,PHITEST,1,1,0.,.0,0.,.15,0., ,.40,0.,.70,0.,1.0DMI,PHITEST,2,1,.15,0.,.45,0.,.70, ,0.,.90,0.,1.0,0. (rest of PHITEST).DMI MXX 0 6 1 0 10 10DMI* MXX 1 1 9.62499976E-0

25、2* 3 1.92499999E-02DMI* MXX 2 2 1.38867334E-01* 4 2.67470982E-02 6 -1.37760025E-02* 8 4.32037748E-03 10 3.94638191E-05. (rest of MXX).DMI PHIX 0 2 1 0 10 6 DMI* PHIX 1 1 -1.02694275E-17* 3.79799381E-02 -3.21330419E-17 3.09179097E-01 1.62494801E-17* 7.69559503E-01 -1.60461922E-17 1.34014440E+00 -4.83

26、748987E-17* 1.95740056E+00DMI* PHIX 2 1 2.53673702E-01* -3.71932167E-18 7.36189783E-01 -2.11487186E-17 1.14664245E+00* -1.32509834E-17 1.44485378E+00 -5.04831637E-19 1.60163271E+00* 1.19194476E-17. (rest of PHIX).ENDDATAPost-Test Evaluation (Cont.)Post-Test Evaluation (Cont.) MATRIX COR (GINO NAME 1

27、01 ) IS A DB PREC 6 COLUMN X 6 ROW SQUARE MATRIX.COLUMN 1 ROWS 1 THRU 6 ROW 1) 5.1380D-01 6.7188D-19 1.8822D-03 -1.6038D-17 8.3091D-03 -1.5467D-16COLUMN 2 ROWS 1 THRU 6 ROW 1) -1.0247D-17 6.1972D-01 3.1542D-18 -5.7035D-03 -1.0390D-17 -8.8287D-09COLUMN 3 ROWS 1 THRU 6 ROW 1) -1.5467D-02 -6.3725D-18 6

28、.3014D-01 -2.7673D-17 -4.6145D-03 9.5503D-17COLUMN 4 ROWS 1 THRU 6 ROW 1) 2.5192D-18 1.1390D-02 8.1214D-17 5.8007D-01 -1.0636D-16 7.5993D-03COLUMN 5 ROWS 1 THRU 6 ROW 1) -1.0452D-17 5.3899D-03 -2.1283D-17 -1.3447D-02 1.2668D-14 7.4474D-01COLUMN 6 ROWS 1 THRU 6 ROW 1) -3.2927D-03 -2.5409D-18 8.7221D-

29、03 -2.3220D-16 -5.6143D-01 9.6348D-15THE NUMBER OF NON-ZERO TERMS IN THE DENSEST COLUMN = 6THE DENSITY OF THIS MATRIX IS 100.00 PERCENT.CROSS-ORTHOGONALITY MATRIX Summary1.COR matrix shows good agreement in mode shapes. (Off diagonal are small compared to the diagonal terms.)2.Mode 5 (test) is Mode

30、6 (analysis) and vice versa. This is shown by large off-diagonal terms in 5,6 and 6,5.3.The magnitude of diagonal terms are not 1.0 because the test modes are not normalized to unit modal mass. (They are normalized to a maximum component of 1.0.)4.Some effort is required to reformat test data for DM

31、I input.5.For a large number of modes, additional DMAP may be written to simplify the cross-correlation output.6.DMAP alter for test-analysis cross-orthogonality, postmaca.vxx, is on the MSC.Nastran delivery.Model RefinementThere are three ways to update an MSC.Nastran model to match test data:1.Usi

32、ng brute force2.Using the sensitivity matrix3.Using design optimizationAll methods update MSC.Nastran model parameters such as I and A for BARs and t for QUADs. Base flexibility can also be a parameter if it was explicitly modeled (such as with ELASs).Brute force:Make model changes based on inspecti

33、on of the results and make an educated “guess” as to the type of changes necessary.After the model is updated, another analysis is made and the MSC.Nastran results are again compared to test data to see if the match is better.If the match is not better, then make more changes and repeat.Model Refine

34、ment (Cont.)Sensitivity matrix:Sensitivity is the gradient of response with respect to model parameter. “Responses” are puted results, and “parameters” are property values.Each term in the sensitivity matrix S is given bywhere Ri is the i-th response and Pj is the j-th parameter.The greater the magn

35、itude Sij, the greater the response sensitivity.SOL 200 in MSC.Nastran computes the sensitivity matrix.S can be used by inspection to indicate which parameters need to be changed to change the response.Model Refinement (Cont.)S can also be output and used in a least-squares sense to minimize the dif

36、ference between test and analysis as follows:Pn = Po + (STS)-1ST(Rt - Ra)wherePn=updated parameters for test-analysis matchPo=parameters from original model S=sensitivity matrixRt=test responseRa=analytical resultsModel Refinement (Cont.)Design optimization:Implemented in SOL 200Want to minimize the

37、 difference between test and analysis but with a model that changes least from the baseline modelUse DEQATN to write an objective function that is the weighted difference between test data and analytical results.where RTi=test dataPF=final parametersRAi=analysis resultsPOj=original parametersWRi=tes

38、t weightingWPj=parameter weighting wt=weighting for test as a whole wp=weighting for model parameters as a wholeSOL 200 minimizes the objective (E) subject to design constraints.Model Refinement (Cont.)Example:Disk drive enclosure with 1406 grid points, 1354 plate elements, four design variables (plate thicknesses), and four measured flexible modesMinimize the difference between computed and measured resonant frequencies.M