Second DA Challenge Case 1: Time-Resolved results

This page presents published results for the Time-Resolved case of 2nd DA Challenge Case 1. The link for result submission, as well as details about the test case and rules for result formatting are given there.

Tables can be ordered according to each of the quantities reported. Clicking on an algorithm name in a table will open a page gathering its result, together with information on the submitter.

Definition of performance metrics

Below, in both the tables and the figures, we provide the bias and random error for:

  • the velocity vector magnitude
  • the vorticity vector magnitude
  • pressure

All quantities are dimensionless, with reference values taken as Vinf = 0.2 m/s for the velocity, Vinf/D (with D = 0.1 m the channel half-width) for the vorticity, and Vinf2/D for the acceleration. For pressure, we also provide the correlation coefficient between submitted result and ground truth.

Below the tables, several groups of figures compare these quantities among participants, as well as obtained streamwise velocity spectra. Additionally, several groups of figures show error slices of streamwise velocity, vorticity and pressure at a selected test point.

For comparison, we provide results obtained by a simple linear interpolation ('Lin. interp'). These are obtained by interpolating towards the output mesh the ground truth at the particles' positions, for each of the requested quantities (velocity components, velocity gradient tensor, pressure). 

ppp: 0.001
Algorithm Sort descending Institution Vel. bias Vel. random error Vort. bias Vort. random error Pres. bias Pres. random error Pres. corr. coef.
AUPINN Auburn University -0.0157 0.0864 -6.9 9.42 -0.0367 0.0194 -0.483
DLR-PINN DLR -0.0031 0.0783 -1.28 6.62 -0.04 0.0156 -0.628
FF3_lin DLR -0.0233 0.125 -3.91 7.33 -0.0297 0.0161
FF3_nonlin DLR -0.0233 0.125 -3.91 7.33 -0.0297 0.0161
FF3_nonlin@DaVis LaVision GmbH -0.0285 0.116 -3.12 7.94 -0.027 0.016 0.37
Lin.Interp N/A -0.0228 0.0879 -2.9 9.16 -0.00193 0.0132 0.919
MTA Dantec Dynamics -0.0208 0.0948 -3.16 7.16 -0.0168 0.0171 0.74
NIPA Penn State -0.00932 0.065 -3.86 6.16 -0.0525 0.0369 -0.496
nudging ONERA 0.012 0.0917 -1.7 6.83 -0.0484 0.0161 -0.732
PIGLET-LINEAR University of Southampton -0.000943 0.0568 -3.41 5.42 -0.0357 0.0131 -0.405
PIGLET-NONLIN University of Southampton -0.000555 0.0563 -3.36 5.38 -0.0323 0.0135 -0.0641
PII Dantec Dynamics -0.021 0.0964 -3 7.19 -0.00443 0.0198 0.804
TUB_PINN TU Berlin;TU Delft;Tohoku University -0.00525 0.0659 -3.35 6.04 -0.0183 0.0235 0.492
ppp: 0.01
Algorithm Sort descending Institution Vel. bias Vel. random error Vort. bias Vort. random error Pres. bias Pres. random error Pres. corr. coef.
AUPINN Auburn University -0.00322 0.0405 -2.17 7.93 0.00675 0.0142 0.697
DLR-PINN DLR -0.000653 0.0361 -0.478 5.14 0.00644 0.0139 0.707
FF3_lin DLR -0.00174 0.0443 -1.17 5.31 0.0171 0.0129
FF3_nonlin DLR -0.00157 0.044 -1.1 5.25 -0.014 0.027 0.69
FF3_nonlin@DaVis LaVision GmbH -0.00956 0.0574 -1.82 6.92 0.0117 0.00913 0.793
Lin.Interp N/A -0.0113 0.0513 -2.35 8.84 0.000382 0.0067 0.95
MTA Dantec Dynamics -0.00645 0.0477 -1.73 6.02 0.0245 0.00973 -0.266
NIPA Penn State -0.00289 0.0385 -2.68 5.00 0.0679 0.0192 -0.683
nudging ONERA 0.0137 0.0553 -1.27 5.89 -0.0086 0.012 0.833
PIGLET-LINEAR University of Southampton -0.0012 0.0351 -1.93 4.86 0.0104 0.00865 0.868
PIGLET-NONLIN University of Southampton -0.000611 0.034 -1.71 4.95 0.0146 0.0108 0.531
PII Dantec Dynamics -0.00554 0.0493 -1.42 6.04 0.0258 0.011 -0.245
TUB_PINN TU Berlin;TU Delft;Tohoku University -0.000911 0.0347 -1.62 4.79 0.0132 0.00982 0.801
ppp: 0.05
Algorithm Sort descending Institution Vel. bias Vel. random error Vort. bias Vort. random error Pres. bias Pres. random error Pres. corr. coef.
AUPINN Auburn University -0.00128 0.0255 -2.11 5.45 -0.00686 0.0129 -0.0192
DLR-PINN DLR 0.0000792 0.0155 -0.491 3.32 -0.0117 0.0135 -0.0702
FF3_lin DLR -0.000622 0.0271 -0.779 4.23 -0.00504 0.0134
FF3_nonlin DLR -0.000893 0.027 -0.811 4.23 0.00757 0.0164 0.608
FF3_nonlin@DaVis LaVision GmbH -0.00239 0.0288 -1.5 4.84 -0.00734 0.00807 0.653
Lin.Interp N/A -0.00639 0.0367 -2.24 6.91 -0.000164 0.0055 0.925
MTA Dantec Dynamics -0.00268 0.0281 -1.35 4.58 -0.00673 0.00907 0.622
NIPA Penn State -0.00117 0.0302 -2.26 4.33 -0.0803 0.0172 -0.269
nudging ONERA 0.00179 0.0397 -1.76 5.08 -0.0149 0.0106 0.0467
PIGLET-LINEAR University of Southampton 0.0000135 0.0246 -1.22 4.25 -0.00777 0.00825 0.614
PIGLET-NONLIN University of Southampton 0.00056 0.0242 -0.729 4.23 -0.00977 0.00913 0.49
PII Dantec Dynamics -0.00171 0.0283 -0.836 4.52 0.00512 0.00956 0.717
TUB_PINN TU Berlin;TU Delft;Tohoku University -0.000712 0.0237 -1.68 4.20 -0.00444 0.00944 0.758
ppp: 0.2
Algorithm Sort descending Institution Vel. bias Vel. random error Vort. bias Vort. random error Pres. bias Pres. random error Pres. corr. coef.
AUPINN Auburn University -0.00105 0.0312 -2.62 4.61 0.00604 0.0133 0.0209
DLR-PINN DLR 0.000473 0.00989 -0.507 2.28 -0.0109 0.0138 0.419
FF3_lin DLR -0.000435 0.0154 -0.324 3.32 0.00548 0.0132
FF3_nonlin DLR -0.000567 0.0152 -0.277 3.26 0.0246 0.0122 -0.0665
FF3_nonlin@DaVis LaVision GmbH -0.00157 0.0196 -1.32 4.49 0.00329 0.0075 0.884
Lin.Interp N/A -0.00434 0.0258 -1.97 6.72 0.00000351 0.00397 0.962
MTA Dantec Dynamics -0.00229 0.0181 -1.18 4.35 0.0079 0.0082 0.608
NIPA Penn State -0.000593 0.0227 -1.95 3.62 0.0463 0.0178 -0.208
nudging ONERA -0.00119 0.0263 -1.45 4.15 -0.00894 0.00935 0.67
PIGLET-LINEAR University of Southampton 0.000356 0.0175 -0.698 3.92 -0.00641 0.00652 0.806
PIGLET-NONLIN University of Southampton 0.000823 0.0187 -0.188 3.90 0.00193 0.00902 0.766
PII Dantec Dynamics -0.00199 0.0165 -0.751 4.24 0.0223 0.00927 0.0125
TUB_PINN TU Berlin;TU Delft;Tohoku University -0.000861 0.0186 -1.57 4.25 0.0025 0.00889 0.848

2nd DA Challenge Case 1 TR results