AI Model Validation for CAE Injury Prediction 30 Cases

Translate a regulatory or design requirement for injury prediction into a written validation plan that specifies the CAE model boundary, response quantities of interest, reference data sources, and quantitative acceptance thresholds, establishing the basis for all subsequent validation cases.

Trace every material parameter, boundary condition, and contact definition in a CAE injury model back to its source test or calibration record, flagging unsupported inputs and producing a traceability matrix that distinguishes validated from assumed model inputs.

Run a systematic mesh refinement study on a body-region model and quantify convergence of peak stress, strain, and injury criteria, selecting a mesh density that balances accuracy against runtime while documenting the convergence evidence.

Configure and run verification cases using analytical or benchmark problems relevant to impact and injury mechanics, confirming that solver settings for time integration, contact, and hourglass control reproduce known solutions within tolerance.

Compare simulated stress-strain and failure response of a tissue or surrogate material model against coupon-level test data, quantify error across the relevant strain range, and adjust calibration parameters only where justified by the data.

Evaluate how the chosen viscoelastic or strain-rate-dependent material formulation reproduces rate-dependent test data, identify the rate range where the model diverges, and document the validated operating envelope for injury prediction.

Set up and run a head impact simulation against published cadaveric or surrogate test data, compare intracranial pressure, skull strain, and head kinematics, and quantify correlation and peak-magnitude error against the experimental corridors.

Reproduce a blunt thoracic impact test in CAE, compare chest deflection, viscous criterion, and rib strain against measured responses, and assess whether the model captures the injury mechanism within the stated acceptance criteria.

Simulate axial loading of a lower extremity model and compare force-displacement response and fracture initiation against test data, identifying whether the model correctly predicts the injury threshold and failure location.

Reproduce a lateral pelvis impact experiment in CAE, compare force-deflection and fracture patterns, and evaluate the model's ability to predict pelvic ring injury under the specified loading condition.

Run a combined compression-flexion loading case on a spine model, compare vertebral force, displacement, and injury location against test data, and assess the model's sensitivity to loading direction and rate.

Simulate a sled test with a full-body human body model, compare occupant kinematics, belt forces, and body-region accelerations against measured data, and quantify the model's ability to reproduce overall body motion before injury prediction is trusted.

Apply a frontal crash pulse to a validated human body model, compare head, chest, and pelvis accelerations and displacements against post-mortem human subject or volunteer data, and identify where the model's kinematic response departs from the reference.

Evaluate how belt and airbag interaction affects predicted injury metrics by comparing simulated belt forces, chest deflection, and head excursion against sled test data, isolating the contribution of restraint modeling to overall validation error.

Verify that the implemented injury criteria, such as HIC, BrIC, and chest deflection criteria, match their published mathematical definitions and produce correct values on controlled synthetic signals, ensuring that post-processing does not introduce validation error.

Run a designed parameter sweep over hourglass control and contact stiffness settings, quantify the resulting variation in predicted injury metrics, and identify which numerical parameters must be tightly controlled to keep validation conclusions stable.

Propagate measured material property scatter through the CAE model using a sampling study, produce a distribution of predicted injury metrics, and compare the predicted confidence interval against the experimental scatter to judge model adequacy.

Test a single body-region model across quasi-static, low-rate, and high-rate loading modes, compare predicted response against the corresponding test data for each mode, and determine whether one calibration can satisfy all validated conditions.

Compare a surrogate-based CAE model against paired experimental measurements from the same test series, quantify bias and random error in injury metrics, and decide whether the surrogate model is fit for the intended validation purpose.

Reproduce a pediatric impact or sled test with a child human body model, compare injury metrics and kinematics against the available pediatric data, and document where scaling assumptions limit the validity of the prediction.

Configure a head model to predict strain-based diffuse axonal injury metrics, compare predicted strain fields and injury volume against the experimental or clinical evidence base, and assess whether the model discriminates injured from uninjured cases.

Evaluate a thoracic model's ability to predict rib fracture location and timing under a known impact, compare predicted fracture sequence against post-test inspection or imaging, and quantify the model's fracture-prediction accuracy.

Simulate a long bone loading scenario and compare predicted fracture load, location, and failure mode against test data, assessing whether the model's failure criterion and element formulation are adequate for the intended injury prediction.

Given a new body region and intended use, design a validation test matrix that covers the relevant loading modes, rates, and injury mechanisms, specifying the reference data, response quantities, and acceptance criteria without running the simulations.

Run a prediction using a model calibrated on one dataset and compare it against a held-out experimental dataset without further tuning, documenting the blind prediction error and assessing the model's predictive credibility.

Given a model that fails an acceptance criterion, systematically isolate the root cause among geometry, material, contact, and boundary conditions, and propose a corrective action supported by targeted diagnostic simulations.

Compare a detailed boundary condition setup against a simplified one, quantify the difference in predicted injury metrics, and determine whether the simplification is acceptable for the validation use case or introduces unacceptable bias.

Build or adapt a model using subject-specific geometry from a post-mortem human subject test, compare predicted response against that subject's measured data, and assess how much of the validation error is attributable to geometry versus material and boundary assumptions.

Apply the ASME V&V 40 credibility framework to a CAE injury prediction model, map the model's context of use to credibility factors, and produce a credibility assessment that identifies the evidence gaps most relevant to the intended injury prediction.

Compile the results of the preceding validation activities into a structured validation report that states the context of use, acceptance criteria, evidence for each criterion, and residual risk, with traceable links to input data, simulation files, and post-processing scripts.