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Physics-Aware GNN Modeling of Crash Loads and Contacts

30 Chapters

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Crash load and contact modeling landscape

explicit finite element baselines, meshfree and reduced-order alternatives, and the role of graph neural networks in surrogate and hybrid workflows.

modelinglandscape

Graph representations for crash structures

nodes as material points or finite element centroids, edges from mesh connectivity and proximity, and feature design for shells, solids, and joints.

representationsstructures

Physics-aware message passing

encoding momentum exchange, contact impulses, and constraint forces into edge and node update functions for transient dynamic simulation.

Physics-awareencoding

Contact graph construction

detecting candidate contact pairs, building contact edges with gap and normal features, and handling self-contact in folded and crushed geometries.

constructiondetecting

Temporal integration with GNNs

explicit time stepping, rollout stability, and physics-informed loss terms for velocity and acceleration consistency.

Temporalintegration

Material and constitutive modeling in GNNs

embedding elastoplasticity, strain-rate sensitivity, and failure criteria as node-level state variables and update rules.

Materialconstitutive

Geometric and structural features

shell normals, curvature, thickness, and joint stiffness as edge and node attributes for crash load prediction.

Geometricstructural

Contact force prediction

learning normal and friction contact laws from simulation data, with penalty and Lagrange multiplier formulations as inductive biases.

predictionfriction

Energy and momentum conservation in learned simulators

architecture constraints, conservative message passing, and diagnostics for drift in crash rollouts.

momentumconservation

Dataset generation from explicit crash simulations

sampling load cases, extracting graph snapshots, and labeling contact forces and deformation states.

generationexplicit

Handling large deformations and topology changes

remeshing, edge splitting, and graph rewiring strategies for crushed and torn structures.

Handlingdeformations

Multi-scale GNN modeling

coupling coarse-grained crash graphs with fine-scale contact patches and submodeling for local stress and force accuracy.

Boundary and loading conditions

encoding impactor velocity, prescribed displacement, and fixture constraints as graph-level and node-level conditioning.

Boundaryconditions

Uncertainty quantification for crash load prediction

ensemble GNNs, Bayesian message passing, and confidence bounds for contact force peaks.

Uncertaintyquantification

Hybrid physics-ML workflows

coupling GNN contact surrogates with explicit FE solvers, co-simulation interfaces, and stability at the coupling boundary.

physics-MLworkflows

Graph pooling and hierarchical architectures for crashworthiness metrics

predicting intrusion, acceleration pulses, and energy absorption from graph-level readouts.

hierarchicalarchitectures

Transfer learning across crash configurations

domain adaptation for vehicle platforms, impact angles, and material grades with limited target data.

Transferconfigurations

Real-time crash load estimation

model compression, graph subsampling, and latency-aware inference for onboard and design-loop applications.

Real-timeestimation

Validation and verification

comparing GNN predictions to explicit FE references, mesh convergence studies, and metrics for contact force and deformation accuracy.

Validationverification

frontal impact contact force prediction using a physics-aware GNN surrogate trained on explicit crash simulations.

predictionphysics-aware

side pole impact and door intrusion modeling with contact-rich graphs and local refinement.

intrusionmodeling

pedestrian head impact and hood contact: graph construction for deformable shells and contact impulse learning.

pedestrianconstruction

battery pack crush and cell contact: multi-material graphs, failure initiation, and short-circuit risk indicators.

multi-materialinitiation

aircraft fuselage section drop test: energy absorption and contact load paths with GNN surrogates.

aircraftfuselage

railway vehicle crashworthiness: coupler and anti-climber contact forces under override scenarios.

crashworthinessanti-climber

composite structures under impact: delamination-aware graph features and contact-induced damage prediction.

compositestructures

crash box and energy absorber design: GNN-driven optimization loops with contact force constraints.

absorberGNN-driven

occupant restraint interaction: coupling GNN structural contact predictions with dummy and airbag models.

occupantrestraint

Deployment and MLOps for crash GNNs

model versioning, simulation data pipelines, and integration with CAE environments.

DeploymentMLOps

Capstone project

end-to-end physics-aware GNN for a selected crash load and contact scenario, from graph construction and training to validation and reporting.

Capstoneend-to-end