Development of a Detailed Finite Element Model of the BIPED and Verification of Fidelity in Two Cases of Blunt Impact
Notice bibliographique
Résumé
Physical surrogates of the human head are commonly used to model cranial impacts and assess head injuries. The Brain Injury Protection Evaluation Device (BIPED mk2) is a head form that contains a brain simulant, fluid layer, connective membranes, a skull, and a skin layer and can measure kinematics, intracranial pressures, and strains. Finite element (FE) models can play a significant role in the development of new head form digital design iterations that better mimic the biological response of the head during impact by allowing researchers to modify material properties and geometries without fully redesigning and manufacturing the head form. This requires digitizing precise geometry, developing accurate material models and implementing realistic boundary conditions within the model. This study aims to create a digital model of the BIPED, perform a comparison of the model using supplied experimental data for both node displacement and pressure, complete a sensitivity study that ascertains whether the location of experimentally instrumented locations affected model outputs, and determine the effectiveness of experimental pressure sensors at capturing the coup and contrecoup phenomenon. The model was developed in ABAQUS based on Computer-Aided Design (CAD) geometry supplied by Defense Research and Development Canada (DRDC). Two different types of BIPED experimental test data were simulated with the developed finite element model: displacement tests and pressure tests. Pressure and displacement time series responses were compared to the experimental data using CORrelation and Analysis (CORA). The CORA values for the pressure comparison indicate an excellent correlation (>0.7) at the front sensor, while the back sensor was not considered just below a good correlation (<0.5). CORA ratings for the x (anterior-posterior) and z (superior-inferior) displacements of the 18 nodes tested resulted in a 0.554 average value, indicating a good correlation to the experimental data (> 0.5). Model simulations and helmeted experimental impacts were used to understand the sensitivity of the pressure sensor locations within the BIPED. Kinematics from helmeted drop tower experiments were input into the model to determine the sensitivity of the simulation output location. One element removed (approximately 5 mm) in the x (anterior-posterior), y (medial-lateral), and z (superior-inferior) directions were compared to the center element (sensor location). A directional bias was observed in the direction parallel to impact, with the average percent difference from the center element being 11.7%. Nodal percent differences were then compared for the displacement tests in the x and z directions. This resulted in a 14.6%, unbiased, percent difference. These large sensitivities indicate that pressure and displacements in a finite element model brain are highly dependant on location. The helmeted impacts were used to determine the effectiveness of the pressure sensor locations at correctly identifying the coup and contrecoup pressures. This was done by extracting the pressure gradient along the line of impact and comparing the values that the sensor locations read. It was determined that the sensors successfully characterised the coup and contrecoup pressure for impacts along its line of action but failed to do so for off-line impacts. Based upon CORA scores, this study demonstrates successful development of a digital twin FE model for the BIPED head surrogate and comparison against experimental pressure and nodal displacement data with both kinematic and force inputs. Additionally, this study underlines the importance of knowing the correct location of the physical sensors while choosing output locations in finite element simulations. Lastly, this thesis helps identify that the locations chosen for pressure sensors in physical surrogate models adequately represented the coup and contrecoup pressures.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».