Starting from the Bottom: Coupling a Genetic Algorithm and a Pore Network Model for Porous Electrode Design
Notice bibliographique
Résumé
Porous electrodes are performance- and cost-defining components in modern electrochemical systems as they provide surfaces for electrochemical reactions, facilitate mass transport, conduct electrons and heat, and determine the hydraulic resistance [1]. Hence, electrode engineering is a valid approach to increase power density and improve const competitiveness. In convection-enhanced technologies, currently used porous materials are fibrous carbonaceous electrodes developed for low temperature fuel cells; yet their microstructure and surface chemistry limits the performance of emerging electrochemical systems such as redox flow batteries. Moreover, the empirical design of these electrodes is time- and resource-intensive which limits exploration of the wider design space. Microstructure-informed multiphysics simulations can be leveraged to aid the theoretical understanding and design of advanced electrode architectures [2]. However, while these simulations have improved our understanding, they have only recently [3] been deployed to realize the bottom-up design of electrode microstructures. The combination of microstructure-informed multiphysics with evolutionary algorithms could accelerate progress in the optimization of porous electrodes for specific applications. In this work, we deploy three-dimensional simulations in combination with a genetic algorithm for the bottom-up design of porous electrodes for redox flow batteries. In the first part of the talk, the coupling of a pore network modeling framework [4] with an evolutionary algorithm [5] is described. The pore network model is a microstructure-informed, electrolyte-agnostic simulation framework for flow batteries utilizing a network-in-series approach to account for species depletion over the entire length of the electrode, developed using an open-access platform (OpenPNM) [6]. The electrochemical model is solved for the electrolyte fluid transport and couples both half-cells by iteratively solving the species and charge transport at a low computational cost. In the genetic algorithm, the electrode microstructure evolves driven by a fitness function that minimizes pumping power requirements and maximizes electrochemical power output, where the optimization only relies on the electrolyte chemistry and initial electrode morphology and flow field geometries as inputs. The analyzed proof-of-concept employs a flow-through cubic lattice structure with fixed pore positions and shows significant improvement of the fitness function over 1000 generations, where the fitness improved by 75% predominantly driven by reducing the pumping requirements by 73%. The evolutionary design resulted in a bimodal pore size distribution containing longitudinal electrolyte flow pathways of larges pores. Additionally, we found an increase in surface area at the membrane-electrode interface resulting in a 42% enhancement of the electrochemical performance. In the second part of the talk, I will discuss our latest progress in the development of the topology optimization by implementing commercial fibrous electrodes as offspring networks, integrated flow field geometries, and extended evolutionary freedom during the optimization. Coupling the genetic optimization to the desired flow field geometry affects the evolution of the fitness function, shifting the balance between electrochemical and hydraulic performance, highlighting the importance of the coupled optimization of flow fields and electrodes. By including more evolutionary freedom in the algorithm (i.e., by allowing merging and splitting of pores outside fixed coordinates), existing fibrous electrodes can be enhanced by for example reducing their pumping losses. The presented genetic algorithm offers great potential for the predictive design of electrode microstructures tailored for specific reactor architectures and redox chemistries. Hence, this framework can be used to accelerate and broaden the design and fabrication process of advanced electrode structures. Although applied to flow batteries in this study, the methodology can be leveraged to advance electrode microstructures in other electrochemical systems by adapting the relevant physics. References [1] M. van der Heijden, A. Forner-Cuenca, Encyclopedia of Energy Storage , 480-499 (2022) [2] B. Chakrabarti et al., Sustainable Energy and Fuels, 4 , 5433-5468 (2022) [3] V. Beck et al., J. Power Sources, 512 , 230453 (2021) [4] M. van der Heijden & R. van Gorp et al. , J. Electrochem. Soc. , 169 040505 (2022) [5] R. van Gorp & M. van der Heijden et al ., Chem. Eng. J. , 139947 (2022) [6] J. Gostick et al., Comput. Sci. Eng. , 18 , 60-74 (2016) Figure 1
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| 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 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».