Reply on RC1

Published: Oct. 30, 2024

Abstract. Deep learning-based surrogate models represent a powerful alternative to numerical for speeding up flood mapping while preserving accuracy. In particular, solutions based on hydraulic-based graph neural networks (SWE-GNN) enable transferability domains not used training and allow including physical constraints. However, these are limited due four main aspects. First, they cannot model rapid differences in flow propagation speeds; secondly, can face instabilities during when using large number of layers, needed effective modelling; third, accommodate time-varying boundary conditions; fourth, require initial conditions from solver. To address issues, we propose multi-scale network (mSWE-GNN) that the at different resolutions speeds. We include via ghost cells, which enforce solution domain's drop need solver conditions. improve generalization over unseen meshes reduce data demand, use invariance principles make inputs independent coordinates' rotations. Numerical results dike-breach floods show predicts full spatio-temporal simulation irregular meshes, topographies, conditions, with mean absolute errors time 0.05 m water depths 0.003 m² s⁻¹ unit discharges. further corroborate mSWE-GNN realistic case study The Netherlands capabilities only one fine-tuning sample, 0.12 m depth, critical success index depth threshold 87.68 %, speed-ups 700 times. Overall, approach opens several avenues probabilistic analyses configurations scenarios.

Language: Английский

A data-driven early warning method for thermal runaway during charging of lithium-ion battery packs in electric vehicles

Measurement Science and Technology, Journal Year: 2024, Volume and Issue: 36(1), P. 016233 - 016233

Published: Dec. 11, 2024

Abstract In recent years, thermal runaway during charging of lithium-ion batteries has become a critical issue. This problem emerged as significant barrier to the development power for electric vehicles (EVs). paper addresses this challenge from data-driven perspective by proposing temperature prediction model EV batteries. The leverages both long short-term memory and Transformer algorithms account time-series characteristics charging. data under varying capacities ambient temperatures are extracted using Newman–Tiedemann–Gaines–Kim batteries, which is then used optimize accuracy hybrid algorithm through training. Additionally, real-world collected further validate model. Experimental results demonstrate that proposed achieves superior compared single models convolutional neural network models. Based on model, residual-based early warning method incorporating sliding window approach proposed. experimental findings indicate when residual predicted EVs exceeds threshold, preemptive termination effectively prevents runaway.

Language: Английский