Towards hydrogen-powered electric aircraft: Physics-informed machine learning based multi-domain modeling and real-time digital twin emulation on FPGA
Bibliographic record
Abstract
In response to environmental concerns related to carbon and nitrogen emissions, hydrogen-powered aircraft (HPA) are poised for significant development over the coming decades, driven by advances in power electronics technology. However, HPA systems present complex multi-domain challenges encompassing electrical, hydraulic, mechanical, and chemical disciplines, necessitating efficient modeling and robust validation platforms. This paper introduces a physics-informed machine learning (PIML) approach for multi-domain HPA system modeling, enhanced by hardware accelerated parallel hardware emulation to construct a real-time digital twin. It delves into the physical analysis of various HPA subsystems, whose equations form the basis for both traditional numerical solution methods like Euler’s and Runge–Kutta methods (RKM), as well as the physics-informed neural networks (PINN) components developed herein. By comparing physics-feature neural networks (PFNN) and PINN with conventional neural network strategies, this paper elucidates their advantages and limitations in practical applications. The final implementation on the Xilinx® UltraScale+™ VCU128 FPGA platform showcases the PIML method’s high efficiency, accuracy, data independence, and adherence to established physical laws, demonstrating its potential for advancing real-time multi-domain HPA emulation. • Comprehensive system integration: This work is at the forefront of modeling a multi-domain hydrogen-powered aircraft system, bringing together diverse complex subsystems into a cohesive framework. This model extends beyond the experimental and conceptual phases of hydrogen-powered aircraft development, offering a detailed simulation of its multi-functional systems. • Enhanced modeling techniques: The paper enhances the utility of the physics-informed machine learning approach by evaluating its performance against conventional electromagnetic transient methods, fully connected neural network, and physics-feature neural networks. This comparison identifies optimal modeling practices based on accuracy, simplicity, and adaptability across various subsystems. • Real-time emulation advancements: By implementing hardware acceleration, the study achieves real-time digital twin emulation of the extensive hydrogen-powered aircraft system, optimizing emulation latency and hardware resource usage while ensuring high accuracy and reduced latency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".