Machine Learning and Artificial Intelligence-Driven Multi-Scale Modeling for High Burnup Accident-Tolerant Fuels for Light Water-Based SMR Applications

• URL: http://arxiv.org/abs/2209.12146v1
• Date: Sun, 25 Sep 2022 04:41:12 GMT
• Title: Machine Learning and Artificial Intelligence-Driven Multi-Scale Modeling for High Burnup Accident-Tolerant Fuels for Light Water-Based SMR Applications
• Authors: Md. Shamim Hassan, Abid Hossain Khan, Richa Verma, Dinesh Kumar, Kazuma Kobayashi, Shoaib Usman and Syed Alam
• Abstract summary: The concept of small modular reactor has changed the outlook for tackling future energy crises. This work focuses on the application of machine learning and artificial intelligence in the design optimization, control, and monitoring of small modular reactors.
• Score: 0.3745741215827112
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The concept of small modular reactor has changed the outlook for tackling future energy crises. This new reactor technology is very promising considering its lower investment requirements, modularity, design simplicity, and enhanced safety features. The application of artificial intelligence-driven multi-scale modeling (neutronics, thermal hydraulics, fuel performance, etc.) incorporating Digital Twin and associated uncertainties in the research of small modular reactors is a recent concept. In this work, a comprehensive study is conducted on the multiscale modeling of accident-tolerant fuels. The application of these fuels in the light water-based small modular reactors is explored. This chapter also focuses on the application of machine learning and artificial intelligence in the design optimization, control, and monitoring of small modular reactors. Finally, a brief assessment of the research gap on the application of artificial intelligence to the development of high burnup composite accident-tolerant fuels is provided. Necessary actions to fulfill these gaps are also discussed.

Related papers

• Design Optimization of Nuclear Fusion Reactor through Deep Reinforcement Learning [4.297070083645049]
  This research explores the application of Deep Reinforcement Learning to optimize the design of a nuclear fusion reactor. The proposed framework enables finding the optimal reactor design that satisfies the operational requirements while reducing building costs.
  arXiv  Detail & Related papers  (2024-09-12T17:23:01Z)
• Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning [53.3760591018817]
  We propose a new benchmarking environment for aquatic navigation using recent advances in the integration between game engines and Deep Reinforcement Learning. Specifically, we focus on PPO, one of the most widely accepted algorithms, and we propose advanced training techniques. Our empirical evaluation shows that a well-designed combination of these ingredients can achieve promising results.
  arXiv  Detail & Related papers  (2024-05-30T23:20:23Z)
• Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation [14.835081385422653]
  We present a novel application of foundation models for chemical reactor modeling. Our model is designed to generalize across three classic reactor types. It shows rapid adaptation to unseen reactions with varying integer orders across different reactor set-ups.
  arXiv  Detail & Related papers  (2024-05-20T03:26:58Z)
• Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis [57.70772230913099]
  Chemist-X automates the reaction condition recommendation (RCR) task in chemical synthesis with retrieval-augmented generation (RAG) technology. Chemist-X interrogates online molecular databases and distills critical data from the latest literature database. Chemist-X considerably reduces chemists' workload and allows them to focus on more fundamental and creative problems.
  arXiv  Detail & Related papers  (2023-11-16T01:21:33Z)
• Machine Learning-Assisted Discovery of Flow Reactor Designs [0.0]
  We propose a machine learning-assisted approach for the design of the next-generation of chemical reactors. We apply high-dimensional parameterisations, computational fluid dynamics, and multi-fidelity Bayesian optimisation. We rationalise the selection of novel design features that lead to experimental plug flow performance improvements of 60% over conventional designs.
  arXiv  Detail & Related papers  (2023-08-17T08:00:20Z)
• CoolPINNs: A Physics-informed Neural Network Modeling of Active Cooling in Vascular Systems [0.0]
  New technologies like hypersonic aircraft, space exploration vehicles, and batteries avail fluid circulation in embedded microvasculatures for efficient thermal regulation. What is lacking is an accurate framework that captures sharp jumps in the thermal flux across complex vasculature layouts. This paper addresses these challenges by availing the power of physics-informed neural networks (PINNs)
  arXiv  Detail & Related papers  (2023-03-09T14:51:10Z)
• A Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction [0.4166512373146748]
  This paper introduces a novel multi-stage deep learning framework for predicting the final steady-state power of reactor transients. We use feed-forward neural networks with both classification and regression stages, and training on a unique dataset that integrates real-world measurements of reactor power and controls state. The incorporation of simulated data with noise significantly improves the model's generalization capabilities, mitigating the risk of overfitting.
  arXiv  Detail & Related papers  (2022-11-23T17:32:52Z)
• Advancing Reacting Flow Simulations with Data-Driven Models [50.9598607067535]
  Key to effective use of machine learning tools in multi-physics problems is to couple them to physical and computer models. The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems.
  arXiv  Detail & Related papers  (2022-09-05T16:48:34Z)
• Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport [49.237577649802034]
  We develop a novel optimal transport-based algorithm termed MROT to enhance their generalization capability for molecular regression problems. MROT significantly outperforms state-of-the-art models, showing promising potential in accelerating the discovery of new substances.
  arXiv  Detail & Related papers  (2022-02-13T04:56:18Z)
• Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation [91.05073136215886]
  "Actor-Learner Distillation" transfers learning progress from a large capacity learner model to a small capacity actor model. We demonstrate in several challenging memory environments that using Actor-Learner Distillation recovers the clear sample-efficiency gains of the transformer learner model.
  arXiv  Detail & Related papers  (2021-04-04T17:56:34Z)
• Knowledge Distillation: A Survey [87.51063304509067]
  Deep neural networks have been successful in both industry and academia, especially for computer vision tasks. It is a challenge to deploy these cumbersome deep models on devices with limited resources. Knowledge distillation effectively learns a small student model from a large teacher model.
  arXiv  Detail & Related papers  (2020-06-09T21:47:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.