Digital Twin and Artificial Intelligence Incorporated with Surrogate Modeling for Hybrid and Sustainable Energy Systems

Khan, Abid Hossain; Omar, Salauddin; Mushtary, Nadia; Verma, Rohit; Kumar, Dinesh; Alam, Syed Bahauddin

doi:10.1007/978-3-030-72322-4_147-1

Cited by 10 publications

(4 citation statements)

References 71 publications

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“…In this scenario, we can use a surrogate model, also referred to as emulators or metamodels, to replace the computationally prohibitive simulation code. Many learning algorithms are available and have been successfully applied to TH applications, such as, Polynomial Regression (PR) [43], Gaussian Process (GP) [41,49], Artificial Neural Networks [50][51][52], etc. 4.…”

Section: Bayesian Iuq Framework Overviewmentioning

confidence: 99%

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Wang,

Wu,

Xie

et al. 2023

Energies

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Inverse Uncertainty Quantification (IUQ) has gained increasing attention in the field of nuclear engineering, especially nuclear thermal-hydraulics (TH), where it serves as an important tool for quantifying the uncertainties in the physical model parameters (PMPs) while making the model predictions consistent with the experimental data. In this paper, we present an extension to an existing Bayesian inference-based IUQ methodology by employing a hierarchical Bayesian model and variational inference (VI), and apply this novel framework to a real-world nuclear TH scenario. The proposed approach leverages a hierarchical model to encapsulate group-level behaviors inherent to the PMPs, thereby mitigating existing challenges posed by the high variability of PMPs under diverse experimental conditions and the potential overfitting issues due to unknown model discrepancies or outliers. To accommodate computational scalability and efficiency, we utilize VI to enable the framework to be used in applications with a large number of variables or datasets. The efficacy of the proposed method is evaluated against a previous study where a No-U-Turn-Sampler was used in a Bayesian hierarchical model. We illustrate the performance comparisons of the proposed framework through a synthetic data example and an applied case in nuclear TH. Our findings reveal that the presented approach not only delivers accurate and efficient IUQ without the need for manual tuning, but also offers a promising way for scaling to larger, more complex nuclear TH experimental datasets.

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Section: Bayesian Iuq Framework Overviewmentioning

confidence: 99%

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Wang,

Wu,

Xie

et al. 2023

Energies

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“…These allow smarter energy systems that can predict demand, optimize production from renewable sources, and improve energy storage solutions [12,13]. In addition, integrating digital twins and AI in energy systems allows real-time monitoring and simulation of processes, thus improving both decision-making and the efficiency of the entire system [14]. The sustainability of cloud infrastructures and the Internet of Things (IoT) also becomes critical with the widespread implementation of digitalization in all sectors of activity [15].…”

Section: Introductionmentioning

confidence: 99%

Shaping Sustainable Futures: Public Policies and Renewable Energy Insights Based on Global Bibliometric Analysis

Androniceanu,

Veith,

Ionescu

et al. 2024

Sustainability

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The paradigm of sustainable energy is gaining ground at the historical juncture of the present worldwide push for sustainable development. This is being driven by the latest technological advancements and by a maturing process of public policy evolution toward support for a sustainable energy transition. This paper analyzes, with the support of bibliometric analysis, the specialized literature in order to capture the main themes of interest, as well as their evolution, thus offering a panoramic view of research trends and the significance of implementing correct environmental measures and policies. Covering the period from 1991 to 2024, our exploration filters 2990 articles from the Web of Science database using a query that intersects “sustainable energy”, “renewable energy”, and “sustainable development”, with a nuanced consideration of the political landscape that shapes these domains. Using the advanced capabilities of the R program, the methodology employed facilitates a nuanced workflow of extraction and analysis, allowing for a detailed examination of the proliferation of research over the decades. This paper provides significant results, demonstrating the increasing impact of research through international collaborations, the importance of high-impact journals on sustainability policies, and the growing focus on “renewable energy” and “CO2 emissions”. The analysis of the relevance of term groups in environmental policy development and the correlation between economic growth and CO2 emissions confirms emerging trends. Furthermore, critical directions for future research and the necessity for formulating and implementing coherent sustainability policies are highlighted.

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“…Relying solely on expensive simulations based on real-time information from various sensors installed in the reactor system and then making control decisions based on the results can lead to sluggish operator response times and, in extreme cases, potentially catastrophic nuclear accidents. This situation calls for a new approach in DT technology for nuclear and energy systems [11][12][13] that simultaneously addresses the need for high calculation accuracy and speed.…”

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confidence: 99%

Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems

Kobayashi,

Alam

2024

Sci Rep

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This paper focuses on the feasibility of deep neural operator network (DeepONet) as a robust surrogate modeling method within the context of digital twin (DT) enabling technology for nuclear energy systems. Machine learning (ML)-based prediction algorithms that need extensive retraining for new reactor operational conditions may prohibit real-time inference for DT across varying scenarios. In this study, DeepONet is trained with possible operational conditions and that relaxes the requirement of continuous retraining - making it suitable for online and real-time prediction components for DT. Through benchmarking and evaluation, DeepONet exhibits remarkable prediction accuracy and speed, outperforming traditional ML methods, making it a suitable algorithm for real-time DT inference in solving a challenging particle transport problem. DeepONet also exhibits generalizability and computational efficiency as an efficient surrogate tool for DT component. However, the application of DeepONet reveals challenges related to optimal sensor placement and model evaluation, critical aspects of real-world DT implementation. Addressing these challenges will further enhance the method’s practicality and reliability. Overall, this study marks an important step towards harnessing the power of DeepONet surrogate modeling for real-time inference capability within the context of DT enabling technology for nuclear systems.

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Digital Twin and Artificial Intelligence Incorporated with Surrogate Modeling for Hybrid and Sustainable Energy Systems

Cited by 10 publications

References 71 publications

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference

Shaping Sustainable Futures: Public Policies and Renewable Energy Insights Based on Global Bibliometric Analysis

Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems

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