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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B4: Nuclear Energy".

Deadline for manuscript submissions: closed (16 May 2022) | Viewed by 20758

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Prof. Dr. Hany Abdel-Khalik

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Guest Editor

School of Nuclear Engineering, Purdue University, 205 Gates Rd, Flex Bldg - 1041B, West Lafayette, IN 47907, USA
Interests: cybersecurity analytics; validation and uncertainty quantification; reduced order modeling

Special Issue Information

Dear Colleagues,

We are pleased to offer you the opportunity to publish your original research paper in the journal Energies, Special Issue: “Latest Advances in Nuclear Energy Systems”. This Special Issue aims to identify recent advances in nuclear technology that are necessary to realize the full potential of nuclear energy. Our aim is to collect approximately 20 articles covering key topics, ranging from modeling and simulation, analysis methods, and algorithms to demonstrative reactor applications, including first-of-a-kind reactor technologies, advanced fuel concepts, and hybrid energy technologies. By way of example, some of the key topics of interest to this Special Issue include advances in multiphysics simulation and associated numerical methods, digital twining applications for condition monitoring, autonomous control, intrusion detection, methods to support validation of first-of-a-kind reactor systems with limited experimental data, advanced methods for machine learning, artificial intelligence, and statistical inference, strategies and algorithms to improve the economy of existing reactors and optimize design of new reactors, application of nuclear technology for both electricity and heat process applications, etc.

Prof. Dr. Hany Abdel-Khalik
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Advanced nuclear energy technology and hybrid nuclear energy systems
Digital twinning and advanced modeling and simulation
Cybersecurity of nuclear facilities
Machine learning and artificial intelligence
Validation and uncertainty quantification
Advances in inference and data assimilation techniques

Published Papers (9 papers)

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Research

13 pages, 2955 KiB

Open AccessArticle

Method to Estimate Thermal Transients in Reactors and Determine Their Parameter Sensitivities without a Forward Simulation

by Sydney A. Holdampf, Andrew G. Osborne and Mark R. Deinert

Energies 2022, 15(19), 7027; https://doi.org/10.3390/en15197027 - 24 Sep 2022

Viewed by 1164

Abstract

Thermal response time is an important parameter for the control of fast reactors. Modern thermal hydraulic codes allow for the modeling of transient responses and can also be used to understand the dominant factors that affect them. However, simulations can be computationally expensive, particularly for performing parametric analyses of how thermophysical properties affect transient behavior. Here, we present a method for using linear stability analysis to estimate thermal response time and determine the key parameters that affect transient behavior without performing a forward simulation. The approach can also be used to corroborate simulation results and is tested against simulation results produced with a 2D finite difference model. The results show that this approach produces time-dependent temperature profiles that are within 2 × 10⁻⁵–0.1% of the numerical results for a single node perturbation. Changes in temperature have the greatest effect on thermal response time, followed by changes to thermal conductivity. Full article

(This article belongs to the Special Issue Latest Advances in Nuclear Energy Systems)

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20 pages, 4776 KiB

Open AccessArticle

A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection

by Ahmad Y. Al Rashdan, Hany S. Abdel-Khalik, Kellen M. Giraud, Daniel G. Cole, Jacob A. Farber, William W. Clark, Abenezer Alemu, Marcus C. Allen, Ryan M. Spangler and Athi Varuttamaseni

Energies 2022, 15(15), 5640; https://doi.org/10.3390/en15155640 - 3 Aug 2022

Cited by 1 | Viewed by 1477

Abstract

To facilitate the automated online monitoring of power plants, a systematic and qualitative strategy for anomaly detection is presented. This strategy is essential to provide credible reasoning on why and when an empirical versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. Empirical methods are usually based on pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The presented strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability, and suitability for training and testing, cause-effect, entropy inference, and model fitting. The strategy is demonstrated through a pilot use case for the application of anomaly detection to capture a valve packing leak at the high-pressure coolant injection system of a nuclear power plant. Full article

(This article belongs to the Special Issue Latest Advances in Nuclear Energy Systems)

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39 pages, 33941 KiB

Open AccessArticle

CTF-PARCS Core Multi-Physics Computational Framework for Efficient LWR Steady-State, Depletion and Transient Uncertainty Quantification

by Gregory K. Delipei, Pascal Rouxelin, Agustin Abarca, Jason Hou, Maria Avramova and Kostadin Ivanov

Energies 2022, 15(14), 5226; https://doi.org/10.3390/en15145226 - 19 Jul 2022

Cited by 2 | Viewed by 1755

Abstract

Best Estimate Plus Uncertainty (BEPU) approaches for nuclear reactor applications have been extensively developed in recent years. The challenge for BEPU approaches is to achieve multi-physics modeling with an acceptable computational cost while preserving a reasonable fidelity of the physics modeled. In this work, we present the core multi-physics computational framework developed for the efficient computation of uncertainties in Light Water Reactor (LWR) simulations. The subchannel thermal-hydraulic code CTF and the nodal expansion neutronic code PARCS are coupled for the multi-physics modeling (CTF-PARCS). The computational framework is discussed in detail from the Polaris lattice calculations up to the CTF-PARCS coupling approaches. Sampler is used to perturb the multi-group microscopic cross-sections, fission yields and manufacturing parameters, while Dakota is used to sample the CTF input parameters and the boundary conditions. Python scripts were developed to automatize and modularize both pre- and post-processing. The current state of the framework allows the consistent perturbation of inputs across neutronics and thermal-hydraulics modeling. Improvements to the standard thermal-hydraulics modeling for such coupling approaches have been implemented in CTF to allow the usage of 3D burnup distribution, calculation of the radial power and the burnup profile, and the usage of Santamarina effective Doppler temperature. The uncertainty quantification approach allows the treatment of both scalar and functional quantities and can estimate correlation between the multi-physics outputs of interest and up to the originally perturbed microscopic cross-sections and yields. The computational framework is applied to three exercises of the LWR Uncertainty Analysis in Modeling Phase III benchmark. The exercises cover steady-state, depletion and transient calculations. The results show that the maximum fuel centerline temperature across all exercises is

2474 K

with

1.7 %

uncertainty and that the most correlated inputs are the ²³⁸U inelastic and elastic cross-sections above 1 MeV. Full article

(This article belongs to the Special Issue Latest Advances in Nuclear Energy Systems)

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14 pages, 1500 KiB

Open AccessArticle

Projecting the Thermal Response in a HTGR-Type System during Conduction Cooldown Using Graph-Laplacian Based Machine Learning

by Molly Ross, T-Ying Lin, Daniel Gould, Sanjoy Das and Hitesh Bindra

Energies 2022, 15(11), 3895; https://doi.org/10.3390/en15113895 - 25 May 2022

Cited by 1 | Viewed by 1902

Abstract

Accurate prediction of an off-normal event in a nuclear reactor is dependent upon the availability of sensory data, reactor core physical condition, and understanding of the underlying phenomenon. This work presents a method to project the data from some discrete sensory locations to the overall reactor domain during conduction cooldown scenarios similar to High Temperature Gas-cooled Reactors (HTGRs). The existing models for conductive cooldown in a heterogeneous multi-body system, such as an assembly of prismatic blocks or pebble beds relies on knowledge of the thermal contact conductance, requiring significant knowledge of local thermal contacts and heat transport possibilities across those contacts. With a priori knowledge of bulk geometry features and some discrete sensors, a machine learning approach was devised. The presented work uses an experimental facility to mimic conduction cooldown with an assembly of 68 cylindrical rods initially heated to 1200 K. High-fidelity temperature data were collected using an infrared (IR) camera to provide training data to the model and validate the predicted temperature data. The machine learning approach used here first converts the macroscopic bulk geometry information into Graph-Laplacian, and then uses the eigenvectors of the Graph-Laplacian to develop Kernel functions. Support vector regression (SVR) was implemented on the obtained Kernels and used to predict the thermal response in a packed rod assembly during a conduction cooldown experiment. The usage of SVR modeling differs from most models today because of its representation of thermal coupling between rods in the core. When trained with thermographic data, the average normalized error is less than

2 %

over 400 s, during which temperatures of the assembly have dropped by more than 500 K. The rod temperature prediction performance was significantly better for rods in the interior of the assembly compared to those near the exterior, likely due to the model simplification of the surroundings. Full article

(This article belongs to the Special Issue Latest Advances in Nuclear Energy Systems)

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23 pages, 2783 KiB

Open AccessArticle

Dispatch Optimization, System Design and Cost Benefit Analysis of a Nuclear Reactor with Molten Salt Thermal Storage

by Gabriel J. Soto, Ben Lindley, Ty Neises, Cory Stansbury and Michael J. Wagner

Energies 2022, 15(10), 3599; https://doi.org/10.3390/en15103599 - 14 May 2022

Cited by 3 | Viewed by 2568

Abstract

Variable renewable energy availability has increased the volatility in energy prices in most markets. Nuclear power plants, with a large ratio of capital to variable costs, have historically operated as base load energy suppliers but the need for more flexible operation is increasing. [...] Read more.

_{t}

nuclear power plant, based on the Westinghouse lead-cooled fast reactor, coupled with molten salt thermal storage as a method for flexible energy dispatch. We use the System Advisor Model to model the nuclear reactor thermal power input and power cycle operating modes. We combine this robust engineering model with a mixed-integer linear program model for optimized dispatch scheduling. We then simulate the coupled nuclear and thermal storage system under different market scenarios with varying price volatility. We find that the coupled plant outperforms the base plant under markets where energy price peaks fluctuate by a factor of two or more about the mean price. We show that a calculated power purchase agreement price for the plant improves by up to 10% when operating under California energy market conditions. Sensitivity analysis on the thermal storage cost shows that the optimal design remains unchanged even when doubling costs. Full article

(This article belongs to the Special Issue Latest Advances in Nuclear Energy Systems)

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25 pages, 8826 KiB

Open AccessArticle

Comparison of Deep Reinforcement Learning and PID Controllers for Automatic Cold Shutdown Operation

by Daeil Lee, Seoryong Koo, Inseok Jang and Jonghyun Kim

Energies 2022, 15(8), 2834; https://doi.org/10.3390/en15082834 - 13 Apr 2022

Cited by 8 | Viewed by 3209

Abstract

Many industries apply traditional controllers to automate manual control. In recent years, artificial intelligence controllers applied with deep-learning techniques have been suggested as advanced controllers that can achieve goals from many industrial domains, such as humans. Deep reinforcement learning (DRL) is a powerful method for these controllers to learn how to achieve their specific operational goals. As DRL controllers learn through sampling from a target system, they can overcome the limitations of traditional controllers, such as proportional-integral-derivative (PID) controllers. In nuclear power plants (NPPs), automatic systems can manage components during full-power operation. In contrast, startup and shutdown operations are less automated and are typically performed by operators. This study suggests DRL-based and PID-based controllers for cold shutdown operations, which are a part of startup operations. By comparing the suggested controllers, this study aims to verify that learning-based controllers can overcome the limitations of traditional controllers and achieve operational goals with minimal manipulation. First, to identify the required components, operational goals, and inputs/outputs of operations, this study analyzed the general operating procedures for cold shutdown operations. Then, PID- and DRL-based controllers are designed. The PID-based controller consists of PID controllers that are well-tuned using the Ziegler–Nichols rule. The DRL-based controller with long short-term memory (LSTM) is trained with a soft actor-critic algorithm that can reduce the training time by using distributed prioritized experience replay and distributed learning. The LSTM can process a plant time-series data to generate control signals. Subsequently, the suggested controllers were validated using an NPP simulator during the cold shutdown operation. Finally, this study discusses the operational performance by comparing PID- and DRL-based controllers. Full article

(This article belongs to the Special Issue Latest Advances in Nuclear Energy Systems)

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14 pages, 2689 KiB

Open AccessArticle

Momentum-Dependent Cosmic Ray Muon Computed Tomography Using a Fieldable Muon Spectrometer

by Junghyun Bae and Stylianos Chatzidakis

Energies 2022, 15(7), 2666; https://doi.org/10.3390/en15072666 - 5 Apr 2022

Cited by 6 | Viewed by 2502

Abstract

Cosmic ray muon tomography has been recently explored as a non-destructive technique for monitoring or imaging dense well-shielded objects, classically not achievable with traditional tomographic methods. As a recent example of technology transition from high-energy physics to real-world engineering applications, cosmic ray muon tomography has been used with various levels of success in nuclear nonproliferation. However, present muon detection systems have no momentum measurement capabilities and recently developed muon-based radiographic techniques rely only on muon tracking. This unavoidably reduces resolution and requires longer measurement times thus limiting the widespread use of cosmic ray muon tomography. Measurement of cosmic ray muon momenta has the potential to significantly improve the efficiency and resolution of cosmic ray muon tomography. In this paper, we propose and explore the use of momentum-dependent cosmic ray muon tomography using multi-layer gas Cherenkov radiators, a new concept for measuring muon momentum in the field. The muon momentum measurements are coupled with a momentum-dependent imaging algorithm (mPoCA) and image reconstructions are presented to demonstrate the benefits of measuring momentum in cosmic ray muon tomography. Full article

(This article belongs to the Special Issue Latest Advances in Nuclear Energy Systems)

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29 pages, 4374 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Operational Resilience of Nuclear-Renewable Integrated-Energy Microgrids

by Bikash Poudel, Linyu Lin, Tyler Phillips, Shannon Eggers, Vivek Agarwal and Timothy McJunkin

Energies 2022, 15(3), 789; https://doi.org/10.3390/en15030789 - 21 Jan 2022

Cited by 3 | Viewed by 2442

Abstract

The increasing prevalence and severity of wildfires, severe storms, and cyberattacks is driving the introduction of numerous microgrids to improve resilience locally. While distributed energy resources (DERs), such as small-scale wind and solar photovoltaics with storage, will be major components in future microgrids, today, the majority of microgrids are backed up with fossil-fuel-based generators. Small modular reactors (SMRs) can form synergistic mix with DERs due to their ability to provide baseload and flexible power. The heat produced by SMRs can also fulfill the heating needs of microgrid consumers. This paper discusses an operational scheme based on distributed control of flexible power assets to strengthen the operational resilience of SMR-DER integrated-energy microgrids. A framework is developed to assess the operational resilience of SMR-DER microgrids in terms of system adaptive real-power capacity quantified as a response area metric (RAM). Month-long simulation results are shown with a microgrid developed in a modified Institute of Electrical and Electronics Engineers (IEEE)-30 bus system. The RAM values calculated along the operational simulation reflect the system resilience in real time and can be used to supervise the microgrid operation and reactor’s autonomous control. Full article

(This article belongs to the Special Issue Latest Advances in Nuclear Energy Systems)

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28 pages, 1964 KiB

Open AccessArticle

A Hybrid Energy System Workflow for Energy Portfolio Optimization

by Jia Zhou, Hany Abdel-Khalik, Paul Talbot and Cristian Rabiti

Energies 2021, 14(15), 4392; https://doi.org/10.3390/en14154392 - 21 Jul 2021

Cited by 1 | Viewed by 2041

Abstract

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems. Full article

(This article belongs to the Special Issue Latest Advances in Nuclear Energy Systems)

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