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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2023

Derivative-based SINDy (DSINDy): Addressing the challenge of discovering governing equations from noisy data

Recent advances in the field of data-driven dynamics allow for the discovery of ODE systems using state measurements. One approach, known as Sparse Identification of Nonlinear Dynamics (SINDy), assumes the dynamics are sparse within a predetermined basis in the states and finds the expansion coefficients through linear regression with sparsity constraints. This approach requires an accurate estimation of the state time derivatives, which is not necessarily possible in the high-noise regime without additional constraints. We present an approach called Derivative-based SINDy (DSINDy) that combines two novel methods to improve ODE recovery at high-noise levels. First, we denoise the state variables by applying a projection operator that leverages the assumed basis for the system dynamics. Second, we use a second order cone program (SOCP) to find the derivative and governing equations simultaneously. We derive theoretical results for the projection-based denoising step, which allow us to estimate the values of hyperparameters used in the SOCP formulation. This underlying theory helps limit the number of required user-specified parameters. Finally, we present results demonstrating that our approach leads to improved system recovery for the Van der Pol oscillator, the Duffing oscillator, the Rössler attractor, and the Lorenz 96 model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, validation of AD models through real-world sensor data is important for their application in nuclear facilities. In this paper, we propose an Autoencoder (AE)— a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD) — as another AD scheme for identifying irregularities within the same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We also validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Benchmarking Soft Sensors for Remote Monitoring of On-Site Wastewater Treatment Plants

On-site wastewater treatment plants (OSTs) are usually unattended, so failures often remain undetected and lead to prolonged periods of reduced performance. To stabilize the performance of unattended plants, soft sensors could expose faults and failures to the operator. In a previous study, we developed soft sensors and showed that soft sensors with data from unmaintained physical sensors can be as accurate as soft sensors with data from maintained ones. The monitored variables were pH and dissolved oxygen (DO), and soft sensors were used to predict nitrification performance. In the present study, we use synthetic data and monitor three plants to test these soft sensors. We find that a long solids retention time and a moderate aeration rate improve the pH soft-sensor accuracy and that the aeration regime is the main operational parameter affecting the accuracy of the DO soft sensor. We demonstrate that integrated design of monitoring and control is necessary to achieve robustness when extrapolating from one OST to another in the absence of plant-specific fine-tuning. Additionally, we provide a unique labeled dataset for further feature and data-driven soft-sensor development. Our benchmarking results indicate that it is feasible to monitor OSTs with unmaintained sensors and without plant-specific tuning of the developed soft sensors. This is expected to drastically reduce monitoring costs for OST-based sanitation systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Tokamak Energy’s pre-concept design for a fusion power plant: an overview of ST-E1

Climate change and rapidly rising energy demand, driven in part by artificial intelligence and data-centre growth, create an urgent need for stable, low-carbon, and abundant power. Fusion is a promising long-term solution, yet its commercialisation faces a fundamental paradox in today’s investment environment: pilot plants are essential to de-risk physics, engineering, and operations, but their limited lifetime energy output and high upfront costs make them difficult to finance. This paper presents Tokamak Energy’s response: ST-E1, a pre-concept design for a low-aspect-ratio tokamak power plant engineered specifically to overcome this challenge. ST-E1 is designed from the outset for phased operation—pilot and commercial phases, with an upgrade phase in between—with emphasis on commercial viability, maintainability, nuclear engineering, modularity, and upgradability. A key design principle is the deliberate separation of long-lived assets, such as the magnet cage and vacuum vessel, from replaceable in-vessel systems. This provides an attractive and credible investment approach to generate operational data and de-risk key technologies while preserving most capital-intensive assets for later commercial phases. The architecture supports continuous optimisation toward high net electric power (targeting 800–1000 MW net electric), a normalised capital expenditure of $\$$ 12–14k/kW of net electric power, and high availability (targeting > 80%). A tokamak core with a 5 m major radius, aspect ratio of 2.3, and on-plasma axis toroidal field of 5.25 T was selected to meet these objectives. This paper summarises the ST-E1 design philosophy, principal features, and development methodology. It introduces a Focus Collection of 11 papers detailing the pre-concept design of the entire tokamak and corresponding plant.

ST-E1↗
DOE OSTI2025

Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines

Neutrinoless double-beta decay ($0\nu\beta\beta$) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the Universe. The large enriched germanium experiment for neutrinoless double-beta decay (LEGEND) will operate in two phases to search for $0\nu\beta\beta$. The first (second) stage will employ 200 (1000) kg of High-Purity Germanium (HPGe) enriched in 76 Ge to achieve a half-life sensitivity of 10 27 (10 28 ) years. In this study, we present a semi-supervised data-driven approach to remove non-physical events captured by HPGe detectors powered by a novel artificial intelligence model. We utilize affinity propagation to cluster waveform signals based on their shape and a support vector machine to classify them into different categories. We train, optimize, and test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. We demonstrate that our model yields a maximum sacrifice of physics events of $0.024 ^{+0.004}_{-0.003} \%$ after data cleaning. Our model is being used to accelerate data cleaning development for LEGEND-200 and will serve to improve data cleaning procedures for LEGEND-1000.

artificial intelligence↗
DOE OSTI2025

Acceleration of Power System Dynamic Simulations Using a Deep Equilibrium Layer and Neural ODE Surrogate

The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. Here, in this paper, we propose a data-driven surrogate model based on implicit machine learningspecifically deep equilibrium layers and neural ordinary differential equationsto learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.

Neural ordinary differential equations↗
DOE OSTI2016

TH-C-204-00: Medical Physics Workshop: An Update On the Journal’s Improvement Activities and Guidance On Writing and Reviewing Papers

In this presentation, the Editors will outline our vision for the future of Medical Physics and review recent work-in-progress initiatives to implement this vision. Finally, we will close with guidance to authors on how to write a good Medical Physics paper. A major focus will be the transition to a new publisher in 2017 following a more than 40-year association with American Institute of Physics Publishing (AIPP). Vision for Medical Physics and status of current initiatives: Jeff Williamson, Editor-in-Chief The broad vision of Medical Physics is “to continue the Journal’s tradition of publishing the very best science that propels our discipline forward and improves our contribution to patient care.” More concretely, the Journal should be the preeminent forum for exchange of cutting edge medical physics science. We seek to identify the best contributions in (a) high impact clinical physics innovations; (b) clinical translation and validation of basic science innovations; and (c) cutting edge basic science developments with potential for patient care improvements. Among the challenges and opportunities, we face are: electronic-only and open access publishing; competition from new radiological science journals; trends towards more interactive, social-media based scientific communities; and diversification of the medical physics research, authorship, and readership domains, including clinical applications quite foreign to core ABR clinical competencies. Recently implemented and ongoing initiatives include: Revised Table of Contents (TOC) and more contemporary topical submission categories Structured review template in HTML format Comprehensive hierarchical taxonomy for identifying reviewer expertise Formal process for soliciting high quality and impact Review and Vision 20/20 Articles We have recruited four Review Article Co-editors: John Rowlands and Ingrid Reiser (imaging physics) and Joao Seco and Tim Zhu (therapy physics). The Co-Editors will identify timely topics and solicit high profile authors to submit review manuscripts. To submit an article, authors will need to work with an assigned Co-Editor to develop a mutually acceptable outline and abstract. 5) A new and exciting class of articles: Medical Physics Dataset Articles (MPDAs) MPDAs describe scientifically or clinically valuable open-access datasets with high potential for contributing to the research of medical physicists working on related problems. In contrast to Research Articles, MPDAs should not include hypothesis testing; or data analyses supporting generalizable conclusions. The publically accessible dataset must be permanently archived before the MPDA can be published. This initiative is being led by Joe Deasy. Update on new publisher transition: The transition of AAPM scientific publishing operations to a major publishing house is a major opportunity to expand Medical Physics readership and its scholarly impact. The advantages include: (a) common manuscript management and web hosting platforms for Medical Physics and its sister journal, JACMP; (b) greater than 4-fold expansion of subscribing institutions; and (c) resources to mount data-driven, highly targeted marketing campaigns to enhance citation and download rates. A transition update of this epochal development, which has only begun as of this writing (3/31/16), will be given. Improving manuscript quality via structured reviews, enhanced scientific category taxonomy, and outreach: Shiva Das, Therapy Physics Editor Medical Physics is committed to continuous improvement with the ultimate goal of improving the potential impact of accepted manuscripts. In order to do so, Medical Physics must be able to tap into important/emerging areas and be able to select high quality contributions consistently via discerning reviews. Improving the quality of reviews is crucial to selecting high quality manuscripts and also to improving manuscript impact via feedback in the review process. With this in mind, Medical Physics is in the process of: (a) fostering outreach to important areas that are currently underrepresented in Medical Physics; (b) implementing a structured template review form; and (c) implementing a comprehensive scientific category taxonomy to identify reviewers who are best suited to an article. Outreach efforts have begun to various scientific areas. Strategies to increase submissions from these areas will be discussed. As a consequence of this effort, a special issue on particle therapy is under development. A review template was implemented in late 2014 on a limited test basis. Based on reviewer feedback, the template was restructured and shortened to capture essential review elements. The restructured template is due to be released shortly. The new scientific category taxonomy is in the process of being deployed to reviewers and associate editors. Salient aspects of the structured review template and scientific category taxonomy will be discussed in this talk. Writing good scientific papers and responding to critiques: Mitch Goodsitt, Imaging Physics Editor The essential components of the abstract, introduction, methods, discussion and conclusion sections of manuscripts, as well as the desired writing style and style of the figures and tables will be reviewed. Publishable Medical Physics manuscripts must include clear and concise statements of the novelty and clinical and/or scientific importance of the authors’ work. Examples of novelty include: new technical solution to an important clinical problem; new generalizable knowledge; and first demonstration that an existing engineering solution solves a clinical problem. Please note that we encourage authors of recently published conference proceedings (e.g., SPIE, IEEE) papers on novel medical physics related work to submit more substantial versions of that work to our journal. All submissions must include: sufficient background information and rationale; enough detail for others to reproduce the authors’ work; sufficient statistical analysis to refute or validate the authors’ hypotheses; a description of how the present work compares to, is distinct from, and improves upon others’ work; and sections devoted to the limitations of the study and future directions. Writing should be polished. Poor wording, grammar and composition frustrate the review process. Our journal does not have copyeditors for revising manuscripts. When authors receive critiques from the referees and associate editor, the authors should provide a detailed point-by-point response to each comment. The authors’ rebuttal should include the text of the original criticism, the authors’ response, and a pasted copy of the modified text along with the line numbers in the revised article. The new text should be highlighted in a different font color in the revised submission. Following these recommendations will improve submissions and facilitate the review process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-C-204-01: Vision for Medical Physics and Status of Current Initiatives

In this presentation, the Editors will outline our vision for the future of Medical Physics and review recent work-in-progress initiatives to implement this vision. Finally, we will close with guidance to authors on how to write a good Medical Physics paper. A major focus will be the transition to a new publisher in 2017 following a more than 40-year association with American Institute of Physics Publishing (AIPP). Vision for Medical Physics and status of current initiatives: Jeff Williamson, Editor-in-Chief The broad vision of Medical Physics is “to continue the Journal’s tradition of publishing the very best science that propels our discipline forward and improves our contribution to patient care.” More concretely, the Journal should be the preeminent forum for exchange of cutting edge medical physics science. We seek to identify the best contributions in (a) high impact clinical physics innovations; (b) clinical translation and validation of basic science innovations; and (c) cutting edge basic science developments with potential for patient care improvements. Among the challenges and opportunities, we face are: electronic-only and open access publishing; competition from new radiological science journals; trends towards more interactive, social-media based scientific communities; and diversification of the medical physics research, authorship, and readership domains, including clinical applications quite foreign to core ABR clinical competencies. Recently implemented and ongoing initiatives include: Revised Table of Contents (TOC) and more contemporary topical submission categories Structured review template in HTML format Comprehensive hierarchical taxonomy for identifying reviewer expertise Formal process for soliciting high quality and impact Review and Vision 20/20 Articles We have recruited four Review Article Co-editors: John Rowlands and Ingrid Reiser (imaging physics) and Joao Seco and Tim Zhu (therapy physics). The Co-Editors will identify timely topics and solicit high profile authors to submit review manuscripts. To submit an article, authors will need to work with an assigned Co-Editor to develop a mutually acceptable outline and abstract. 5) A new and exciting class of articles: Medical Physics Dataset Articles (MPDAs) MPDAs describe scientifically or clinically valuable open-access datasets with high potential for contributing to the research of medical physicists working on related problems. In contrast to Research Articles, MPDAs should not include hypothesis testing; or data analyses supporting generalizable conclusions. The publically accessible dataset must be permanently archived before the MPDA can be published. This initiative is being led by Joe Deasy. Update on new publisher transition: The transition of AAPM scientific publishing operations to a major publishing house is a major opportunity to expand Medical Physics readership and its scholarly impact. The advantages include: (a) common manuscript management and web hosting platforms for Medical Physics and its sister journal, JACMP; (b) greater than 4-fold expansion of subscribing institutions; and (c) resources to mount data-driven, highly targeted marketing campaigns to enhance citation and download rates. A transition update of this epochal development, which has only begun as of this writing (3/31/16), will be given. Improving manuscript quality via structured reviews, enhanced scientific category taxonomy, and outreach: Shiva Das, Therapy Physics Editor Medical Physics is committed to continuous improvement with the ultimate goal of improving the potential impact of accepted manuscripts. In order to do so, Medical Physics must be able to tap into important/emerging areas and be able to select high quality contributions consistently via discerning reviews. Improving the quality of reviews is crucial to selecting high quality manuscripts and also to improving manuscript impact via feedback in the review process. With this in mind, Medical Physics is in the process of: (a) fostering outreach to important areas that are currently underrepresented in Medical Physics; (b) implementing a structured template review form; and (c) implementing a comprehensive scientific category taxonomy to identify reviewers who are best suited to an article. Outreach efforts have begun to various scientific areas. Strategies to increase submissions from these areas will be discussed. As a consequence of this effort, a special issue on particle therapy is under development. A review template was implemented in late 2014 on a limited test basis. Based on reviewer feedback, the template was restructured and shortened to capture essential review elements. The restructured template is due to be released shortly. The new scientific category taxonomy is in the process of being deployed to reviewers and associate editors. Salient aspects of the structured review template and scientific category taxonomy will be discussed in this talk. Writing good scientific papers and responding to critiques: Mitch Goodsitt, Imaging Physics Editor The essential components of the abstract, introduction, methods, discussion and conclusion sections of manuscripts, as well as the desired writing style and style of the figures and tables will be reviewed. Publishable Medical Physics manuscripts must include clear and concise statements of the novelty and clinical and/or scientific importance of the authors’ work. Examples of novelty include: new technical solution to an important clinical problem; new generalizable knowledge; and first demonstration that an existing engineering solution solves a clinical problem. Please note that we encourage authors of recently published conference proceedings (e.g., SPIE, IEEE) papers on novel medical physics related work to submit more substantial versions of that work to our journal. All submissions must include: sufficient background information and rationale; enough detail for others to reproduce the authors’ work; sufficient statistical analysis to refute or validate the authors’ hypotheses; a description of how the present work compares to, is distinct from, and improves upon others’ work; and sections devoted to the limitations of the study and future directions. Writing should be polished. Poor wording, grammar and composition frustrate the review process. Our journal does not have copyeditors for revising manuscripts. When authors receive critiques from the referees and associate editor, the authors should provide a detailed point-by-point response to each comment. The authors’ rebuttal should include the text of the original criticism, the authors’ response, and a pasted copy of the modified text along with the line numbers in the revised article. The new text should be highlighted in a different font color in the revised submission. Following these recommendations will improve submissions and facilitate the review process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-C-204-02: Improving Manuscript Quality Via Structured Reviews, Enhanced Scientific Category Taxonomy, and Outreach

In this presentation, the Editors will outline our vision for the future of Medical Physics and review recent work-in-progress initiatives to implement this vision. Finally, we will close with guidance to authors on how to write a good Medical Physics paper. A major focus will be the transition to a new publisher in 2017 following a more than 40-year association with American Institute of Physics Publishing (AIPP). Vision for Medical Physics and status of current initiatives: Jeff Williamson, Editor-in-Chief The broad vision of Medical Physics is “to continue the Journal’s tradition of publishing the very best science that propels our discipline forward and improves our contribution to patient care.” More concretely, the Journal should be the preeminent forum for exchange of cutting edge medical physics science. We seek to identify the best contributions in (a) high impact clinical physics innovations; (b) clinical translation and validation of basic science innovations; and (c) cutting edge basic science developments with potential for patient care improvements. Among the challenges and opportunities, we face are: electronic-only and open access publishing; competition from new radiological science journals; trends towards more interactive, social-media based scientific communities; and diversification of the medical physics research, authorship, and readership domains, including clinical applications quite foreign to core ABR clinical competencies. Recently implemented and ongoing initiatives include: Revised Table of Contents (TOC) and more contemporary topical submission categories Structured review template in HTML format Comprehensive hierarchical taxonomy for identifying reviewer expertise Formal process for soliciting high quality and impact Review and Vision 20/20 Articles We have recruited four Review Article Co-editors: John Rowlands and Ingrid Reiser (imaging physics) and Joao Seco and Tim Zhu (therapy physics). The Co-Editors will identify timely topics and solicit high profile authors to submit review manuscripts. To submit an article, authors will need to work with an assigned Co-Editor to develop a mutually acceptable outline and abstract. 5) A new and exciting class of articles: Medical Physics Dataset Articles (MPDAs) MPDAs describe scientifically or clinically valuable open-access datasets with high potential for contributing to the research of medical physicists working on related problems. In contrast to Research Articles, MPDAs should not include hypothesis testing; or data analyses supporting generalizable conclusions. The publically accessible dataset must be permanently archived before the MPDA can be published. This initiative is being led by Joe Deasy. Update on new publisher transition: The transition of AAPM scientific publishing operations to a major publishing house is a major opportunity to expand Medical Physics readership and its scholarly impact. The advantages include: (a) common manuscript management and web hosting platforms for Medical Physics and its sister journal, JACMP; (b) greater than 4-fold expansion of subscribing institutions; and (c) resources to mount data-driven, highly targeted marketing campaigns to enhance citation and download rates. A transition update of this epochal development, which has only begun as of this writing (3/31/16), will be given. Improving manuscript quality via structured reviews, enhanced scientific category taxonomy, and outreach: Shiva Das, Therapy Physics Editor Medical Physics is committed to continuous improvement with the ultimate goal of improving the potential impact of accepted manuscripts. In order to do so, Medical Physics must be able to tap into important/emerging areas and be able to select high quality contributions consistently via discerning reviews. Improving the quality of reviews is crucial to selecting high quality manuscripts and also to improving manuscript impact via feedback in the review process. With this in mind, Medical Physics is in the process of: (a) fostering outreach to important areas that are currently underrepresented in Medical Physics; (b) implementing a structured template review form; and (c) implementing a comprehensive scientific category taxonomy to identify reviewers who are best suited to an article. Outreach efforts have begun to various scientific areas. Strategies to increase submissions from these areas will be discussed. As a consequence of this effort, a special issue on particle therapy is under development. A review template was implemented in late 2014 on a limited test basis. Based on reviewer feedback, the template was restructured and shortened to capture essential review elements. The restructured template is due to be released shortly. The new scientific category taxonomy is in the process of being deployed to reviewers and associate editors. Salient aspects of the structured review template and scientific category taxonomy will be discussed in this talk. Writing good scientific papers and responding to critiques: Mitch Goodsitt, Imaging Physics Editor The essential components of the abstract, introduction, methods, discussion and conclusion sections of manuscripts, as well as the desired writing style and style of the figures and tables will be reviewed. Publishable Medical Physics manuscripts must include clear and concise statements of the novelty and clinical and/or scientific importance of the authors’ work. Examples of novelty include: new technical solution to an important clinical problem; new generalizable knowledge; and first demonstration that an existing engineering solution solves a clinical problem. Please note that we encourage authors of recently published conference proceedings (e.g., SPIE, IEEE) papers on novel medical physics related work to submit more substantial versions of that work to our journal. All submissions must include: sufficient background information and rationale; enough detail for others to reproduce the authors’ work; sufficient statistical analysis to refute or validate the authors’ hypotheses; a description of how the present work compares to, is distinct from, and improves upon others’ work; and sections devoted to the limitations of the study and future directions. Writing should be polished. Poor wording, grammar and composition frustrate the review process. Our journal does not have copyeditors for revising manuscripts. When authors receive critiques from the referees and associate editor, the authors should provide a detailed point-by-point response to each comment. The authors’ rebuttal should include the text of the original criticism, the authors’ response, and a pasted copy of the modified text along with the line numbers in the revised article. The new text should be highlighted in a different font color in the revised submission. Following these recommendations will improve submissions and facilitate the review process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-C-204-03: Writing Good Scientific Papers and Responding to Critiques

In this presentation, the Editors will outline our vision for the future of Medical Physics and review recent work-in-progress initiatives to implement this vision. Finally, we will close with guidance to authors on how to write a good Medical Physics paper. A major focus will be the transition to a new publisher in 2017 following a more than 40-year association with American Institute of Physics Publishing (AIPP). Vision for Medical Physics and status of current initiatives: Jeff Williamson, Editor-in-Chief The broad vision of Medical Physics is “to continue the Journal’s tradition of publishing the very best science that propels our discipline forward and improves our contribution to patient care.” More concretely, the Journal should be the preeminent forum for exchange of cutting edge medical physics science. We seek to identify the best contributions in (a) high impact clinical physics innovations; (b) clinical translation and validation of basic science innovations; and (c) cutting edge basic science developments with potential for patient care improvements. Among the challenges and opportunities, we face are: electronic-only and open access publishing; competition from new radiological science journals; trends towards more interactive, social-media based scientific communities; and diversification of the medical physics research, authorship, and readership domains, including clinical applications quite foreign to core ABR clinical competencies. Recently implemented and ongoing initiatives include: Revised Table of Contents (TOC) and more contemporary topical submission categories Structured review template in HTML format Comprehensive hierarchical taxonomy for identifying reviewer expertise Formal process for soliciting high quality and impact Review and Vision 20/20 Articles We have recruited four Review Article Co-editors: John Rowlands and Ingrid Reiser (imaging physics) and Joao Seco and Tim Zhu (therapy physics). The Co-Editors will identify timely topics and solicit high profile authors to submit review manuscripts. To submit an article, authors will need to work with an assigned Co-Editor to develop a mutually acceptable outline and abstract. 5) A new and exciting class of articles: Medical Physics Dataset Articles (MPDAs) MPDAs describe scientifically or clinically valuable open-access datasets with high potential for contributing to the research of medical physicists working on related problems. In contrast to Research Articles, MPDAs should not include hypothesis testing; or data analyses supporting generalizable conclusions. The publically accessible dataset must be permanently archived before the MPDA can be published. This initiative is being led by Joe Deasy. Update on new publisher transition: The transition of AAPM scientific publishing operations to a major publishing house is a major opportunity to expand Medical Physics readership and its scholarly impact. The advantages include: (a) common manuscript management and web hosting platforms for Medical Physics and its sister journal, JACMP; (b) greater than 4-fold expansion of subscribing institutions; and (c) resources to mount data-driven, highly targeted marketing campaigns to enhance citation and download rates. A transition update of this epochal development, which has only begun as of this writing (3/31/16), will be given. Improving manuscript quality via structured reviews, enhanced scientific category taxonomy, and outreach: Shiva Das, Therapy Physics Editor Medical Physics is committed to continuous improvement with the ultimate goal of improving the potential impact of accepted manuscripts. In order to do so, Medical Physics must be able to tap into important/emerging areas and be able to select high quality contributions consistently via discerning reviews. Improving the quality of reviews is crucial to selecting high quality manuscripts and also to improving manuscript impact via feedback in the review process. With this in mind, Medical Physics is in the process of: (a) fostering outreach to important areas that are currently underrepresented in Medical Physics; (b) implementing a structured template review form; and (c) implementing a comprehensive scientific category taxonomy to identify reviewers who are best suited to an article. Outreach efforts have begun to various scientific areas. Strategies to increase submissions from these areas will be discussed. As a consequence of this effort, a special issue on particle therapy is under development. A review template was implemented in late 2014 on a limited test basis. Based on reviewer feedback, the template was restructured and shortened to capture essential review elements. The restructured template is due to be released shortly. The new scientific category taxonomy is in the process of being deployed to reviewers and associate editors. Salient aspects of the structured review template and scientific category taxonomy will be discussed in this talk. Writing good scientific papers and responding to critiques: Mitch Goodsitt, Imaging Physics Editor The essential components of the abstract, introduction, methods, discussion and conclusion sections of manuscripts, as well as the desired writing style and style of the figures and tables will be reviewed. Publishable Medical Physics manuscripts must include clear and concise statements of the novelty and clinical and/or scientific importance of the authors’ work. Examples of novelty include: new technical solution to an important clinical problem; new generalizable knowledge; and first demonstration that an existing engineering solution solves a clinical problem. Please note that we encourage authors of recently published conference proceedings (e.g., SPIE, IEEE) papers on novel medical physics related work to submit more substantial versions of that work to our journal. All submissions must include: sufficient background information and rationale; enough detail for others to reproduce the authors’ work; sufficient statistical analysis to refute or validate the authors’ hypotheses; a description of how the present work compares to, is distinct from, and improves upon others’ work; and sections devoted to the limitations of the study and future directions. Writing should be polished. Poor wording, grammar and composition frustrate the review process. Our journal does not have copyeditors for revising manuscripts. When authors receive critiques from the referees and associate editor, the authors should provide a detailed point-by-point response to each comment. The authors’ rebuttal should include the text of the original criticism, the authors’ response, and a pasted copy of the modified text along with the line numbers in the revised article. The new text should be highlighted in a different font color in the revised submission. Following these recommendations will improve submissions and facilitate the review process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Avista CEF2 Shared Energy Economy: Modeling and Simulation

This research project supported Avista’s Shared Energy Economy (SEE) Project in Spokane’s University District (UDistrict), as funded by the WA Department of Commerce Clean Energy Fund (CEF) Phase 2. The SEE project comprises a microgrid with two campus buildings, two solar photovoltaic generators, and two batteries. The microgrid can operate grid-connected or off-grid. This research project included modeling the buildings and other microgrid components, simulating a transactive energy system, and supporting Avista’s testing of different use cases for the WA Department of Commerce. The main research finding is that buildings can be modeled successfully in a data-driven approach.

14 SOLAR ENERGY↗
DOE OSTI2022

Mapping of a Novel Zero-Liquid Discharge Desalination System Based on Humidification–Dehumidification onto the Field of Existing Desalination Technologies

It is well-established that increasing demands for fresh water are paving the way for desalination technologies. However, this correlates with an increase in brine production whose treatment and disposal can be complicated and expensive. This paper presents a thermodynamic model to bound the operation and development of a novel Humidification–Dehumidification-based system featuring Zero-Liquid Discharge and off-grid capabilities. The model employs conservation laws to find feasible state points to meet a baseline operation of 10 kg/h of product water separated from a hypersaline feed stream with 100 g/kg salt concentration. The system incurs in a 1039 kWh/m3 energy intensity that can be supplied completely by an electric source or in combination with heating steam. Follow-up sensitivity analysis highlights the robustness of the system in handling variations of 25% in product flowrate and 75% in feed salinity, practically without incurring any additional energy demands. The proposed system operating costs between 72 USD/m3 and 96 USD/m3 are comparable to those of existing brine disposal techniques. Furthermore, an operational map of existing desalination technologies suggests a niche characterized by high recovery rates and high feed salinities that are generally unfulfilled by conventional desalination methods. Overall, the proposed system shows potential for off-grid hypersaline brine treatment. This study sets the stage for future development of physics-based and data-driven predictive models as the proposed system iterates into a pilot plant deployment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data

We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data-driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine learning methods and template fitting methods by building template SEDs directly from the spectroscopic training data. This is made computationally tractable with Gaussian processes operating in flux–redshift space, encoding the physics of redshifts and the projection of galaxy SEDs onto photometric bandpasses. This method alleviates the need to acquire representative training data or to construct detailed galaxy SED models; it requires only that the photometric bandpasses and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data with reliable redshifts, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the i -magnitude-selected, spectroscopically confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST ) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes or to simulate populations of galaxies with realistic fluxes and redshifts, for example.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Dynamically Learning Incentives for Load Control

As electrical generation becomes more distributed and volatile, and loads become more uncertain, controllability of distributed energy resources (DERs), regardless of their ownership status, will be necessary for grid reliability. Grid operators lack direct control over end-users' grid interactions, such as energy usage, but incentives can influence behavior -- for example, an end-user that receives a grid-driven incentive may adjust their consumption or expose relevant control variables in response. A key challenge in studying such incentives is the lack of data about human behavior, which usually motivates strong assumptions, such as distributional assumptions on compliance or rational utility-maximization. In this paper, we propose a general incentive mechanism in the form of a constrained optimization problem -- our approach is distinguished from prior work by modeling human behavior (e.g., reactions to an incentive) as an arbitrary unknown function. We propose feedback-based optimization algorithms to solve this problem that each leverage different amounts of information and/or measurements. We show that each converges to an asymptotically stable incentive with (near)-optimality guarantees given mild assumptions on the problem. Finally, we evaluate our proposed techniques in voltage regulation simulations on standard test beds. We test a variety of settings, including those that break assumptions required for theoretical convergence (e.g., convexity, smoothness) to capture realistic settings. In this evaluation, our proposed algorithms are able to find near-optimal incentives even when the reaction to an incentive is modeled by a theoretically difficult (yet realistic) function.

demand response↗
DOE OSTI2022

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗
DOE OSTI2017

Strain control of composite superconductors to prevent degradation of superconducting magnets due to a quench: II. High-strength, laminated Ag-sheathed Bi-2223 tapes

In article I of this series, we described a spiral coil quench technique for probing the influence of the superconductor stress and strain state during normal operation on its margin to degradation during a quench and applied to a Bi-2212 round wire. Here we extend this technique to study the failure mechanisms and limits of high-strength Bi-2223 tapes experiencing a quench while carrying a large current in a high magnetic field. In contrast to Bi-2212 magnets made via a wind-and-react technique for which bending strains can be ignored, Bi-2223 magnets are made with a react-and-wind technique for which bending strain is significant. The critical tensile stress of Bi-2223 tapes (type HT-NX) decreases from >440 MPa for straight samples to 185 MPa after being bent to a diameter D of 50 mm. For HT-NX tapes with D = 50 mm, the quench degradation limit, measured using maximum allowable temperature during a quench T allowable , is greater than 300 K for axial tensile stress ${\sigma }_{a}$ < 94 MPa; it decreases with increasing tensile axial stress ${\sigma }_{a}$ nonlinearly, dropping to 230 K for ${\sigma }_{a}$ = 125 MPa. T allow able $({\sigma }_{a})$ experimental data at D = 50 mm is consistently predicted by a general strain model that assumes that quench degradation in NX/Bi-2223 is driven by axial tensile strain in Bi-2223 filaments exceeding the irreversible strain limit. The T allowable $({\sigma }_{a})$ is then predicted for various D including D = 80 mm important for NMR magnets. In conclusion, the given T allowable $(D,{\sigma }_{a})$ is easy to use and important for finding the balance between operation stress, and therefore magnetic field generation efficiency, and operation margin when designing a superconducting magnet using Bi-2223 tapes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Evaluation of Machine Learning Approaches to Estimate Aerosol Mixing State Metrics in Atmospheric Models

Aerosol mixing state describes how aerosol compositions are distributed among atmospheric aerosol particles in a population. Oversimplified assumptions of aerosol mixing state in atmospheric modeling can introduce errors in estimations of weather and climate-relevant aerosol microphysical properties. A more comprehensive representation of the aerosol mixing state can be achieved in principle with a Particle-resolved Monte Carlo (PartMC) model but at added computational cost that may be prohibitive for direct invocation in operational numerical weather prediction or multi-year climate simulations. The aim of our research is to explore the machine learning (ML) methodologies for estimating aerosol mixing state metrics, which we define here in three different ways: with respect to (a) hygroscopicity; (b) optical properties; and (c) chemical species abundance. We adopted a data-driven approach, leveraging deep learning and statistical learning techniques, to take advantage of massive PartMC model simulations. First, we performed particle-resolved simulations by PartMC to create a series of scenarios considering a range of global environmental conditions. Each scenario consists of aerosol populations and corresponding mixing state metrics. The gas concentration, aerosol mass concentration, environmental variables and mixing state metrics of each population constitute the datasets for machine learning implementations. We have adopted and evaluated various configurations of machine learning methodologies in this investigation, embracing deep learning, Extreme Gradient Boosting (XGBoost) algorithm, and the ensemble approaches. After a rigorous model selection process, we identified an appropriate model to derive estimates of aerosol mixing state metrics. We used the computational and data resources of the Oak Ridge Leadership Computing Facility (OLCF) and the ORNL Compute and Data Environment for Science (CADES). The NVIDIA DGX-1 hardware was used for the prototyping of the ML models. Our approach has allowed us to gain a new understanding of how machine learning methodologies can be applied to improve the representation of aerosol mixing state in atmospheric models and benefit the atmospheric research community. Next, we plan to extend our research and methodology to quantify some of the aerosol-related uncertainties in the E3SM Atmospheric Model (EAM) Version 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Application of Margin-Based Methods to Assess System Health

Health management of complex systems such as nuclear power plants is an essential task to guarantee system reliability. This task can be greatly enhanced by constantly monitoring asset status and performances and process such data (through anomaly detection, diagnostic, and prognostic computational algorithms) to identify asset degradation trends and faulty states. While such information and data are typically available for many of the assets, they are not propagated from the asset to the system level in order to identify the most critical assets and prioritize maintenance and surveillance activities. The main reason is driven by the fact that current reliability modeling techniques are inadequate to process such information/data. This is due to the nature of these techniques which are based on the concept of failure rate/probability that do not serve an operational context where quantitative asset health information is available. Simply stated, current reliability techniques serve a run-to-failure operational setting and not a predictive maintenance one where the goal is to perform maintenance and surveillance activities only when they are needed based on asset health. The risk informed asset management (RIAM) project is focusing on the development of a different kind of reliability modeling techniques designed to adequately serve a predictive operational setting. Such reliability techniques move aways from a failure rate/probability to a margin-based mindset where margin is here used as a metric to quantify asset health based only on current and past operational experience of the asset under consideration. In addition, margin-based reliability techniques are able to propagate asset health information from the component to system level and provide importance measure to each asset. This report summarizes a recent activity performed in collaboration with plant modernization pathway designed to integrate monitoring data into margin-based reliability models. Such activity focuses on a specific system of an existing nuclear power plant where large amount of historic monitoring data is used to monitor asset and system health.

97 MATHEMATICS AND COMPUTING↗