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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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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Ion Exchange Column Thermal Modeling: Prevention of Process Effluent Boiling - 18577

The Savannah River Site treats and disposes of legacy high-level waste, which resulted from 60 years of nuclear material production, medical research, and other nuclear related work. Complex mixtures of soluble and insoluble solids occupy tanks of which the South Carolina Department of Health and Environmental Control recognizes as the single largest environmental risk state-wide. The risk is reduced through removal, treatment, and solidification of decontaminated salt waste. Presently, SRS decontaminates the soluble salt component of the waste, a solution containing actinides and cesium, via implementation of the Actinide Removal Process and the Modular Caustic Side Solvent- Extraction Unit. Caustic solvent extraction is set to increase in scale by late 2018, with startup of the newly constructed Salt Waste Processing Facility. In conjunction with Salt Waste Processing Facility startup, Savannah River Remediation has been tasked by the Department of Energy to demonstrate modular ion exchange technology. The Tank Closure Cesium Removal project aims to demonstrate the feasibility of modular ion exchange processes for future Department of Energy applications. The Tank Closure Cesium Removal system is scheduled to begin operation in mid-2018. Dissolved salt solution, or influent, will be fed through a filter and ion exchange columns in lead-lag configuration, producing effluent. The effluent is fed to a separate high-level waste tank, for future disposition at Salt-stone Production Facility. Once the demonstration project is complete, the loaded ion exchange columns will be purged, dried, and placed in interim safe storage, awaiting final disposition. The ion exchange media selected for the Tank Closure Cesium Removal system is Honeywell UOP IONSIV{sup C} R-9120. The media is a prime candidate for separation of cesium from radioactive high-level waste. UOP R-9120{sup C} is inorganic and has high capacity, selectivity, and affinity for cesium. During operation, influent is fed to the ion exchange columns, contacting UOP R-9210{sup C} packed within the media vessel. Contact initiates loading of isotopes, and given ample time the solid phase (ion exchange media) and solution achieve thermodynamic equilibrium. UOP R-9120{sup C} is a prime candidate for separation of cesium and dissolved fission products. Although the properties of UOP R-9120{sup C} are favorable for separation, the same properties lead to accumulation of a heat source in the ion exchange bed. Cesium-137 and its daughter product, barium-137 m, generate a clear majority of heat (i.e. ∼ 94%). During operation salt solution flows through each column and transports heat from the ion exchange bed. Once operation is terminated, the effluent contained within the porous bed volume becomes essentially static. In the absence of engineering controls, the heat generated may boil the effluent. Rapid vaporization of aqueous salt solution would lead to bed over pressurization. Over pressurization and an associated release of high-level waste would result in adverse impacts to human health and the environment. This paper focuses on modeling the heat transfer associated with the Tank Closure Cesium Removal system ion exchange column. The model evaluates the maximum heat generation and the associated radionuclide loadings that prevent boiling within of the media vessel, under no flow conditions. A safety significant control, maintaining loadings less than the established loading limit ensures associated impacts to human health and the environment are prevented. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Long‐Term Large‐Scale Atmospheric Forcing Data From Three‐Dimensional Constrained Variational Analysis for the ARM SGP Site

Here, this study presents a long‐term three‐dimensional large‐scale forcing data set (VARANAL3D) derived from the three‐dimensional constrained variational analysis (3DCVA) method at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) site from 2004 to 2018. Building on the same input data sets as the conventional continuous forcing data set (VARANAL), VARANAL3D maintains overall consistency in domain‐averaged fields while introducing spatial variability, offering critical insights into the influence of mesoscale synoptic systems on cloud‐related processes. Evaluations are conducted across four cloud and precipitation regimes: Clear‐sky, Shallow‐clouds, Afternoon‐precipitation, and Nocturnal‐precipitation, presenting high consistency of the domain‐mean forcing data sets while emphasizing the role of subdomain forcing variability particularly in precipitating regimes. Single column model (SCM) simulations demonstrate that subdomain VARANAL3D forcing improves cloud and precipitation representation, with the ensemble outperforming domain‐mean forcing in three cloudy and precipitating regimes. Overall, these results highlight VARANAL3D's value for investigating the impacts of spatial variability of large‐scale forcing on atmospheric processes. The VARANAL3D data set provides new opportunities for evaluating model physics, advancing the development of scale‐aware parameterizations and deepening our understanding of cloud and precipitation dynamics.

Environmental sciences↗
DOE OSTI2018

Search for pair production of up-type vector-like quarks and for four-top-quark events in final states with multiple b-jets with the ATLAS detector

A search for pair production of up-type vector-like quarks ( T ) with a significant branching ratio into a top quark and either a Standard Model Higgs boson or a Z boson is presented. The same analysis is also used to search for four-top-quark production in several new physics scenarios. The search is based on a dataset of pp collisions at $\sqrt{s}$ =13 TeV recorded in 2015 and 2016 with the ATLAS detector at the CERN Large Hadron Collider and corresponds to an integrated luminosity of 36.1 fb -1 . Data are analysed in the lepton+jets final state, characterised by an isolated electron or muon with high transverse momentum, large missing transverse momentum and multiple jets, as well as the jets + E T miss final state, characterised by multiple jets and large missing transverse momentum. The search exploits the high multiplicity of jets identified as originating from b -quarks, and the presence of boosted, hadronically decaying top quarks and Higgs bosons reconstructed as large-radius jets, characteristic of signal events. No significant excess above the Standard Model expectation is observed, and 95% CL upper limits are set on the production cross sections for the different signal processes considered. These cross-section limits are used to derive lower limits on the mass of a vector-like T quark under several branching ratio hypotheses assuming contributions from T → W b , Zt , Ht decays. The 95% CL observed lower limits on the T quark mass range between 0 . 99 TeV and 1 . 43 TeV for all possible values of the branching ratios into the three decay modes considered, significantly extending the reach beyond that of previous searches. Additionally, upper limits on anomalous four-top-quark production are set in the context of an effective field theory model, as well as in an universal extra dimensions model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Reformulation of the No-Free-Lunch Theorem for Entangled Datasets

The No-Free-Lunch (NFL) theorem is a celebrated result in learning theory that limits one’s ability to learn a function with a training data set. With the recent rise of quantum machine learning, it is natural to ask whether there is a quantum analog of the NFL theorem, which would restrict a quantum computer’s ability to learn a unitary process with quantum training data. However, in the quantum setting, the training data can possess entanglement, a strong correlation with no classical analog. In this work, we show that entangled data sets lead to an apparent violation of the (classical) NFL theorem. This motivates a reformulation that accounts for the degree of entanglement in the training set. As our main result, we prove a quantum NFL theorem whereby the fundamental limit on the learnability of a unitary is reduced by entanglement. We employ Rigetti's quantum computer to test both the classical and quantum NFL theorems. In conclusion, our work establishes that entanglement is a commodity in quantum machine learning.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Raman Spectroscopy Based On-Line, Real-Time Monitoring to Reduce Composition Uncertainties: Enhanced sensitivity through optimization of Raman Parameters

Optical spectroscopy-based on-line monitoring of Hanford processing streams can enable real-time characterization of chemical composition of process streams and batches, ultimately enabling and enhancing process control. It can provide immediate feedback on process conditions and has the potential to reduce the needed number of grab sample collections, thereby reducing times and costs associated with laboratory processing. Here we discuss the utilization of Raman spectroscopy to quantify multiple target analytes that are common within Hanford tanks and waste processing streams. Analytes include: nitrate, nitrite, carbonate, chromate, sulfate, phosphate, hydroxide, oxalate, ammonia, and aluminate. Most notably in this work, Raman applications to low-concentration streams are explored and optimized. Raman instrument specifications are compared; specifically, the impact of utilizing three different Raman excitation wavelengths, 405, 532, and 671 nm, is discussed. Also, Raman data collection parameters such as collection time and spectral averaging are measured and discussed. Finally, optical libraries of chemical targets were collected using optimized collection parameters and chemometric models were built to automate quantification of chemical targets. These models were validated through application to simulants and real Hanford process samples. Chemometric models performed well on both training and validation sets, suggesting these approaches can be successfully applied to on-line and real-time monitoring of low concentration Hanford processing streams. The use of the combine Raman wave lengths with the enhanced chemometric models for the low concentration streams significantly improved the chemical detection levels and significantly reduced uncertainties of those measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Expanding the role of reactive transport models in critical zone processes

We report that models test our understanding of processes and can reach beyond the spatial and temporal scales of measurements. Multi-component Reactive Transport Models (RTMs), initially developed more than three decades ago, have been used extensively to explore the interactions of geothermal, hydrologic, geochemical, and geobiological processes in subsurface systems. Driven by extensive data sets now available from intensive measurement efforts, there is a pressing need to couple RTMs with other community models to explore non-linear interactions among the atmosphere, hydrosphere, biosphere, and geosphere. Here we briefly review the history of RTM development, summarize the current state of RTM approaches, and identify new research directions, opportunities, and infrastructure needs to broaden the use of RTMs. In particular, we envision the expanded use of RTMs in advancing process understanding in the Critical Zone, the veneer of the Earth that extends from the top of vegetation to the bottom of groundwater. We argue that, although parsimonious models are essential at larger scales, process-based models offer tools to explore the highly nonlinear coupling that characterizes natural systems. We present seven testable hypotheses that emphasize the unique capabilities of process-based RTMs for (1) elucidating chemical weathering and its physical and biogeochemical drivers; (2) understanding the interactions among roots, micro-organisms, carbon, water, and minerals in the rhizosphere; (3) assessing the effects of heterogeneity across spatial and temporal scales; and (4) integrating the vast quantity of novel data, including “omics” data (genomics, transcriptomics, proteomics, metabolomics), elemental concentration and speciation data, and isotope data into our understanding of complex earth surface systems. In conclusion, with strong support from data-driven sciences, we are now in an exciting era where integration of RTM framework into other community models will facilitate process understanding across disciplines and across scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Constitutive Model Development for Aging Polymer Encapsulants (ASC P&EM FY2021 L2 Milestone 7836)

This SAND report fulfills the completion requirements for the ASC Physics and Engineering Modeling Level 2 Milestone 7836 during Fiscal Year 2021. The Sandia Simplified potential energy clock (SPEC) non-linear viscoelastic constitutive model was developed to predict a whole host of polymer glass physical behaviors in order to provide a tool to assess the effects of stress on these materials over their lifecycle. Polymer glasses are used extensively in applications such as electronics packaging, where encapsulants and adhesives can be critical to device performance. In this work, the focus is on assessing the performance of the model in predicting material evolution associated with long-term physical aging, an area that the model has not been fully vetted in. These predictions are key to utilizing models to help demonstrate electronics packaging component reliability over decades long service lives, a task that is very costly and time consuming to execute experimentally. The initiating hypothesis for the work was that a model calibration process can be defined that enables confidence in physical aging predictions under ND relevant environments and timescales without sacrificing other predictive capabilities. To test the hypothesis, an extensive suite of calibration and aging data was assembled from a combination of prior work and collaborating projects (Aging and Lifetimes as well as the DoD Joint Munitions Program) for two mission relevant epoxy encapsulants, 828DGEBA/DEA and 828DGEBA/T403. Multiple model calibration processes were developed and evaluated against the entire set of data for each material. A qualitative assessment of each calibration's ability to predict the wide range of aging responses was key to ranking the calibrations against each other. During this evaluation, predictions that were identified as non-physical, i.e., demonstrated something that was qualitatively different than known material behavior, were heavily weighted against the calibration performance. Thus, unphysical predictions for one aspect of aging response could generate a lower overall rating for a calibration process even if that process generated better quantitative predictions for another aspect of aging response. This insurance that all predictions are qualitatively correct is important to the overall aim of utilizing the model to predict residual stress evolution, which will depend on the interplay amongst the different material aging responses. The DSC-focused calibration procedure generated the best all-around aging predictions for both materials, demonstrating material models that can qualitatively predict the whole host of different physical aging responses that have been measured. This step forward in predictive capability comes from an unanticipated source, utilization of calorimetry measurements to specify model parameters. The DSC-focused calibration technique performed better than compression-focused techniques that more heavily weigh measurements more closely related to the structural responses to be predicted. Indeed, the DSC-focused calibration procedure was only possible due to recent incorporation of the enthalpy and heat capacity features into SPEC that was newly verified during this L2 milestone. Fundamentally similar aspects of the two material model calibrations as well as parametric studies to assess sensitives of the aging predictions are discussed within the report. A perspective on the next steps to the overall goal of residual stress evolution predictions under stockpile conditions closes the report.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Learning from crowds with variational Gaussian processes

Solving a supervised learning problem requires to label a training set. This task is traditionally performed by an expert, who provides a label for each sample. The proliferation of social web services (e.g., Amazon Mechanical Turk) has introduced an alternative crowdsourcing approach. Anybody with a computer can register in one of these services and label, either partially or completely, a dataset. The effort of labeling is then shared between a great number of annotators. However, this approach introduces scientifically challenging problems such as combining the unknown expertise of the annotators, handling disagreements on the annotated samples, or detecting the existence of spammer and adversarial annotators. All these problems require probabilistic sound solutions which go beyond the naive use of majority voting plus classical classification methods. In this work we introduce a new crowdsourcing model and inference procedure which trains a Gaussian Process classifier using the noisy labels provided by the annotators. Variational Bayes inference is used to estimate all unknowns. The proposed model can predict the class of new samples and assess the expertise of the involved annotators. Moreover, the Bayesian treatment allows for a solid uncertainty quantification. Since when predicting the class of a new sample we might have access to some annotations for it, we also show how our method can naturally incorporate this additional information. Furthermore, a comprehensive experimental section evaluates the proposed method with synthetic and real experiments, showing that it consistently outperforms other state-of-the-art crowdsourcing approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Design for Remanufacturing

The objective of this project was to develop relationships between published scholarship, industry experience, and remanufacturing design rules and identify a clear path for integrating remanufacturing design rules into CAD tools and design practices. To accomplish this, remanufacturing knowledge was compiled, and then the relationships between this knowledge and a prioritized set of product design guidelines was developed. Next, a process to create rules out of guidelines was developed by first defining life extension as a primary function, and remanufacturing as effective tasks to control the failure modes that would restrict the product life. The reliability centered maintenance (RCM) process was used to identify proactive tasks and default actions to mitigate the effects of failure modes. These tasks were developed into expert system rules. The impact of design on remanufacturing was then validated through implementing a prioritized set of design rules for a Heavy Duty Off-Road (HDOR) techno-economic case study. Finally, detailed steps on how to define, develop, and integrate these rules into CAD were created.

42 ENGINEERING↗
DOE OSTI2019

Fiber motion in highly confined flows of carbon fiber and non-Newtonian polymer [Advanced modeling of non-Newtonian polymer and carbon fibers]

Inks compounded of short carbon fibers suspended in polymer resin can be extruded to produce composite materials during additive manufacturing or 3D printing processes. The flow process induces anisotropic orientation of the fibers which is set into the matrix and significantly affects the mechanical and physical properties of the final product. Therefore, the flow of fiber suspensions needs to be understood in order to predict the orientation distribution of the fibers during such manufacturing processes. There is still a lack of knowledge for extrusion of the complex mixture of a non-Newtonian polymer containing a high-volume fraction of fibers with high fiber aspect ratio, where both inter-particle, fluid-particle and fiber-wall interactions are computationally evaluated. This paper presents predictive numerical simulations and experimental results of such confined flow in a concentrated regime. The code is based on Lagrange multiplier technique and resolves each particle and interaction between the fibers and surrounding fluid and nozzle walls. We investigate numerically how the fiber length impacts the fiber alignment during extrusion. We found that a fiber length above 67% of the nozzle's diameter induces dramatic change in the fiber flow, causing fibers to concentrate at the nozzle boundaries with very low concentration of fibers in the center of the nozzle. It was found that the best fiber alignment is reached for fiber lengths equal to 40–50% of the nozzle diameters. Numerical findings are supported by experimental results. In conclusion, this work improves understanding of fiber orientation during 3D printing and is an important milestone for the prediction of the complex mechanical properties of additively manufactured fiber composites.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Advancements in Life Cycle Human Exposure and Toxicity Characterization

The Life Cycle Initiative, hosted at the United Nations Environment Programme, selected human toxicity impacts from exposure to chemical substances as an impact category that requires global guidance to overcome current assessment challenges. The initiative leadership established the Human Toxicity Task Force to develop guidance on assessing human exposure and toxicity impacts. Based on input gathered at three workshops addressing the main current scientific challenges and questions, the task force built a roadmap for advancing human toxicity characterization, primarily for use in life cycle impact assessment (LCIA). The present paper aims at reporting on the outcomes of the task force workshops along with interpretation of how these outcomes will impact the practice and reliability of toxicity characterization. The task force thereby focuses on two major issues that emerged from the workshops, namely considering near-field exposures and improving dose–response modeling. The task force recommended approaches to improve the assessment of human exposure, including capturing missing exposure settings and human receptor pathways by coupling additional fate and exposure processes in consumer and occupational environments (near field) with existing processes in outdoor environments (far field). To quantify overall aggregate exposure, the task force suggested that environments be coupled using a consistent set of quantified chemical mass fractions transferred among environmental compartments. With respect to dose–response, the task force was concerned about the way LCIA currently characterizes human toxicity effects, and discussed several potential solutions. A specific concern is the use of a (linear) dose–response extrapolation to zero. Another concern addresses the challenge of identifying a metric for human toxicity impacts that is aligned with the spatiotemporal resolution of present LCIA methodology, yet is adequate to indicate health impact potential. Further research efforts are required based on our proposed set of recommendations for improving the characterization of human exposure and toxicity impacts in LCIA and other comparative assessment frameworks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Data-driven design of metal–organic frameworks for wet flue gas CO 2 capture

Limiting the increase of CO 2 in the atmosphere is one of the largest challenges of our generation. Because carbon capture and storage is one of the few viable technologies that can mitigate current CO 2 emissions, much effort is focused on developing solid adsorbents that can efficiently capture CO 2 from flue gases emitted from anthropogenic sources. One class of materials that has attracted considerable interest in this context is metal-organic frameworks (MOFs), in which the careful combination of organic ligands with metal-ion nodes can, in principle, give rise to innumerable structurally and chemically distinct nanoporous MOFs. However, many MOFs that are optimized for the separation of CO 2 from nitrogen do not perform well when using realistic flue gas that contains water, because water competes with CO 2 for the same adsorption sites and thereby causes the materials to lose their selectivity. Although flue gases can be dried, this renders the capture process prohibitively expensive. Here we show that data mining of a computational screening library of over 300,000 MOFs can identify different classes of strong CO 2 -binding sites-which we term 'adsorbaphores'-that endow MOFs with CO 2 /N 2 selectivity that persists in wet flue gases. We subsequently synthesized two water-stable MOFs containing the most hydrophobic adsorbaphore, and found that their carbon-capture performance is not affected by water and outperforms that of some commercial materials. Testing the performance of these MOFs in an industrial setting and consideration of the full capture process-including the targeted CO 2 sink, such as geological storage or serving as a carbon source for the chemical industry-will be necessary to identify the optimal separation material.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Chromium (VI) reduction in acetate- and molasses-amended natural media: empirical model development

Stimulating indigenous microbes to reduce heavy metals from highly toxic oxidized species to more benign reduced species is a promising groundwater remediation technique that has already seen successful field applications. Designing such a bio-remediation scheme requires a model incorporating the kinetics of nonlinear bio-geochemical interactions between multiple species. With this motivation, we performed a set of microcosm experiments in natural sediments and their indigenous pore water and microbes, generating simultaneous time series for concentrations of Cr(VI), an electron donor (both molasses and acetate were considered), and biomass. Molasses was found to undergo a rapid direct abiotic reaction which eliminated all Cr(VI) before any biomass had time to grow. This was not found in the acetate microcosms, and a distinct zero-order bio-reduction process was observed. Existing models were found inappropriate and a new set of three coupled governing equations representing these process dynamics were developed and their parameters calibrated against the time series from the acetate-amended microcosms. Cell suspension batch experiments were also performed to calibrate bio-reduction rates in the absence of electron donor and sediment. The donor used to initially grow the cells (molasses or acetate) was found not to impact the reduction rate constants in suspension, which were orders of magnitude larger than those explaining the natural media microcosm experiments. This suggests the limited utility of kinetics determined in suspension for remedial design. Scoping studies on the natural media microcosms were also performed, suggesting limited impact of foreign abiotic material and minimal effect of diffusion limitation in the vertical dimension. These analyses may be of independent value to future researchers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Light source and method for making a light source

A light source fabrication process to fabricate a customized light source includes attaching a plurality of light emitting diodes to a circuit board, where each light emitting diode is configured to emit light within a same wavelength range. Further, the light source fabrication process includes applying a first phosphor layer to a first set of light emitting diodes of the plurality of light emitting diodes and applying a second phosphor layer to a second set of light emitting diodes of the plurality of light emitting diodes. The phosphor layers are applied to the light emitting diodes using one of a screen printing process and an inkjet printing process. The first phosphor layer is different from and has different optical properties from the second phosphor layer.

42 ENGINEERING↗
DOE OSTI2022

A dictionary learning algorithm for compression and reconstruction of streaming data in preset order

There has been an emerging interest in developing and applying dictionary learning (DL) to process massive datasets in the last decade. Many of these efforts, however, focus on employing DL to compress and extract a set of important features from data, while considering restoring the original data from this set a secondary goal. On the other hand, although several methods are able to process streaming data by updating the dictionary incrementally as new snapshots pass by, most of those algorithms are designed for the setting where the snapshots are randomly drawn from a probability distribution. In this paper, we present a new DL approach to compress and denoise massive dataset in real time, in which the data are streamed through in a preset order (instances are videos and temporal experimental data), so at any time, we can only observe a biased sample set of the whole data. Here, our approach incrementally builds up the dictionary in a relatively simple manner: if the new snapshot is adequately explained by the current dictionary, we perform a sparse coding to find its sparse representation; otherwise, we add the new snapshot to the dictionary, with a Gram-Schmidt process to maintain the orthogonality. To compress and denoise noisy datasets, we apply the denoising to the snapshot directly before sparse coding, which deviates from traditional dictionary learning approach that achieves denoising via sparse coding. Compared to full-batch matrix decomposition methods, where the whole data is kept in memory, and other mini-batch approaches, where unbiased sampling is often assumed, our approach has minimal requirement in data sampling and storage: i) each snapshot is only seen once then discarded, and ii) the snapshots are drawn in a preset order, so can be highly biased. Through experiments on climate simulations and scanning transmission electron microscopy (STEM) data, we demonstrate that the proposed approach performs competitively to those methods in data reconstruction and denoising.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Causally‐Informed Deep Learning to Improve Climate Models and Projections

Abstract Climate models are essential to understand and project climate change, yet long‐standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid‐scale processes, particularly clouds and convection. Deep learning can learn these subgrid‐scale processes from computationally expensive storm‐resolving models while retaining many features at a fraction of computational cost. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious non‐physical correlations learned by the neural networks due to the complexity of the physical dynamical system. Here, we show that the combination of causality with deep learning helps removing spurious correlations and optimizing the neural network algorithm. To resolve this, we apply a causal discovery method to unveil causal drivers in the set of input predictors of atmospheric subgrid‐scale processes of a superparameterized climate model in which deep convection is explicitly resolved. The resulting causally‐informed neural networks are coupled to the climate model, hence, replacing the superparameterization and radiation scheme. We show that the climate simulations with causally‐informed neural network parameterizations retain many convection‐related properties and accurately generate the climate of the original high‐resolution climate model, while retaining similar generalization capabilities to unseen climates compared to the non‐causal approach. The combination of causal discovery and deep learning is a new and promising approach that leads to stable and more trustworthy climate simulations and paves the way toward more physically‐based causal deep learning approaches also in other scientific disciplines.

Meteorology & Atmospheric Sciences↗
DOE OSTI2017

Development of utility generic functional requirements for electronic work packages and computer-based procedures

The Nuclear Electronic Work Packages - Enterprise Requirements (NEWPER) initiative is a step toward a vision of implementing an eWP framework that includes many types of eWPs. This will enable immediate paper-related cost savings in work management and provide a path to future labor efficiency gains through enhanced integration and process improvement in support of the Nuclear Promise (Nuclear Energy Institute 2016). The NEWPER initiative was organized by the Nuclear Information Technology Strategic Leadership (NITSL) group, which is an organization that brings together leaders from the nuclear utility industry and regulatory agencies to address issues involved with information technology used in nuclear-power utilities. NITSL strives to maintain awareness of industry information technology-related initiatives and events and communicates those events to its membership. NITSL and LWRS Program researchers have been coordinating activities, including joint organization of NEWPER-related meetings and report development. The main goal of the NEWPER initiative was to develop a set of utility generic functional requirements for eWP systems. This set of requirements will support each utility in their process of identifying plant-specific functional and non-functional requirements. The NEWPER initiative has 140 members where the largest group of members consists of 19 commercial U.S. nuclear utilities and eleven of the most prominent vendors of eWP solutions. Through the NEWPER initiative two sets of functional requirements were developed; functional requirements for electronic work packages and functional requirements for computer-based procedures. This paper will describe the development process as well as a summary of the requirements.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Predicting resistive wall mode stability in NSTX through balanced random forests and counterfactual explanations

Abstract Recent progress in the disruption event characterization and forecasting framework has shown that machine learning guided by physics theory can be easily implemented as a supporting tool for fast computations of ideal stability properties of spherical tokamak plasmas. In order to extend that idea, a customized random forest (RF) classifier that takes into account imbalances in the training data is hereby employed to predict resistive wall mode (RWM) stability for a set of high beta discharges from the NSTX spherical tokamak. More specifically, with this approach each tree in the forest is trained on samples that are balanced via a user-defined over/under-sampler. The proposed approach outperforms classical cost-sensitive methods for the problem at hand, in particular when used in conjunction with a random under-sampler, while also resulting in a threefold reduction in the training time. In order to further understand the model’s decisions, a diverse set of counterfactual explanations based on determinantal point processes (DPP) is generated and evaluated. Via the use of DPP, the underlying RF model infers that the presence of hypothetical magnetohydrodynamic activity would have prevented the RWM from concurrently going unstable, which is a counterfactual that is indeed expected by prior physics knowledge. Given that this result emerges from the data-driven RF classifier and the use of counterfactuals without hand-crafted embedding of prior physics intuition, it motivates the usage of counterfactuals to simulate real-time control by generating the β N levels that would have kept the RWM stable for a set of unstable discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗