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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Report on Progress of correlation of in-situ and ex-situ data and the use of artificial intelligence to predict defects

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to fabricate nuclear components which will be assembled into a fully functional microreactor core. Compared with traditional manufacturing technologies, AM technologies allow (1) real-time observation of the manufacturing process at a much higher resolution using in-situ monitoring technologies to capture the sensor signatures that scientifically describe each event occurring over time and space and (2) validation of the manufacturing process quality using domain-informed data analytics techniques as a potential qualification and certification methodology for the final component. This report provides an update on the program work on in-situ and ex-situ data correlation and associated data analytics results. Examples are provided to illustrate progress with respect to laser powder bed fusion (L-PBF), binder jetting, computed tomography (CT) reconstruction, and mechanical testing. Elements of the Digital Thread and data management infrastructure are discussed in the main document, and an extensive supplemental appendix is provided detailing the Digital Platform, as well as its implementation and subcomponents. In conclusion, the path forward for the next fiscal year is also discussed.

42 ENGINEERING↗
DOE OSTI2023

Molecular identification of wines using in situ liquid SIMS and PCA analysis

Composition analysis in wine is gaining increasing attention because it can provide information about the wine quality, source, and nutrition. In this work, in situ liquid secondary ion mass spectrometry (SIMS) was applied to 14 representative wines, including six wines manufactured by a manufacturer in Washington State, United States, four Cabernet Sauvignon wines, and four Chardonnay wines from other different manufacturers and locations. In situ liquid SIMS has the unique advantage of simultaneously examining both organic and inorganic compositions from liquid samples. Principal component analysis (PCA) of SIMS spectra showed that red and white wines can be clearly differentiated according to their aromatic and oxygen-contained organic species. Furthermore, the identities of different wines, especially the same variety of wines, can be enforced with a combination of both organic and inorganic species. Meanwhile, in situ liquid SIMS is sample-friendly, so liquid samples can be directly analyzed without any prior sample dilution or separation. Taken together, we demonstrate the great potential of in situ liquid SIMS in applications related to the molecular investigation of various liquid samples in food science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Predictive Indicators of the Performance of Large Language Models

In several mission contexts, it is desirable to estimate the performance of large language models (LLMs) on tasks that we cannot run directly. In light of published “scaling laws” our hypothesis is that some tasks should be consistently more challenging than others based on characteristics of the task. The goal of this project was to begin quantifying how much information about LLM performance can be gained from the features of a model and a task. Two of our statistical models struggled to converge. Pass/fail test results may provide limited information for inference beyond model quality and task difficulty, but we see no evidence at this time for significant feature interaction effect sizes, arguing for simple models. Future work extending the models to capitalize on perplexity of ground truth answers is suggested. This project also introduces “Depth of Knowledge Variant Testing” as a strategy for more finely assessing language models on open domain question and answer tasks. We developed sets of questions that ask a language model to produce similar information while demonstrating increasing depth of knowledge, and also relabeled existing Q&A test questions with their depth of knowledge. Our results suggest further consideration of Bloom’s taxonomy and further refinement of prompts to properly elicit information at varying depths. In the course of this work, we set up a basic infrastructure for standardizing tasks and testing many language models on these tasks. In addition to testing the predictive quality of model features and performance across test suites, with this project we have introduced two new task features to contextualize each test question: the Dewey Classification main category of information covered, and the Bloom’s taxonomy level that corresponds to the depth of knowledge probed by the question. Splits across these and other features produced over five hundred task subtypes with distinct feature vectors, which we tested on half a dozen models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The global spectrum of plant form and function: enhanced species-level trait dataset

Here we provide the ‘Global Spectrum of Plant Form and Function Dataset’, containing species mean values for six vascular plant traits. Together, these traits –plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass – define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

ORNL Package Testing Program Software Quality Assurance Plan

The Oak Ridge National Laboratory (ORNL) Package Testing Program (PTP) uses commercial off-the-shelf (COTS) software in performing data collection of thermal test results for package designs that contain radioactive materials. Specifically, this software is used to collect temperature data from the furnace, packages, and ambient air to prepare and execute the thermal test specified in 10 CFR 71.73, “Thermal Test.” This software quality assurance (SQA) plan sets forth the guidelines, standards, and procedures that shall be used to provide SQA for PTP software applications. This is a living document that will be maintained for the lifecycle of the PTP program. The SQA plan follows the requirements set forth in ORNL Standards Based Management System (SBMS): Information Technology; Subject Area: Software Quality Assurance. When applicable to the requirements as described in ORNL SBMS, Software Quality Assurance, the software shall be listed in the ORNL Software Registration System (SRS). Exemptions to this SBMS are COTS and firmware that are not modified; spreadsheet applications and personal productivity tools that do not have a utility or safety application, research applications, legacy software, system software, vendor-supplied software used to interface with the vendor’s services, software used within the organization to facilitate processing or management of information, and software developed for applications not specific to the US Department of Energy (DOE).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Quality Ranking of Unary Fluoride Salt Property Data in MSTDB-TP

Molten salt reactor developers rely on thermal property data to design, license and operate the reactors they are developing. The Molten Salt Thermal Database-Thermophysical Properties (MSTDB-TP) is being developed under the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program and managed by Oak Ridge National Laboratory. The MSTDB-TP is intended to serve as a single source of measured thermophysical property values for a wide variety of molten salt systems available to researchers, molten salt reactor developers, and regulators. These properties include melting and boiling points, heat capacity, density, viscosity and thermal diffusivity and conductivity. Published measurements of molten salt properties are lacking for many salts and the data that are available are often inconsistent. This creates a challenge for MSR developers in determining which property values to select when designing their reactors. The MSDTDB-TP collects all available property data and down-selects to preferred data sets or correlations. It is the purpose of this work to apply a recently developed ranking system that indicates the quality of property values listed in the database to inform decisions about how to use the available data. Quality assessments and rankings are being applied to data in MSTDB-TP to provide an indication of the quality of the data available. A previous report detailed the ranking system that was followed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network

Abstract Background Estimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality of protein models. Inter-residue distances are key information for predicting protein’s tertiary structures and therefore have good potentials to predict the quality of protein structural models. However, few methods have been developed to fully take advantage of predicted inter-residue distance maps to estimate the accuracy of a single protein structural model. Result We developed an attentive 2D convolutional neural network (CNN) with channel-wise attention to take only a raw difference map between the inter-residue distance map calculated from a single protein model and the distance map predicted from the protein sequence as input to predict the quality of the model. The network comprises multiple convolutional layers, batch normalization layers, dense layers, and Squeeze-and-Excitation blocks with attention to automatically extract features relevant to protein model quality from the raw input without using any expert-curated features. We evaluated DISTEMA’s capability of selecting the best models for CASP13 targets in terms of ranking loss of GDT-TS score. The ranking loss of DISTEMA is 0.079, lower than several state-of-the-art single-model quality assessment methods. Conclusion This work demonstrates that using raw inter-residue distance information with deep learning can predict the quality of protein structural models reasonably well. DISTEMA is freely at https://github.com/jianlin-cheng/DISTEMA

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Information theory optimization of signals from small-angle scattering measurements

Small-angle X-ray scattering (SAXS) of particles in solution informs on the conformational states and assemblies of biological macromolecules (bioSAXS) outside of cryo- and solid-state conditions. In bioSAXS, the SAXS measurement under dilute conditions is resolution limited, and through an inverse Fourier transform, the measured SAXS intensities directly relate to the physical space occupied by the particles via the P (r)-distribution. Yet, this inverse transform of SAXS data has been historically cast as an ill-posed, ill-conditioned problem requiring an indirect approach. Here, we show that through the applications of matrix and information theories, the inverse transform of SAXS intensity data is a well-conditioned problem. The so-called ill-conditioning of the inverse problem is directly related to the Shannon number. By exploiting the oversampling enabled by modern detectors, a direct inverse Fourier transform of the SAXS data is possible, provided the recovered information does not exceed the Shannon number. The Shannon limit corresponds to the maximum number of significant singular values that can be recovered in a SAXS experiment, suggesting this relationship is a fundamental property of band-limited inverse integral transform problems. This correspondence reduces the complexity of the inverse problem to the Shannon limit and maximum dimension. We propose a hybrid scoring function using an information theory framework that assesses both the quality of the model-data fit as well as the quality of the recovered P (r)-distribution. The hybrid score utilizes the Akaike information criteria and Durbin-Watson statistic that considers parameter-model complexity, i.e., degrees of freedom, and the randomness of the model-data residuals. The described tests and findings extend the boundaries for bioSAXS by completing the information theory formalism initiated by Peter B. Moore to enable a quantitative measure of resolution in SAXS, robustly determine maximum dimension, and more precisely define the best parameter model appropriately representing the observed scattering data.

Rambo, Robert P. [Science and Technology Facilitie↗
DOE OSTI2022

Air Quality Analysis and Tools For Policy

South Asia is home to some of the most significant air quality challenges in the world. More than 99% of the region’s population is exposed to unhealthy fine particulate matter (PM2.5) concentrations, as defined by the World Health Organization. Recent policy initiatives in South Asia show that air quality policy is a priority, given the health impacts of air pollution. At the same time, capacity and data to assess and address air pollution remains limited in many areas. The main objective of this report is to outline a set of publicly-available tools that can be used to inform policies to improve air quality in South Asia. The report primarily focuses on tools such as emission inventories and integrated modeling systems with country-level resolution and illustrates their air quality assessment capabilities through examples and brief technical summaries. The report focuses on: (a) the Community Emissions Data System (CEDS) that provides historical emissions of key pollutants for all countries in South Asia, and (b) profiles an analysis of Indian air pollution and policy scenarios, using several integrated modeling tools. Both of these examples draw on recent research and data that outlines the scale of the challenge as well as the policy options for reducing air pollution in the region. The report concludes with key recommendations for regional stakeholders working towards mitigating air pollution in South Asia.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Clarifying the trophic state concept to advance macroscale freshwater science and management

For over a century, ecologists have used the concept of trophic state (TS) to characterize an aquatic ecosystem's biological productivity. However, multiple TS classification schemes, each relying on a variety of measurable parameters as proxies for productivity, have emerged to meet use‐specific needs. Frequently, chlorophyll a, phosphorus, and Secchi depth are used to classify TS based on autotrophic production, whereas phosphorus, dissolved organic carbon, and true color are used to classify TS based on both autotrophic and heterotrophic production. Both classification approaches aim to characterize an ecosystem's function broadly, but with varying degrees of autotrophic and heterotrophic processes considered in those characterizations. Moreover, differing classification schemes can create inconsistent interpretations of ecosystem integrity. For example, the US Clean Water Act focuses exclusively on algal threats to water quality, framed in terms of eutrophication in response to nutrient loading. This usage lacks information about non‐algal threats to water quality, such as dystrophication in response to dissolved organic carbon loading. Consequently, the TS classification schemes used to identify eutrophication and dystrophication may refer to ecosystems similarly (e.g., oligotrophic and eutrophic), yet these categories are derived from different proxies. These inconsistencies in TS classification schemes may be compounded when interdisciplinary projects employ varied TS frameworks. Even with these shortcomings, TS can still be used to distill information on complex aquatic ecosystem function into a set of generalizable expectations. The usefulness of distilling complex information into a TS index is substantial such that usage inconsistencies should be explicitly addressed and resolved. To emphasize the consequences of diverging TS classification schemes, we present three case studies for which an improved understanding of the TS concept advances freshwater research, management efforts, and interdisciplinary collaboration. To increase clarity in TS, the aquatic sciences could benefit from including information about the proxy variables, ecosystem type, as well as the spatiotemporal domains used to classify TS. As the field of aquatic sciences expands and climatic irregularity increases, we highlight the importance of re‐evaluating fundamental concepts, such as TS, to ensure their compatibility with evolving science.

classification↗
DOE OSTI2021

A Multi-Analysis Approach for Estimating Regional Health Impacts from the 2017 Northern California Wildfires

In the evening of October 8 and early hours of October 9, 2017, high winds in Northern California downed trees and power lines, igniting some of the most devastating wildfires the state had seen, and within hours unhealthy air quality impacted millions of people. We simulated these air quality conditions using fire detection information from the MODIS, VIIRS, and GOES-16 ABI satellite instruments, and applying a set of three WRF–CMAQ simulations, one data fusion, and three machine learning methods. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ air quality modeling system. Interestingly, this approach did not necessarily improve results compared to the baseline case, which used a default time profile. However, this approach was key to simulating the initial 12-hr explosive fire activity and smoke impacts. The WRF-CMAQ simulations compared well with observational data for the October 8-15 time period and tended to overestimate concentrations October 16-20. To improve these results, we applied three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across the WRF-CMAQ, data fusion, and machine learning datasets, the best Pearson correlation was 0.5. The data fusion and machine learning results were biased low and WRF-CMAQ results were biased high. Finally, we applied the optimized PM2.5 exposure estimate in a short-term exposure-response function. Total estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to wildland fire smoke.

O'Neill, Susan↗
DOE OSTI2020

A general-purpose method for Pareto optimal placement of flow rate and concentration sensors in networked systems – With application to wastewater treatment plants

The advent of affordable computing, low-cost sensor hardware, and high-speed and reliable communications have spurred installation of ubiquitous sensors in complex engineered systems. However, ensuring reliable data quality remains a challenge. Exploitation of redundancy among sensor signals can help improving the precision of measured variables, detecting the presence of gross errors, and identifying faulty sensors. The cost of sensor ownership, maintenance efforts in particular, can still be cost-prohibitive however. Maximizing the ability to assess and control data quality while minimizing the cost of ownership thus requires a careful sensor placement. To solve this challenge, in this work we develop a generally applicable method to solve the multi-objective sensor placement problem in systems governed by linear and bilinear balance equations. Importantly, the method computes all Pareto-optimal sensor layouts with conventional computational resources and requires no information about the expected sensor quality.

42 ENGINEERING↗
DOE OSTI2022

Development and Validation of Southeast Asia Solar Resource Data [Slides]

Lack of access to high-quality, publicly available, time series solar data to inform decisions that will transform energy sectors in Southeast Asia is a challenge. The solution is to level the playing field by offering free, high-quality, robust solar data to inform private sector investment and policymaking. This is done by (1) leveraging deep NREL expertise in atmospheric science, solar resource assessment, high-performance computing, and cloud-based data dissemination, (2) producing and validating high spatial and temporal resolution solar resource data, (3) making data available on the USAID-funded global Renewable Energy Data Explorer platform, (4) providing capacity building for data and applications, and (5) informing future demand-driven tool development.

14 SOLAR ENERGY↗
DOE OSTI2023

Improved quality metrics for association and reproducibility in chromatin accessibility data using mutual information

Correlation metrics are widely utilized in genomics analysis and often implemented with little regard to assumptions of normality, homoscedasticity, and independence of values. This is especially true when comparing values between replicated sequencing experiments that probe chromatin accessibility, such as assays for transposase-accessible chromatin via sequencing (ATAC-seq). Such data can possess several regions across the human genome with little to no sequencing depth and are thus non-normal with a large portion of zero values. Despite distributed use in the epigenomics field, few studies have evaluated and benchmarked how correlation and association statistics behave across ATAC-seq experiments with known differences or the effects of removing specific outliers from the data. Here, we developed a computational simulation of ATAC-seq data to elucidate the behavior of correlation statistics and to compare their accuracy under set conditions of reproducibility. Using these simulations, we monitored the behavior of several correlation statistics, including the Pearson’s R and Spearman’s ρ coefficients as well as Kendall’s τ and Top–Down correlation. We also test the behavior of association measures, including the coefficient of determination R 2 , Kendall’s W, and normalized mutual information. Our experiments reveal an insensitivity of most statistics, including Spear man’s ρ, Kendall’s τ, and Kendall’s W, to increasing differences between simulated ATAC-seq replicates. The removal of co-zeros (regions lacking mapped sequenced reads) between simulated experiments greatly improves the estimates of correlation and association. After removing co-zeros, the R 2 coefficient and normalized mutual information display the best performance, having a closer one-to-one relationship with the known portion of shared, enhanced loci between simulated replicates. When comparing values between experimental ATAC-seq data using a random forest model, mutual information best predicts ATAC-seq replicate relationships. Collectively, this study demonstrates how measures of correlation and association can behave in epigenomics experiments. We provide improved strategies for quantifying relationships in these increasingly prevalent and important chromatin accessibility assays.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Deep Gravity model for mobility flows generation

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model’s prediction with explainable AI techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Solution Irregularity Remediation for Spatial Discretization Error Estimation for S N Transport Solutions

The discrete ordinates linear Boltzmann transport equation is typically solved in its spatially discretized form, incurring spatial discretization error. Quantification of this error for purposes such as adaptive mesh refinement or error analysis requires an a posteriori estimator, which utilizes the numerical solution to the spatially discretized equation to compute an estimate. Because the quality of the numerical solution informs the error estimate, irregularities, present in the true solution for any realistic problem configuration, tend to cause the largest deviation in the error estimate vis-a-vis the true error. In this paper, an analytical partial singular characteristic tracking (pSCT) procedure for reducing the estimator’s error is implemented within our novel residual source estimator for a zeroth-order discontinuous Galerkin scheme, at the additional cost of a single inner iteration. Here, a metric-based evaluation of the pSCT scheme versus the standard residual source estimator is performed over the parameter range of a Method of Manufactured Solutions test suite. The pSCT scheme generates near-ideal accuracy in the estimate in problems where the dominant source of the estimator’s error is the solution irregularity, namely, problems where the true solution is discontinuous and problems where the true solution’s first derivative is discontinuous and the scattering ratio is low. In problems where the scattering ratio is high and the true solution is discontinuous in the first derivative, the error in the scattering source, which is not converged by the pSCT scheme, is greater than the error incurred due to the irregularity. Ultimately, a pSCT scheme is judged to be useful for error estimation in problems where the computational cost of the scheme is justified. In the presence of many irregularities, such a scheme may be intractable for general use, but in benchmarks, as an analytical tool, or in problems that have nondissipative discontinuities, the scheme may prove invaluable.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗