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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Application of Orthogonal Defect Classification for Software Reliability Analysis

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify if the software is reliable (or unreliable) while still leveraging the strength of the existing methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Application of Orthogonal Defect Classification for Software Reliability Analysis

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify software reliability (or unreliability) while still leveraging the strength of the existing methods.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Environmental, Health and Safety Assessment Application of a Heat Integrated, Post-combustion CO2 Capture System Using Monoethanolamine and Hitachi H3-1 Solvent at the E.W. Brown Coal-fired Power Plant

An Environmental Health and Safety (EH&S) Assessment was completed for operation of a pilot scale carbon dioxide (CO2) capture system (CCS) at the Louisville Gas & Electric (LG&E) and Kentucky Utilities (KU) Energy, LLC (LG&E-KU) E. W. Brown Generating Station in Harrodsburg, Mercer County, Kentucky. The pilot system was evaluated using two CO2 capture solvents, monoethanolamine (MEA) and Hitachi’s H3-1 solvent. The purpose of the EH&S Assessment was to determine if there were any unacceptable environmental, health or safety concerns that may prevent operation or environmental permitting of a larger scale pilot scale plant. The assessment included review of process flow diagrams, input and output flow rates for primary materials, literature describing similar processes and chemicals used or created during the process, actual air emission and wastewater test data and air emission calculations. The evaluation included identification of risks related to hazardous chemicals, air emissions, wastewater discharges, solid wastes generated and employee hazards. Most potential EH&S issues that were identified are commonly found and successfully managed at large industrial facilities. No risks were identified that could not be successfully managed or would likely prevent implementation or environmental permitting of the pilot or larger scale plant. Potential exposures and resulting health risks from exposure to low concentrations of nitrosamines generated from degradation of the Hitachi H3-1 and possibly MEA solvents may be a health concern for site workers, but additional investigation is needed to accurately assess this risk. Applying the information gained from operating the pilot plant, performing air emission testing and used solvent testing to a large-scale facility at a commercial coal-fired electric generating plant suggests additional air emission control measures may be necessary to reduce potential site worker health risks and possibly community risks. Depending upon the location and regional air quality, considerable effort may be required to obtain the necessary air permit for construction and operation of a similarly configured large-scale PCCCS facility.

gas separation↗
DOE OSTI2020

Environmental, Health and Safety Assessment Application of a Heat Integrated, Post-combustion CO 2 Capture System Using Monoethanolamine and Hitachi H3-1 Solvent at the E.W. Brown Coal-Fired Power Plant

An Environmental Health and Safety (EH&S) Assessment was completed for operation of a pilot scale carbon dioxide (CO 2 ) capture system (CCS) at the Louisville Gas & Electric (LG&E) and Kentucky Utilities (KU) Energy, LLC (LG&E-KU) E. W. Brown Generating Station in Harrodsburg, Mercer County, Kentucky. The pilot system was evaluated using two CO 2 capture solvents, monoethanolamine (MEA) and Hitachi’s H3-1 solvent. The purpose of the EH&S Assessment was to determine if there were any unacceptable environmental, health or safety concerns that may prevent operation or environmental permitting of a larger scale pilot scale plant. The assessment included review of process flow diagrams, input and output flow rates for primary materials, literature describing similar processes and chemicals used or created during the process, actual air emission and wastewater test data and air emission calculations. The evaluation included identification of risks related to hazardous chemicals, air emissions, wastewater discharges, solid wastes generated and employee hazards. Most potential EH&S issues that were identified are commonly found and successfully managed at large industrial facilities. No risks were identified that could not be successfully managed or would likely prevent implementation or environmental permitting of the pilot or larger scale plant. Potential exposures and resulting health risks from exposure to low concentrations of nitrosamines generated from degradation of the Hitachi H3-1 and possibly MEA solvents may be a health concern for site workers, but additional investigation is needed to accurately assess this risk. Applying the information gained from operating the pilot plant, performing air emission testing and used solvent testing to a large-scale facility at a commercial coal-fired electric generating plant suggests additional air emission control measures may be necessary to reduce potential site worker health risks and possibly community risks. Depending upon the location and regional air quality, considerable effort may be required to obtain the necessary air permit for construction and operation of a similarly configured large-scale PCCCS facility.

gas separation↗
DOE OSTI2025

Predictive Assessment of the Chemical Composition of Coal Ash in Reserve at U.S. Disposal Sites

In the United States, more than 2 Gt of coal combustion residuals (i.e., coal ash) are stored in hundreds of disposal units. Recent federal regulations mandate the closure or retrofitting of most coal ash impoundments, presenting significant challenges for waste management. These regulatory pressures also present opportunities to reuse coal ash. However, the quality and quantity of discarded coal ash across the U.S. are not well known, even though this information is crucial for spurring its reuse for conventional and new material applications. This study describes a predictive model for the major element composition of coal ash in reserve at disposal sites of major U.S. coal-fired power plants. This model was constructed from coal purchase records of 705 power stations from 1973 to 2022 and was trained on coal ash composition data, showing that coal ash elemental composition is strongly associated with the source of feedstock coal. The model showed regional shifts in the major element contents of ash produced by power plants in the last 50 years, particularly for calcium and iron (expressed as %CaO and %Fe2O3), as power stations changed their source of coal over this time frame. Our approach enables an estimation of chemical composition for ash stored in waste impoundments at individual power stations. Such information can help to delineate the regional market resource potential of supplementary cements for concrete and other material innovations that would utilize coal ash harvested from disposal sites across the U.S.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2012

Satellite Contributions to the Quantitative Characterization of Biomass Burning for Climate Modeling

Characterization of biomass burning from space has been the subject of an extensive body of literature published over the last few decades. Given the importance of this topic, we review how satellite observations contribute toward improving the representation of biomass burning quantitatively in climate and air-quality modeling and assessment. Satellite observations related to biomass burning may be classified into five broad categories: (i) active fire location and energy release, (ii) burned areas and burn severity, (iii) smoke plume physical disposition, (iv) aerosol distribution and particle properties, and (v) trace gas concentrations. Each of these categories involves multiple parameters used in characterizing specific aspects of the biomass-burning phenomenon. Some of the parameters are merely qualitative, whereas others are quantitative, although all are essential for improving the scientific understanding of the overall distribution (both spatial and temporal) and impacts of biomass burning. Some of the qualitative satellite datasets, such as fire locations, aerosol index, and gas estimates have fairly long-term records. They date back as far as the 1970s, following the launches of the DMSP, Landsat, NOAA, and Nimbus series of earth observation satellites. Although there were additional satellite launches in the 1980s and 1990s, space-based retrieval of quantitative biomass burning data products began in earnest following the launch of Terra in December 1999. Starting in 2000, fire radiative power, aerosol optical thickness and particle properties over land, smoke plume injection height and profile, and essential trace gas concentrations at improved resolutions became available. The 2000s also saw a large list of other new satellite launches, including Aqua, Aura, Envisat, Parasol, and CALIPSO, carrying a host of sophisticated instruments providing high quality measurements of parameters related to biomass burning and other phenomena. These improved data products have enabled significant progress in the study of biomass burning from space. However, appreciable uncertainty remains in many of the measurements that still needs to be addressed. Nevertheless, climate and other atmospheric models are

Ichoku, Charles↗
DOE OSTI2021

Probabilistic Voltage Sensitivity Analysis to Quantify Impact of High PV Penetration on Unbalanced Distribution System

From an operational and planning perspective, it is important to quantify the impact of increasing penetration of photovoltaics on the distribution system. Most existing impact assessment studies are scenario-based where derived results are scenario specific and not generalizable. Moreover, stochasticity in the temporal behavior of spatially distributed PVs requires a large number of scenarios that increase with the size of the network and the level of penetration. Therefore, we propose a new computationally efficient analytical framework of voltage sensitivity analysis that allows for stochastic analysis of voltage change due to random changes in PV generation. We first derive an analytical approximation for voltage change at any node of the network due to change in power at other nodes in an unbalanced distribution network. The quality of this approximation is reinforced via bounds on the approximation error. Then, we derive the probability distribution of voltage change at a certain node due to random changes in power injections/consumptions at multiple locations of the network. The accuracy of the proposed PVSA is illustrated using a modified version of the IEEE 37 bus test system. As a result, the proposed PVSA can serve as a powerful tool for proactive monitoring/control and ease the computational burden associated with perturbation based cybersecurity mechanisms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

United States Nuclear Power Reactor Used Nuclear Fuel Database and Applications

The Unified Database (UDB) within STANDARDS serves as the foundational data infrastructure for managing the United States' spent nuclear fuel inventory of 315,111 discharged assemblies totaling 91,036 metric tons of heavy metal. The database organizes this complex inventory through over 200 interconnected tables structured into eight primary attribute categories, supporting integrated analyses across storage, transportation, and disposal domains. Data enters the UDB through the GC-859 Nuclear Fuel Data Survey, which transitioned to web-based collection in 2023, improving data quality through real-time validation. The UDB enables automated generation of input files for nuclear safety analyses, reducing preparation time from weeks to hours while maintaining traceability. Applications include national inventory reporting, Certificate of Compliance assessments, and facility optimization. The three-tier distribution model balances accessibility with security requirements for federal agencies, national laboratories, and research organizations. The UDB provides essential data infrastructure as spent fuel management transitions from site-specific to integrated national campaigns.

Stefanovic, Peter↗
DOE OSTI2023

Field Validation of Air-Source Heat Pumps for Cold Climates

Heating energy is the largest end-use for U.S. residential buildings accounting for approximately one-third of residential building energy consumption (EIA 2021). Historically, air-source heat pumps have been limited to temperate climates because of subpar performance at extremely cold outdoor air temperatures. However, recent advances to cold-climate air-source heat pump technology, which typically rely on inverter-driven, variable-speed compressors and variable-speed fans, have significantly improved low-temperature heat pump performance enabling the technology to save energy for many homes in cold climates. The primary objective of this project was to measure in-field performance of centrally ducted, variable-capacity air-source heat pumps in cold climates to validate performance and develop field-based performance maps. The project focused on quantifying heat pump performance at cold temperatures. The sites identified for the study were primarily located in the Northwest United States since homes in the region tend to have all-electric space heating systems and high-efficiency heat pumps have been incentivized in the region for several years. NREL partnered with Ecotope, Inc., a small energy consulting firm located in Seattle, WA, for site recruitment, monitoring equipment installation, data quality management. All the sites included in the study had previously installed a high-efficiency, central heat pump system. One site was in a Denver, CO suburb, which was the only dual fuel heat pump in the study. We used airside and power measurements, collected at 5-second intervals, to quantify heat pump capacity, coefficient of performance (COP), and auxiliary heater energy consumption. We developed algorithms to automatically determine the heat pump operating mode including defrost and auxiliary heating operation. A whole-house thermal and duct audit was completed during the initial site visit to estimate winter heating loads and assess heat pump sizing. Whole-home heating design loads were calculated at ASHRAE 99% design temperatures and compared to manufacturer-reported maximum capacities to assess the heat pump sizing at each site.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Assessment of Economic Impact of Permitting Timelines on Produced Geothermal Power in Imperial County, California

Despite having a large geothermal power potential in the United States, only a small fraction has been developed for power generation. Various barriers, including technical, financial, and regulatory permit delays, are attributed to lower contribution of geothermal energy in the national grid. Unpredictable environmental reviews and permitting timelines are some of the non-technical barriers that can cause delays in geothermal exploration and utilization plans. Our study shows that the geothermal permitting timelines can vary from six months to several years, depending on the presence or absence of biological resources, cultural resources, and sensitive environmental issues at the project site. The potential impacts of these permit barriers can range from investors abandoning geothermal development to making the product (i.e., electricity) more expensive and uncompetitive. In this study, we conducted economic analysis to assess the impact of permitting timelines on cost of produced electricity from geothermal resources using data from existing geothermal plants as well as prospective sites. In this paper, we present collected timelines data, approach, and results of economic impact of permitting timelines on geothermal power. We evaluated the various environmental management and permit review processes by considering a hypothetical geothermal project in the Salton Sea Known Geothermal Resource Area. Because of the variety of the biological and environmental issues and the involvement of local, state, and federal agencies with overlapping jurisdictions, this project could go through one of the many California Environmental Quality Act (CEQA) and National Environmental Policy Act (NEPA) review scenarios that range from the least to the most complex in its circumstances. The fastest CEQA/NEPA review timelines would have the project completed in six years. In contrast, the project would substantially need longer time to complete if it were located in an area with significant environmental resources or cultural issues that required permitting from various agencies. With increasing project completion timelines, the simplified levelized cost of electricity (sLCOE) can be 4 to 11% higher with longer CEQA/NEPA review timelines than the sLCOE value with the fastest CEQA/NEPA review timeline. Lengthier CEQA/NEPA review timelines could also result in loss of $64 million to $227 million in potential revenue. Such significant economic impacts could determine the success of a geothermal project.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Sensitivity and Detection Limit of Spectroscopic-Grade Perovskite CsPbBr 3 Crystal for Hard X-Ray Detection

We report spectroscopic-grade single crystal detectors can register the energies of individual X-ray interactions enabling photon-counting systems with superior resolution over traditional photoconductive X-ray detection systems. Current technical challenges have limited the preparation of perovskite semiconductors for energy-discrimination X-ray photon-counting detection. Here, this work reports the deployment of a spectroscopic-grade CsPbBr 3 Schottky detector under reverse bias for continuum hard X-ray detection in both the photocurrent and spectroscopic schemes. High surface barriers of approximate to ≈ 1 eV are formed by depositing solid bismuth and gold contacts. The spectroscopic response under a hard X-ray source is assessed in resolving the characteristic X-ray peak. The methodology in enhancing X-ray sensitivity by controlling the X-ray energies and flux, and voltage, is described. The X-ray sensitivity varies between a few tens to over 8000 μC Gy air -1 cm -2 . The detectable dose rate of the CsPbBr 3 detectors is as low as 0.02 nGy air s -1 in the energy discrimination configuration. Finally, the unbiased CsPbBr 3 device forms a spontaneous contact potential difference of about 0.7 V enabling high quality of the CsPbBr 3 single crystals to operate in "passive" self-powered X-ray detection mode and the X-ray sensitivity is estimated as 14 μC Gy air -1 cm -2 . The great potential of spectroscopic-grade CsPbBr 3 devices for X-ray photon-counting systems is anticipated in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Guide to Nonaqueous Electrochemistry of f-Element Complexes

Electrochemistry is a powerful tool for assessing and understanding the redox chemistry of molecular complexes. Cyclic voltammetry enables the f-element community to study molecules in unusually high or low oxidation states, which potentially pose important broad-scope questions of electronic structure. In the pursuit of boundary-pushing compounds, reactive air- and moisture-sensitive species are often encountered, which can be challenging to characterize, especially when they are chemically incompatible with certain solvents, electrolytes, or electrodes or when their potentials lie outside of common electrochemical windows. Nonaqueous solvents and pseudo reference electrodes complicate many of the standard practices in acquiring high-quality and reproducible electrochemical data. This guide presents a detailed discussion of selecting appropriate cell conditions and referencing and addresses metrics for evaluating electrochemical and chemical reversibility. These methodological approaches have been extended to best practices for the electrochemical analysis of radioactive transuranic complexes.

Electrodes↗
DOE OSTI2021

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO)

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Economic Impact of Permitting Timelines on Geothermal Power in California, Nevada, and Utah

The United States has great geothermal power potential; however, only a small fraction of this resource has been utilized for power generation. Various barriers, including technical, financial, and regulatory permit delays, are attributed to the lower penetration of geothermal energy into the national grid. Unpredictable environmental reviews and permitting timelines are some of the non-technical barriers that can cause delays in geothermal exploration and utilization plans. This document provides an assessment of the potential economic impact of permitting costs and timelines on geothermal power in California, Nevada, and Utah. Development of geothermal resources requires multi-layered regulatory permitting by local, state, and federal agencies. In this study, we collected and reviewed permit timelines and associated cost data for several existing geothermal power plants as well as for geothermal projects currently undergoing permitting processes for exploration and development activities in California, Nevada, and Utah. We interviewed several geothermal developers and staff from multiple permitting agencies to obtain insight from both sides (i.e., people applying for permits and people processing those applications for California Environmental Quality Act [CEQA] and National Environmental Policy Act [NEPA] reviews and other regulatory compliance). The intent of this project was also to identify informative pathways for the geothermal stakeholder community by which the permitting process could be streamlined.

15 GEOTHERMAL ENERGY↗
NASA NTRS2019

Bumper: A Tool for Analyzing Spacecraft Micrometeoroid and Orbital Debris Risk

“Bumper” is NASA’s computer program for analyzing spacecraft micrometeoroid and orbital debris (MMOD) risk. Bumper was developed in the late-1980s and has been continuously used and maintained since. The user base has grown from a few government entities to now include numerous commercial entities as well. The NASA Johnson Space Center (JSC) Hypervelocity Impact Technology (HVIT) Team is responsible for all aspects of the Bumper software. Bumper has been used to characterize MMOD risk on hundreds of spacecraft. All of the International Space Station (ISS) modules, visiting vehicles and numerous external components and systems have been analyzed. Bumper was used to analyze each of the Space Shuttle missions since STS-50. The Orion Multi-Purpose Crew Vehicle (MPCV) MMOD shielding is being developed using Bumper as well. Bumper has also been used on numerous telescopes (Hubble, James Webb, and Fermi Gamma-ray Space Telescopes), scientific probes (Stardust, New Horizons, Parker Solar Probe), and Earth observation satellites (Landsat, Joint Polar Satellite System). Bumper is also being used to analyze the micrometeoroid risk and support design of the Deep Space Gateway (DSG) and Mars Sample Return (MSR) missions. The HVIT Bumper Configuration Control Board (CCB) ensures that all changes to the code are approved, reviewed, and documented. Most of the changes are made to add new MMOD damage “ballistic limit equations” (BLEs). BLEs are typically added in response to completion of a hypervelocity impact (HVI) test series and development of an associated BLE. Other less frequent changes include updates of the debris or meteoroid environment models, feature enhancements, and feature retirement. Some BLEs are commercially sensitive and/or proprietary, so the CCB also manages code user-version control and software distribution. The current version – “Bumper 3” – is a FORTRAN executable that utilizes a 64-bit architecture. Bumper 3 has numerous features that make it a powerful tool for analyzing spacecraft MMOD risk. Bumper uses the latest orbital debris and micrometeoroid environment models. Bumper also easily processes large spacecraft geometry models, recognizes hidden surfaces, permits BLE assignment by name or number, and conducts quality checks of the spacecraft geometry model. Bumper 3 can also be used to estimate the effects of particle penetration through thin, high-standoff distance hardware components such as solar arrays and radiators. This is done using a special HVIT-developed technique know as the “3-Part Analysis.” The paper introduces the Bumper 3 MMOD risk analysis code and provides an example MMOD risk assessment showing Bumper’s role in the overall MMOD protection design process.

Lear, Dana M.↗
DOE OSTI2025

Addressing Critical Challenges of Coal Mines for Underground Pumped Storage Hydropower Using Numerical Calculations

Using existing coal mines to build underground pumped storage hydropower (UPSH) power plants could help reduce the costs associated with starting a new PSH operation. This innovative approach can leverage the existing infrastructure of coal mine facilities available in many regions of the United States. However, technical challenges associated with water quality, mineral chemistry and integrity of subsurface topologies have thus far prevented any of these facilities from going online. Rye Development is investigating the feasibility of UPSH at existing coal mine facilities. In this project, Rye Development received the technical assistance from Oak Ridge National Laboratory to investigate the impacts of minerals, metal particles, and chemical substances in water flow on hydraulic equipment and assess the overall structural integrity of hydraulic equipment in former coal mines.

13 HYDRO ENERGY↗
DOE OSTI2023

Review of machine learning and deep learning models for toxicity prediction

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

Research & Experimental Medicine↗
DOE OSTI2025

Selection of Global Climate Model Data for Downscaling With Generative Machine Learning and Use in the Power Planning for Alignment of Climate and Energy Systems Project

The range of results from climate models and scenarios is important to the understanding of uncertainty in power planning analysis. A U.S. Department of Energy-funded analytic project called Power Planning for Alignment of Climate and Energy Systems is developing data and analytic methods to reflect the effects of climate change on key variables for power system planning, as part of the Grid Modernization Lab Consortium. This project will select and prepare global climate model results for use in power system planning models. A related report (Evaluation of Global Climate Models for Use in Energy Analysis) assesses the performance of various global climate models from the Coupled Model Intercomparison Project Phase 6 data archive for their historical skill with respect to energy system performance and for their future projections under multiple climate change scenarios. Building from that report, we describe the selection of a climate scenario (Shared Socioeconomic Pathway [SSP] 2-4.5) and five climate models: TaiESM1, EC-Earth3-CC, GFDL-CM4, EC-Earth3-Veg, and MPI-ESM1-2-HR. We describe the model selection criteria, which were based on the quality of the match between model results under historical conditions and on the representation of the range of future values for several variables. These results will be downscaled via an open-source generative machine learning method called Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗