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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Electromagnetic Transient Simulation of Photovoltaic Inverter Using Implicit-Explicit Solver

This paper introduces the implementation of electromagnetic transient (EMT) simulations of a photovoltaic (PV) inverter module using the Implicit-Explicit (ImEx) solver in the Suite of Nonlinear and Differential/Algebraic Equation Solvers (SUNDIALS). This study demonstrates the effectiveness of the ImEx solver in overcoming the challenges inherent in simulating the complex dynamics of PV inverter modules. Furthermore, using SUNDIALS’ ImEx solver module ARKODE for EMT simulation automates key aspects of the process, such as numerical integration, providing substantial benefits including enhanced consistency, faster implementation, reduced human error, and the capability to handle the complexities of advanced numerical integration. By conducting comparative simulations with an implicit method used in commercial software, the research showcases the ImEx solver’s capability in achieving high accuracy and reliability. Results indicate that leveraging the ImEx approach significantly enhances modeling fidelity and reduces simulation setup times, offering a promising tool for the EMT analysis of PV inverter systems in power electronics-dominated power grids.

Choi, Jongchan [ORNL] (ORCID:000000025952455X)↗
DOE OSTI2024

Tethys: A Spatiotemporal Downscaling Model for Global Water Demand

Humans use water for many important tasks, such as drinking, growing food, and cooling power plants. Since future water demands depend on complex global interactions between economic sectors (e.g., demand for wheat in one country causing demand for water to grow that wheat in another country), it is often modeled at coarse spatial and temporal scales as part of models that account for complex, multi-sector system dynamics. However, models that project future water availability typically simulate physical processes at much finer scales. Tethys enables integration between these kinds of models by downscaling region-scale water demand projections using sector-specific proxies and formulas.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Understanding Soil Microbial Sources of Nitrous Acid and their Effect on Carbon-Nitrogen Cycle Interactions

Descriptions of soil emissions of reactive nitrogen (NO y ) in climate models are underdeveloped or non-existent, due to the fact that details of the mechanisms leading to nitrous acid (HONO) and nitrogen di-oxide (NO 2 ) formation in soil are lacking. This represents a major gap in our understanding of a significant land-atmosphere interaction that prevents us from scaling these processes from the laboratory scale to the ecosystem and global scales. There is a critical need to include these mechanisms into climate models since NO y controls the oxidative capacity of the atmosphere and the lifetime of greenhouse gases and the rate of secondary aerosol formation that directly and indirectly affect climate. The first objective of this proposal is to conduct laboratory and field measurements of NO y fluxes from diverse soil types and determine the mechanism of biogenic NO y formation. The working hypothesis based on preliminary data is that soil HONO and NO 2 is ultimately derived from ammonia-oxidizing archaea (AOA) and bacteria (AOB) that are widespread, but whose abundance varies across ecosystems. In the case of NO 2 , reactive oxygen species derived from iron-containing minerals and heterotrophic bacteria drive NO-to-NO 2 conversion. The approach is to link soil fluxes of HONO and NO 2 to AOA, AOB, and other heterotrophs using a combination of laboratory and field experiments, isotopic analysis, and molecular techniques to address how variability in land surfaces and edaphic properties impact emissions. In addition, we will determine the effect of HONO and NO 2 on nitrogen immobilization and the photo-oxidative capacity of soil. The working hypothesis is that HONO uptake in soil will be a source of nitrosonium and hydroxyl radical that will lead to thermal- and photodegradation of soil organic matter to CO 2 and CO, and the incorporation of N in soil organic matter. We will use surface-sensitive mass spectrometry techniques and gas phase detection to study N-immobilization in soil and subsequent enhancements in reactivity that lead to decomposition of organic matter. The results will be used to reduce the uncertainty in projections from the Community Earth System Model (CESM) stemming from inaccurate representations of soil NO y emissions. The proposed research is significant because, in addition to demonstrating new mechanisms of NO y formation and loss, it will be a crucial first step towards modeling the land-atmosphere exchange of HONO and NO 2 in the CESM. Improved model treatment of land-air exchange of NO y is key for understanding feedbacks between human activity and climate, and addressing societal concerns about the fate of N and C emitted to the atmosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Investigating lab-scaled offshore wind aerodynamic testing failure and developing solutions for early anomaly detections

As offshore wind systems become more complex, the risk of human error or equipment malfunction increases during experimental testing. This study investigates a lab-scale incident involving a 1 : 50 scale 5 MW wind turbine, where a generator failure led to rotor overspeed and a blade–tower strike. To improve early fault detection, we propose a data-driven method based on multivariate long short-term memory (LSTM) models. High-frequency measurements are projected onto principal components, and anomalies are identified using reconstruction error and its time derivative. Two models are trained on different healthy datasets and tested using single- and multi-principal component (1PC and MPC) variations. Results show that combining both error and error derivative improves detection accuracy. The 1PC model detects faults faster, has a higher recall rate, and achieves a 43 % improvement in anomaly detection accuracy, while the MPC model yields higher precision. This approach provides a simple and effective tool for early anomaly detection in lab-scale experiments, helping to reduce the risk of future failures during the testing of new technologies.

17 WIND ENERGY↗
DOE OSTI2026

CONTROL AND DATA ACQUISITION IN A CYBER-PHYSICAL MIDSTREAM TESTBED

This thesis presents the development of a laboratory-scale cyber–physical midstream pipeline testbed designed to address this gap and support research in industrial control systems security. The platform integrates pumps, valves, sensors, programmable logic controllers (PLCs), and a human–machine interface (HMI) to emulate the monitoring and control architecture of real pipeline operations. The physical process is implemented as a closed-loop liquid circulation system designed to replicate flow behavior characteristic of midstream pipeline infrastructure. The testbed enables real-time data acquisition of key process variables, including flow rate and pressure facilitating the generation of datasets representative of normal pipeline operation. A threat model encompassing common ICS attack vectors was developed, including sensor spoofing, command injection, false data injection, denial-of-service attacks, and relay manipulation. Multiple attack scenarios were implemented and evaluated to demonstrate how cyber intrusions targeting sensors, actuators, networks, and software propagate into measurable physical consequences in pipeline flow and pressure. The developed platform serves as a practical, cost-effective environment for experimentation, education, and future cybersecurity research in midstream pipeline systems.

42 ENGINEERING↗
DOE OSTI2020

AeDES: a next-generation monitoring and forecasting system for environmental suitability of Aedes-borne disease transmission

Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth, more frequent travels and ecological changes. In the United States of America, the vast majority of Aedes-borne infections are imported from endemic regions by travelers, who can become new sources of mosquito infection upon their return home if the exposed population is susceptible to the disease, and if suitable environmental conditions for the mosquitoes and the virus are present. Since the susceptibility of the human population can be determined via periodic monitoring campaigns, the environmental suitability for the presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. We present a next-generation monitoring and forecasting system for Aedes-borne diseases’ environmental suitability (AeDES) of transmission in the conterminous United States and transboundary regions, using calibrated ento-epidemiological models, climate models and temperature observations. After analyzing the seasonal predictive skill of AeDES, we briefly consider the recent Zika epidemic, and the compound effects of the current Central American dengue outbreak happening during the SARS-CoV-2 pandemic, to illustrate how a combination of tailored deterministic and probabilistic forecasts can inform key prevention and control strategies .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Enhancing integrated analysis of national and global goal pursuit by endogenizing economic productivity

Analysis with integrated assessment models (IAMs) and multisector dynamics models (MSDs) of global and national challenges and opportunities, including pursuit of Sustainable Development Goals (SDGs), requires projections of economic growth. In turn, the pursuit of multiple interacting goals affects economic productivity and growth, generating complex feedback loops among actions and objectives. Yet, most analysis uses either exogenous projections of productivity and growth or specifications endogenously enriched with a very small set of drivers. Extending endogenous treatment of productivity to represent two-way interactions with a significant set of goal-related variables can considerably enhance analysis. Among such variables incorporated in this project are aspects of human development (e.g., education, health, poverty reduction), socio-political change (e.g., governance capacity and quality), and infrastructure (e.g. water and sanitation and modern energy access), all in conditional interaction with underlying technological advance and economic convergence among countries. Using extensive datasets across countries and time, this project broadly endogenizes total factor productivity (TFP) within a large-scale, multi-issue IAM, the International Futures (IFs) model system. We demonstrate the utility of the resultant open system via comparison of new TFP projections with those produced for Shared Socioeconomic Pathways (SSP) scenarios, via integrated analysis of economic growth potential, and via multi-scenario analysis of progress toward the SDGs. We find that the integrated system can reproduce existing SSP projections, help anticipate differential economic progress across countries, and facilitate extended, integrated analysis of trade-offs and synergies in pursuit of the SDGs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Robust Explanations using Diverse Adversarially Trained Ensembles, Multi-Modal Contrastive Learning, and Attribution-based Confidence Metrics

The primary objective of this project is to strengthen the trustworthiness of AI systems by designing algorithms that make their internal decision-making processes more understandable to human users. This involves creating clear, interpretable explanations for AI decisions and developing metrics to assess these explanations' validity and reliability. Significant progress has been achieved through (i) developing symbolic explanations, (ii) generating meaningful interpretive insights, (iii) establishing accuracy and confidence metrics, and (iv) devising methods to evaluate the knowledge boundaries of AI models. To date, the research findings have been shared in peer-reviewed publications, with accompanying scientific and technical information (STI) detailed below.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

SNAPSHOT USA 2020: A second coordinated national camera trap survey of the United States during the COVID-19 pandemic

Managing wildlife populations in the face of global change requires regular data on the abundance and distribution of wild animals, but acquiring these over appropriate spatial scales in a sustainable way has proven challenging. Here, in this study, we present the data from Snapshot USA 2020, a second annual national mammal survey of the USA. This project involved 152 scientists setting camera traps in a standardized protocol at 1485 locations across 103 arrays in 43 states for a total of 52,710 trap-nights of survey effort. Most (58) of these arrays were also sampled during the same months (September and October) in 2019, providing a direct comparison of animal populations in 2 years that includes data from both during and before the COVID-19 pandemic. All data were managed by the eMammal system, with all species identifications checked by at least two reviewers. In total, we recorded 117,415 detections of 78 species of wild mammals, 9236 detections of at least 43 species of birds, 15,851 detections of six domestic animals and 23,825 detections of humans or their vehicles. Spatial differences across arrays explained more variation in the relative abundance than temporal variation across years for all 38 species modeled, although there are examples of significant site-level differences among years for many species. Temporal results show how species allocate their time and can be used to study species interactions, including between humans and wildlife. These data provide a snapshot of the mammal community of the USA for 2020 and will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, and the impacts of species interactions on daily activity patterns. There are no copyright restrictions, and please cite this paper when using these data, or a subset of these data, for publication.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Marginal Damage of Methane Emissions: Ozone Impacts on Agriculture

Methane directly contributes to air pollution, as an ozone precursor, and to climate change, generating physical and economic damages to different systems, namely agriculture, vegetation, energy, human health, or biodiversity. The methane-related damages to climate, measured as the Social Cost of Methane, and to human health have been analyzed by different studies and considered by government rulemaking in the last decades, but the ozone-related damages to crop revenues associated to methane emissions have not been incorporated to policy agenda. Using a combination of the Global Change Analysis Model and the TM5-FASST Scenario Screening Tool, we estimate that global marginal agricultural damages range from ~ 423 to 556 $2010/t-CH 4 , of which 98 $2010/t-CH 4 occur in the USA, which is the most affected region due to its role as a major crop producer, followed by China, EU-15, and India. These damages would represent 39–59% of the climate damages and 28–64% of the human health damages associated with methane emissions by previous studies. The marginal damages to crop revenues calculated in this study complement the damages from methane to climate and human health, and provides valuable information to be considered in future cost-benefits analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning

Supramolecular polymer blends (SPBs) offer tunable morphologies that dictate their macroscopic properties, yet their rational design is limited by the absence of predictive structure−morphology models. Here, we introduce a data-driven highthroughput workflow that integrates modular polymer synthesis, robotic formulation, automated morphology characterization, and machine learning (ML) for accelerated SPB discovery. Using a plug-and-play synthetic strategy, 33 hydrogen-bonding endfunctional homopolymers were prepared and orthogonally combined to generate 260 SPBs in 1 day. A fully automated atomic force microscopy (AFM) pipeline enabled systematic imaging, producing 2340 morphology data sets with minimal human intervention. Domain spacings were extracted through complementary imageprocessing methods and used to train ML models. A support vector regression (SVR) model accurately predicted target phase-separation sizes (50, 100, and 150 nm), which were experimentally validated. This work demonstrates the power of coupling high-throughput experimentation with ML to accelerate morphology discovery and provides one of the first large-scale experimental data sets for supramolecular polymer systems.

ML-guided polymer design↗
DOE OSTI2024

In-vivo neuronal dysfunction by Aβ and tau overlaps with brain-wide inflammatory mechanisms in Alzheimer’s disease

The molecular mechanisms underlying neuronal dysfunction in Alzheimer’s disease (AD) remain uncharacterized. Here, we identify genes, molecular pathways and cellular components associated with whole-brain dysregulation caused by amyloid-beta (Aβ) and tau deposits in the living human brain. We obtained in-vivo resting-state functional MRI (rs-fMRI), Aβ- and tau-PET for 47 cognitively unimpaired and 16 AD participants from the Translational Biomarkers in Aging and Dementia cohort. Adverse neuronal activity impacts by Aβ and tau were quantified with personalized dynamical models by fitting pathology-mediated computational signals to the participant’s real rs-fMRIs. Then, we detected robust brain-wide associations between the spatial profiles of Aβ-tau impacts and gene expression in the neurotypical transcriptome (Allen Human Brain Atlas). Within the obtained distinctive signature of in-vivo neuronal dysfunction, several genes have prominent roles in microglial activation and in interactions with Aβ and tau. Moreover, cellular vulnerability estimations revealed strong association of microglial expression patterns with Aβ and tau’s synergistic impact on neuronal activity (q < 0.001). These results further support the central role of the immune system and neuroinflammatory pathways in AD pathogenesis. Neuronal dysregulation by AD pathologies also associated with neurotypical synaptic and developmental processes. In addition, we identified drug candidates from the vast LINCS library to halt or reduce the observed Aβ-tau effects on neuronal activity. Top-ranked pharmacological interventions target inflammatory, cancer and cardiovascular pathways, including specific medications undergoing clinical evaluation in AD. Our findings, based on the examination of molecular-pathological-functional interactions in humans, may accelerate the process of bringing effective therapies into clinical practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Human and Technology Integration Evaluation of Advanced Automation and Data Visualization

While the existing United States (U.S.) light water reactors are highly reliable, safe, and provide a significant proportion of carbon-free electricity, the cost of operating and maintaining them has become less competitive compared to other electricity generating sources. The reason for the gap in operating and maintenance (O&M) costs can be at least in part attributed to the advent of new digital technologies that other electricity generating industries are currently using. Advanced capabilities including digital instrumentation and control (I&C) systems, advanced automation and analytics, and greater span of data integration (i.e., connectedness) across these non-nuclear plants has transformed the way work is performed and ultimately given them a competitive advantage in terms of the cost required for operating, maintaining, and supporting them. To reduce O&M cost and address obsolescence of the aging I&C infrastructure of the existing U.S. light water reactors, the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program Plant Modernization Pathway is conducting targeting multidisciplinary research that 1) delivers a sustainable business model to enable a cost-competitive U.S. nuclear industry and 2) is developing technology modernization solutions that address aging and obsolescence challenges. The work described in this report supports these two objectives and describes the demonstration of human and technology integration across recent industry collaborations to support their large-scale digital I&C modifications. This technical report describes the demonstration of the human and technology integration methodology in performing full-scale performance-based human-in-the-loop tests to evaluate plant-specific advanced automation and data visualization applications within these collaborators’ digital modifications. This technical report also documents future applications of human and technology integration that expand beyond main control room modernization and digital I&C upgrades, which have been a central focus to date. Thus, this technical report discusses how to implement human and technology integration across new business opportunities and how to develop an evaluation plan that defines measures and criteria, and documents key assumptions to support full plant modernization.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Integrated multi-omic characterizations of the synapse reveal RNA processing factors and ubiquitin ligases associated with neurodevelopmental disorders

The molecular composition of the excitatory synapse is incompletely defined due to its dynamic nature across developmental stages and neuronal populations. To address this gap, we apply proteomic mass spectrometry to characterize the synapse in multiple biological models including the fetal human brain and hiPSC-derived neurons. To prioritize the identified proteins, we develop an orthogonal multi-omic screen of genomic, transcriptomic, interactomic, and structural data. This data-driven framework identifies proteins with key molecular features intrinsic to the synapse, including characteristic patterns of biophysical interactions and cross-tissue expression. The multi-omic analysis captures synaptic proteins across developmental stages and experimental systems, including 493 synaptic candidates supported by proteomics. We further investigate three such proteins that are associated with neurodevelopmental disorders – the CUL3 E3 ubiquitin ligase, the DDX3X and YBX1 nucleic-acid binding proteins – by mapping their networks of physically interacting synapse proteins or transcripts. Our study demonstrates the potential of an integrated multi-omic approach to systematically and more comprehensively resolve the synaptic architecture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Evaluating the effectiveness of a camera-based detection system to support informed curtailment and minimize eagle fatalities at wind energy facilities

The American Wind Wildlife Institute (AWWI – now Renewable Energy Wildlife Institute, REWI) – was appointed prime awardee of DOE award number DE-EE0007880 to lead a team of scientists, wind developers, and technology manufacturers in a collaborative evaluation of the effectiveness of a technology intended to help reduce eagle collisions with wind turbines. IdentiFlight, developed by Renewable Energy Systems (RES) in collaboration with Boulder Imaging, is an autonomous aerial monitoring and detection system. REWI partnered with ecological consultants, Western Ecosystems Technology, Inc. (WEST), to independently evaluate the performance of IdentiFlight reducing eagle collision risk under a suite of environmental conditions relative to the performance of a Biomonitor (human observer-based) Program. The project objectives were to 1) evaluate how IdentiFlight compared to biomonitors in detecting and identifying eagles under a suite of environmental conditions and 2) use the observation data to model how IdentiFlight and biomonitors reduced the eagle fatality prediction through informed curtailment. Field testing was to take place at two geographically distinct wind farms over two field seasons at each wind farm with a study design appropriate for meta-analyses across sites and years.

17 WIND ENERGY↗
DOE OSTI2024

Improving Self-Driving Labs: Quantifying System-Level Experiment Repeatability and Broadening Instrument-Level Compatibility

Modular Autonomous Research System (MARS) is a self-driving laboratory (SDL) which performs wet-lab science with peptide-lanthanide combinations in an automated and, ultimately, an autonomous manner to aid in soil analysis for domestic lithium mining. Autonomous experimentation involves automated experimentation, experiment planning, and active learning. MARS consists of a 6-axis robotic arm (UR5e) on a linear rail, pipette robots (Opentrons 2), and microplate readers. These components transport, operate on, and collect data with chemical solutions in standard labware. For effective autonomy, MARS must perform system-level labware operations repeatably, plan experiments autonomously, and be portable between research-domains. Repeatability is evaluated by labware placement precision, such that future operations can properly locate labware, as well as the elapsed time, so that low variance mean estimates of experiment duration can inform high-level researcher decision making. Autonomous experiment planning is the next step to decouple experimentation from human management; however, there is a conflict between the ideal system-level experiment goals and the constraints imposed by instruments’ limitations. Sub-domain portability is a long-term goal to extend MARS’ research beyond the chemistry of peptide-lanthanide binding to other sub-domains without having to invest significant overhead to system retrofitting. To address these goals, we manually trained the robotic arm labware placement and modelled statistical failurerate and uncertainty Additionally, we benchmarked the duration and variance of each experiment sub-operation as a heuristic for research decision making. Next, we use a parameterized geometric program (PGP) approach to design experiments that optimize system-level objectives and satisfy instrument-level constraints. Lastly, we proposed a Python framework to maximize MARS’ extensibility to other scientific sub-domains through a JSON-based experiment specification.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Social vulnerability and power loss mitigation: A case study of Puerto Rico

The increasing occurrence of extreme weather events urges us to reevaluate the resiliency and vulnerability aspects of our most critical infrastructures — such as power grids — as their failures result in both economic loss and severe human hardship. Seen through the lens of alleviating human suffering, it is crucial to be able to identify critical system components of the infrastructure for targeted hardening given resource constraints. This effort is of particular importance in islanded areas such as Puerto Rico where hurricanes are frequent and resources are limited, and where the spatially diverse effects of power loss on human suffering are all the more severe. Recent studies on evaluating infrastructure networks during extreme weather events have taken a simulation based approach that incorporates a variety of component models, such as weather realizations, topological network models, fragility models, and power flow models to estimate expected loss of service. Here, in this work, we expand such a Component Based Event Simulation (CBES) methodology proposed in the literature and integrate it with a social vulnerability modeling component. This paradigm-advancing approach of synthesizing the cutting edge capability of power network modeling and the social impacts of the power transmission network failure is demonstrated for the island of Puerto Rico. Our work exemplifies the efficacy of this integrated modeling framework in developing a decision metric for targeted transmission line hardening.

24 POWER TRANSMISSION AND DISTRIBUTION↗