SEARCH · Search NASA

Results for “Data Driven Systems”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,027 records · Page 57

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Enhanced Component Performance Study: Motor-Driven Pumps 1998-2020

This report presents an enhanced performance evaluation of motor-driven pumps (MDPs) at U.S. commercial nuclear power plants. The data used in this study are based on the operating experience failure reports from calendar year 1998 through 2020 as reported in the Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS). The MDP failure modes considered for standby systems are fail to start (FTS), fail to run (FTR) for one hour of operation (FTR≤1H), FTR after one hour of operation (FTR>1H), and for normally running systems FTS and FTR. An eight-hour unreliability estimate is also calculated and trended. The component reliability estimates and the reliability data are trended for the most recent 10-year period while yearly estimates for reliability are provided for the entire study period. The following increasing trends were identified for MDPs for the most recent 10-year period: • Standby MDP frequency of start demands (demands per reactor year) • Standby MDP frequency of FTR≤1H hours (hours per reactor year) • Standby MDP frequency of FTR>1H hours • Normally running MDP frequency of run hours. The following decreasing trends were identified for MDPs for the most recent 10-year period: • Standby MDP FTR≤1H failure probability • Normally running MDP FTR failure rate • Standby MDP unavailability • Normally running MDP total unreliability (8-hour mission) • Standby MDP frequency of FTR≤1H events (failures per reactor year) • Normally running MDP frequency of FTR events.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Enhanced Component Performance Study: Turbine Driven Pumps 1998–2022

This report presents an enhanced performance evaluation of turbine driven pumps (TDPs) at U.S. commercial nuclear power plants. The data used in this study are based on the operating experience failure reports from calendar year 1998 through 2022 as reported in the Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS). The TDP failure modes considered for standby systems are fail to start (FTS), fail to run (FTR) for one hour of operation (FTR=1H), FTR after one hour of operation (FTR>1H), and for normally running systems FTS and FTR. An eight hour unreliability estimate is also calculated and trended. The component reliability estimates and the reliability data are trended for the most recent 10 year period while yearly estimates for reliability are provided for the entire study period. No increasing trends were identified for TDPs for the most recent 10 year period: The following decreasing trends were identified for TDPs for the most recent 10 year period: • Standby TDP FTR>1H failure rate • Normally running TDP FTR failure rate • Standby TDP unavailability • Standby MDP total unreliability (8-hour mission) • Standby TDP frequency of start demands (demands per reactor year) • Standby TDP frequency of FTR=1H hours (hours per reactor year) • Standby TDP frequency of FTR>1H events (failures per reactor year) • Normally running TDP frequency of start demands • Normally running TDP frequency of run hours • Normally running TDP frequency of FTR events.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Enhanced Component Performance Study: Motor Driven Pumps 1998–2022

This report presents an enhanced performance evaluation of motor-driven pumps (MDPs) at U.S. commercial nuclear power plants. The data used in this study are based on the operating experience failure reports from calendar year 1998 through 2022 as reported in the Institute of Nuclear Power Operations (INPO) Industry Reporting and Information System (IRIS). The MDP failure modes considered for standby systems are fail to start (FTS), fail to run (FTR) for one hour of operation (FTR=1H), FTR after one hour of operation (FTR>1H), and for normally running systems FTS and FTR. An eight-hour unreliability estimate is also calculated and trended. The component reliability estimates and the reliability data are trended for the most recent 10-year period while yearly estimates for reliability are provided for the entire study period. The following increasing trends were identified for MDPs for the most recent 10-year period: (1) Standby MDP frequency of start demands (demands per reactor year), (2) Standby MDP frequency of FTR=1H hours (hours per reactor year), (3) Standby MDP frequency of FTR>1H hours, and (4) Normally running MDP frequency of run hours. The following decreasing trends were identified for MDPs for the most recent 10-year period: (1) Standby MDP FTR=1H failure probability, (2) Normally running MDP FTR failure rate, (3) Standby MDP unavailability, (4) Standby MDP total unreliability (8-hour mission), and (5) Normally running MDP total unreliability (8-hour mission).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Omics-driven onboarding of the carotenoid producing red yeast Xanthophyllomyces dendrorhous CBS 6938

Transcriptomics is a powerful approach for functional genomics and systems biology, yet it can also be used for genetic part discovery. Here, we derive constitutive and light-regulated promoters directly from transcriptomics data of the basidiomycete red yeast Xanthophyllomyces dendrorhous CBS 6938 (anamorph Phaffia rhodozyma) and use these promoters with other genetic elements to create a modular synthetic biology parts collection for this organism. X. dendrorhous is currently the sole biotechnologically relevant yeast in the Tremellomycete class-it produces large amounts of astaxanthin, especially under oxidative stress and exposure to light. Thus, we performed transcriptomics on X. dendrorhous under different wavelengths of light (red, green, blue, and ultraviolet) and oxidative stress. Differential gene expression analysis (DGE) revealed that terpenoid biosynthesis was primarily upregulated by light through crtI, while oxidative stress upregulated several genes in the pathway. Further gene ontology (GO) analysis revealed a complex survival response to ultraviolet (UV) where X. dendrorhous upregulates aromatic amino acid and tetraterpenoid biosynthesis and downregulates central carbon metabolism and respiration. The DGE data was also used to identify 26 constitutive and regulated genes, and then, putative promoters for each of the 26 genes were derived from the genome. Simultaneously, a modular cloning system for X. dendrorhous was developed, including integration sites, terminators, selection markers, and reporters. Each of the 26 putative promoters were integrated into the genome and characterized by luciferase assay in the dark and under UV light. The putative constitutive promoters were constitutive in the synthetic genetic context, but so were many of the putative regulated promoters. Notably, one putative promoter, derived from a hypothetical gene, showed ninefold activation upon UV exposure. Thus, this study reveals metabolic pathway regulation and develops a genetic parts collection for X. dendrorhous from transcriptomic data. Therefore, this study demonstrates that combining systems biology and synthetic biology into an omics-to-parts workflow can simultaneously provide useful biological insight and genetic tools for nonconventional microbes, particularly those without a related model organism. This approach can enhance current efforts to engineer diverse microbes.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗

DOE OSTI2026

Finding the perfect imperfection: Accelerated, computationally driven discovery and design of quantum defects

Optically addressable spin defects have emerged as the leading platforms for quantum sensing and communication in solid-state systems. While traditional efforts have concentrated on a focused set of well-studied defects, recent advances in high-throughput computational methods have shown promise for large-scale exploration of defects across diverse semiconductor hosts. By cataloging key properties of quantum defects in computational databases, high-throughput screening techniques can systematically suggest and design novel candidates. In this article, we highlight recent advances in data-driven quantum defect design aimed at addressing critical materials science challenges such as host materials selection, defect stability, and desirable electronic and optical properties. Here, we emphasize the importance of electronic-structure-guided searches across various materials and illustrate how high-throughput computations contribute to our understanding of design principles for quantum defects. Additionally, we outline ongoing challenges and emerging opportunities in this rapidly developing field.

Xiong, Yihuang [Dartmouth College, Hanover, NH (Un↗

DOE OSTI2021

Learning Optimal Power Flow Solutions using Linearized Models in Power Distribution Systems

Solving nonlinear optimal power flow (OPF) problem is computationally expensive, and poses scalability challenges for power distribution networks. An alternative to solving the original nonlinear OPF is the linear approximated OPF models. Although, these linear approximated OPF models are fast, the resulting solutions may result in significant optimality gap. Lately, the application of machine learning (ML) methods in successfully solving the nonlinear OPF has been reported. These methods learn and estimate the nonlinear control policies using a purely data-driven approach. In this paper, we propose an approach to complements the ML based approach to solving OPF using solutions from known linearized OPF model. Specifically, we use supervised learning to map the solutions of linear OPF to nonlinear control variables. Unlike, the traditional ML based methods for OPF that approximate the full distribution feeder model using function approximation, our approach uses a two-node approximation of radial networks. The proposed approach is validated using IEEE 123 bus test system for OPF solutions obtained using the nonlinear OPF models.

optimal power flow, power distribution systems, su↗

DOE OSTI2023

DSS-SimPy-RL (Open-DSS and SimPy based Cyber-Physical RL environment) [SWR-23-29]

Recently, numerous data-driven approaches to control an electric grid using machine learning techniques have been investigated. With the advancement of reinforcement learning (RL) based techniques, gradually the conventional optimization based solvers are being replaced with RL approach where there is uncertainty in the environment such as renewable generation or cyber system emulation. However, to train an agent efficiently, it requires numerous interactions with an environment to learn the best policies. There are numerous RL environments for the power systems based on some well-known simulators, similarly there are environment for communication domains. While majority of the cyber emulators are based in an UNIX environment, the power simulators are based in the Windows-based operating system, the generation of cyber-physical mixed domain RL environment has been challenging. Existing co-simulation methods are efficient but resource and time intensive to generate large scale data set for training RL agents. Hence, this software focuses on development and validation of a mixed domain RL environment using Open DSS for the physical side and leverages a discrete event simulator python package, SimPy, for cyber-side emulation which is Operating Systems agnostic. Further utilizing this software co-simulation and training RL agents for re-routing based resilient control for network reconfiguration and volt-var control in power distribution feeder are performed.

Sahu, Abhijeet↗

DOE OSTI2022

Model-based interface design for smart field-device integration

Operational complexity is ever-increasing for electric utilities that face challenges including integration of DERs, customer expectation of energy choices, the proliferation of non-utility-owned resources, new business models with energy service providers, and new technology with IT/OT convergence. To maintain and improve the quality of operations, planning, and decision-making in general, utilities need to manage and navigate the complexity. Managing complexity requires a modular, scalable, and flexible solution. Connecting large amounts of DERs and introducing new services requires increased grid control and evolving applications. In this paper, we show an approach utilizing model-based standardized interfaces that simplifies integration and deployment of new algorithms and smart field devices for interoperability across legacy or new systems. A modular design is presented for a reference implementation of widely used DNP3 and IEEE 2030.5 interfaces within an open-source, standards-based data integration platform for integration of smart field devices with independently developed, best-of-breed applications.

Model-driven development, system integration, smar↗

DOE OSTI2020

Dynamical Mass Estimates of the β Pictoris Planetary System through Gaussian Process Stellar Activity Modeling

Nearly 15 yr of radial velocity (RV) monitoring and direct imaging enabled the detection of two giant planets orbiting the young, nearby star β Pictoris. The δ Scuti pulsations of the star, which overwhelm planetary signals, need to be carefully suppressed. In this work, we independently revisit the analysis of the RV data following a different approach than available in the literature to model the activity of the star. We show that a Gaussian process (GP) with a stochastically driven damped harmonic oscillator kernel can model the δ Scuti pulsations. It provides similar results to parametric models but with a simpler framework, using only three hyperparameters. It also enables us to model poorly sampled RV data that were excluded from previous analyses, hence extending the RV baseline by nearly five years. Altogether, the orbit and mass of both planets can be constrained from RV only, which was not possible with the parametric modeling. To characterize the system more accurately, we also perform a joint fit of all available relative astrometry and RV data. Our orbital solutions for β Pic b favor a low eccentricity of 0.029$_{−0.024}^{+0.061}$ and a relatively short period of 21.1$_{−0.8}^{+2.0}$ yr. The orbit of β Pic c is eccentric with 0.206$_{−0.063}^{+0.074}$ with a period of 3.36 ± 0.03 yr. We find model-independent masses of 11.7 ± 1.4 and 8.5 ± 0.5 M Jup for β Pic b and c, respectively, assuming coplanarity. The mass of β Pic b is consistent with the hottest start evolutionary models, at an age of 25 ± 3 Myr. A direct detection of β Pic c would provide a second calibration measurement in a coeval system.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Hierarchical Convolutional Neural Networks for Event Classification on PMU Measurements

Event classification is one of the central components of automated disturbance analysis based on PMU measurements. Obtaining high-quality event labels remains a challenge for supervised learning-based classification of local and system-wide events in power grids due to its labor-intensive requirement. We present a sensitivity study considering rapidly refined, partially and fully inspected event labels that leads to evidence that hierarchical convolutional neural networks (HCNNs) outperform traditional classification models regardless of the quality of the available event labels. Furthermore, it is demonstrated that performance similar to the one obtained using entirely domain-driven labeling can be achieved as long as the involved expert does not mislabel more than ~5% of the event data captured by PMU measurements.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Data-driven surrogate modeling of hPIC ion energy-angle distributions for high-dimensional sensitivity analysis of plasma parameters' uncertainty

In this work, we present a data-driven strategy for effective construction of a surrogate model in high-dimensional parameter space for the ion energy-angle distribution (IEAD) output of hPIC simulations of plasma-surface interactions. The methodology is based on a bin-by-bin least-squares fitting of the IEAD in the parameter space. The fitting is performed in a transformed coordinate system to normalize the IEAD, and it employs sparse grids for sampling the parameter space to overcome sampling challenges in high dimensions. The surrogate model is significantly cheaper computationally than direct hPIC simulations yet maintains high fidelity to them, providing a fast emulator for hPIC simulations. Sensitivity analysis based on the surrogate model is utilized to characterize the dependence of the ion impact angle and energy moments on the physical parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

3D city modeling↗

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 OSTI2020

What Factors Drive the Changes in Water Withdrawals in the U.S. Agriculture and Food Manufacturing Industries between 1995 and 2010?

Climate change and increasing world population will directly impact the global food supply chain linkages. In the United States, agricultural production requires less irrigated water than before but it still accounts for a third of total water withdrawals. To better understand the evolution of its water use, we perform a structural decomposition analysis of water withdrawals across eight different crops and six livestock categories and differentiate the trends over 1995–2005 vs 2005–2010 to account for the role of the economic crisis in the second period. Based on USGS data, the results show that both periods experienced an overall decline in water withdrawals in the production of all crops except oilseeds. This trend is driven by a decrease in water intensity, reflecting greater efficiency of irrigation systems, and by reduced local per capita income in the second period. However, increased foreign demand for water-intensive sectors like oilseeds from NAFTA and Asian partners mitigated the decline. Results indicate also a decreasing water use in livestock production partially due to a shift from red to white meat consumption in the country. Arguably, recent tariff wars and border closures have greatly reduced the virtual water embodied in American exports.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

Nweye, Kingsley↗

DOE OSTI2022

Discovery of Signatures, Anomalies, and Precursors in Synchrophasor Data with Matrix Profile and Deep Recurrent Neural Networks (Final Project Report)

The widespread deployment of phasor measurement unit (PMU) across the U.S. together with the burgeoning machine learning technology made it possible to develop data-driven PMU data analytics to improve grid security and reliability in a more insightful and effective manner. Although PMU applications have been explored for over a decade, the representative PMU usage is limited to the bulk power system monitoring mainly due to the data integrity issues associated with PMUs (typically missing, fragmented, and wrongly amplified data). To forge a breakthrough on this stalemate and embrace PMUs for power system control and protection as well, we applied various advanced machine learning and big data analysis technology to the power system event detection and classification as the first step toward the power system control and protection pertaining to grid security enhancement.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Integration and Validation of Multi-Layer Mitigation Strategies for Cyber Physical Systems Resilience (RD2C Capstone I Project) (Technical Report)

This project demonstrated the effectiveness of RD2C-developed, resilience-driven control strategies on high-fidelity system models with high penetrations of inverter-based resources, under the impacts of unexpected natural events and cyber-physical attacks. Controller performance and overall system resilience were quantified using data from a comprehensive suite of scenarios, and provided a clear understanding on how to operate and deploy these mitigation strategies.

42 ENGINEERING↗