Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “system value”

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.

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

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The isospin-3 three-particle K -matrix at NLO in ChPT

The three-particle K-matrix, K df,3 , is a scheme-dependent quantity that parametrizes short-range three-particle interactions in the relativistic-field-theory three-particle finite-volume formalism. In this work, we compute its value for systems of three pions at maximal isospin through next-to-leading order (NLO) in Chiral Perturbation Theory (ChPT). We compare the values to existing lattice QCD results and find that the agreement between lattice QCD data and ChPT in the first two coefficients of the threshold expansion of K df,3 is significantly improved with respect to leading order once NLO effects are incorporated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Geothermal well testing pressure prediction by using a hybrid transformer model system: FORGE well use case

Geothermal has huge potential to become an indispensable component in achieving the goal of sustainable energy economy, given its capability to provide consistent baseload power to the electric grid. Injection tests are crucial in geothermal energy system as they naturally help to evaluate reservoir properties, understand fluid flow and even enhance reservoir performance. In this research, we developed a hybrid model system that integrates machine learning (ML) regression, a physics-based mathematical model, and transformer deep learning. Trained and validated using FORGE injection test dataset, this system can forecast the pressure variations both upward and downward over time. The pressure prediction achieved prediction accuracy within 3-6% variance of true pressure values. The system can significantly save time and reduce costs by testing only a few cycles and then using model predictions for further analysis, instead of conducting additional real injection cycle tests. The developed model system also holds promise for designing injection test processes and maintaining well production in geothermal energy. Presented at the IMAGE ‘25 Conference led by Shell.

FORGE↗
DOE OSTI2020

Benefit Analysis of Long-Duration Energy Storage in Power Systems with High Renewable Energy Shares

The integration of high shares of variable renewable energy raises challenges for the reliability and cost-effectiveness of power systems. The value of long-duration energy storage, which helps address variability in renewable energy supply across days and seasons, is poised to grow significantly as power systems shift to larger shares of variable generation such as wind and solar. This study explores the system-level services and associated benefits of long-duration energy storage on the 2050 Western Interconnection (WI). The operation of the future WI system with 85% renewable penetration is simulated using a two-stage production cost model. The impact of long duration energy storage on systemwide operations is examined for the 2050 WI system, using a range of round-trip efficiencies corresponding to four different energy storage technologies. The analysis projects the energy storage dispatch profile, system-wide production cost savings (from both diurnal and seasonal operation), and impacts on generation mix, and change in renewable generation curtailment.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

High Value Risk Targets in Human System Risk

The Human System Risk Board (HSRB) has the overall responsibility for tracking the evolution of the top ~30 human system risks that it has identified to be associated with human spaceflight. The Board is also charged with maintaining a consistent, integrated process to mitigate those risks, and developing evidence-based risk posture recommendations. Each Risk Custodian Team (RCT) identifies High Value Risk Mitigation Targets (HVRMT) that have the highest likelihood or greatest impact in reducing their risk. These typically include areas where there are major gaps in knowledge or capability; or other targets that promise to yield returns worthy of investments in time, money and other resources by stakeholders interested in reducing Human System Risk. As part of the review of each risk the Board members consider and discuss the HVRMT statements, which are then approved by the Board Chair. High Value Risk Mitigation Targets can include research activities funded by the Human Research Program, but often involve recommending standards, monitoring, or other operational goals to reduce risk. This presentation will discuss common themes and highlights of the approved HVRTs to inform IWS attendees and the wider research community.

D M Buckland↗
DOE OSTI2023

Impacts of Dispatch Strategies and Forecast Errors on the Economics of Behind-the-Meter PV-Battery Systems: Preprint

To assess the economic value of batteries in hybrid PV-battery systems, one must create a dispatch profile for the battery. Many analyses of battery value assume perfect forecasts of PV generation and load, determining an upper limit on the value of the battery. Prior work that accounts for forecast uncertainty often does so in the context of a single dispatch algorithm, which does not provide a baseline for comparison. Furthermore, when multiple dispatch algorithms are assessed with uncertainty, the benefits considered are for diesel generation in a microgrid, not retail rate savings. This work addresses the gaps in the literature by comparing the performance of both heuristic and optimal dispatch algorithms for retail rate savings under forecast uncertainty, and provides comparisons of the robustness of these algorithms and their associated estimates of economic value. We find that using a perfect forecast can overestimate the value of hybrid PV-battery systems between 1% and 8% compared to the reality of using a day-ahead forecast, depending on the dispatch algorithm used. Thus, accounting for forecast uncertainty in system design and analysis will significantly improve the accuracy of modeled system values.

batteries↗
NASA NTRS2009

Detection of presence of chemical precursors

Methods and systems for determining if one or more target molecules are present in a gas, by exposing a functionalized carbon nanostructure (CNS) to the gas and measuring an electrical parameter value EPV(n) associated with each of N CNS sub-arrays. In a first embodiment, a most-probable concentration value C(opt) is estimated, and an error value, depending upon differences between the measured values EPV(n) and corresponding values EPV(n;C(opt)) is computed. If the error value is less than a first error threshold value, the system interprets this as indicating that the target molecule is present in a concentration C.apprxeq.C(opt). A second embodiment uses extensive statistical and vector space analysis to estimate target molecule concentration.

Li, Jing↗
NASA NTRS1977

Applications of Algebraic Geometry to Systems Theory

Basic theorems of algebraic geometry are applied to prove some pole-placement theorems, including an improved version of pole placement with output feedback. Examples are given which show the limitations of the algebro-geometric theorems and their potential value for systems theory. This paper and those to follow might contribute towards making the powerful theorems of modern algebraic geometry accessible and applicable to problems of engineering.

Hermann, Robert↗
NASA NTRS2011

Method for Predicting and Optimizing System Parameters for Electrospinning System

An electrospinning system using a spinneret and a counter electrode is first operated for a fixed amount of time at known system and operational parameters to generate a fiber mat having a measured fiber mat width associated therewith. Next, acceleration of the fiberizable material at the spinneret is modeled to determine values of mass, drag, and surface tension associated with the fiberizable material at the spinneret output. The model is then applied in an inversion process to generate predicted values of an electric charge at the spinneret output and an electric field between the spinneret and electrode required to fabricate a selected fiber mat design. The electric charge and electric field are indicative of design values for system and operational parameters needed to fabricate the selected fiber mat design.

Wincheski, Russell A.↗
DOE OSTI2020

Ambient Synchrophasor Measurement Based System Inertia Estimation

This paper develops an algorithm to estimate the system inertia value based on ambient synchrophasor measurement. Informative features are extracted from ambient synchrophasor measurements for machine-learning-based inertia estimation. Besides ambient synchrophasor measurements of FNET/GridEye, other available data relevant to inertia (such as weather and system load data) are also used to improve the inertia estimation accuracy. Then a machine learning algorithm to estimate system inertia is developed. A test dataset including ambient synchrophasor data from FNET/GridEye measurements and the WECC system inertia data from NERC is used to evaluate the performance of the developed inertia estimation method. The average and maximum estimation errors of the developed inertia estimation method is lower than 5% and 10%, respectively. This accuracy is higher than reported accuracy values in existing literature.

CUI, YI↗
DOE OSTI2026

CHESS 2025: Discrete-return LiDAR point clouds from NEON AOP surveys

This dataset provides Level 1 (L1) discrete-return light detection and ranging (LiDAR) point cloud data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary unclassified discrete-return LiDAR data delivered by NEON and are provided per flightline as LASzip (LAZ) 1.4 Format 6 files. Data were processed following the workflow described in the NEON L0-to-L1 Discrete Return LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022). Each record in the unclassified point clouds represents a geolocated laser target/return recorded by the LiDAR system, with values for X, Y, Z position and return intensity. All point coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Flight metadata describing flightline boundaries and positional uncertainty by point are also included. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRS2020

Enabling Space Exploration Medical System Development Using a Tool Ecosystem

The NASA Human Research Program’s (HRP) Exploration Medical Capability (ExMC) Element is utilizing a Model Based Systems Engineering (MBSE) approach to enhance the development of systems engineering products that will be used to advance medical system designs for exploration missions beyond Low Earth Orbit. In support of future missions, the team is capturing content such as system behaviors, functional decompositions, architecture, system requirements and interfaces, and recommendations for clinical capabilities and resources in Systems Modeling Language (SysML) models. As these products mature, SysML models provide a way for ExMC to capture relationships among the various products, which includes supporting more integrated and multi-faceted views of future medical systems. In addition to using SysML models, HRP and ExMC are developing supplementary tools to support two key functions: 1) prioritizing current and future research activities for exploration missions in an objective manner; and 2) enabling risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This paper will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include dynamic Probabilistic Risk Assessment (PRA) capabilities, additional SysML models, a database of system component options, and data visualizations. It also includes a review of an initial Pilot Project focused on enabling medical system trade studies utilizing data that is coordinated across tools for consistent outputs (e.g., mission risk metrics that are associated with medical system mass values and medical conditions addressed). This first Pilot Project demonstrated successful operating procedures and integration across tools. Finally, the paper will also cover a second Pilot Project that utilizes tool enhancements such as medical system optimization capabilities, post-processing, and visualization of generated data for subject matter expert review, and increased integration amongst the tools themselves.

Amador, Jennifer R.↗
NASA NTRS2020

Enabling Space Exploration Medical System Development Using a Tool Ecosystem

The NASA Human Research Program's (HRP) Exploration Medical Capability (ExMC) Element is utilizing a Model Based Systems Engineering (MBSE) approach to enhance the development of systems engineering products that will be used to advance medical system designs for exploration missions beyond Low Earth Orbit. In support of future missions, the team is capturing content such as system behaviors, functional decompositions, architecture, system requirements and interfaces, and recommendations for clinical capabilities and resources in Systems Modeling Language (SysML) models. As these products mature, SysML models provide a way for ExMC to capture relationships among the various products, which includes supporting more integrated and multi-faceted views of future medical systems. In addition to using SysML models, HRP and ExMC are developing supplementary tools to support two key functions: 1) prioritizing current and future research activities for exploration missions in an objective manner; and 2) enabling risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This paper will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include dynamic Probabilistic Risk Assessment (PRA) capabilities, additional SysML models, a database of system component options, and data visualizations. It also includes a review of an initial Pilot Project focused on enabling medical system trade studies utilizing data that is coordinated across tools for consistent outputs (e.g., mission risk metrics that are associated with medical system mass values and medical conditions addressed). This first Pilot Project demonstrated successful operating procedures and integration across tools. Finally, the paper will also cover a second Pilot Project that utilizes tool enhancements such as medical system optimization capabilities, post-processing, and visualization of generated data for subject matter expert review, and increased integration amongst the tools themselves.

Amador, Jennifer R.↗
DOE OSTI2026

RIKEN TRIP Magnets Database

This dataset contains ab-initio calculation results for the temperature-dependent anomalous Hall conductivity, the anomalous Nernst effect, and the Seebeck coefficient. All calculations are based on ab-inito Quantum Espresso (PWSCF v.6.3) + Wannier90 (v.3.0.0). The dependence on carrier doping is also calculated. For all calculations a ferromagnetic order has been assumed, which might not correspond to the true ground state of the system. Tabulated values for the magnetic moments and essential input files for Quantum Espresso are available for download as attachments. This project has been supported by the RIKEN Transformative Research Innovation Platform (TRIP), Use Case: Many-body Electron Systems.

36 MATERIALS SCIENCE↗
NASA NTRS1988

Propagation measurements for an Australian land mobile satellite system

Measurements of attenuation statistics using a helicopter and an instrumented van are discussed. Results are given for two different tree densities, for elevation angles of 30, 45 and 60 degrees and for frequencies of 893, 1550 and 2660 MHz. These results show that at 1550 MHz and 45 degrees elevation angle, attenuation values of 5.0 and 8.6 dB were exceeded 10 percent of the time for roadside tree densities of 35 percent and 85 percent respectively. Comparisons with other results made in the Northern Hemisphere are made and show general agreement. The implication of the measured values on system design are discussed, and it is shown that, for Australia, an adaptive margin allocation scheme would require an average margin of approximately 5 dB.

Bundrock, Anthony J.↗
DOE OSTI2020

Development and Application of a Risk Analysis Toolkit for Plant Resources Optimization

This report summarizes the R&D activities of the Risk Informed Asset Management (RIAM) project during fiscal year 2020 (FY20). This project focuses on the development of methods designed to optimize plant operations (e.g., maintenance/replacement schedule, optimal maintenance posture) provided system/component health/cost data. This project development lives in cooperation with the Plant Health Management (PHM) project which focuses on the development of methods that integrate component health data and propagate such information at the system level to evaluate most relevant sources of risk. This year’s activities for the RIAM project focused on the continuation of schedule optimization algorithms developed in FY19. While in FY19 we focused on both deterministic and stochastic capital budgeting methods, in FY20 we moved forward by implementing two versions of schedule optimization methods. The first one reformulates the capital budgeting problem in a distributionally robust form which allows the user to rely on data directly rather than proposing a distribution from the data itself. The second version reformulates the capital budgeting explicitly using risk measures as variables to maximize/minimize. Lastly, we focused on the development of methods designed to identify the optimal maintenance posture based on the Pareto Frontier analysis. Rather than performing a tradeoff analysis (i.e., identify the absolute best posture), the Pareto Frontier analysis performs a trade space exploration approach (i.e., identify value and costs of several postures and have the analyst perform the task of imposing desired value and cost constraints). This is performed by identifying maintenance postures that maximize value (e.g., system availability) and minimize operational costs, i.e., the Pareto frontier in a value-cost trade space.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Physics-informed State-space Neural Networks for transport phenomena

This work introduces Physics -informed State -space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transportdominated systems such as chemical, biomedical, and power plants. Traditional data -driven methods fall short due to a lack of physical constraints like mass conservation; PSMs address this issue by training deep neural networks with sensor data and physics -informing using components' Partial Differential Equations (PDEs), resulting in a physics -constrained, end -to -end differentiable forward dynamics model. Further, through two in silico experiments - a heated channel and a cooling system loop - we demonstrate that PSMs offer a more accurate approach than a purely data -driven model. In the former experiment, PSMs demonstrated significantly lower average root -mean -square errors across test datasets compared to a purely data -driven neural network, with reductions of 44 %, 48 %, and 94 % in predicting pressure, velocity, and temperature, respectively. Beyond accuracy, PSMs demonstrate a compelling multitask capability, making them highly versatile. In this work, we showcase two: supervisory control of a nonlinear system through a sequentially updated state -space representation and the proposal of a diagnostic algorithm using residuals from each of the PDEs. The former demonstrates PSMs' ability to handle constant and time -dependent constraints, while the latter illustrates their value in system diagnostics and fault detection.

42 ENGINEERING↗
NASA NTRSDate not supplied

On-Orbit Glass Stress Measurement Tool Development

Exposed window surfaces on-orbit are vulnerable to a variety of damage including impacts. These impacts impart stress into the panes and reduce the remaining life of the window. Spacecraft windows are evaluated before and after flight to determine the minimum strength of the glass prior to launch. Currently, no method exists to accurately evaluate the windows on-orbit. An on-orbit optical system is under development to evaluate spacecraft windowpane material for remaining flight life by directly measuring the stress in the glass. This optical system builds on the industry-standard practice of calculating the amount of impact-induced stress through birefringence measurement. In industry, the birefringence is imaged through the pane with a detector and light source on opposite sides. On-orbit, there is only access to one side of the pane. This system has been designed to image birefringence by reflecting the light off the pane to a detector on the same side of the glass. Previous studies have shown a correlation between material stress distribution and the associated K-value. This system is designed to determine low strength areas which can be caused by defects due to impact damage. Glass samples will be imaged and broken in order to correlate impact-induced stress to the fracture strength of the material. Once this correlation is determined, a quantitative assessment of the stress areas in spacecraft windows will occur prior to and during mission operations to reliably determine the overall strength of the glass.

Max J Schneiderman↗