Results for “model integration” — Page 44

NASA NTRS2006

Zero to Integration in Eight Months, the Dawn Ground Data System Engineering Challange

The Dawn Project has presented the Ground Data System (GDS) with technical challenges driven by cost and schedule constraints commonly associated with National Aeronautics and Space Administration (NASA) Discovery Projects. The Dawn mission consists of a new and exciting Deep Space partnership among: the Jet Propulsion Laboratory (JPL), responsible for project management and flight operations; Orbital Sciences Corporation (OSC), spacecraft builder and responsible for flight system test and integration; and the University of California, at Los Angeles (UCLA), responsible for science planning and operations. As a cost-capped mission, one of Dawn s implementation strategies is to leverage from both flight and ground heritage. OSC's ground data system is used for flight system test and integration as part of the flight heritage strategy. Mission operations, however, are to be conducted with JPL s ground system. The system engineering challenge of dealing with two heterogeneous ground systems emerged immediately. During the first technical interchange meeting between the JPL s GDS Team and OSC's Flight Software Team, August 2003, the need to integrate the ground system with the flight software was brought to the table. This need was driven by the project s commitment to enable instrument engineering model integration in a spacecraft simulator environment, for both demonstration and risk mitigation purposes, by April 2004. This paper will describe the system engineering approach that was undertaken by JPL's GDS Team in order to meet the technical challenge within a non-negotiable eight-month schedule. Key to the success was adherence to an overall systems engineering process and fundamental systems engineering practices: decomposition of the project request into manageable requirements; definition of a structured yet flexible development process; integration of multiple ground disciplines and experts into a focused team effort; in-process risk management; and aggregation of the intermediate products to an integrated final product. In addition, this paper will highlight the role of lessons learned from the integration experience. The lessons learned from an early GDS deployment have served as the foundation for the design and implementation of the Dawn Ground Data System.

systems engineering↗

NASA NTRS2006

Zero to Integration in Eight Months, the Dawn Ground Data System Engineering Challenge

The Dawn Project has presented the Ground Data System (GDS) with technical challenges driven by cost and schedule constraints commonly associated with National Aeronautics and Space Administration (NASA) Discovery Projects. The Dawn mission consists of a new and exciting Deep Space partnership among: the Jet Propulsion Laboratory (JPL), manages the project and is responsible for flight operation; Orbital Sciences Corporation (OSC), is the spacecraft builder and is responsible for flight system test and integration; and the University of California, at Los Angeles (UCLA), is responsible for science planning and operations. As a cost-capped mission, one of Dawn's implementation strategies is to leverage from both flight and ground heritage. OSC's ground data system is used for flight system test and integration as part of the flight heritage strategy. Mission operations, however, are to be conducted with JPL's ground system. The system engineering challenge of dealing with two heterogeneous ground systems emerged immediately. During the first technical interchange meeting between the JPL's GDS Team and OSC's Flight Software Team, August 2003, the need to integrate the ground system with the flight software was brought to the table. This need was driven by the project's commitment to enable instrument engineering model integration in a spacecraft simulator environment, for both demonstration and risk mitigation purposes, by April 2004. This paper will describe the system engineering approach that was undertaken by JPL's GDS Team in order to meet the technical challenge within a non-negotiable eight-month schedule. Key to the success was adherence to fundamental systems engineering practices: decomposition of the project request into manageable requirements; integration of multiple ground disciplines and experts into a focused team effort; definition of a structured yet flexible development process; definition of an in-process risk reduction plan; and aggregation of the intermediate products to an integrated final product. In addition, this paper will highlight the role of lessons learned from the integration experience. The lessons learned from an early GDS deployment have served as the foundation for the design and implementation of the Dawn Ground Data System.

Ground Data System (GDS)↗

DOE OSTI2025

Co-Simulation of PSS/E, OpenDSS, and PSCAD for Power Systems Stability Analysis With Inverter-Based Resources: Preprint

The increasing penetration of inverter-based resources (IBRs) is reshaping the dynamic behavior of power systems. IEEE standard 1547-2018 suggests that distributed energy resources (DERs) should provide grid services such as voltage and frequency supports. On the other hand, dynamic events caused by IBRs such as sub-synchronous oscillation have been reported. These developments necessitate improvement in the current modeling capabilities to better understand the interdependencies within power systems. These include interactions between transmission and distribution systems, among IBRs themselves, and between IBRs and conventional resources. In this paper, we present a co-simulation model integrating PSS/E, OpenDSS, and PSCAD to analyze IBR impacts on the stability of transmission and distribution systems. A key challenge in developing a co-simulation model is ensuring interoperability among different simulators (interfacing and data flow) while maintaining accurate results. Using the developed model, we simulate the impact of IBRs on power systems in two test cases: 1) fault ride through (FRT) capability during a generation trip contingency; 2) IBR-induced sub-synchronous oscillation. The results show the effectiveness of the co-simulation model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1985

MOS integrated circuit fault modeling

Three digital simulation techniques for MOS integrated circuit faults were examined. These techniques embody a hierarchy of complexity bracketing the range of simulation levels. The digital approaches are: transistor-level, connector-switch-attenuator level, and gate level. The advantages and disadvantages are discussed. Failure characteristics are also described.

Sievers, M.↗

DOE OSTI2024

Using an Isotope Enabled Mass Balance to Evaluate Existing Land Surface Models

Abstract Land surface models (LSMs) play a crucial role in elucidating water and carbon cycles by simulating processes such as plant transpiration and evaporation from bare soil, yet calibration often relies on comparing LSM outputs of landscape total evapotranspiration ( ET ) and discharge with measured bulk fluxes. Discrepancies in partitioning into component fluxes predicted by various LSMs have been noted, prompting the need for improved evaluation methods. Stable water isotopes serve as effective tracers of component hydrologic fluxes, but data and model integration challenges have hindered their widespread application. Leveraging National Ecological Observation Network measurements of water isotope ratios at 16 US sites over 3 years combined with LSM‐modeled fluxes, we employed an isotope‐enabled mass balance framework to simulate ET isotope values ( δET ) within three operational LSMs (Mosaic, Noah, and VIC) to evaluate their partitioning. Models simulating δET values consistent with observations were deemed more reflective of water cycling in these ecosystems. Mosaic exhibited the best overall performance (Kling‐Gupta Efficiency of 0.28). For both Mosaic and Noah there were robust correlations between bare soil evaporation fraction and error (negative) as well as transpiration fraction and error (positive). We found the point at which errors are smallest ( x ‐intercept of the multi‐site regression) is at a higher transpiration fraction than is currently specified in the models. Which means that transpiration fraction is underestimated on average. Stable isotope tracers offer an additional tool for model evaluation and identifying areas for improvement, potentially enhancing LSM simulations and our understanding of land‐surface hydrologic processes.

58 GEOSCIENCES↗

DOE OSTI2026

Community-Engaged Modeling of Urban Flood Adaptation Pathways

Climate change is intensifying the hydrologic cycle, leading to more frequent and severe rainfall-driven (pluvial) flooding in urban areas. In the mid-Atlantic US cities, aging and under-designed stormwater infrastructure is increasingly strained by these events, resulting in recurring damage to property and disruptions to transportation networks. In this study, we combine community engagement with hydrologic modeling to develop and evaluate potential urban flood adaptation strategies. Over a three-year period, local technical experts and community representatives met regularly to discuss flooding concerns, identify priorities, and co-develop adaptation strategies. These discussions informed the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed, the focus location of this study. The flooding model integrates complex surface and subsurface stormwater infrastructure data, local expert knowledge, and community insights. We simulate stakeholder-prioritized adaptations, such as green and gray infrastructure strategies. Model results demonstrate that enhanced infrastructure maintenance is the most effective adaptation for reducing flood depths, but has varied effects across the watershed, and can increase flooding in some locations. Spatially concentrated greening provides limited benefit to the watershed as a whole, but moderate benefit in community priority areas. Together, these adaptations have the potential to reduce flood depths by as much as 58% in some locations, greatly reducing property damage and mobility impacts, primary concerns of stakeholders. Future work will implement robust optimization tools to search for adaptations which meet stakeholder objectives and perform highly under varied future climate conditions. This work contributes to the expanding literature on collaborative modeling and demonstrates that community-engaged approaches can enhance model credibility and generate more actionable insights for communities seeking to strengthen climate resilience.

Spangler, Ava [Pennsylvania State University] (ORC↗

DOE OSTI2026

Community-Informed Urban Flood Modeling for Impact Mitigation

Climate change is intensifying the hydrologic cycle, leading to more frequent and severe rainfall-driven (pluvial) flooding in urban areas. In the mid-Atlantic US cities, aging and under-designed stormwater infrastructure is increasingly strained by these events, resulting in recurring damage to property and disruptions to transportation networks. In this study, we combine community engagement with hydrologic modeling to develop and evaluate potential urban flood adaptation strategies. Over a three-year period, local technical experts and community representatives met regularly to discuss flooding concerns, identify priorities, and co-develop adaptation strategies. These discussions informed the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed, the focus location of this study. The flooding model integrates complex surface and subsurface stormwater infrastructure data, local expert knowledge, and community insights. We simulate stakeholder-prioritized adaptations, such as green and gray infrastructure strategies. Model results demonstrate that enhanced infrastructure maintenance is the most effective adaptation for reducing flood depths, but has varied effects across the watershed, and can increase flooding in some locations. Spatially concentrated greening provides limited benefit to the watershed as a whole, but moderate benefit in community priority areas. Together, these adaptations have the potential to reduce flood depths by as much as 58% in some locations, greatly reducing property damage and mobility impacts, primary concerns of stakeholders. Future work will implement robust optimization tools to search for adaptations which meet stakeholder objectives and perform highly under varied future climate conditions. This work contributes to the expanding literature on collaborative modeling and demonstrates that community-engaged approaches can enhance model credibility and generate more actionable insights for communities seeking to strengthen climate resilience.

Baltimore MD↗

NASA NTRS1986

An optimization model for the US Air-Traffic System

A systematic approach for monitoring U.S. air traffic was developed in the context of system-wide planning and control. Towards this end, a network optimization model with nonlinear objectives was chosen as the central element in the planning/control system. The network representation was selected because: (1) it provides a comprehensive structure for depicting essential aspects of the air traffic system, (2) it can be solved efficiently for large scale problems, and (3) the design can be easily communicated to non-technical users through computer graphics. Briefly, the network planning models consider the flow of traffic through a graph as the basic structure. Nodes depict locations and time periods for either individual planes or for aggregated groups of airplanes. Arcs define variables as actual airplanes flying through space or as delays across time periods. As such, a special case of the network can be used to model the so called flow control problem. Due to the large number of interacting variables and the difficulty in subdividing the problem into relatively independent subproblems, an integrated model was designed which will depict the entire high level (above 29000 feet) jet route system for the 48 contiguous states in the U.S. As a first step in demonstrating the concept's feasibility a nonlinear risk/cost model was developed for the Indianapolis Airspace. The nonlinear network program --NLPNETG-- was employed in solving the resulting test cases. This optimization program uses the Truncated-Newton method (quadratic approximation) for determining the search direction at each iteration in the nonlinear algorithm. It was shown that aircraft could be re-routed in an optimal fashion whenever traffic congestion increased beyond an acceptable level, as measured by the nonlinear risk function.

Mulvey, J. M.↗

NASA NTRS2006

NASA Tech Briefs, June 2006

Topics covered include: Magnetic-Field-Response Measurement-Acquisition System; Platform for Testing Robotic Vehicles on Simulated Terrain; Interferometer for Low-Uncertainty Vector Metrology; Rayleigh Scattering for Measuring Flow in a Nozzle Testing Facility; "Virtual Feel" Capaciflectors; FETs Based on Doped Polyaniline/Polyethylene Oxide Fibers; Miniature Housings for Electronics With Standard Interfaces; Integrated Modeling Environment; Modified Recursive Hierarchical Segmentation of Data; Sizing Structures and Predicting Weight of a Spacecraft; Stress Testing of Data-Communication Networks; Framework for Flexible Security in Group Communications; Software for Collaborative Use of Large Interactive Displays; Microsphere Insulation Panels; Single-Wall Carbon Nanotube Anodes for Lithium Cells; Tantalum-Based Ceramics for Refractory Composites; Integral Flexure Mounts for Metal Mirrors for Cryogenic Use; Templates for Fabricating Nanowire/Nanoconduit- Based Devices; Measuring Vapors To Monitor the State of Cure of a Resin; Partial-Vacuum-Gasketed Electrochemical Corrosion Cell; Theodolite Ring Lights; Integrating Terrain Maps Into a Reactive Navigation Strategy; Reducing Centroid Error Through Model-Based Noise Reduction; Adaptive Modeling Language and Its Derivatives; Stable Satellite Orbits for Global Coverage of the Moon; and Low-Cost Propellant Launch From a Tethered Balloon

Source record↗

NASA NTRS1990

Simulations of winter blocking episodes using observed sea surface temperatures

The formation of major Northern Hemisphere blockings during January 1979 is studied in numerical simulations of the global atmosphere with a sea surface temperature (SST) field updated with observations during the model integration. Both the standard 4 x 5 degrees latitude-longitude and high-resolution 2 x 2.5 degrees versions of the Goddard Laboratory for Atmospheres general circulation models (GCMs) are employed. The SST field is provided by a blend analysis of in situ and satellite-retrieved data. The simulations by the high-resolution GCM with a realistic SST field of the ocean surface are shown to be capable of producing two successive realistic major blockings in the Pacific and Atlantic through a one-month period. The skill in predicting the blocking formation as obtained in the simulations is due to improved skill in forecasting of ultralog waves. Although the results are encouraging, only one initial state is involved in this series of simulations; therefore, the results should not be generalized yet at this stage of investigation.

Kung, Ernest C.↗

NASA NTRS2011

Lessons Learned from Assimilating Altimeter Data into a Coupled General Circulation Model with the GMAO Augmented Ensemble Kalman Filter

Satellite altimetry measurements have provided global, evenly distributed observations of the ocean surface since 1993. However, the difficulties introduced by the presence of model biases and the requirement that data assimilation systems extrapolate the sea surface height (SSH) information to the subsurface in order to estimate the temperature, salinity and currents make it difficult to optimally exploit these measurements. This talk investigates the potential of the altimetry data assimilation once the biases are accounted for with an ad hoc bias estimation scheme. Either steady-state or state-dependent multivariate background-error covariances from an ensemble of model integrations are used to address the problem of extrapolating the information to the sub-surface. The GMAO ocean data assimilation system applied to an ensemble of coupled model instances using the GEOS-5 AGCM coupled to MOM4 is used in the investigation. To model the background error covariances, the system relies on a hybrid ensemble approach in which a small number of dynamically evolved model trajectories is augmented on the one hand with past instances of the state vector along each trajectory and, on the other, with a steady state ensemble of error estimates from a time series of short-term model forecasts. A state-dependent adaptive error-covariance localization and inflation algorithm controls how the SSH information is extrapolated to the sub-surface. A two-step predictor corrector approach is used to assimilate future information. Independent (not-assimilated) temperature and salinity observations from Argo floats are used to validate the assimilation. A two-step projection method in which the system first calculates a SSH increment and then projects this increment vertically onto the temperature, salt and current fields is found to be most effective in reconstructing the sub-surface information. The performance of the system in reconstructing the sub-surface fields is particularly impressive for temperature, but not as satisfactory for salt.

Keppenne, Christian↗

NASA NTRS2013

Integrated Main Propulsion System Performance Reconstruction Process/Models

The Integrated Main Propulsion System (MPS) Performance Reconstruction process provides the MPS post-flight data files needed for postflight reporting to the project integration management and key customers to verify flight performance. This process/model was used as the baseline for the currently ongoing Space Launch System (SLS) work. The process utilizes several methodologies, including multiple software programs, to model integrated propulsion system performance through space shuttle ascent. It is used to evaluate integrated propulsion systems, including propellant tanks, feed systems, rocket engine, and pressurization systems performance throughout ascent based on flight pressure and temperature data. The latest revision incorporates new methods based on main engine power balance model updates to model higher mixture ratio operation at lower engine power levels.

Lopez, Eduardo↗

DOE OSTI2024

Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification

This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based on first principles dynamic models with a multi-step time–frequency LSTM model in predicting the temperature profiles of a sodium cold trap purification system. The long short-term memory–model predictive controller (LSTM-MPC) model employs a sliding window scheme to gather training samples for multi-step prediction, leveraging historical data to construct predictive models that capture the non-linearities of the complex system dynamics without explicitly modeling the underlying physical processes. The performance of the LSTM-MPC and MPC were evaluated through simulation experiments, where both models were assessed on their capacity to maintain the cold trap temperature within predefined set-points while minimizing deviations and overshoots. Results obtained show how the data-driven LSTM-MPC model demonstrates stability and adaptability. In contrast, the traditional MPC model exhibits irregularities, particularly evident as overshoots around set-point limits, which can potentially compromise its effectiveness over long prediction time intervals. The findings obtained offer valuable insights into integrating data-driven techniques for enhancing real-time monitoring systems.

LSTM-MPC↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRS2009

Real-Time Simulation of Ares I Launch Vehicle

The Ares Real-Time Environment for Modeling, Integration, and Simulation (ARTEMIS) has been developed for use by the Ares I launch vehicle System Integration Laboratory (SIL) at the Marshall Space Flight Center (MSFC). The primary purpose of the Ares SIL is to test the vehicle avionics hardware and software in a hardware-in-the-loop (HWIL) environment to certify that the integrated system is prepared for flight. ARTEMIS has been designed to be the real-time software backbone to stimulate all required Ares components through high-fidelity simulation. ARTEMIS has been designed to take full advantage of the advances in underlying computational power now available to support HWIL testing. A modular real-time design relying on a fully distributed computing architecture has been achieved. Two fundamental requirements drove ARTEMIS to pursue the use of high-fidelity simulation models in a real-time environment. First, ARTEMIS must be used to test a man-rated integrated avionics hardware and software system, thus requiring a wide variety of nominal and off-nominal simulation capabilities to certify system robustness. The second driving requirement - derived from a nationwide review of current state-of-the-art HWIL facilities - was that preserving digital model fidelity significantly reduced overall vehicle lifecycle cost by reducing testing time for certification runs and increasing flight tempo through an expanded operational envelope. These two driving requirements necessitated the use of high-fidelity models throughout the ARTEMIS simulation. The nature of the Ares mission profile imposed a variety of additional requirements on the ARTEMIS simulation. The Ares I vehicle is composed of multiple elements, including the First Stage Solid Rocket Booster (SRB), the Upper Stage powered by the J- 2X engine, the Orion Crew Exploration Vehicle (CEV) which houses the crew, the Launch Abort System (LAS), and various secondary elements that separate from the vehicle. At launch, the integrated vehicle stack is composed of these stages, and throughout the mission, various elements separate from the integrated stack and tumble back towards the earth. ARTEMIS must be capable of simulating the integrated stack through the flight as well as propagating each individual element after separation. In addition, abort sequences can lead to other unique configurations of the integrated stack as the timing and sequence of the stage separations are altered.

Tobbe, Patrick↗

DOE OSTI2021

A hierarchical gray-box dynamic modeling methodology for direct-expansion cooling systems to support control stability analysis; Méthodologie de modélisation dynamique hiérarchique de type boîte grise pour les systèmes de refroidissement à détente directe afin de soutenir l'analyse de stabilité de la commande

In this paper, a gray-box dynamic modeling approach for direct-expansion cooling systems is presented. The overall approach incorporates a multi-stage training procedure that consists of 1) identification of component sub-models from quasi-steady-state performance data, 2) system model integration with estimation of refrigerant charge and 3) fine tuning of thermal capacitances of the evaporator and condenser to capture the system dynamic responses. Compared to traditional physics-based models, the proposed modeling approach has advantages including reduced engineering efforts in the model development phase, improved computational efficiency and enhanced prediction accuracy. The modeling method was validated using a 3-ton variable-speed heat pump and proved to be capable of accurately predicting the system transient behaviors over a wide range of operating conditions. The established dynamic model was then applied for control stability analysis, with a specific goal of determining a proper control execution time step. The case study results showed that the stable control execution time step could change significantly, from 3 sec to 19 sec, as the operating conditions and control settings vary, and a proper selection of the execution time step is critical to ensure stable and reliable operations.

42 ENGINEERING↗