Results for “performance model” — Page 27

NASA NTRS1999

On-Orbit Thermal Performance and Model Correlation of the Fast Auroral Snapshot Explorer

The Fast Auroral SnapshoT explorer (FAST) spacecraft, the second of NASA's Small Explorer (SMEX) series of scientific satellites, was launched on August 21, 1996 by a Pegasus XL launch vehicle. Due to slightly higher than expected temperatures during early orbit operations, an extensive thermal model correlation effort was undertaken to understand and characterize FAST's thermal performance in order to properly orient the spacecraft's attitude during its mission. FAST's thermal design and the on-orbit thermal model correlation and resolution are described. Finally, the correlated model's predictions are compared with nine months of flight data.

Parrish, Keith↗

NASA NTRS1993

A Performance Prediction Model for a Fault-Tolerant Computer During Recovery and Restoration

The modeling and design of a fault-tolerant multiprocessor system is addressed. In particular, the behavior of the system during recovery and restoration after a fault has occurred is investigated. Given that a multicomputer system is designed using the Algorithm to Architecture to Mapping Model (ATAMM), and that a fault (death of a computing resource) occurs during its normal steady-state operation, a model is presented as a viable research tool for predicting the performance bounds of the system during its recovery and restoration phases. Furthermore, the bounds of the performance behavior of the system during this transient mode can be assessed. These bounds include: time to recover from the fault (t(sub rec)), time to restore the system (t(sub rec)) and whether there is a permanent delay in the system's Time Between Input and Output (TBIO) after the system has reached a steady state. An implementation of an ATAMM based computer was developed with the Generic VHSIC Spaceborne Computer (GVSC) as the target system. A simulation of the GVSC was also written based on the code used in ATAMM Multicomputer Operating System (AMOS). The simulation is in turn used to validate the new model in the usefulness and accuracy in tracking the propagation of the delay through the system and predicting the behavior in the transient state of recovery and restoration. The model is validated as an accurate method to predict the transient behavior of an ATAMM based multicomputer during recovery and restoration.

Stoughton, John W.↗

NASA NTRS1973

Analyses of flight model spacecraft performance during thermal-vacuum tests

Malfunction data from the thermal-vacuum tests of 39 flight-model spacecraft have been analyzed. The results are interpreted in terms of the test variables and the spacecraft performance. The malfunction data are correlated with the test time as a single variable, and also with the composite variable of time plus temperature. The improvement in spacecraft performance is examined by means of malfunction rates, malfunctions per spacecraft, and the probability of no failure related to test time. The minimum thermal-vacuum test profile required for Goddard Space Flight Center spacecraft is verified, and the probability of a defect remaining undetected is estimated.

Timmins, A. R.↗

NASA NTRS1972

Analyses of flight model spacecraft performance during thermal-vacuum tests

Malfunction data from the thermal-vacuum tests of 39 flight-model spacecraft were analyzed. The results are interpreted in terms of the test variables, and in terms of the spacecraft performance. The malfunction data are correlated with the test time as a single variable, and also with the composite variable of time plus temperature. The improvement in spacecraft performance is examined by means of malfunction rates, malfunctions per spacecraft, and the probability of no failure related to test time. The minimum thermal-vacuum test profile required for Goddard Space Flight Center spacecraft is verified, and the probability of a defect remaining undetected is estimated.

Timmins, A. R.↗

NASA NTRS2014

Predicting Ground Delay Program at an Airport Based on Meteorological Conditions

In this paper, we present two supervised-learning models, logistic regression and decision tree, to predict occurrence of ground delay program at an airport based on meteorological conditions and scheduled traffic demand. Such predictive capabilities can help the Federal Aviation Administration traffic managers and airline dispatchers to prepare mitigation strategies to reduce the impact of adverse weather. The models are applied to predict ground delay program occurrence at two major U.S. airports: Newark Liberty Intl. and San Francisco Intl. airports. The logistic regression model estimates the probability that a ground delay program will occur during a given hour. Decision tree, on the other hand, classifies an hour as a ground delay program or not based on the input variables. Results indicate that both models perform significantly better than a purely random prediction of ground delay program occurrence at the two airports. The logistic regression model performs better than the decision tree model. The degree to which various input variables impact the probability of ground delay program vary between the two airports. While the enroute convective weather is a dominant factor causing ground delay programs at New York airports, poor visibility and low cloud ceiling caused by marine stratus are major drivers of ground delay programs at San Francisco Intl. airport.

traffic flow management↗

NASA NTRS2017

Measured Boundary Layer Transition and Rotor Hover Performance at Model Scale

An experiment involving a Mach-scaled, 11:08 f t: diameter rotor was performed in hover during the summer of 2016 at NASA Langley Research Center. The experiment investigated the hover performance as a function of the laminar to turbulent transition state of the boundary layer, including both natural and fixed transition cases. The boundary layer transition locations were measured on both the upper and lower aerodynamic surfaces simultaneously. The measurements were enabled by recent advances in infrared sensor sensitivity and stability. The infrared thermography measurement technique was enhanced by a paintable blade surface heater, as well as a new high-sensitivity long wave infrared camera. The measured transition locations showed extensive amounts, x=c>0:90, of laminar flow on the lower surface at moderate to high thrust (CT=s > 0:068) for the full blade radius. The upper surface showed large amounts, x=c > 0:50, of laminar flow at the blade tip for low thrust (CT=s < 0:045). The objective of this paper is to provide an experimental data set for comparisons to newly developed and implemented rotor boundary layer transition models in CFD and rotor design tools. The data is expected to be used as part of the AIAA Rotorcraft SimulationWorking Group

Overmeyer, Austin D.↗

NASA NTRS2022

MSL MMRTG Power Modeling for Lifetime Performance Predictions

The Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) has been providing power to the Mars Science Laboratory (MSL) rover, Curiosity, for surface operations since 2012. The Jet Propulsion Laboratory (JPL) has employed the Life Performance Prediction Model (LPPM) to generate predictions and outputs based upon flight data from the rover and experimental data from the thermoelectric (TE) couples. In order to make these predictions, LPPM requires various spacecraft inputs relevant to RTG performance such as fin root temperature, load voltage, general purpose heat source (GPHS) inventory, etc., combined with couple data such as thermoelectric properties, interface degradation, and sublimation properties. With tens of thousands of hours of flight data to date, LPPM can not only track performance to date, but also make predictions on the power output of the MMRTG through the 17-year end-of-design-life (EODL). These predictions are useful for mission planners and surface operators who rely on the MMRTG for power in order to perform the critical science necessary during the mission. This paper will showcase the power data extracted from the flight electronics, comparing it to the predictions and outputs generated from LPPM.

Pinkowski, Stanley↗

NASA NTRS1992

Modelling and performance of Nb SIS mixers in the 1.3 mm and 0.8 mm bands

We describe the modeling and subsequent improvements of SIS waveguide mixers for the 200-270 and 330-370 GHz bands (Blundell, Carter, and Gundlach 1988, Carter et al 1991). These mixers are constructed for use in receivers on IRAM radiotelescopes on Pico Veleta (Spain, Sierra Nevada) and Plateau de Bure (French Alps), and must meet specific requirements. The standard reduced height waveguide structure with suspended stripline is first analyzed and a model is validated through comparison with scale model and working scale measurements. In the first step, the intrinsic limitations of the standard mixer structure are identified, and the parameters are optimized bearing in mind the radioastronomical applications. In the second step, inductive tuning of the junctions is introduced and optimized for minimum noise and maximum bandwidth. In the 1.3 mm band, a DSB receiver temperature of less than 110 K (minimum 80 K) is measured from 180 through 260 GHz. In the 0.8 mm band, a DSB receiver temperature of less than 250 K (minimum 175 K) is obtained between 325 and 355 GHz. All these results are obtained with room-temperature optics and a 4 GHz IF chain having a 500 MHz bandwidth and a noise temperature of 14 K.

Karpov, A.↗

DOE OSTI2024

Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net-Load Forecasting

Accurate net-load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net-load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net-load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models.

Bhattacharjee, Kaustav↗

NASA NTRSDate not supplied

Models for Facilitating Government-Funded Activities in the Post-ISS LEO Ecosystem

NASA is preparing for the retirement of the ISS and transition of LEO activities to one or more Commercial LEO Destinations (CLDs) by 2030. This transition necessitates new models for connecting NASA and other government-funded users of the LEO environment to platforms and opportunities. This paper describes for consideration six models for facilitating government-funded activities in the post-ISS LEO ecosystem. These six models are illustrative and represent a wide trade space of potential options, each relying on unique mechanisms for facilitating activities on one or more commercial LEO platforms or vehicles. We assessed each model across three possible future scenarios varying in number and diversity of LEO activities and commercial offerings, and across five stakeholder-driven model evaluation criteria. We present the highlights of the analysis, including ways to modify and strengthen each model. The Government Research Broker model performs best across all future scenarios, followed by Innovation Campus, Anchor Tenant, and Fee for Service. While Matchmaker and Institute Network exhibit positive aspects, these models perform most favorably in future scenarios with well-established communities and markets. While each model has strengths and weaknesses, no single model in its current form performs well across all criteria in all three future scenarios. NASA leadership can adjust models as desired to align closer to their priorities using combinations of unique model mechanisms. A model that meets leadership priorities is likely a combination of features from multiple models.

Erica Rodgers↗

NASA NTRSDate not supplied

Models for Facilitating Government-Funded Activities in the Post-ISS LEO Ecosystem

NASA is preparing for the retirement of the ISS and transition of LEO activities to one or more Commercial LEO Destinations (CLDs) by 2030. This transition necessitates new models for connecting NASA and other government-funded users of the LEO environment to platforms and opportunities. This paper describes for consideration six models for facilitating government-funded activities in the post-ISS LEO ecosystem. These six models are illustrative and represent a wide trade space of potential options, each relying on unique mechanisms for facilitating activities on one or more commercial LEO platforms or vehicles. We assessed each model across three possible future scenarios varying in number and diversity of LEO activities and commercial offerings, and across five stakeholder-driven model evaluation criteria. We present the highlights of the analysis, including ways to modify and strengthen each model. The Government Research Broker model performs best across all future scenarios, followed by Innovation Campus, Anchor Tenant, and Fee for Service. While Matchmaker and Institute Network exhibit positive aspects, these models perform most favorably in future scenarios with well-established communities and markets. While each model has strengths and weaknesses, no single model in its current form performs well across all criteria in all three future scenarios. NASA leadership can adjust models as desired to align closer to their priorities using combinations of unique model mechanisms. A model that meets leadership priorities is likely a combination of features from multiple models.

Erica Rodgers↗

NASA NTRSDate not supplied

Models for Facilitating Government-Funded Activities in the Post-ISS LEO Ecosystem

NASA is preparing for the retirement of the ISS and transition of LEO activities to one or more Commercial LEO Destinations (CLDs) by 2030. This transition necessitates new models for connecting NASA and other government-funded users of the LEO environment to platforms and opportunities. This paper describes for consideration six models for facilitating government-funded activities in the post-ISS LEO ecosystem. These six models are illustrative and represent a wide trade space of potential options, each relying on unique mechanisms for facilitating activities on one or more commercial LEO platforms or vehicles. We assessed each model across three possible future scenarios varying in number and diversity of LEO activities and commercial offerings, and across five stakeholder-driven model evaluation criteria. We present the highlights of the analysis, including ways to modify and strengthen each model. The Government Research Broker model performs best across all future scenarios, followed by Innovation Campus, Anchor Tenant, and Fee for Service. While Matchmaker and Institute Network exhibit positive aspects, these models perform most favorably in future scenarios with well-established communities and markets. While each model has strengths and weaknesses, no single model in its current form performs well across all criteria in all three future scenarios. NASA leadership can adjust models as desired to align closer to their priorities using combinations of unique model mechanisms. A model that meets leadership priorities is likely a combination of features from multiple models.

Erica Rodgers↗

DOE OSTI2020

The response of stomatal conductance to seasonal drought in tropical forests

Stomata regulate CO2 uptake for photosynthesis and water loss through transpiration. The approaches used to represent stomatal conductance (gs) in models vary. In particular, current understanding of drivers of the variation in a key parameter in those models, the slope parameter (i.e. a measure of intrinsic plant water-use-efficiency), is still limited, particularly in the tropics. Here we collected diurnal measurements of leaf gas exchange and water potential (?leaf), and a suite of plant traits from the upper canopy of 15 tropical trees in two contrasting Panamanian forests throughout the dry season of the 2016 El Niño. The plant traits included wood density, leaf-mass-per-area (LMA), leaf carboxylation capacity (Vc,max25), leaf water content, the degree of isohydry, and predawn ?leaf. We first investigated how the choice of four commonly used leaf-level gs models with and without the inclusion of ?leaf as an additional predictor variable influence the ability to predict gs, and then explored the abiotic (i.e. month, site-month interaction) and biotic (i.e. tree-species-specific characteristics) drivers of slope parameter variation. Our results show that the inclusion of ?leaf did not improve model performance and that the models that represent the response of gs to vapor pressure deficit performed better than corresponding models that respond to relative humidity. Within each gs model, we found large variation in the slope parameter, and this variation was attributable to the biotic driver, rather than abiotic drivers. We further investigated potential relationships between the slope parameter and the six available plant traits mentioned above, and found that only one trait, LMA, had a significant correlation with the slope parameter (R2=0.66, n=15), highlighting a potential path towards improved model parameterization. This study advances understanding of gs dynamics over seasonal drought, and identifies a practical, trait-based approach to improve modeling of carbon and water exchange in tropical forests.

carbon and water exchange, stomatal conductance mo↗

DOE OSTI2023

Quantifying Uncertainty in PV Energy Estimates Final Report

Uncertainty in PV energy estimates is "one of the most critical areas of lack of understanding" according to independent engineers, financiers, PV model developers, and other industry stakeholders. The primary problem is a lack of rigorous, transparent, widely accepted methods for quantifying uncertainty in energy production estimates. Uncertainty in energy production estimates arises from variability of the solar resource, inexact PV performance models and their parameters, and system reliability considerations. Uncertainty in annual energy production is frequently calculated for larger projects in order to quantify financial risk. Key statistics for energy, such as the P-values "P50" and "P90" (the annual energy values that are exceeded in future years with 50\% and 90\% probability, respectively) are used by financing institutions to calculate the repayment risk for the project. The current methods to estimate these statistics are typically proprietary, specialized, and involve significant post-processing of commercial performance model results. This black-box approach leads to inconsistent P-value estimates from different parties, which reduces investors' confidence in the results. Since the financial community bases its risk assessment on these estimates, reduced confidence increases perceived project risk, and consequently financing costs. The goal of this project was to establish a set of best practices for quantifying uncertainty in energy production estimates, including identifying what sources of uncertainty must be considered with clear definitions and metrics, determining which sources are the biggest drivers of uncertainty, and providing a computationally efficient framework for combining different sources of uncertainty that is flexible enough to accommodate substitutions of data or methods when better information is available. We engaged a wide set of stakeholders to ensure industry endorsement and adoption, and leveraged complementary projects investigating individual sources of uncertainty in great detail, as well as others' work that started down this path.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗

DOE OSTI2022

Causality guided machine learning model on wetland CH 4 emissions across global wetlands

Wetland CH 4 emissions are among the most uncertain components of the global CH 4 budget. The complex nature of wetland CH 4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH 4 emissions. In this study, we used the flux measurements of CH 4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH 4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH 4 emissions in all studied wetland types. Ecosystem respiration (CO 2 ) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH 4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH 4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH 4 emissions within earth system land models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Comparative investigations of multi-fidelity modeling on performance of electrostatically-actuated cracked micro-beams

Silicon is a commonly used material for the fabrication of beams for use in micro-electrical-mechanical systems (MEMS). Although silicon is a brittle material, it has been shown to accumulate fatigue damage at the micro-scale. Understanding the effect this has on the overall device performance is critical to the design of reliable devices. Analytical methods for modeling damage provide expedient results but are limited by broad modeling assumptions. Numerical models account for more detailed physical phenomena but can be computationally intensive. In this work, two different crack scenarios are modeled using both analytical techniques and 3D computational simulations. First, the effects of a single surface crack on the static deflection and natural frequency of an electrostatically actuated micro-beam are formulated and compared. Then, a new method for approximating damage associated with realistic distributed crack networks is formulated for use in an analytical model and numerical simulations. A method for utilizing experimentally derived crack statistics to inform the analytical and numerical distributed crack models is developed. Good agreement between the analytical and numerical models is obtained for both crack scenarios. Altogether, these models can be used to effectively simulate a variety of damage and fatigue behaviors in silicon-based MEMS devices.

42 ENGINEERING↗

DOE OSTI2021

Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: A Mechanistic Modeling Analysis

Earth system models have predicted that there will be more frequent and severe precipitation and drought events in terrestrial ecosystems. Microbially mediated decomposition of soil organic carbon (SOC) tends to increase as soils wet and decrease as soils dry. However, the long-term SOC change under intensified moisture extremes remains poorly known as it depends on the frequency and intensity of soil drying and wetting. In this study, we explored long-term SOC dynamics under scenarios of alternating drying-wetting cycles using the Microbial-ENzyme Decomposition model, a mechanistic microbial model. The model was parameterized with 11 years of observations from a temperate deciduous broadleaf forest site, showing satisfactory model performance in both model calibration (R 2 = 0.67) and validation (R 2 = 0.69) against heterotrophic respiration. We then used the model to simulate the long-term SOC dynamics under five scenarios of alternating drying-wetting cycles with different frequencies and severities over a period of 100 years. Results showed that the changes in active microbial biomass C and the corresponding turnover rates of SOC pools were more sensitive to soil drying than soil wetting. As a result, the cumulative soil carbon emission from microbial respiration decreased by 433.7 g C m -2 after the 100-year simulation in the highest frequency and intensity moisture scenario, but was not significantly affected by the lowest frequency and intensity scenario. This study emphasizes the nonlinear response of SOC decomposition to soil moisture changes, which causes decreased decomposition by microbes under drying that is, not compensated by increased decomposition under wetting conditions.

58 GEOSCIENCES↗

DOE OSTI2024

Image-based novel fault detection with deep learning classifiers using hierarchical labels

One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based fault classifiers. Specifically, we propose a methodology on how, when available, labels regarding the fault taxonomy can be used to increase unknown fault detection performance without sacrificing model performance. To achieve this, we propose to utilize soft label techniques to improve the state-of-the-art deep novel fault detection techniques during the training process and novel hierarchically consistent detection statistics for online novel fault detection. Lastly, we demonstrated increased detection performance on novel fault detection in inspection images from the hot steel rolling process, with results well replicated across multiple scenarios and baseline detection methods.

42 ENGINEERING↗