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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,801 records · Page 100
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Predicting contamination accumulation in facilities with limited data

"In preparation for the James Webb Space Telescope (JWST) launch at Centre Spatial Guyanias (CSG) in French Guiana, particulate contamination accumulation predictions were necessary for each facility in which the hardware would be exposed because the Telescope would be uncovered in each of the facilities and had strict particulate requirements. These included facilities for final integration and testing, fueling, transportation and encapsulation. Minimal heritage data existed from CSG and previous launch campaigns to use as a basis for contamination predictions. Data from the Automatic Transfer Vehicle and Herschel & Planck launch campaigns were used in conjunction with facility monitoring data provided by ESA and data collected during JWST working group visits to CSG. These campaigns were conducted at varying cleanliness levels that were typically less stringent than JWST requirements. Each facility was evaluated using the data provided and likely performance improvement with the addition of High Efficiency Particulate Air filter (HEPA) banks operating when possible. Once the launch campaign was completed, the predicted fallout was compared with actual data collected throughout the campaign. Based on the actual measurements, JWST’s primary and secondary mirrors are likely much cleaner than was expected with the predicted fallout in each facility. "

James Webb Space Telescope, contamination, cleanro↗
NASA NTRSDate not supplied

GOES-T Goddard Magnetometer Sensor Unit Flight Thermal Predictions and On-Orbit Thermal Performance

The GOES-T Goddard Magnetometer (GMAG) sensor unit (SU) thermal design was validated by SU level thermal balance (TB) testing. The correlated SU thermal model was integrated to the observatory thermal model to predict the outboard SU temperatures and operating heater duty cycles in the worst cold case and worst hot case orbits. An orbit with the highest solar beta angle (23.5°) and an orbit with the lowest solar beta angle (0°) were run for each case. A comparison in the SU temperature predictions and heater duty cycles were made. The Autumnal Equinox (AE) cold case orbit is slightly colder than the Winter Solstice (WS) cold case orbit. The WS hot case orbit is slightly warmer than the March Equinox (VE) hot case orbit. Additionally, the lunar eclipse cold case orbit was run. It is the coldest environment. Using a 23°C set point for the SU cover heater controller, there is positive heater control for all the orbits. The heater duty cycle predictions for the AE worst cold case orbit and WS worst hot case orbit were 44.4% and 33% respectively. During the lunar eclipse, the heater cycle increases to 68.8% at the end of the eclipse. The temperature predictions for the bobbins are in the 21.9°C to 22.8°C range in the AE worst cold case orbit and in the 21.8°C to 23.0°C range in the WS worst hot case orbit. The SU temperatures during nominal operation on orbit were expected to be within the design goal, despite seasonal variations in solar beta angle and solar irradiance, and no solar flux in a lunar eclipse. The maximum operating SU cover flight heater duty cycle was expected to comply with GOLD Rule 4.25. The SU thermal design is now flight proven based on its long-term (>1 year) thermal performance on orbit.

GOES-T↗
NASA NTRSDate not supplied

Predicting Supersonic Noise at the Airport—Tech Challenge Wrapup

NASA has completed a Technical Challenge to reduce uncertainty of predicting the noise of future commercial supersonic aircraft around the airport. Noise prediction models being used to guide manufacturers and regulators have previously been based on conventional aircraft configurations. Aircraft configurations required for supersonic flight are not predicted well by these models. NASA used physics-based simulations along with some historical data to extend noise prediction models to cover aircraft designs typical of near-term market concepts. The presentation gives an overview of this work and its lasting accomplishments.

supersonic↗
DOE OSTI2013

Behavior-Based Budget Management Using Predictive Analytics

Historically, the mechanisms to perform forecasting have primarily used two common factors as a basis for future predictions: time and money. While time and money are very important aspects of determining future budgetary spend patterns, organizations represent a complex system of unique individuals with a myriad of associated behaviors and all of these behaviors have bearing on how budget is utilized. When looking to forecasted budgets, it becomes a guessing game about how budget managers will behave under a given set of conditions. This becomes relatively messy when human nature is introduced, as different managers will react very differently under similar circumstances. While one manager becomes ultra conservative during periods of financial austerity, another might be un-phased and continue to spend as they have in the past. Both might revert into a state of budgetary protectionism masking what is truly happening at a budget holder level, in order to keep as much budget and influence as possible while at the same time sacrificing the greater good of the organization. To more accurately predict future outcomes, the models should consider both time and money and other behavioral patterns that have been observed across the organization. The field of predictive analytics is poised to provide the tools and methodologies needed for organizations to do just this: capture and leverage behaviors of the past to predict the future.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2016

Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs

Estimating the confidence for a link is a critical task for Knowledge Graph construction. Link prediction, or predicting the likelihood of a link in a knowledge graph based on prior state is a key research direction within this area. We propose a Latent Feature Embedding based link recommendation model for prediction task and utilize Bayesian Personalized Ranking based optimization technique for learning models for each predicate. Experimental results on large-scale knowledge bases such as YAGO2 show that our approach achieves substantially higher performance than several state-of-art approaches. Furthermore, we also study the performance of the link prediction algorithm in terms of topological properties of the Knowledge Graph and present a linear regression model to reason about its expected level of accuracy.

knowledge graphs↗
DOE OSTI2016

Comparison of silver release predictions using PARFUME with results from the AGR-2 irradiation experiment

The PARFUME (PARticle FUel ModEl) code was used to predict silver release from tristructural isotropic (TRISO) coated fuel particles and compacts during the second irradiation experiment (AGR-2) of the Advanced Gas Reactor Fuel Development and Qualification program. The PARFUME model for the AGR-2 experiment used the fuel compact volume average temperature for each of the 559 days of irradiation to calculate the release of fission product silver from a representative particle for a select number of AGR-2 compacts and individual fuel particles containing either mixed uranium carbide/oxide (UCO) or 100% uranium dioxide (UO2) kernels. Post-irradiation examination (PIE) measurements were performed to provide data on release of silver from these compacts and individual fuel particles. The available experimental fractional releases of silver were compared to their corresponding PARFUME predictions. Preliminary comparisons show that PARFUME under-predicts the PIE results in UCO compacts and is in reasonable agreement with experimental data for UO2 compacts. The accuracy of PARFUME predictions is impacted by the code limitations in the modeling of the temporal and spatial distributions of the temperature across the compacts. Nevertheless, the comparisons on silver release lie within the same order of magnitude.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

A First-order Prediction-Correction Algorithm for Time-varying (Constrained) Optimization: Preprint

This paper focuses on the design of online algorithms based on prediction-correction steps to track the optimal solution of a time-varying constrained problem. Existing prediction-correction methods have been shown to work well for unconstrained convex problems and for settings where obtaining the inverse of the Hessian of the cost function can be computationally affordable. The prediction-correction algorithm proposed in this paper addresses the limitations of existing methods by tackling constrained problems and by designing a first-order prediction step that relies on the Hessian of the cost function (and do not require the computation of its inverse). Analytical results are established to quantify the tracking error. Numerical simulations corroborate the analytical results and showcase performance and benefits of the algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Next Day Building Load Predictions based on Limited Input Features Using an On-Line Laterally Primed Adaptive Resonance Theory Artificial Neural Network.

Optimal integration of thermal energy storage within commercial building applications requires accurate load predictions. Several methods exist that provide an estimate of a buildings future needs. Methods include component-based models and data-driven algorithms. This work implemented a previously untested algorithm for this application that is called a Laterally Primed Adaptive Resonance Theory (LAPART) artificial neural network (ANN). The LAPART algorithm provided accurate results over a two month period where minimal historical data and a small amount of input types were available. These results are significant, because common practice has often overlooked the implementation of an ANN. ANN have often been perceived to be too complex and require large amounts of data to provide accurate results. The LAPART neural network was implemented in an on-line learning manner. On-line learning refers to the continuous updating of training data as time occurs. For this experiment, training began with a singe day and grew to two months of data. This approach provides a platform for immediate implementation that requires minimal time and effort. The results from the LAPART algorithm were compared with statistical regression and a component-based model. The comparison was based on the predictions linear relationship with the measured data, mean squared error, mean bias error, and cost savings achieved by the respective prediction techniques. The results show that the LAPART algorithm provided a reliable and cost effective means to predict the building load for the next day.

25 ENERGY STORAGE↗
DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

Storm-DEPART (Damage Estimate Prediction and Restoration Tool): Each year hurricanes and tropical storms in the United States damage critical infrastructure assets, disrupt the services they provide, and cause millions to billions of dollars in economic impacts due to extended recovery times. The Storm-DEPART tool and analytical output enable more impactful data-driven decision-making capabilities and strengthen national-level disaster preparedness, response, and recovery. Storm-DEPART, built through multi-month collaboration between Entergy and INL, combines Entergy’s critical infrastructure inventory data with weather forecasts to predict damages to Electric utility’s assets due to natural disasters and the estimated recovery support needed, including time, materials, and resource allocation. In the event of an approaching hurricane, this innovative solution can assess potential damage to power generation capacity, transmission grids, distribution networks, and communications assets from wind bands, storm surge, and flooding. With more effective predictions, Entergy can more efficiently allocate resources to mitigate impacts and optimize recovery for customers. Storm-DEPART also allows Electric utilities the ability to apply a planning scenario and model expected damage to better inform infrastructure restoration needs leading to enhance system resiliency. The technology is fully transferrable to other electric utilities with the same damage estimating challenges. The INL team is working on the evolution of Storm-DEPART to include ice event damage prediction framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

Storm-DEPART (Damage Estimate Prediction and Restoration Tool): Each year hurricanes and tropical storms in the United States damage critical infrastructure assets, disrupt the services they provide, and cause millions to billions of dollars in economic impacts due to extended recovery times. The Storm-DEPART tool and analytical output enable more impactful data-driven decision-making capabilities and strengthen national-level disaster preparedness, response, and recovery. Storm-DEPART, built through multi-month collaboration between Entergy and INL, combines Entergy’s critical infrastructure inventory data with weather forecasts to predict damages to Electric utility’s assets due to natural disasters and the estimated recovery support needed, including time, materials, and resource allocation. In the event of an approaching hurricane, this innovative solution can assess potential damage to power generation capacity, transmission grids, distribution networks, and communications assets from wind bands, storm surge, and flooding. With more effective predictions, Entergy can more efficiently allocate resources to mitigate impacts and optimize recovery for customers. Storm-DEPART also allows Electric utilities the ability to apply a planning scenario and model expected damage to better inform infrastructure restoration needs leading to enhance system resiliency. The technology is fully transferrable to other electric utilities with the same damage estimating challenges. The INL team is working on the evolution of Storm-DEPART to include ice event damage prediction framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

General-Purpose Heat Source Radioisotope Thermoelectric Generator Flight Unit 5 Refurbished (GPHS-RTG F5R) Power Prediction

INL has evaluated and refurbished General Purpose Heat Source (GPHS) Radioisotope Thermoelectric Generator (RTG) Flight Unit 5 (GPHS-RTG F5R or F5R) that was defueled in 2005. The generator has passed all the INL internal reviews, an end item data package has been delivered to NASA and is ready fueling for a mission. NASA requested a user’s guide for F5R with enough detail for a prospective mission proposal. This report documents the development of the power prediction model used in the user’s guide. The prediction evaluated the thermoelectric “burn-in” and the graceful degradation modes of the missions and developed a thermal inventory dependent equation that predicts the power that matches Galileo, Cassini, and PNH within a three-standard deviation of ±1%. This prediction is only valid for the 30 VDC load voltage of these missions.

30 - DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Systematic feature design for cycle life prediction of lithium-ion batteries during formation

Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid-electrolyte interphase formation and the long testing time (∼100 days) for cells to reach the end of life. We propose a systematic feature-design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. By only using two simple Q (V) features designed from our framework, extracted from formation data without any additional diagnostic cycles, we achieved an average of 9.87% error for cycle life prediction. Here, the physics-based investigation guided by the two designed features shows that the voltage ranges identified by our framework capture the effects of formation temperature and microscopic-particle resistance heterogeneity. By designing highly predictive, robust, and interpretable features, our approach can accelerate industrial battery formation research, leveraging the interplay between data-driven feature design and mechanistic understanding.

25 ENERGY STORAGE↗
DOE OSTI2023

Experimental and computational design tools for industrial drying processes: Challenges in process‐limit prediction

Abstract The coating and drying of inks and slurries are important steps to manufacture a plethora of products. Drying processes, particularly, comprise energy‐intensive steps that affect product cost and quality. Prior work has highlighted failures of various multicomponent diffusivity models to conserve mass in dryer modeling and challenges in predicting process limits given variability in published values of key thermodynamic parameters. Herein, we develop a computational model and benchtop drying experiments to investigate these concerns for drying polymer‐laden coatings. Model predictions of process limits in a single‐zone drying oven demonstrate that published variability in Flory–Huggins parameter yields large variations in predicted operating temperatures above which blistering occurs. This indicates that caution should be exercised when choosing approaches to obtain or predict the Flory–Huggins parameter, and that both benchtop drying experiments and a set of additional experiments, such as sorption experiments, are needed to fully characterize and optimize a given drying process.

Parrish, Chance↗
DOE OSTI2020

Predicting a Sharp Decline in Selectivity for Catalytic Esterification of Alcohols from van der Waals Interactions

Controlling the selectivity of catalytic reactions is a critical aspect of improving energy efficiency in the chemical industry; thus, predictive models are of key importance. Herein the performance of a heterogeneous, nanoporous Au catalyst is predicted for the complex catalytic self-coupling of the series of C 2 -C 4 alkyl alcohols, based solely on the known kinetics of the elementary steps of the catalytic cycle for methanol coupling, using scaling methods augmented by density functional theory. Notably, a sharp decrease in selectivity for ester formation with increasing molecular weight to favor the aldehyde due to van der Waals interactions of reaction intermediates with the surface was predicted and subsequently verified quantitatively by experiment. Furthermore, the agreement between theory and experiment clearly demonstrates the efficacy of this approach for building a predictive model of catalytic behavior for a homologous set of reactants using a small set of experimental information.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Predicting a Sharp Decline in Selectivity for Catalytic Esterification of Alcohols from van der Waals Interactions

Controlling the selectivity of catalytic reactions is a critical aspect of improving energy efficiency in the chemical industry; thus, predictive models are of key importance. Herein the performance of a heterogeneous, nanoporous Au catalyst is predicted for the complex catalytic self-coupling of the series of C 2 –C 4 alkyl alcohols, based solely on the known kinetics of the elementary steps of the catalytic cycle for methanol coupling, using scaling methods augmented by density functional theory. Notably, a sharp decrease in selectivity for ester formation with increasing molecular weight to favor the aldehyde due to van der Waals interactions of reaction intermediates with the surface was predicted and subsequently verified quantitatively by experiment. Further, the agreement between theory and experiment clearly demonstrates the efficacy of this approach for building a predictive model of catalytic behavior for a homologous set of reactants using a small set of experimental information.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A nomogram for prediction of stage III/IV gastric cancer outcome after surgery: A multicenter population‐based study

Abstract Most patients with gastric cancer (GC) are first diagnosed at stage III‐IV and surgery resection remains the primary therapeutic modality for these patients. However, clinical staging used for prediction of those patients provides limited information. We collected clinicopathological data and disease‐progression information from 508 patients with stage III‐IV GC at three Chinese hospitals and 1298 patients from the Surveillance, Epidemiology, and End Results database. Based on the stepwise multivariate regression model, we constructed a novel nomogram to predict overall survival (OS). The performance of discrimination for this model was measured using Harrell's concordance index (C‐index) and receiver‐operating characteristic curve (ROC), and was validated using calibration plots. Multivariate Cox regression analyses showed that tumor size, age at diagnosis, N stage, tumor grade, and distant metastases were outstanding independent prognostic factors of stage III‐IV GC. We developed a nomogram based on these five prognostic predictors. In the training set, the C‐index of the nomogram was 0.645 (95% CI: 0.611‐0.679), which was higher than that of the American Joint Committee on Cancer TNM system alone (sixth TNM: 0.544; seventh TNM: 0.575; eighth TNM: 0.568). Similar results were observed in validation cohort. Moreover, calibration blots demonstrated good consistency between the actual and predicted OS probabilities. According to the nomogram, GC individuals could be classified into three groups (low‐, middle‐, and high‐risk) ( P < .001). Our nomogram complements the current staging system for prediction of individual prognosis with stage III‐IV GC, and may be helpful for making individualized treatment decisions.

Gao, Zhiying↗
DOE OSTI2025

Structure‐Aware Representation Learning for Effective Performance Prediction

ABSTRACT Application performance is a function of several unknowns stemming from the interactions between the application, runtime, OS, and underlying hardware, making it challenging to model performance using deep learning techniques, especially without a large labeled dataset. Collecting such labeled longitudinal datasets can take weeks. Intuitively, developers could save analysis time during code development by taking a comparative approach between multiple applications. However, the unknown dynamic interactions between applications and execution environments make it difficult for deep learning‐based models to predict the performance of new applications. In this paper, we address these problems by presenting a labeled dataset for the community and taking a comparative analysis approach to explore the source code differences between different correct implementations of the same problem. This paper assesses the feasibility of using purely static information, for example, Abstract Syntax Tree (AST), of applications to predict performance change based on code structure. We evaluate several deep learning‐based representation learning techniques for source code and propose an architecture for the tree‐based Long Short‐Term Memory (LSTM) models to discover latent representations for a source code's hierarchical structure. We demonstrate that our proposed architecture enables feed‐forward predictive models to predict change in performance using source code with up to 84% accuracy.

Ramadan, Tarek [Department of Computer Science Tex↗
DOE OSTI2014

Predicting future coexistence in a North American ant community

Global climate change will remodel ecological communities worldwide. However, as a consequence of biotic interactions, communities may respond to climate change in idiosyncratic ways. This makes predictive models that incorporate biotic interactions necessary. We show how such models can be constructed based on empirical studies in combination with predictions or assumptions regarding the abiotic consequences of climate change. Specifically, we consider a well-studied ant community in North America. First, we use historical data to parameterize a basic model for species coexistence. Using this model, we determine the importance of various factors, including thermal niches, food discovery rates, and food removal rates, to historical species coexistence. We then extend the model to predict how the community will restructure in response to several climate-related changes, such as increased temperature, shifts in species phenology, and altered resource availability. Interestingly, our mechanistic model suggests that increased temperature and shifts in species phenology can have contrasting effects. Nevertheless, for almost all scenarios considered, we find that the most subordinate ant species suffers most as a result of climate change. More generally, our analysis shows that community composition can respond to climate warming in nonintuitive ways. For example, in the context of a community, it is not necessarily the most heatsensitive species that are most at risk. Our results demonstrate how models that account for niche partitioning and interspecific trade-offs among species can be used to predict the likely idiosyncratic responses of local communities to climate change.

54 ENVIRONMENTAL SCIENCES↗

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