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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Hierarchical Linear Regression (Rev. 1)

We describe an algorithm for making inferences in a hierarchical linear model. The model allows different slopes and intercepts for different groups, and ties together the different slopes by representing them as samples from a higher level distribution. (The intercepts are treated as statistically independent.) Our model and code permit many options for the variance of this hierarchical distribution for the slopes, including gamma distributions on the precision, t-distributions on the standard error, pooled slopes (all equal), and independent slopes. The algorithm is described in the paper; an implementation of the algorithm in R is available on request.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Presentation: Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning: An overview of methods and results

This presentation is a culmination of work which has occurred over the course of a 10-week internship. Anomaly detection methods must be both robust enough to detect subtle anomalies yet not so sensitive to report false positives, which would result significant loss of revenue. Methods currently being developed for autonomous systems are often pursuing a Digital Twin method, which will look at the entire system and model it as a whole. This presentation, however, focuses less on direct application to an NPP, rather acting as a proof of concept for the methods developed. For the project, we look to develop methods to analyze steady-state data and report anomalies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Synthesis, molecular docking, and apoptogenic efficacy of novel N-heterocycle analogs to target B-cell lymphoma 2/X-linked inhibitors of apoptosis proteins to regress melanoma

The novel series of piperidine conjugated benzophenone analogs with amide link 11a–l were synthesized in a multistep process. The structures of these compounds were confirmed by IR, {sup 1}H, {sup 13}C, NMR, and mass spectra and also by elemental analyses. The newly synthesized molecules were screened for selectivity against cancers of different origin through cell based assay system using B16F10, A375, A549, HepG2, ACHN, and MCF7 cells. The results postulated that compound 11f with two bromo groups at the para position in rings A and E and two methyl groups at ortho position in rings B and D evokes target specific action against melanoma highlighting the importance of substituted groups. Down the line studies further inferred compound 11f evokes the apoptotic cellular event leading to cell death. Investigation of eventual mechanism revealed that compound 11f turned out to be a dual inhibitor of B-cell lymphoma-2 and X-linked inhibitors of apoptosis causing the up regulation of Bax and Bad. Further, the antiproliferative effects were mimicked in murine melanoma with similar mechanisms. Molecular docking experiments further confirmed that compound 11f possessed a superior affinity for of B-cell lymphoma-2 and X-linked inhibitors of apoptosis through strong hydrogen bonds. The study implies the identification of compound 11f with selective target against melanoma by inducing apoptogenic effect, which could be the future hope for the treatment of skin cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗
DOE OSTI2023

Estimation of hydraulic conductivity in a watershed using sparse multi-source data via Gaussian process regression and Bayesian experimental design

Enhanced water management systems depend on accurate estimation of subsurface hydraulic properties. However, geologic formations can vary significantly, so information from a single source (e.g., widely spaced boreholes) is insufficient in characterizing subsurface aquifer properties. Therefore, multiple sources of information are needed to complement the hydrogeology understanding of a region. Here, this study presents a numerical framework in which information from different measurement sources is combined to characterize the 3D random field in a multi-fidelity prediction model. Coupled with the model, a Bayesian experimental design was used to determine the best future sampling locations. The Upper Sangamon watershed in east-central Illinois was selected as the case study site, where the multi-fidelity Gaussian process model was used to estimate the hydraulic conductivity in the region of interest. Multi-source observation data were obtained from electrical resistivity and borehole pumping tests. The accuracy of the model prediction is dependent on the locations and the distribution of both high- and low-fidelity data. Furthermore, the multi-fidelity model was compared with the single-fidelity model. The uncertainties and confidence in the measurements and parameter estimates were quantified and used to design future cycles of data collection to further improve the confidence intervals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A physics-based parametric regression approach for feedwater pump system diagnosis

Here in this paper, we assessed the performance of a model-based approach for fault diagnosis of a nuclear power plant feedwater pump system. Physics-based models were constructed to monitor the performance of the system components. Plant data were used for the calibration of the models and subsequently in the diagnosis of abnormal events. We considered two real-time events representing scenarios of a pump fault and component performance degradation. The two events were correctly diagnosed, and the results demonstrated the high detection sensitivity of the physics-based models. Various sensor fault scenarios were simulated to show the capability of the approach to detect and uniquely identify sensor faults. Results for a scenario in which the plant operated in flexible power mode also showed that the diagnostic approach is insensitive to changes of operating conditions, which is one of the advantages of the model-based approach using physics-based models over purely data-driven approach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

Residential electricity load profiles and their diversity have become increasingly important to realize the benefits of Smart or Transactive Energy Networks (TENs). An important element of TENs will be practical, accurate, and implementable residential load forecasting techniques. While there have been many approaches to short-term load forecasting, few have included forecasting for individual households, partly because the high volatility and idiosyncrasies present in individual household load data can pose significant challenges. In this study, we develop a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features (termed a CLSAF model) to improve short-term household electricity load forecasting accuracy by employing three strategies: autoregressive features selection, exogenous features selection, and a “default” state to avoid overfitting at times of high load volatility. We include aggregations of apartments to floor and building level, because utilities may favor transactive approaches that rely on aggregator models, e.g., a cluster of consumers as opposed to an individual. We demonstrate that the CLSAF model, by virtue of its enhanced feature representation and modest computational resources, can accomplish load forecasting in a multi-family residential building across three spatial granularities (individual apartment/household, floor, and building levels), with an accuracy improvement of up to 25% compared to a persistence model. We propose a data screening technique to characterize time-series electricity-load data. This technique is suitable for integration into a TEN ecosystem and allows one to estimate confidence levels of the load forecasts to optimize computational resources and the risks associated with uncertain forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗