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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Sequential Bayesian inference of transition rates in the hidden Markov model for multi-state system degradation

The more easily available system performance data and advances in data analytics have provided us with opportunities to optimize maintenance programs for engineered systems, for example nuclear power plants. One key task in maintenance optimization is to obtain an accurate model for system degradation. Here, in this research, we propose a Bayesian method to address this problem. Noting that systems usually exhibit multiple states and that the actual state of a system usually is not directly observable, in the method we first model the system degradation process and the observation process based on a hidden Markov model. Then we develop a sequential Bayesian inference algorithm based on importance sampling and the forward algorithm to infer the posterior distributions of the transition rates in the hidden Markov model based on available observations. The proposed Bayesian method allows us to take advantage of evidence from multiple sources, and also allows us to perform Bayesian inference sequentially, without the need to use the entire history of observations every time new observations are collected. We demonstrate the proposed method using both synthetic data for a nuclear power plant feedwater pump and realistic data for a nuclear power plant chemistry analytical device.

42 ENGINEERING↗
DOE OSTI2024

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. Additionally, an error compensation mechanism is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The result show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92% - 95% from one hour to one week ahead forecasting.

14 SOLAR ENERGY↗
DOE OSTI2022

Improvements on the Diagnostic Residual Gas Analyzer at Wendelstein 7-X

Exhaust gas analysis provides key information on fusion processes, divertor operation, and wall state in fusion experiments and future reactors. The diagnostic residual gas analyzer (DRGA) concept has been developed for ITER with a focus on fast helium and hydrogen isotope detection. The first operation of the prototype DRGA (P-DRGA) at the stellarator Wendelstein 7-X showed potential for improvement in terms of magnetic sensor shielding, data acquisition automation, and potential new additions to the cluster of sensors on the P-DRGA. More recently, a Monte Carlo simulation of the flow of the mixed gas species effluent from the pressure-reducing orifice, down to about 8-m sampling tube and into the analysis region of the sensors, has been found to generally agree with previous calculations and measurements but revealed potential back-streaming effects for light gases, with impact on detection limits both for the prototype and for the ITER DRGA currently in design. For the upcoming campaign of the prototype, an enhanced soft iron shield will safeguard the gauges against magnetic stray field influence. The newly introduced shielding has been tested for its effect on magnetic stray fields and found to reduce the inside residual field by about two orders of magnitude.

Schlisio, G↗
DOE OSTI2026

4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer’s Diagnosis

Multimodal neuroimaging provides complementary structural and functional insights into both human brain organization and disease-related dynamics. Recent studies demonstrate enhanced diagnostic sensitivity for Alzheimer’s disease (AD) through synergistic integration of neuroimaging data (e.g., sMRI, fMRI) with tabular data (e.g., behavioral and cognitive tests). However, the intrinsic heterogeneity across modalities (e.g., 4D spatiotemporal fMRI dynamics vs. 3D anatomical sMRI structure) presents critical challenges for discriminative feature fusion, often leading to information loss or biased fusion. To bridge this gap, we propose M2M-AlignNet: a multimodal co-attention network with latent alignment for early AD diagnosis using sMRI and fMRI. At the core of our approach is a multi-patch-to-multi-patch (M2M) contrastive loss function that quantifies and reduces representational discrepancies via weighted patch correspondence, explicitly aligning fMRI components across brain regions with their sMRI structural substrates without one-to-one constraints. Additionally, we propose a latent-as-query co-attention module to autonomously discover fusion patterns, circumventing modality prioritization biases while minimizing feature redundancy. We conduct extensive experiments to confirm the effectiveness of our method and highlight the correspondence between fMRI and sMRI as AD biomarkers.

Wei, Yuxiang [Georgia Institute of Technology]↗
DOE OSTI2023

Uncertainty-aware photovoltaic generation estimation through fusion of physics with harmonics information using Bayesian neural networks

We develop an aggregate photovoltaic generation estimation methodology that uses diverse inputs and can reason on its current input-dependent predictive uncertainty. Named PVPHEst, for PhotoVoltaic Physics- & Harmonics-driven Estimator, the resulting tool is intelligently weighing and fusing information carried by the output of physics models, harmonics, and line sensors, using Bayesian neural networks and related techniques aimed at solving machine learning problems with intrinsic uncertainty quantification. As each of the three input classes carries heterogeneous information that only sheds light on one facet of the estimation problem but its value can diminish in the face of diverse grid phenomena, PV-PHEst with its estimation and uncertainty reasoning capabilities perform a nontrivial and potentially mission-critical task of value to grid operators.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Gamma-ray measurements for inertial confinement fusion applications

High-energy γ rays generated from inertial confinement fusion (ICF) experiments have become an important signature for studying the dynamics of implosion processes. Due to their high-energy and penetrating nature, γ rays are the most unperturbed fusion products, which can preserve the original birth information of the fusion process. Fusion γ rays provide a direct measure of nuclear reaction rates (unlike x rays) without being compromised by Doppler spreading (unlike neutrons). However, unambiguous γ-ray measurements for ICF study further required a decade-long period of technological development, which included a deepening understanding of fusion γ-ray characteristics and innovations in instrument performance. This review article introduces the production mechanism of the prompt and secondary γ rays and various ICF performance parameters (e.g., bang time and burn width), which can be derived from γ-ray measurement. A technical overview will be followed by summarizing γ-ray detectors fielded or proposed, especially for high-yield ICF experiments at the Omega Laser Facility and National Ignition Facility. Over the past few years, γ-ray diagnostic technologies have been extended beyond ICF research. A few examples of non-ICF applications of γ-ray detectors are introduced at the end of this article.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Predicting fusion ignition at the National Ignition Facility with physics-informed deep learning

Here, an inertial confinement fusion experiment, carried out at the National Ignition Facility, has achieved ignition by generating fusion energy exceeding the laser energy that drove the experiment. Prior to the experiment, a generative machine learning model that combines radiation hydrodynamics simulations, deep learning, experimental data, and Bayesian statistics was used to predict, with a probability greater than 70%, that ignition was the most likely outcome for this shot.

Spears, Brian K. [Lawrence Livermore National Labo↗
DOE OSTI2024

Simultaneous analysis of collinear neutron time-of-flight (nToF) traces applied to pulsed fusion experiment

On pulsed fusion experiments, the neutron time of flight (nToF) diagnostic provides critical information on the fusion neutron energy spectrum. This work presents an analysis technique that uses two collinear nToF detectors, potentially to measure nuclear bang time and directional flow velocities. Two collinear detectors may be sufficient to disambiguate the contributions of nuclear bang time and directional flow velocities to the first moment of the neutron energy spectrum, providing an independent measurement of nuclear bang time. In conclusion, preliminary results from measured nToF traces on the National Ignition Facility and additional applications of this technique are presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Burn mode signatures enabled by high dynamic range fusion reaction history

The evolution of the fusion burn of a compressed inertial confinement fusion (ICF) implosion gives information about the evolution of the temperature, mass, and volume of the hot spot. Currently, the fusion reaction history has been measured with about a decade of dynamic range, giving information about just the peak of the fusion burn. There are proposals for extending the dynamic range to 1000×, measuring the rising edge of the burn earlier in time. Using fusion hot spot theory and a set of ICF simulations, we identify and categorize different stages of fusion burn and what signatures could be measured. For ice layered implosions, the details, conditions, and dynamics of the fusion burn propagation could be observed. For double shell and OMEGA scale implosions, the evolution of the final compression and rebounding shock before ignition could be observed. Higher dynamic range expands investigation into the logarithmic derivative, α, as a signature of various mechanisms.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

An experimentally informed design process for future inertial confinement fusion facilities

The achievement of ignition in the laboratory has renewed interest in defining the requirements for a future high-gain inertial confinement fusion (ICF) facility. Our best chance of predicting future ICF performance is with 3-D radiation hydrodynamic simulations that have been benchmarked against experimental data, but their high computational cost is prohibitive for use in practical design studies. We introduce a hierarchical approach where 3-D simulations are tuned to match experimental measurements and used to train 3-D degradation models in 1-D simulations allowing for accurate predictions over the entire OMEGA direct-drive database. A genetic algorithm was used in combination with the trained 1-D simulations to search for optimal direct-drive implosion designs at driver energies ranging from 20 kJ to 10 MJ. As the fidelity of 3-D codes improves, this approach will provide a viable experimentally informed tool for defining the next ICF facility.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Data Federation Challenges in Remote Near-Real-Time Fusion Experiment Data Processing

Fusion energy experiments and simulations provide critical information needed to plan future fusion reactors. As next-generation devices like ITER move toward long-pulse experiments, analyses, including AI and ML, should be performed in a wide range of time and computing constraints, from near-real-time constraints, between-shot analysis, and to campaign-wide long-term analysis. However, the data volume, velocity, and variety make it extremely challenging for analyses using only local computational resources. Researchers need the ability to compose and execute workflows spanning edge resources to large-scale high-performance computing facilities.We present Delta, a system to address data analysis challenges, including AI/ML, in fusion science, by leveraging the ADIOS I/O library and middleware, to support executing science workflows over the wide area network for near-real-time streaming. We discuss the data federation challenges in performing remote workflows, focusing on on-going research work in (1) managing, reducing, and streaming data to minimize I/O and data movement overheads, (2) decompressing and reorganizing data for analysis, and (3) executing workflows for automated data analysis. We introduce examples for deep-learning based data analysis for the fusion domain and demonstrate how we use Delta to construct end-to-end workflows for a fusion device in Korea, connecting a remote DOE facility in the USA. The capability demonstrated by this project is the basis for improving the state of the art for near-real-time data federation amongst remote facilities.

Choi, Jong Youl↗