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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Decentralized Data-Driven Estimation of Generator Rotor Speed and Inertia Constant Based on Adaptive Unscented Kalman Filter

This paper proposes an online decentralized and data-driven method for synchronous generator rotor speed and inertia estimation. First, an analytical relationship between the generator terminal voltage/current phasors and its internal rotor speed is derived using the Thevenin equivalent. The parameters of the latter are obtained via a recursive estimator, which allows us to estimate the generator's internal rotor speed from voltage/current phasors without the need for any generator information. Second, we reformulate the equivalent swing equation into the Kalman filter form, which enables us to use a priori-estimated internal rotor speed along with measured real and reactive power injections to obtain an estimate of the inertia for each online synchronous generator. This is achieved through an adaptive unscented Kalman filter. We demonstrate that the use of terminal bus frequency to approximate the rotor speed for inertia estimation by existing methods leads to large estimation biases/errors. Numerical simulations carried out on the IEEE 39-bus and Texas 2000-bus systems reveal that our proposed method is consistently more accurate than existing methods. Moreover, our proposed method is insensitive to both the type and location of system disturbances, and it is robust to different levels of measurement noise.

bus frequency↗
DOE OSTI2023

Extending Data-Driven Anomaly Detection Methods to Transient Power Conditions in Nuclear Power Plants

Historically, nuclear power plants have operated predominantly at or near full power, meaning that data driven anomaly detection methods can likely perform well at full power operations. This presents a challenge when the power drops (referred to as a transient) and may result in false alarms due to the lack of historical data at those new power levels. The current approach to handling this challenge is to turn detectors off during transients, which makes it impossible to use the algorithms to detect anomalies during these periods, i.e., causing missed detection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Data-Driven Modeling and Control of Systems with Plasma-Surface Interactions (Final Technical Report)

This final technical report summarizes the activities and accomplishments in the period from February 2023 thru January 2026. The objective of the proposed research is to investigate the physical mechanisms and processes underlying the formation of structures and patterns in systems with plasma-surface interactions. In the past decades, there have been extensive studies on the interaction of glow discharges, dielectric barrier discharges, and arc discharges with confining or intervening surfaces. The advancement of the understanding of these phenomena is not only of fundamental scientific interest and relevance to the knowledge of the plasma state, but also with profound implications in various technological applications. The research will integrate theoretical, computational, and experimental work within an innovative framework of data assimilation, i.e., optimally combining model predictions with measurements. The scientific merit of this research has three aspects. Firstly, it extends the studies of plasma-surface interactions to systems with insulator surfaces and multi-layer systems, while existing studies are predominantly on electrode surfaces. Secondly, it expects to develop a novel data-driven modeling approach based on data assimilation to enhance the predictive and control capabilities, which could make transformative contributions to basic plasma research. Thirdly, it will shed new light on outstanding problems related to formation of patterns interfacing plasmas. This project also aims to launch an education and outreach initiative at Texas A&M University-Kingsville, a non-R1, minority-serving institution in South Texas. The initiative is structured as a four-tier pyramid. Tier one will be a webinar series for culture and capacity building to inform broader audience in the region about the research fields of plasma science and engineering. Tier two will be the creation and offering of an upper-level undergraduate course on introductory plasma physics, which will help with the recruitment for the upper tiers. On tier three, we will engage and mentor senior design students to conduct work toward the research goal of this project. There will also be a certificate program on general plasma science for undergrad and graduate students, part of which will be lab training at Princeton University. Tier four will be the supervision and mentoring of Ph.D. students. Therefore, this project will systematically expand the talent pipeline, broaden participation from communities historically and geographically underrepresented in DOE SC research portfolio, significantly improve the research and education capacity at the PI’s institution, and contribute to developing a diverse workforce in plasma science and engineering.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Design-Space Exploration for Inverse-Design of Wind Turbine Blades Using Data-Driven Methods

The state-of-the-practice aerodynamic design methods for wind turbine blades is typically based on Blade-Element Momentum (BEM) theory using a pre-designed frozen family of airfoils. The airfoils are themselves typically designed using panel methods. The design of the next-generation of large flexible rotors will need to capture the non-linear aerodynamics and three-dimensional flow to reduce the levelized cost of wind energy. Data-driven methods for aerodynamic design using data generated by computational fluid dynamics offer an attractive alternative to BEM-based methods that captures the non-linear aerodynamics of the component airfoils as well as the root and tip sections. In this work, we develop and demonstrate a framework to "smartly" explore the relevant design space in combination with an appropriate automated CFD pipeline to evaluate the aerodynamics of each design. The design-space exploration framework uses appropriate perturbations to the airfoil shape and induction profile from a baseline shape in combination with the inverse-design using BEM. The resulting designs are evaluated using an automated CFD pipeline using the in-house CFD solver framework "Mercury". We perform verification and validation to establish the capability of the Mercury framework to predict the aerodynamic performance of wind turbines. The CFD simulation of the perturbed blade shapes are optimized to restart from the converged baseline simulation to reduce the computational time. Finally, we demonstrate the design-space exploration technique for the design of the outboard section and the full rotor using perturbations to the shape and operating conditions of the NREL 5-MW turbine.

aerodynamic design methods↗
NASA NTRS2017

HMI Data Driven Magnetohydrodynamic Model Predicted Active Region Photospheric Heating Rates: Their Scale Invariant, Flare Like Power Law Distributions, and Their Possible Association With Flares

A data driven, near photospheric, 3 D, non-force free magnetohydrodynamic model pre- dicts time series of the complete current density, and the resistive heating rate Q at the photosphere in neutral line regions (NLRs) of 14 active regions (ARs). The model is driven by time series of the magnetic field B observed by the Helioseismic & Magnetic Imager on the Solar Dynamics Observatory (SDO) satellite. Spurious Doppler periods due to SDO orbital motion are filtered out of the time series for B in every AR pixel. Errors in B due to these periods can be significant. The number of occurrences N(q) of values of Q > or = q for each AR time series is found to be a scale invariant power law distribution, N(Q) / Q−s, above an AR dependent threshold value of Q, where 0.3952 < or = s < or = 0.5298 with mean and standard deviation of 0.4678 and 0.0454, indicating little variation between ARs. Observations show that the number of occurrences N(E) of coronal flares with a total energy released > or = E obeys the same type of distribution, N(E) / E−S, above an AR dependent threshold value of E, with 0.38 < or approx. S < or approx. 0.60, also with little variation among ARs. Within error margins the ranges of s and S are nearly identical. This strong similarity between N(Q) and N(E) suggests a fundamental connection between the process that drives coronal flares and the process that drives photospheric NLR heating rates in ARs. In addition, results suggest it is plausible that spikes in Q, several orders of magnitude above background values, are correlated with times of the subsequent occurrence of M or X flares.

Goodman, Michael L.↗
NASA NTRS2017

HMI Data Driven Magnetohydrodynamic Model Predicted Active Region Photospheric Heating Rates: Their Scale Invariant, Flare Like Power Law Distributions, and Their Possible Association With Flares

A data driven, near photospheric, 3 D, non-force free magnetohydrodynamic model predicts time series of the complete current density, and the resistive heating rate Q at the photosphere in neutral line regions (NLRs) of 14 active regions (ARs). The model is driven by time series of the magnetic field B observed by the Helioseismic and Magnetic Imager on the Solar Dynamics Observatory (SDO) satellite. Spurious Doppler periods due to SDO orbital motion are filtered out of the time series for B in every AR pixel. Errors in B due to these periods can be significant. The number of occurrences N(q) of values of Q > or = q for each AR time series is found to be a scale invariant power law distribution, N(Q) / Q−s, above an AR dependent threshold value of Q, where 0.3952 < or = s < or = 0.5298 with mean and standard deviation of 0.4678 and 0.0454, indicating little variation between ARs. Observations show that the number of occurrences N(E) of coronal flares with a total energy released > or = E obeys the same type of distribution, N(E) / E−S, above an AR dependent threshold value of E, with 0.38 < or approx. S < or approx. 0.60, also with little variation among ARs. Within error margins the ranges of s and S are nearly identical. This strong similarity between N(Q) and N(E) suggests a fundamental connection between the process that drives coronal flares and the process that drives photospheric NLR heating rates in ARs. In addition, results suggest it is plausible that spikes in Q, several orders of magnitude above background values, are correlated with times of the subsequent occurrence of M or X flares.

Goodman, Michael L.↗
DOE OSTI2024

Vertical instability forecasting and controllability assessment of multi-device tokamak plasmas in DECAF with data-driven optimization

Abstract Reliable vertical position control will be an essential element of any future tokamak-based fusion power plant in order to reduce disruptions and maximize performance. We investigate methods to improve vertical controllability boundary determination in plasma operational space and demonstrate a data-driven approach based on direct pseudoinversion of operational space data that is rigorously quantitative, applicable in real-time plasma control systems, and physically intuitive to interpret. Applied to historical shot data from entire run campaigns on the MAST-U, KSTAR, and NSTX tokamaks, this approach, implemented in DECAF, improves vertical displacement event identification accuracy to 98.9%–100%. Further, we explore the application of a physics-based vertical stability metric as an early warning forecaster for vertical displacement events. The development of a linear surrogate model for the plasma current density profile, with a coefficient of determination of 0.992 on the training dataset, enables potential employment of this forecaster in real-time. The application of this approach on historical data from the MAST-U MU02 campaign yields a forecaster with 62.6% accuracy, indicating promise for this method when further refined and potentially coupled with other stability metrics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Optimal PV Inverter Control in Distribution Systems via Data-Driven Distributionally Robust Optimization

Distribution systems with high penetration of uncertain solar generation call for advanced control strategies of photovoltaics (PVs) inverters. This paper proposes a data-driven distributionally robust optimization (DDDRO) approach to optimally controlling the PV inverters to improve the system operation performance under solar power uncertainties. In the proposed DDDRO approach, a Wasserstein ball-based method is proposed to construct the distributional ambiguity set to model the uncertainties of PV generation through partial observations of historical data without knowing exact probability distributions. We further reformulate the computationally intractable DDDRO model to a mixed integer second order cone programming (MISOCP) problem. The effectiveness and out-of-sample performance of the proposed approach have been demonstrated on a modified IEEE 33-node system. We conduct a comparative study to compare the proposed method with traditional chance constrained programming (CCP). It shows that the proposed DDDRO approach can provide a less conservative yet robust solution to minimize the worse-case expectation of the total network loss while maintaining nodal voltages in a secure range.

Xue, Yaosuo↗
DOE OSTI2023

A simple data-driven level finding method of many-electron atoms and heavy nuclei based on statistical outlier detection

Here, we report a simple and pure data-driven method to find new energy levels of quantum many-body systems only from observed line wavelengths. In our method, all the possible combinations are computed from known energy levels and wavelengths of unidentified lines. As each excited state exhibits many transition lines to different lower levels, the true levels should be reconstructed coincidentally from many level-line combinations, while the wrong combinations distribute randomly. Such a coincidence can be easily detected statistically. We demonstrate this statistical method by finding new levels for various atomic and nuclear systems from unidentified line lists available online.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Accelerated data-driven materials science with the Materials Project

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

Horton, Matthew K↗
DOE OSTI2024

A Variable Eddington Factor Model for Thermal Radiative Transfer with Closure Based on Data-Driven Shape Function

Here, a new variable Eddington factor (VEF) model is presented for nonlinear problems of thermal radiative transfer (TRT). The VEF model is data-driven and acts on known (a-priori) radiation-diffusion solutions for material temperatures in the TRT problem. A linear auxiliary problem is constructed for the radiative transfer equation (RTE) whose emission source and opacities are evaluated at these known material temperatures. The solution to this RTE approximates the specific intensity distribution in phase-space and time. It is applied as a shape function to define the Eddington tensor for the presented VEF model. The shape function computed via the auxiliary RTE problem will capture some degree of transport effects within the TRT problem. The VEF moment equations closed with this approximate Eddington tensor will thus carry with them these captured transport effects. In this study, the temperature data comes from multigroup P 1 , P 1/3 , and flux-limited diffusion radiative transfer models. The proposed VEF model can be interpreted as a transport-corrected diffusion reduced-order model. Numerical results are presented on the Fleck-Cummings test problem which models a supersonic wavefront of radiation. The VEF model is shown to improve accuracy by 1–2 orders of magnitude compared to the considered radiation-diffusion model solutions to the TRT problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Sizing ramping reserve using probabilistic solar forecasts: A data-driven method

Ramping products have been introduced or proposed in several U.S. power markets to mitigate the impact of load and renewable uncertainties on market efficiency and reliability. Current methods often rely on historical data to estimate the requirements of ramping products and fail to take into account the effects of the latest weather conditions and their uncertainties, which could lead to overly conservative or insufficient requirements. This study proposes a k-nearest-neighbor-based method to give weather-informed estimates of ramping needs based on short-term probabilistic solar irradiance forecasts. Forecasts from multiple sites are employed in conjunction with principal component analysis to derive numerical classifiers to characterize system-level weather conditions. In addition, we develop a data-driven method to optimize the model parameters in a rolling-forward manner. By using real-world data from the California Independent System Operator, we design two metrics to evaluate method performance: 1) frequency of shortage and 2) oversupply of ramping product. Our proposed method presents advantages in comparison with the baseline and a set of benchmark methods: without compromising system reliability, it reduces system ramping requirements by up to 25%, therefore improving both system reliability and economics.

14 SOLAR ENERGY↗
DOE OSTI2023

Lifetime and Degradation Study of Poly(Methyl Methacrylate) via a Data-Driven Study Protocol Approach

We report to optimize and extend the service life of polymeric materials in outdoor environments, a domain knowledge-based and data-driven approach was utilized to quantitatively investigate the temporal evolution of degradation modes, mechanisms, and rates under various stepwise accelerated exposure conditions. Six formulations of poly(methyl methacrylate) (PMMA) with different combinations of stabilizing additives, including one unstabilized formulation, were exposed in three accelerated weathering conditions. Degradation was dependent on wavelength as samples in UV light at 340 nm (UVA) exposure showed the most yellowing. The unstabilized PMMA formulation showed much higher yellowness index values (59.5) than stabilized PMMA formulations (2–12). Urbach edge analysis shows a shift toward longer wavelength from 285 to 500 nm with increasing exposure time and an increased absorbance around 400 nm of visible region as the unstabilized samples increase in yellowing. The degradation mechanisms of PMMA were tracked using induced absorbance to dose at specific wavelengths that correspond to known degradation mechanisms. The degradation pathway of PMMA was modeled in a framework using network structural equation modeling (netSEM). netSEM showed changes in degradation pathway as PMMA transition stages of degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Data-Driven Analysis Tool Plays Critical Role in Climate Neutral Buildings: Improving energy efficiency and reducing emissions

A new web application that advances the science of data-driven, remote building energy analysis to increase the speed and scale of retrofits worldwide could play a key role in reducing greenhouse gas (GHG) emissions and meeting the Paris Agreement's targets. Here, the multi-award-winning Building Efficiency Targeting Tool for Energy Retrofits (BETTER) is a public access web application (better.lbl.gov) sponsored by the U.S. Department of Energy (DOE) and developed by Lawrence Berkeley National Laboratory (Berkeley Lab) and Johnson Controls.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm

Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency and subjective human factors. We developed a novel data-driven geological feature characterization approach based on pre-stack seismic measurements. Our characterization method employs an efficient and accurate machine learning method to extract useful subsurface geologic features automatically. Specifically, we use kernel ridge regression to account for the nonlinear relationship between seismic data and geological features. We moreover employ kernel tricks to avoid the explicit nonlinear mapping and infinite dimension of feature space. Yet, the conventional kernel ridge regression can be computationally prohibitive because of the large volume of seismic measurements. We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage. In particular, we utilize a randomized numerical linear algebra technique, named Nyström method, to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate characterization. We provide thorough computational cost analysis to show the efficiency of our new geological feature characterization methods. We validate the performance of our method in characterizing geologic fault zones because faults play an important role in various subsurface applications. Our numerical examples demonstrate that our new characterization method significantly improves the computational efficiency while maintaining comparable accuracy. Interestingly, we demonstrate that our approach yields a speedup ratio on the order of ~102 to ~103 in a multicore computational environment.

58 GEOSCIENCES↗
DOE OSTI2025

End-to-end microgrid protection using distributed data-driven methods

This paper introduces an end-to-end microgrid protection framework that offers real-time system monitoring, fault-related decision making, and circuit breaker control. This is achieved through the design of distributed data-driven techniques based on the support vector machine method, where each relay is responsible for distributed data collection, fault detection, fault localization, and fault isolation. Local communication is established among neighboring relays, fostering cooperative fault localization and isolation. This decentralized design not only reduces the computational and communication requirements but also enables the adaptability of each relay under varying operational dynamics. The proposed end-to-end protection framework was validated using MATLAB/Simulink simulations on a 100% renewable microgrid, achieving an accuracy of 93.1% with response time of 0.0523 s, in protecting against a range of fault scenarios that are characterized by various types, locations, impedances, load conditions, photovoltaic power levels, and microgrid operating modes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.

In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model’s input parameters. Here we develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model’s response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier–Stokes) flow simulator, using shock-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are the limited information content of the heat flux and pressure measurements and the large model-form error encountered in a certain part of the flow.

42 ENGINEERING↗