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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Bayesian analysis of classical shadows

Abstract The method of classical shadows proposed by Huang, Kueng, and Preskill heralds remarkable opportunities for quantum estimation with limited measurements. Yet its relationship to established quantum tomographic approaches, particularly those based on likelihood models, remains unclear. In this article, we investigate classical shadows through the lens of Bayesian mean estimation (BME). In direct tests on numerical data, BME is found to attain significantly lower error on average, but classical shadows prove remarkably more accurate in specific situations—such as high-fidelity ground truth states—which are improbable in a fully uniform Hilbert space. We then introduce an observable-oriented pseudo-likelihood that successfully emulates the dimension-independence and state-specific optimality of classical shadows, but within a Bayesian framework that ensures only physical states. Our research reveals how classical shadows effect important departures from conventional thinking in quantum state estimation, as well as the utility of Bayesian methods for uncovering and formalizing statistical assumptions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Assessing Teleconnections-Induced Predictability of Regional Water Cycle on Seasonal to Decadal Timescales Using Machine Learning Approaches

Focal Area: (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: Predictive understanding of the regional water cycle based on teleconnections of climate modes of variability using AI methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

New method for the determination of photoabsorption from transmittance measurements in the extreme ultraviolet

We have developed a new method for the determination of photoabsorption at extreme ultraviolet wavelengths longer than 20 nm, where reliable refractive index values are sparse or non-existent. Our method overcomes the obstacle of multiple reflections that occur inside thin films in this spectral range, which up until now has prevented the accurate determination of photoabsorption from transmittance measurements. We have derived a mathematical expression that is independent of internal reflection amplitudes, while taking advantage of the transmittance oscillations stemming from such reflections. The method is validated on measurements of aluminum thin films. This advance will enable accurate refractive index values for many important materials for optical instrumentation, thus facilitating high-priority research on topics including coherent light sources, planetary and solar physics, and semiconductor manufacturing.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data

Today's scientific simulations and instruments are producing a large amount of data, leading to difficulties in storing, transmitting, and analyzing these data. While error-controlled lossy compressors are effective in significantly reducing data volumes and efficiently developing databases for multiple scientific applications, they mainly support error controls on raw data, which leaves a significant gap between the data and user's downstream analysis. This may cause unqualified uncertainties in the outcomes of the analysis, a.k.a quantities of interest (QoIs), which are the major concerns of users in adopting lossy compression in practice. In this paper, we propose rigorous mathematical theories to preserve four families of QoIs that are widely used in scientific analysis during lossy compression along with practical implementations. Specifically, we first develop the error control theory for univariate QoIs which are essential for computing physical properties such as kinetic energy, followed by multivariate QoIs that are more commonly used in real-world applications. The proposed method is integrated into a state-of-the-art compression framework in a modular fashion, which could easily adapt to new QoIs and new compression algorithms. Experiments on real-world datasets demonstrate that the proposed method provides faithful error control on important QoIs including kinetic energy, regional average, and isosurface without trials and errors, while offering compression ratios that are up to 4x of the compression ratios provided by state-of-the-art compressors.

Jiao, Pu↗
DOE OSTI2021

Additive manufacturing system and method having toolpath analysis

An additive manufacturing (AM) system is disclosed for constructing a three dimensional (3D) part with optimized orthotropy. The system combines an electronic processor which calculates an optimal set of physically achievable toolpaths to meet a given design objective, and a 3D printing direct ink write machine capable of printing inks with reinforcing particles that result in orthotropic materials. The electronic processor may combine a Domain of Interest subsystem that transforms a mathematical description of a desired part orthotropy to a plurality of guidepaths, a toolpath generate subsystem that develops a plurality of physically realizable toolpaths from those guidepaths with a minimum number of starts and stops, a finite element subsystem that computes spatially varying material orthotropy from those toolpaths and then solves a boundary value problem to determine a figure of merit for the design, and an optimization subsystem that uses that figure of merit to update the mathematical description of the part orthotropy to iteratively develop an improved part. The optimization subsystem also includes convergence criteria to indicate when toolpaths have been achieved that yield a sufficiently optimal part has been achieved. The toolpath generate can then output the final toolpaths, which are converted to suitable code that controls the motion of the 3D printer toolhead and allows the optimized 3D part to be manufactured.

King, Michael J.↗
DOE OSTI2020

Improving High-Energy Particle Detectors with Machine Learning

Microseconds after the Big Bang, the universe existed in a state called the quark-gluon plasma (QGP). To experimentally study its properties, the QGP is recreated in high-energy nuclear collisions at the LHC, and the particles produced from the QGP are reconstructed from their energy deposition in the ATLAS calorimeter. This requires both classifying the particles and calibrating their deposited energy. The objective of this project is to improve the reconstruction by using machine learning techniques, where the energy depositions of clusters of cells, formed by ATLAS topo-clustering methods, are treated as three-dimensional images when inputted to neural networks. This approach significantly improves the calibration of deposited energies when cross-validating while training, and models trained on idealized data predict the calibrated energies of particles in more complex data sets well. Additionally, implementation of a data generator using uproot allows the program to load input data into memory as needed while training or predicting, significantly reducing the amount of memory used. The data generator also allows for use of multiprocessing to speed up training and evaluating. This work illustrates that using machine learning methods for both classification and calibration has the potential to significantly improve particle reconstruction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

SCEPTRE 2.6 User's Guide

Sandia’s Computational Engine for Particle Transport for Radiation Effects (SCEPTRE) is a computer code that solves the linear Boltzmann transport equation, particularly targeting coupled photon-electron problems. It uses unstructured finite element meshes in space, multigroup in energy, and discrete ordinates (S n ) or other methods in angle. SCEPTRE uses an xml-based input file to specify the problem. This report documents the options and syntax of that input file.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Hybrid large-eddy simulation with adaptive downstream anisotropic eddy viscosity model

Turbulence pervades most flows of engineering interest, and its prediction remains a challenge on both accuracy and cost. One promising predictive approach that reduces cost combines large-eddy simulation (LES) with simulation based on Reynolds averaged Navier–Stokes equations (RANS). This study presents a method to overcome stability and accuracy issues associated with these hybrid LES-RANS methods. The method developed involves extracting the Reynolds stresses from the upstream LES solution and then using that information to convert the downstream RANS model from a scalar eddy-viscosity model to an anisotropic nonlinear eddy-viscosity model. The method developed differs from the downstream tensorial eddy-viscosity model by being independent of the coordinate system. The method developed was evaluated by computing film cooling of a flat plate with the coolant injected through one row of circular holes. Results obtained show instabilities at the LES-to-RANS interface to be eliminated. Also, the method developed yielded solutions that compare reasonably well with those from LES, even though a significant portion of the flow is computed by the adapted anisotropic RANS model instead of LES, which significantly reduced the number of grid points and computational time needed. Since modification of the downstream RANS model is based on information extracted from the upstream LES solution, the method developed is adaptive to the problem being studied.

42 ENGINEERING↗
DOE OSTI2023

Sequence2Self: Self-supervised image sequence denoising of pixel-level spray breakup morphology

Optical imaging of fast and transient phenomena such as the turbulent breakup of liquid sprays exhibit low signal-to-noise ratios due to the limited illumination intensity relative to the short exposure time. Image denoising is required to facilitate physical studies over these data but is challenging due to the absence of clean ground-truths and the stringency of the denoising task (e.g., strong and complex noise, limited resolution, preserving physical fidelity), preventing supervised and existing un-/self-supervised deep learning methods. To this end, Sequence2Self (Seq2S) is proposed, an extension of Self2Self (S2S) to image sequences that leverages both the signal’s spatial and temporal correlation. Seq2S is demonstrated on time-resolved x-ray phase contrast imaging of liquid jet fuel sprays in a gas turbine combustor, which possesses all of challenges detailed above. Experiments are conducted across four fuels with different breakup morphology using various state-of-the-art methods. Overall, many of the methods failed and Seq2S was most successful: (1) Accurate spray structures were reconstructed with consistent evolution across frames void of artifacts. (2) The performance was robust, invariant to the hyperparameter choice. (3) Computational time is short and can be made eligible for real-time denoising. In particular, the images denoised by Seq2S showed spray droplet diameter distributions with near-zero Kullback–Leibler divergence (0.01 ± 0.01) to a cleaner reference, whereas the second best method yielded 0.06 ± 0.03. In conclusion, this suggests that Seq2S can be reliably used prior to subsequent quantitative spray analyses as it retains (if not, improves) the statistical physical properties of the data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Accurate numerical, integral methods for computing drift-kinetic Trubnikov-Rosenbluth potentials

A novel numerical method is employed to compute the integral form of the axi-symmetric Trubnikov-Rosenbluth potentials. Two methods for quadrature in pitch-angle are described and their convergence properties are studied. Careful attention is given to quadrature over a singular Green's function. Here it is shown that an infinite series representation of the Green's function can be used more efficiently than its closed form involving complete elliptic integrals. Then a collocation method in speed, with its associated quadrature scheme, is laid out and its convergence properties are studied. Using the proposed scheme, accurate low-order moments of the field collision operator are obtained using relatively few velocity space degrees of freedom. The scheme is showcased by solving for the equilibrium, axi-symmetric bootstrap current in tokamaks. A C 0 Gauss-Lobatto-Legendre finite element pitch-angle basis with vertex nodes at the trapped/passing boundary is shown, in the context of the integral methods used, to be much more efficient than the more common Legendre polynomial expansion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Numerical schemes for a multi-species BGK model with velocity-dependent collision frequency

Here, we consider a kinetic description of multi-species gas mixture modeled with Bhatnagar-Gross-Krook (BGK) collision operators, in which the collision frequency varies not only in time and space but also with the microscopic velocity. In this model, the Maxwellians typically used in standard BGK operators are replaced by a generalization of such target functions, which are defined by a variational procedure. In this paper we present a numerical method for simulating this model, which uses an Implicit-Explicit (IMEX) scheme to minimize a certain potential function, mimicking the Lagrange functional that appears in the theoretical derivation. We show that theoretical properties such as conservation of mass, total momentum and total energy as well as positivity of the distribution functions are preserved by the numerical method, and illustrate its usefulness and effectiveness with numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Development and Evaluation of Occupancy-Aware Model Predictive Control for Residential Building Energy Efficiency and Occupant Comfort

The residential sector accounts for 25% of global primary energy consumption. Two methods have previously been proposed to reduce residential energy use associated with the provision of occupant thermal comfort: 1. Occupancy-based HVAC control, operating systems only during confirmed occupancy, and 2. model predictive control (MPC), harnessing a mathematical model and forecasts to find optimal operating strategies. Previous studies estimate the average energy savings of the two methods individually in the range of 21% and 16%, respectively. The research presented herein was carried out to evaluate the energy savings potential in residential buildings by combining both approaches across different climates, house vintages, and occupancy patterns. Occupancy and eight different physical modalities (e.g. CO2 and VOC) data were collected from five homes for time periods of 4–9 weeks. Collected data sets were used to train occupancy prediction models suggested by an extensive literature survey of occupancy model types. The trained prediction models were combined with MPC and detailed EnergyPlus building simulation models to evaluate residential building performance in terms of annual energy savings and thermal comfort, along with discomfort exceedance metrics. Multiple home types and regions were analyzed to understand regional and climate-dependent potential. Based on actual field data, the occupancy models had a prediction inaccuracy between 8% and 35% across the investigated homes. Average occupancy for the collected data ranged from 56% to 86%, a typical range reported in the literature. Building simulations were conducted for three control scenarios: conventional thermostatic control, occupancy-based, and occupancy-based MPC. The results indicate that all advanced strategies improve upon the conventional control, with some scenarios cutting energy use in half with only occasional incurrence of discomfort. The findings indicate that occupancy-aware model predictive residential building control has the potential to drastically reduce energy use and associated emissions while maintaining occupant comfort for both new and existing buildings.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2024

Uniformly decaying subspaces for error-mitigated quantum computation

Here, we present a general condition to obtain subspaces that decay uniformly in a system governed by the Lindblad master equation and use them to perform error-mitigated quantum computation. The expectation values of dynamics encoded in such subspaces are unbiased estimators of noise-free expectation values. In analogy to the decoherence free subspaces which are left invariant by the action of Lindblad operators, we show that the uniformly decaying subspaces are left invariant (up to orthogonal terms) by the action of the dissipative part of the Lindblad equation. We apply our theory to a system of qubits and qudits undergoing relaxation with varying decay rates and show that such subspaces can be used to eliminate bias up to first-order variations in the decay rates without requiring full knowledge of noise. Since such a bias cannot be corrected through standard symmetry verification, our method can improve error mitigation in dual-rail qubits and, given partial knowledge of noise, can perform better than probabilistic error cancellation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Scalable Wind Turbine Generator Bearing Fault Prediction Using Machine Learning: A Case Study

Operation and maintenance (O&M) costs for wind turbines pose a risk to competitiveness and asset owners. With machine-learning technologies and digitalization rapidly maturing, the wind industry is actively investigating these new technologies to optimize O&M practices and reduce costs. This paper reviews recent work on machine-learning approaches to generator bearing failure prediction and presents a relevant real-world case study through a collaboration between the National Renewable Energy Laboratory and Envision Digital Corporation. In the case study, we evaluate the performance of representative machine-learning algorithms for predicting wind turbine generator bearing failures. Operational supervisory control and data acquisition data from one wind power plant was used to train and test the machine-learning models. The investigated data channels are chosen based on whether physically they reflect the failed generator bearing conditions and the component historical usage, including both environmental and operational conditions. Benefits and drawbacks of different methods are identified.

generator bearing failures↗
DOE OSTI2021

Multifractal Characterization of Distribution Synchrophasors for Cybersecurity Defense of Smart Grids

“Source ID Mix” spoofing emerged as a new type of cyber-attack on Distribution Synchrophasors (DS) where adversaries have the capability to swap the source information of DS without changing the measurement values. Accurate detection of such a highly-deceptive attack is a challenging task especially when the spoofing attack happens on short fragments of DS recorded within a relatively small geographical scale. Herein this letter proposes an effective approach to detect this cyber-attack by realizing the multifractal characteristics of DS measurements. First, the multifractal cross-correlation of DS measured at multiple intra-state locations is revealed. Then the derived correlation is integrated with weighted two-dimensional multifractal surface interpolation to reconstruct quasi high-resolution signals. Finally, informative location-specific signatures are extracted from the high-resolution DS and they are integrated with advanced machine learning techniques for source authentication. Experiments using the real-life DS are performed to verify the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Finding Electronic Structure Machine Learning Surrogates without Training

A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations – this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of machine-learning surrogate models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Reconstruction of 6D Phase Space

A ‘Phase Space’ is a mathematical representation of all possible physical states of matter of a physical volume or system. Particle beams at Fermilab exist in a six-dimensional phase space: three position dimensions, and three momentum dimensions. To ‘reconstruct’ that space means to take measurement data from virtual detectors along those beams and map out where particles are and how they’re moving, using computational methods. In this experiment, we simulated a particle beam (Fig 1) and used the position distribution of each particle at each detector to map out the magnitude of the beam’s initial momentum.

Shirlee, Jermaine [DuPage Coll.]↗
DOE OSTI2022

Recent Improvements in the National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) has significantly evolved since the first release of the point source database in 1993. The NSRDB has been widely used by the solar energy industry to provide long-term time-series solar resource data for various project phases. The NSRDB represents the state of the art in the satellite-based estimation of solar resource information and uses a unique physics-based modeling approach that allows improvements in accuracy with the deployment of the next-generation geostationary satellites. This poster provides an overview of (1) the improved spatiotemporal resolution; (2) on-demand services and their applications; (3) future Improvements, such as a new direct normal irradiance model and new methods to gap-fill missing data using physics-guided machine learning; (4) data quality; and (5) data dissemination.

MATHEMATICS AND COMPUTING,SOLAR ENERGY↗