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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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter

The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

An insight into actual energy use and its drivers in high-performance buildings

Using portfolio analysis and individual detailed case studies, we studied the energy performance and drivers of energy use in 51 high-performance office buildings in the U.S., Europe, China, and other parts of Asia. Portfolio analyses revealed that actual site energy use intensity (EUI) of the study buildings varied by a factor of as much as 11, indicating significant variation in real energy use in HPBs worldwide. Nearly half of the buildings did not meet the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) Standard 90.1-2004 energy target, raising questions about whether a building’s certification as high performing accurately indicates that a building is energy efficient and suggesting that improvement in the design and operation of HPBs is needed to realize their energy-saving potential. We studied the influence of climate, building size, and building technologies on building energy performance and found that although all are important, none are decisive factors in building energy use. EUIs were widely scattered in all climate zones. There was a trend toward low energy use in small buildings, but the correlation was not absolute; some small HPBs exhibited high energy use, and some large HPBs exhibited low energy use. We were unable to identify a set of efficient technologies that correlated directly to low EUIs. In two case studies, we investigated the influence of occupant behavior as well as operation and maintenance on energy performance and found that both play significant roles in realizing energy savings. We conclude that no single factor determines the actual energy performance of HPBs, and adding multiple efficient technologies does not necessarily improve building energy performance; therefore, an integrated design approach that takes account of climate, technology, occupant behavior, and operations and maintenance practices should be implemented to maximize energy savings in HPBs. As a result, these findings are intended to help architects, engineers, operators, and policy makers improve the design and operation of HPBs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

High-order matrix-free incompressible flow solvers with GPU acceleration and low-order refined preconditioners

In this work, we present a matrix-free flow solver for high-order finite element discretizations of the incompressible Navier-Stokes and Stokes equations with GPU acceleration. For high polynomial degrees, assembling the matrix for the linear systems resulting from the finite element discretization can be prohibitively expensive, both in terms of computational complexity and memory. For this reason, it is necessary to develop matrix-free operators and preconditioners, which can be used to efficiently solve these linear systems without access to the matrix entries themselves. The matrix-free operator evaluations utilize GPU-accelerated sum-factorization techniques to minimize memory movement and maximize throughput. The preconditioners developed in this work are based on a low-order refined methodology with parallel subspace corrections, as described for diffusion problems in [1]. The saddle-point Stokes system is solved using block-preconditioning techniques, which are robust in mesh size, polynomial degree, time step, and viscosity. For the incompressible Navier-Stokes equations, we make use of projection (fractional step) methods, which require Helmholtz and Poisson solves at each time step. The performance of our flow solvers is assessed on several benchmark problems in two and three spatial dimensions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior

Most multinomial choice models (e.g., the multinomial logit model) adopted in practice assume an extreme-value Gumbel distribution for the random components (error terms) of utility functions. This distributional assumption offers a closed-form likelihood expression when the utility maximization principle is applied to model choice behaviors. As a result, model coefficients can be easily estimated using the standard maximum likelihood estimation method. However, maximum likelihood estimators are consistent and efficient only if distributional assumptions on the random error terms are valid. It is therefore critical to test the validity of underlying distributional assumptions on the error terms that form the basis of parameter estimation and policy evaluation. In this paper, a practical yet statistically rigorous method is proposed to test the validity of the distributional assumption on the random components of utility functions in both the multinomial logit (MNL) model and multiple discrete-continuous extreme value (MDCEV) model. Based on a semi-nonparametric approach, a closed-form likelihood function that nests the MNL or MDCEV model being tested is derived. The proposed method allows traditional likelihood ratio tests to be used to test violations of the standard Gumbel distribution assumption. Simulation experiments are conducted to demonstrate that the proposed test yields acceptable Type-I and Type-II error probabilities at commonly available sample sizes. The test is then applied to three real-world discrete and discrete-continuous choice models. For all three models, the proposed test rejects the validity of the standard Gumbel distribution in most utility functions, calling for the development of robust choice models that overcome adverse effects of violations of distributional assumptions on the error terms in random utility functions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.

Computer simulations↗
DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗
DOE OSTI2020

Neutrino interaction classification with a convolutional neural network in the DUNE far detector

The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure $CP$-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2-5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to $CP$-violating effects.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Advanced Multi-Tube Mixer Combustion for 65% Efficiency (Final Report)

This project targeted advanced low NOx combustion for advanced gas turbines capable of 65%, or greater, efficiency in combined cycle application. This technology advancement has further potential to benefit gas turbines used in coal based IGCC applications with pre-combustion carbon capture and hydrogen as the resulting fuel. The program developed and synthesized the most advanced combustion system capable of achieving low NOX emissions up to turbine inlet temperatures of 3100F while also supporting the load-following needs of a modern grid. The combustion system contributes to the overall gas turbine efficiency goal by setting the maximum cycle temperature achievable for a given NOX level and by minimizing the through-combustor air flow pressure drop. Focus areas for this project targeted maximizing the turbine inlet temperature entitlement, as constrained by emissions considerations. The design also minimized parasitic air flow pressure drop by using advanced cooling techniques and performance materials selections and by minimizing hot surface area. These two technology objectives (maximum, emissions-compliant cycle temperature and minimum air flow pressure drop) were integrated into a prototype design. The primarily analytical project sought to identify the most promising technologies to meet these objectives. Additional critical “jugular” data were obtained from multi-tube mixer tests to realize the potential of leveraging “micro flames” for minimizing overall hot surface area. This data was used, in conjunction with an understanding of advanced material and cooling design technologies, to analytically develop multiple design concepts. Phase I focused on in-depth engineering analysis and design, with minimal supporting laboratory testing to enable a selection of the top three combustion architectures for achieving these overall objectives. Phase II of the program developed the selected design through a combination of sub-scale testing and analytical efforts. Early tests included a cold-flow cascade to establish aerodynamic performance characteristics and a sub-scale fired test at GE Global Research in Niskayuna, NY, to establish cooling and heat transfer characteristics in conjunction with combustion performance. The data from these tests validated the analytical models to ultimately design a full-scale, test article to evaluate at prototypical pressure and temperature conditions at GE Gas Power’s Gas Turbine Technology Laboratory in Greenville, SC. GE Gas Power also developed, tested, and recommended a suitable seal design to be applied to the unique features of the combustor. To assess the technology challenges from prospective future production of the combustor from a ceramic matrix composite material, screening tests of Environmental Barrier Coatings were completed.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2010

Automation for Accommodating Fuel-Efficient Descents in Constrained Airspace

Continuous descents at low engine power are desired to reduce fuel consumption, emissions and noise during arrival operations. The challenge is to allow airplanes to fly these types of efficient descents without interruption during busy traffic conditions. During busy conditions today, airplanes are commonly forced to fly inefficient, step-down descents as airtraffic controllers work to ensure separation and maximize throughput. NASA in collaboration with government and industry partners is developing new automation to help controllers accommodate continuous descents in the presence of complex traffic and airspace constraints. This automation relies on accurate trajectory predictions to compute strategic maneuver advisories. The talk will describe the concept behind this new automation and provide an overview of the simulations and flight testing used to develop and refine its underlying technology.

Coopenbarger, Richard A.↗
DOE OSTI2017

Structure and color-tunable luminescence properties of Ce{sup 3+} and Tb{sup 3+}-activated Mg{sub 2}La{sub 8}(SiO{sub 4}){sub 6}O{sub 2} phosphors based on energy transfer behavior

A series of novel luminescent emission-tunable phosphors Mg{sub 2}La{sub 8}(SiO{sub 4}){sub 6}O{sub 2}:Ce{sup 3+},Tb{sup 3+} (MLS:Ce{sup 3+},Tb{sup 3+}) have been prepared by a solid-state reaction. The phase formation was firstly confirmed through X-ray diffraction technique and refined by the Rietveld method. The MLS:Ce{sup 3+},Tb{sup 3+} phosphors, which crystallized in apatite-type hexagonal phase, exhibited a broad excitation band ranging from 200 to 400 nm and several emission bands centered at 426 nm and 551 nm. Energy transfer from Ce{sup 3+} to Tb{sup 3+} ions via a dipole-dipole mechanism occurred in the as-synthesized phosphors upon ultraviolet (UV) excitation. The energy transfer efficiency increases with increasing doping content of Tb{sup 3+} ions, which was confirmed by the luminescence spectra and fluorescence decay curves of corresponding ions simultaneously. The energy transfer critical distance was calculated and evaluated by both the concentration quenching and spectral overlap methods. The chromaticity of emission-tunable phosphors was also characterized by the Commission International de l'éclairage (CIE) chromaticity indexes, and the color tone can be tuned from blue (0.179, 0.122) to green (0.267, 0.408) by controlling the ratio of Ce{sup 3+}/Tb{sup 3+}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

On optimal infinite impulse response edge detection filters

The authors outline the design of an optimal, computationally efficient, infinite impulse response edge detection filter. The optimal filter is computed based on Canny's high signal to noise ratio, good localization criteria, and a criterion on the spurious response of the filter to noise. An expression for the width of the filter, which is appropriate for infinite-length filters, is incorporated directly in the expression for spurious responses. The three criteria are maximized using the variational method and nonlinear constrained optimization. The optimal filter parameters are tabulated for various values of the filter performance criteria. A complete methodology for implementing the optimal filter using approximating recursive digital filtering is presented. The approximating recursive digital filter is separable into two linear filters operating in two orthogonal directions. The implementation is very simple and computationally efficient, has a constant time of execution for different sizes of the operator, and is readily amenable to real-time hardware implementation.

Sarkar, Sudeep↗
DOE OSTI2019

3D dynamics and morphology of bow-shock pulsar wind nebulae

Bow-shock pulsar wind nebulae (PWNe) show a variety of morphological shapes. We attribute this diversity to the geometrical factors: relative orientations of the pulsar rotation axis, proper velocity, and the line of sight (magnetic inclination angle may also have certain influence on the morphology). We identify three basic types of bow-shock nebulae: (i) a rifle bullet (pulsar spin axis and proper velocity are aligned), (ii) a ‘Frisbee’ (pulsar spin axis and proper velocity are orthogonal with the spin axis lying in the plane of the sky), and (iii) a ‘Cart wheel’ (such as frisbee but the spin axis is perpendicular to the plane of the sky). Using results of the 3D magnetohydrodynamics simulations, as well as analytical calculations, we reproduce the key morphological features of the bow-shock PWNe, as well as variations seen across different systems. Magnetic stresses within the shocked pulsar wind affect the overall structure strongly, producing ‘whiskers’, ‘tails’, ‘filled-in’, and ‘mushroom’ shapes, as well as non-symmetric morphologies. On the other hand, the interstellar medium inhomogeneities and the anisotropy of the energy flux in the pulsar wind have only a mild impact of the PWN morphology. In a few cases, when we clearly identify specific morphological structures, our results do not favour alignment of the pulsar spin axis and proper velocity. Here, our calculations of the underlying emission processes explain the low-synchrotron X-ray efficiency (in terms of the spin-down luminosity) and imply an energetically subdominant contribution of the inverse Compton process.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Regarding the optimization of O1-mode ECRH and the feasibility of EBW startup on NSTX-U

Recently published scenarios for fully non-inductive startup and operation on the National Spherical Torus eXperiment Upgrade (NSTX-U) [Menard J et al 2012 Nucl. Fusion 52 083015] show Electron Cyclotron Resonance Heating (ECRH) as an important component in preparing a target plasma for efficient High Harmonic Fast Wave and Neutral Beam heating. The modelling of the propagation and absorption of EC waves in the evolving plasma is required to define the most effective window of operation, and to optimize the launcher geometry for maximal heating and current drive during this window. Here in this paper, we extend a previous optimization of O1-mode ECRH on NSTX-U to account for the full time-dependent performance of the ECRH using simulations performed with TRANSP. We find that the evolution of the density profile has a prominent role in the optimization by defining the time window of operation, which in certain cases may be a more important metric to compare launcher performance than the average power absorption. This feature cannot be captured by analysis on static profiles, and should be accounted for when optimizing ECRH on any device that operates near the cutoff density. Additionally, the utility of the electron Bernstein wave (EBW) in driving current and generating closed flux surfaces in the early startup phase has been demonstrated on a number of devices. Using standalone GENRAY simulations, we find that efficient EBW current drive is possible on NSTX-U if the injection angle is shifted below the midplane and aimed towards the top half of the vacuum vessel. However, collisional damping of the EBW is projected to be significant, in some cases accounting for up to 97\% of the absorbed EBW power.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Improving the energy efficiency of room air conditioners in China: Costs and benefits

China is the world's largest consumer of room air conditioners, and it contributes about a quarter of global space cooling CO 2 emissions. We model the costs and benefits of recently proposed new room air conditioner minimum energy performance standards (MEPS) in China. Our results suggest that newly proposed MEPS brings accumulative CO 2 emissions reductions of 12.8% between 2019 and 2050, and accumulative bill saving of 2620 billion RMB to China's consumers. The benefits of the proposed MEPS decrease with longer MEPS revision intervals and increase with shorter intervals—indicating that the intervals should be balanced to maximize benefits while accommodating constraints due to air conditioner manufacturer design cycles. We also model potential nationwide benefits from higher MEPS. Across two increasingly aggressive MEPS scenarios, China's room air conditioner electricity consumption and CO 2 emissions in 2050 are both reduced by 15–53% compared to the proposed MEPS. The highest-efficiency scenario (reaching MEPS of annual performance factor 5.4 in 2025) provides the largest long-term national benefits. These results could inform development of a Chinese regulatory regime that effectively updates room air conditioner MEPS. Because China is the world's largest manufacturer of room air conditioners, the economic, energy, and emissions benefits resulting from higher Chinese MEPS could also have a global reach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Accelerated Search for BaTiO 3 -Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design

The problem that is considered is that of maximizing the energy storage density of Pb-free BaTiO3-based dielectrics at low electric fields. It is demonstrated that how varying the size of the combinatorial search space influences the efficiency of material discovery by comparing the performance of two machine learning based approaches where different levels of physical insights are involved. It is started with physics intuition to provide guiding principles to find better performers lying in the crossover region in the composition–temperature phase diagram between the ferroelectric phase and relaxor ferroelectric phase. Such an approach is limiting for multidopant solid solutions and motivates the use of two data-driven machine learning and design strategies with a feedback loop to experiments. Strategy I considers learning and property prediction on all the compounds, and strategy II learns to preselect compounds in the crossover region on which prediction is carried out. By performing only two active learning loops via strategy II, the compound (Ba0.86Ca0.14)(Ti0.79Zr0.11Hf0.10)O3 is synthesized with the largest energy storage density ≈73 mJ cm-3 at a field of 20 kV cm-1 , and an insight into the relative performance of the strategies using varying levels of knowledge is provided.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Defect Detection in Fuel Cell Gas Diffusion Electrodes Using Infrared Thermography

Polymer electrolyte membrane fuel cells are energy conversion devices that offer high power densities and high efficiencies for mobile and other applications. Successful introduction into the marketplace requires addressing cost barriers such as production volumes and platinum loading. For cost reduction, it is vital to minimize waste and maximize quality during the manufacturing of platinum-containing electrodes, including gas diffusion electrodes (GDEs). In this work, we report on developing a quality control diagnostic for GDEs, involving creating an ex situ exothermic reaction on the electrode surface and using infrared thermography to measure the resulting temperature profile. Experiments with a moving GDE containing created defects were conducted to demonstrate the applicability of the diagnostic for real-time web-line inspection.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

A Strong Stability Preserving Analysis for Explicit Multistage Two-Derivative Time-Stepping Schemes Based on Taylor Series Conditions

High-order strong stability preserving (SSP) time discretizations are usually needed to ensure the nonlinear (and sometimes non-inner-product) strong stability properties of spatial discretizations specially designed for the solution of hyperbolic PDEs. Multi-derivative time-stepping methods have recently been increasingly used for evolving hyperbolic PDEs, and the strong stability properties of these methods are of interest. In our prior work we explored time discretizations that preserve the strong stability properties of spatial discretizations coupled with forward Euler and a second-derivative formulation. Yet, many spatial discretizations do not satisfy strong stability properties when coupled with this second-derivative formulation, but rather with a more natural Taylor series formulation. Here, we demonstrate sufficient conditions for an explicit two-derivative multistage method to preserve the strong stability properties of spatial discretizations in a forward Euler and Taylor series formulation. We call these strong stability preserving Taylor series (SSP-TS) methods. We also prove that the maximal order of SSP-TS methods is p=6, and define an optimization procedure that allows us to find such SSP methods. Several types of these methods are presented and their efficiency compared. Finally, these methods are tested on several PDEs to demonstrate the benefit of SSP-TS methods, the need for the SSP property, and the sharpness of the SSP time-step in many cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Swarm intelligence–based distributed stochastic model predictive control for transactive operation of networked building clusters

To maximize potential energy and cost savings, networked clusters of local buildings are formed for energy transactions. Both centralized and distributed decision approaches were explored in past decades as a way to enable efficient transactive operations. However, online distributed stochastic transactive operation has been overlooked in the literature. To bridge gaps in the research, a bi-level distributed stochastic model predictive control framework was proposed to study the transactive operations of building clusters where a system-level agent is employed to coordinate multiple building agents at the subsystem level. The energy transaction is optimized by a marginal price-based particle swarm optimizer at the system level. Given the energy transaction decisions, each building can independently solve a scenario-based two-stage stochastic model to optimally dispatch the electricity and ancillary services for optimal energy performance. The effectiveness of the proposed framework and coordination algorithm are demonstrated in deterministic, stochastic, and online operations and compared to centralized decisions using several sets of experiments. Additionally, the proposed approach can realize autonomous transactive operation and be extended to community-level building clusters in a plug-and-play way.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗