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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dark Energy Survey Year 3 results: Cosmology from cosmic shear and robustness to modeling uncertainty

Here, this work and its companion paper, Amon et al. [Phys. Rev. D 105, 023514 (2022)], present cosmic shear measurements and cosmological constraints from over 100 million source galaxies in the Dark Energy Survey (DES) Year 3 data. We constrain the lensing amplitude parameter 𝑆 8 ≡𝜎 8 ⁢$\sqrt{Ω_{m}/0.3}$ at the 3% level in Λ⁢ CDM: 𝑆 8 =0.75⁢9$^{+0.025}_{−0.023}$ (68% CL). Our constraint is at the 2% level when using angular scale cuts that are optimized for the Λ⁢ CDM analysis: 𝑆 8 =0.77⁢2$^{+0.018}_{−0.017}$ (68% CL). With cosmic shear alone, we find no statistically significant constraint on the dark energy equation-of-state parameter at our present statistical power. We carry out our analysis blind, and compare our measurement with constraints from two other contemporary weak lensing experiments: the Kilo-Degree Survey (KiDS) and Hyper-Suprime Camera Subaru Strategic Program (HSC). We additionally quantify the agreement between our data and external constraints from the Cosmic Microwave Background (CMB). Our DES Y3 result under the assumption of Λ⁢ CDM is found to be in statistical agreement with Planck 2018, although favors a lower 𝑆 8 than the CMB-inferred value by 2.3⁢𝜎 (a 𝑝-value of 0.02). This paper explores the robustness of these cosmic shear results to modeling of intrinsic alignments, the matter power spectrum and baryonic physics. We additionally explore the statistical preference of our data for intrinsic alignment models of different complexity. The fiducial cosmic shear model is tested using synthetic data, and we report no biases greater than 0.3⁢𝜎 in the plane of 𝑆 8 ×Ω m caused by uncertainties in the theoretical models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Combined search for electroweak production of winos, binos, higgsinos, and sleptons in proton-proton collisions at s = 13 TeV

A combination of the results of several searches for the electroweak production of the supersymmetric partners of standard model bosons, and of charged leptons, is presented. All searches use proton-proton collision data at s = 13 TeV recorded with the CMS detector at the LHC in 2016–2018. The analyzed data correspond to an integrated luminosity of up to 137 fb − 1 . The results are interpreted in terms of simplified models of supersymmetry. Two new interpretations are added with this combination: a model spectrum with the bino as the lightest supersymmetric particle together with mass-degenerate Higgsinos decaying to the bino and a standard model boson, and the compressed-spectrum region of a previously studied model of slepton pair production. Improved analysis techniques are employed to optimize sensitivity for the compressed spectra in the wino and slepton pair production models. The results are consistent with expectations from the standard model. The combination provides a more comprehensive coverage of the model parameter space than the individual searches, extending the exclusion by up to 125 GeV, and also targets some of the intermediate gaps in the mass coverage. © 2024 CERN, for the CMS Collaboration 2024 CERN

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Solving the homogeneous Bethe-Salpeter equation with a quantum annealer

The homogeneous Bethe-Salpeter equation (hBSE), describing a bound system in a genuinely relativistic quantum-field theory framework, was solved for the first time by using a D-Wave quantum annealer. After applying standard techniques of discretization, the hBSE, in ladder approximation, can be formally transformed in a generalized eigenvalue problem (GEVP), with two square matrices: one symmetric and the other nonsymmetric. The latter matrix poses the challenge of obtaining a suitable formal approach for investigating the GEVP by means of a quantum annealer, i.e., to recast it as a quadratic unconstrained binary optimization problem. A broad numerical analysis of the proposed algorithms, applied to matrices of dimension up to 64, was carried out by using both the simulated-annealing package and the D-Wave . The numerical results very nicely compare with those obtained with standard classical algorithms, and also show interesting scalability features. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Genetic Algorithm Design of a 3D Printed Heat Sink

In this paper, a genetic algorithm- (GA-) based approach is discussed for designing heat sinks based on total heat generation and dissipation for a pre-specified size andshape. This approach combines random iteration processesand genetic algorithms with finite element analysis (FEA) to design the optimized heat sink. With an approach that prefers survival of the fittest , a more powerful heat sink can bedesigned which can cool power electronics more efficiently. Some of the resulting designs can only be 3D printed due totheir complexity. In addition to describing the methodology, this paper also includes comparisons of different cases to evaluate the performance of the newly designed heat sinkcompared to commercially available heat sinks.

3D Printing↗
DOE OSTI2021

Identifying Light-Duty Vehicle Travel from Large-Scale Multimodal Wearable GPS Data with Novelty Detection Algorithms

Identifying travel mode within travel survey data sets, especially light-duty vehicle (LDV) travel, is foundational, though nontrivial, to travel behavior analysis and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. They are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the data requirements. This paper proposes an LDV travel mode detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique-one-class support vector machines (OCSVMs)-and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multimodal prediction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Impact of Heat Dissipation Profiles on Power Electronics Packaging Design

This work focuses on understanding the thermal impacts of using discrete power devices and limitations of applying conventional thermal design methods. Empirically, a thermal system is designed based on selecting a heat sink with the required thermal resistance from the manufacturer datasheet. This method, as an approximate estimation, has been proven effective as a rough design of Si-based power module. However, wide bandgap (WBG) bare dies bring additional thermal design concerns that have been overlooked. The benefits of WBG devices, such as smaller chip sizes and higher power ratings, on the other hand, lead to thermal concentration issues. Detailed analyses and impacts of the thermal concentration are presented in this paper. Finally, a more accurate model involving Finite Element Analysis (FEA) and Genetic Algorithm optimization is also proposed for a more accurate thermal design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Light-Duty Vehicle Trip Classification Using One-Class Novelty Detection and Exhaustive Feature Extraction

Travel mode classification within travel survey data sets, especially light-duty vehicle (LDV) trips, is foundational, though nontrivial, to emerging mobility systems, travel behavior analysis, and fuel consumption estimation. Current travel mode detection approaches require well-sampled and balanced data sets with ground truth travel mode labels. The detection approaches are rarely applied and validated on large-scale, real-world data sets, which may not satisfy the dataset requirements. This work proposes an LDV trip detection model as a supplement to current travel mode detection methods, for the case when the training set is highly (and/or completely) unbalanced, to the extent that classical machine-learning approaches become difficult or impossible to deploy. The proposed model uses a novelty detection technique - one-class support vector machines (OCSVMs) - and a novel exhaustive feature extraction (EFE) technique on continuous time series data (i.e., Global Positioning System [GPS] speed profiles) for single-mode trip trajectories. Training and validation of the model are conducted on a large-scale, real-world data set. The proposed method accurately identifies LDV trips from a broad set of multimodal trips by leveraging a wealth of preexisting in-vehicle GPS travel data. Additional sensitivity analysis sheds light on the optimal training size and feature selection, which will benefit applications limited by highly imbalanced data. The paper also discusses performance comparison with regular machine-learning approaches, the model's robustness, and the potential to extend the proposed model to multi-modal trip prediction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Tailoring the Toughening Effects in Two-Dimensional Nanomaterial-Reinforced Ceramic Matrix Composites

Abstract Ceramic matrix composites (CMCs) reinforced by two-dimensional (2D) nanomaterials have shown extraordinary load-carrying capacities, even in the harsh environments required by emerging applications. Their exceptional mechanical performance, especially fracture toughness, primarily arises from their heterogeneous microstructures. The deliberate dispersion of 2D reinforcements enables toughening mechanisms that are extrinsic to the matrix and thus endows the composites with substantial resistance to catastrophic failure. However, the incomplete understanding of the fracture behavior of such nanocomposites, especially the complex energy dissipation process of the matrix/reinforcement interface, limits the development of stronger and tougher CMCs. To overcome these limitations, we investigate crack deflection and energy dissipation in nanocomposites using an extended cohesive shear-lag model. This new model accounts for interfacial debonding and friction, which critically control the toughening of nanocomposites. Our analysis provides mechanistic insights for optimizing the toughening effects of CMCs.

Mechanics↗
DOE OSTI2016

SU-F-T-97: Outlier Identification in Radiation Therapy Knowledge Modeling

Purpose: To investigate the impact of outliers on knowledge modeling in radiation therapy, and develop a systematic workflow for identifying and analyzing geometric and dosimetric outliers using pelvic cases. Methods: Four groups (G1-G4) of pelvic plans were included: G1 (37 prostate cases), G2 (37 prostate plus lymph node cases), and G3 (37 prostate bed cases) are all clinical IMRT cases. G4 are 10 plans outside G1 re-planned with dynamic-arc to simulate dosimetric outliers. The workflow involves 2 steps: 1. identify geometric outliers, assess impact and clean up; 2. identify dosimetric outliers, assess impact and clean up.1. A baseline model was trained with all G1 cases. G2/G3 cases were then individually added to the baseline model as geometric outliers. The impact on the model was assessed by comparing leverage statistic of inliers (G1) and outliers (G2/G3). Receiver-operating-characteristics (ROC) analysis was performed to determine optimal threshold. 2. A separate baseline model was trained with 32 G1 cases. Each G4 case (dosimetric outliers) was then progressively added to perturb this model. DVH predictions were performed using these perturbed models for remaining 5 G1 cases. Normal tissue complication probability (NTCP) calculated from predicted DVH were used to evaluate dosimetric outliers’ impact. Results: The leverage of inliers and outliers was significantly different. The Area-Under-Curve (AUC) for differentiating G2 from G1 was 0.94 (threshold: 0.22) for bladder; and 0.80 (threshold: 0.10) for rectum. For differentiating G3 from G1, the AUC (threshold) was 0.68 (0.09) for bladder, 0.76 (0.08) for rectum. Significant increase in NTCP started from models with 4 dosimetric outliers for bladder (p<0.05), and with only 1 dosimetric outlier for rectum (p<0.05). Conclusion: We established a systematic workflow for identifying and analyzing geometric and dosimetric outliers, and investigated statistical metrics for detecting. Results validated the necessity for outlier detection and clean-up to enhance model quality in clinical practice. Research Grant: Varian master research grant.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Improved Guarantees for Optimal Nash Equilibrium Seeking and Bilevel Variational Inequalities

We consider a class of hierarchical variational inequality (VI) problems that subsumes VI-constrained optimization and several other problem classes, including the optimal solution selection problem and the optimal Nash equilibrium (NE) seeking problem. Our main contribution is threefold. (i) We consider bilevel VIs with monotone and Lipschitz continuous mappings and devise a single-timescale iteratively regularized extragradient method, named IR-EG 𝚖,𝚖 . We improve the existing iteration complexity results for addressing both bilevel VI and VI-constrained convex optimization problems. (ii) Under the strong monotonicity of the outer-level mapping, we develop a method named IR-EG 𝚜,𝚖 and derive faster guarantees than those in (i). We also study the iteration complexity of this method under a constant regularization parameter. These results appear to be new for both bilevel VIs and VI-constrained optimization. (iii) To our knowledge, complexity guarantees for computing the optimal NE in nonconvex settings do not exist. Motivated by this lacuna, we consider VI-constrained nonconvex optimization problems and devise an inexactly projected gradient method, named IPR-EG, where the projection onto the unknown set of equilibria is performed using IR-EG 𝚜,𝚖 with a prescribed termination criterion and an adaptive regularization parameter. We obtain new complexity guarantees in terms of a residual map and an infeasibility metric for computing a stationary point. Here, we validate the theoretical findings using preliminary numerical experiments for computing the best and the worst NEs.

bilevel optimization↗
DOE OSTI2022

Studsvik R2 materials test reactor Ad Hoc depletion strategy for the derivation of the fuel isotopic composition of the MPCMIV benchmark

The Ad Hoc Depletion Strategy elaborated by the NINE company, developed in support of the organization of the MPCMIV (Multi-physics Pellet Cladding Mechanical Interaction Validation) benchmark input and output specifications, is presented. This work aims at illustrating the strategy itself and then showing the results obtained with its application over the Studsvik R2 Testing Reactor, which is analyzed in the benchmark. The objective of the application of the strategy is to compute the fuel elements isotopic compositions at the beginning of some core loadings of interest for the benchmark. To this objective, it is necessary to implement first the simulation model of the three single assembly types and perform the infinite lattice depletions, then, to build the full core model and to perform the simulation of the core cycle. All the models and simulations were carried out with the use of the Monte Carlo particle transport code Serpent 2. Finally, the simulations results are assessed against Studsvik isotopic compositions of the fuel elements discharged from the R2 Reactor at the end of the core loading. Several assumptions were necessary during all the steps of the strategy, to overcome the lack of information regarding the core management. For this reason, the solution found at the end of the current analysis may not be completely optimized and further improvements regarding the model assumptions will be tested in a future work. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Evolution of artificial intelligence for application in contemporary materials science

Abstract Contemporary materials science has seen an increasing application of various artificial intelligence techniques in an attempt to accelerate the materials discovery process using forward modeling for predictive analysis and inverse modeling for optimization and design. Over the last decade or so, the increasing availability of computational power and large materials datasets has led to a continuous evolution in the complexity of the techniques used to advance the frontier. In this Review, we provide a high-level overview of the evolution of artificial intelligence in contemporary materials science for the task of materials property prediction in forward modeling. Each stage of evolution is accompanied by an outline of some of the commonly used methodologies and applications. We conclude the work by providing potential future ideas for further development of artificial intelligence in materials science to facilitate the discovery, design, and deployment workflow. Graphical abstract

Materials Science↗
DOE OSTI2016

Thermal contact conductance as a method of rectification in bulk materials

A thermal rectifier that utilizes thermal expansion to directionally control interfacial conductance between two contacting surfaces is presented. The device consists of two thermal reservoirs contacting a beam with one rough and one smooth end. When the temperature of reservoir in contact with the smooth surface is raised, a similar temperature rise will occur in the beam, causing it to expand, thus increasing the contact pressure at the rough interface and reducing the interfacial contact resistance. However, if the temperature of the reservoir in contact with the rough interface is raised, the large contact resistance will prevent a similar temperature rise in the beam. As a result, the contact pressure will be marginally affected and the contact resistance will not change appreciably. Owing to the decreased contact resistance of the first scenario compared to the second, thermal rectification occurs. A parametric analysis is used to determine optimal device parameters including surface roughness, contact pressure, and device length. Modeling predicts that rectification factors greater than 2 are possible at thermal biases as small as 3 K. Lastly, thin surface coatings are discussed as a method to control the temperature bias at which maximum rectification occurs.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Nanostructures of Boron, Carbon and Magnesium Diboride for High Temperature Superconductivity

Direct fabrication of Mg x B y nanostructures is achieved by employing metal (Ni,Mg) incorporated MCM-41 in the Hybrid Physical-Chemical Vapor Deposition (HPCVD) reaction. Different reaction conditions are tested to optimize the fabrication process. TEM analysis shows the fabrication of Mg x B y nanostructures starting at the reaction temperature of 600°C, with the yield of the nanostructures increasing with increasing reaction temperature. The as-synthesized Mg x B y nanostructures have the diameters in the range of 3-5nm, which do not increase with the reaction temperature consistent with templated synthesis. EELS analysis of the template removed nanostructures confirms the existence of B and Mg with possible contamination of Si and O. NEXAFS and Raman spectroscopy analysis suggested a concentric layer-by-layer MgxBy nanowire/nanotube growth model for our as-synthesized nanostructures. Ni k-edge XAS indicates that the formation of MgNi alloy particles is important for the Vapor-Liquid-Solid (VLS) growth of Mg x B y nanostructures with fine diameters, and the presence of Mg vapor not just Mg in the catalyst is crucial for the formation of Ni-Mg clusters. Physical templating by the MCM-41 pores was shown to confine the diameter of the nanostructures. DC magnetization measurements indicate possible superconductive behaviors in the as-synthesized samples.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Final Technical Report on Development of an Economic and Efficient Biodiesel production Process (NC)

The Biofuels Team at The University of North Carolina at Pembroke and North Carolina A&T State University carried out a joint research project aimed at developing an efficient process to produce biodiesel. In this project, the team developed and tested various types of homogeneous and heterogeneous catalysts which could replace the conventionally used soluble potassium hydroxide catalyst which, traditionally, must be separated and disposed of at the end of the process. As a result of this screening, the homogeneous catalyst choline hydroxide was identified as a potential replacement for the traditional catalyst used in this process, potassium hydroxide, due to its decreased corrosiveness and toxicity. A large number of heterogeneous catalysts were produced and tested in order to determine the scaffold, ion type and ion concentration which would produce optimum yield of biodiesel. The catalyst with 12% calcium on Zeolite β was identified as being highly effective and optimal reaction conditions were identified. Furthermore, a packed bed reactor utilizing this type of catalyst was designed, constructed and tested in order to further optimize the process. An economic analysis of the viability of the project showed that the cost of an independent farmer to produce the fuelstock required to produce biodiesel exceeds the cost of petroleum diesel under current conditions and that therefore without incentives, farmers would not be able to benefit economically from producing their own fuel. An educational website on biodiesel production and analysis was produced and a laboratory experiment demonstrating the production of biodiesel was developed and implemented into the Organic Chemistry II laboratory curriculum at UNCP. Five workshops for local farmers and agricultural agents were held in order to inform the broader community about the various fuelstock available, their cultivation and the process and advantages of biodiesel use and production. This project fits both Universities’ goals in the Biofuels Research Initiative, since it uses an alternative fuelstock: namely canola. The outcomes of this project may eventually aid in reducing the state’s consumption of corn and soybean, which are important food crops. The project will also encourage regional farmers to grow alternative crops for biofuel production. The success of this project has contributed towards the development of Robeson County, an economically disadvantaged region. Additionally it should be noted that Robeson County serves a large Native American population. Therefore, training and engaging this minority group in the energy industry was an important accomplishment.

09 BIOMASS FUELS↗
DOE OSTI2015

Thermostatic Radiator Valve Evaluation

A large stock of multifamily buildings in the Northeast and Midwest are heated by steam distribution systems. Losses from these systems are typically high and a significant number of apartments are overheated much of the time. Thermostatically controlled radiator valves (TRVs) are one potential strategy to combat this problem, but have not been widely accepted by the residential retrofit market. In this project, the ARIES team sought to better understand the current usage of TRVs by key market players in steam and hot water heating and to conduct limited experiments on the effectiveness of new and old TRVs as a means of controlling space temperatures and reducing heating fuel consumption. The project included a survey of industry professionals, a field experiment comparing old and new TRVs, and cost-benefit modeling analysis using BEopt™ (Building Energy Optimization software).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Limited-memory adaptive snapshot selection for proper orthogonal decomposition

Reduced order models are useful for accelerating simulations in many-query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, offline training of reduced order models can have prohibitively expensive memory and floating-point operation costs in high-performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits offline training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time-stepping ordinary differential equation solvers. The error estimator used in this work is related to theory bounding the approximation error in time of proper orthogonal decomposition-based reduced order models, and memory usage is minimized by computing the singular value decomposition using a single-pass incremental algorithm. Results for a viscous Burgers’ test problem demonstrate convergence in the limit as the algorithm error tolerances go to zero; in this limit, the full order model is recovered to within discretization error. The resulting method can be used on supercomputers to generate proper orthogonal decomposition-based reduced order models, or as a subroutine within hyperreduction algorithms that require taking snapshots in time, or within greedy algorithms for sampling parameter space.

97 MATHEMATICS AND COMPUTING↗