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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

PHOTOMETRIC REDSHIFTS FOR QUASARS IN MULTI-BAND SURVEYS

The Multi Layer Perceptron with Quasi Newton Algorithm (MLPQNA) is a machine learning method that can be used to cope with regression and classification problems on complex and massive data sets. In this paper, we give a formal description of the method and present the results of its application to the evaluation of photometric redshifts for quasars. The data set used for the experiment was obtained by merging four different surveys (Sloan Digital Sky Survey, GALEX, UKIDSS, and WISE), thus covering a wide range of wavelengths from the UV to the mid-infrared. The method is able (1) to achieve a very high accuracy, (2) to drastically reduce the number of outliers and catastrophic objects, and (3) to discriminate among parameters (or features) on the basis of their significance, so that the number of features used for training and analysis can be optimized in order to reduce both the computational demands and the effects of degeneracy. The best experiment, which makes use of a selected combination of parameters drawn from the four surveys, leads, in terms of {Delta}z{sub norm} (i.e., (z{sub spec} - z{sub phot})/(1 + z{sub spec})), to an average of {Delta}z{sub norm} = 0.004, a standard deviation of {sigma} = 0.069, and a median absolute deviation, MAD = 0.02, over the whole redshift range (i.e., z{sub spec} {<=} 3.6), defined by the four-survey cross-matched spectroscopic sample. The fraction of catastrophic outliers, i.e., of objects with photo-z deviating more than 2{sigma} from the spectroscopic value, is <3%, leading to {sigma} = 0.035 after their removal, over the same redshift range. The method is made available to the community through the DAMEWARE Web application.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Design, Modeling, and Testing of a Novel Inductor for Electric Vehicles: Iron Nitride Soft Magnetic Composites

New technology for electric vehicles (EVs) must meet the requirements of higher energy usage, lower costs, and more sustainable source materials. One promising material for EV power system components is iron nitride (IN) soft magnetic composites (SMCs) because of their competitive magnetic properties and high abundance of the source materials. As part of an ongoing program at Sandia National Laboratories, this project focused on using computer modeling to optimize the prototyping process for an iron nitride SMC toroidal inductor to reach a target inductance of 600 μH. Four inductors with different combinations of wiring (26 AWG and 20 AWG) and vol% loading of iron nitride (65 vol% and 50 vol%) were fabricated at Cal Poly and characterized using an LCR meter. These inductors were also modeled using COMSOL Multiphysics™ with the Magnetic Fields module. The inductance data from the experiment and the model show that the 65 vol% IN prototypes and models agree with about 8% difference, while the 50 vol% IN samples show about a 9% difference between the prototype and the model. These results suggest that the model can predict inductance with both accuracy and precision with low confidence for the given sample size of four. An additional parameter of AC resistance is studied but the AC resistance results from the inductors and from the model generally do not agree closely, suggesting that the current model used in the project does not fully capture the mechanisms behind AC resistance of the inductor. With the focus of the project on inductance, the percent difference results of less than 9% across the four inductors that were tested increases confidence in the model’s predictive capabilities for inductance only. Using the inductance results from both the model and experiment, the final suggested inductor design is a 65 vol% core with 150 windings of 20 AWG wire that is 8 cm across and 1.5 cm tall to reach the inductance goal of 600 μH based on analysis using the optimized COMSOLTM model.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Moderator Optimization for the Second Target Station Final Design

This report details the results from the optimization simulations performed for the final design of the Second Target Station (STS). This is a continuation of the analysis performed in 2022 for the preliminary design. To evaluate the impact of the design changes since 2022, the analysis was repeated with updated target and moderator models. Additionally, more degrees of freedom have been considered in the premoderator dimensions which provide a refinement compared to the previous optimization analysis.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Optimization of stepwise clustering algorithm in backward trajectory analysis

In recent years, the backward trajectory model has been widely used in the research of meteorological and atmospheric environmental quality. This paper presents a comprehensive study on a stepwise clustering analysis algorithm in the clustering process of backward trajectory model and an application of the clustering analysis of single-particle backward trajectory in 2016 in Changchun City. This study starts with an analysis of the original stepwise clustering algorithm and its application to a clustering process of 8784 backward trajectories during 48 h in Changchun City as a benchmark test case. Then, two improvements are made in the algorithm: First, in the process of finding the optimal classification, the algorithm complexity is improved from original O(n 3 ) to O(log(n)*n 2 ) through algorithm improvement. The algorithm performance is enhanced by log(n) times. Next, in the process of re-establishing the classification, the algorithm complexity is improved from the original O(m*n 2 ) to O(m*log(n)*n), that is another algorithm performance improvement by a factor of log(n). Therefore, the accumulative execution efficiency improvement through the algorithm optimization is 2*log(n) times, which has been further verified in the practical application in Changchun City.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

New opportunities in h → 4ℓ

The Higgs decay h → 4ℓ has played an important role in discovering the Higgs and measuring its mass thanks to low background and excellent resolution. Current cuts in this channel have been optimized for Higgs discovery via the dominant tree level ZZ contribution arising from electroweak symmetry breaking. Going forward, one of the primary objectives of this sensitive channel will be to probe other Higgs couplings and search for new physics on top of the tree level ZZ ‘background’. Thanks to interference between these small couplings and the large tree level contribution to ZZ, the h → 4ℓ decay is uniquely capable of probing the magnitude and CP phases of the Higgs couplings to γγ and Zγ as well as, to a lesser extent, ZZ couplings arising from higher dimensional operators. With this in mind we examine how much relaxing current cuts can enhance the sensitivity while also accounting for the dominant non-Higgs continuum \( q\overline{q}\to 4\ell \) background. We find the largest enhancement in sensitivity for the hZγ couplings (≳100%) followed by hγγ (≳40%) and less so for the higher dimensional hZZ couplings (a few percent). With these enhancements, we show that couplings of order Standard Model values for hγγ may optimistically be probed by end of Run-II at the LHC while for hZγ perhaps towards the end of a high luminosity LHC. In addition, an appropriately optimized h → 4ℓ analysis can complement direct decays of the Higgs to on-shell γγ and Zγ pairs giving a unique opportunity to directly access the CP properties of these couplings.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

SCALE/AMPX multigroup libraries for sodium-cooled fast reactor systems

New multigroup cross section libraries and associated covariance files that are optimized for the analysis of sodium-cooled fast reactors were generated with the AMPX tools distributed with the SCALE code system. These new libraries account for the fast neutron flux spectrum with sufficient resolution of resonances in the higher energy range to reproduce continuous-energy reference results and to provide the basis for sensitivity and uncertainty analyses of fast spectrum systems with the SCALE code system. The performance of the new libraries was investigated in terms of the eigenvalue, the neutron flux and reaction rates in criticality calculations, and the generation of group constants. A library using 302 energy groups and a fast neutron flux spectrum as the weighting function led to very good agreement with reference continuous-energy results. As a result, the performance of the new library was demonstrated in calculations of experiments from the International Criticality Safety Benchmark Evaluation Project handbook.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Thermal battery for portable climate control

Current technologies that provide climate control in the transportation sector are quite inefficient. In gasoline-powered vehicles, the use of air-conditioning is known to result in higher emissions of greenhouse gases and pollutants apart from decreasing the gas-mileage. On the other hand, for electric vehicles (EVs), a drain in the onboard electric battery due to the operation of heating and cooling system results in a substantial decrease in the driving range. As an alternative to the conventional climate control system, we are developing an adsorption-based thermal battery (ATB), which is capable of storing thermal energy, and delivering both heating and cooling on demand, while requiring minimal electric power supply. Analogous to an electrical battery, the ATB can be charged for reuse. Furthermore, it promises to be compact, lightweight, and deliver high performance, which is desirable for mobile applications. In this study, we describe the design and operation of the ATB-based climate control system. We present a general theoretical framework to determine the maximum achievable heating and cooling performance using the ATB. The framework is then applied to study the feasibility of ATB integration in EVs, wherein we analyze the use of NaX zeolite-water as the adsorbent-refrigerant pair. In order to deliver the necessary heating and cooling performance, exceeding 2.5 kW h thermal capacity for EVs, the analysis determines the optimal design and operating conditions. While the use of the ATB in EVs can potentially enhance its driving range, it can also be used for climate control in conventional gasoline vehicles, as well as residential and commercial buildings as a more efficient and environmentally-friendly alternative. (C) 2015 Elsevier Ltd. All rights reserved.

Narayanan, S↗
DOE OSTI2024

Processing and characterization of the homologous Zr x Ta 2 O 2x+5 series

In this study, Zr x Ta 2 O 2x+5 (ZTOx) was systematically studied to determine ideal solid-state synthesis and pressureless sintering conditions. For Zr 6 Ta 2 O 17 (ZTO6) and other compositions with values of x in ZTOx the optimal synthesis temperature of 1100 °C with 1 h dwell time was optimal. XRD phase analysis of the homologous series indicated that for ZTOx, x = 5, 6, 7, and 8 were within the phase stability field while x = 4 and x = 9 were outside the phase stability field. Unit cell lattice parameters changed anisotropically with Zr:Ta ratio with an overall <0.1% change in unit cell volume across the solid solution. Some Raman modes red shifted while others blue shifted, indicating competing tensile and compressive strains. Sintering of ZTOx required a progressively higher temperature as Zr-content increased despite the same starting powder grain sizes and synthesis temperature. Higher Vickers hardness for Zr-rich (12.7–12.8 GPa) than Ta-rich Zr x Ta 2 O 2x+5 (11.6–11.7 GPa) disappeared when accounting for different sintering temperatures. Indentation toughness was similar for all compositions (1.2–1.4 MPa m 1/2 ). Properties of Zr x Ta 2 O 2x+5 series indicate tunability of Zr:Ta composition ratio while maintaining the structure and room temperature mechanical properties comparable to Hf 6 Ta 2 O 17 and 8 mol% yttria stabilized zirconia ceramics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Vascular Carbon/Carbon composites for concentrated solar power

Due to their exceptional mechanical and thermal properties under high temperature conditions, for the first time, a novel vascular (embedded channels) Carbon/Carbon (C/C) composite was developed for use as a modular concentrated solar power (CSP) gas receiver. The fabrication process involves the combination of the vaporization of sacrificial component (VaSC) technique with conventional methods to produce C/C composites with well preserved channels. Different heat treatments were evaluated together with X-ray diffraction (XRD) analysis to determine optimal fabrication conditions that maintains the material and channel’s integrity. Mechanical testing confirms that structural integrity is maintained, with statistical analyses indicating no compromise in flexural strength or modulus. Furthermore, this research introduces an innovative pathway for efficient CSP gas receivers, which can bridge the gap towards improved energy generation efficiency in next-gen CSP plants through higher operating temperatures with the use of C/C composites.

14 SOLAR ENERGY↗
DOE OSTI2025

Automated model generation and parameter estimation of building energy models using an ontology-based framework

This study presents a methodology for automated model generation and parameter estimation of building energy models using semantic modeling and Bayesian estimation. Semantic modeling techniques are used to represent the system components and their interactions, facilitating the automatic generation of a simulation model from dynamic component models. The proposed approach is applied to a case study of a ventilation system where a simulation model is generated, calibrated, and assessed through different performance metrics. These metrics demonstrate the accuracy and reliability of both model point estimates and probabilistic prediction intervals across all model outputs. Overall, the proposed methodology offers a systematic and automated approach to model development and calibration in building energy systems, with potential applications in building performance analysis, monitoring, and optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Experimental investigation of injection-induced fracturing during supercritical CO 2 sequestration

Leakage risk assessment is an inevitable procedure in permanent sequestration and storage of CO 2 in deep saline aquifers and depleted oil and gas reservoirs, where the integrity of caprock is most critical. Here, low porosity and low permeability concrete cubes were employed as caprock analogs to investigate the supercritical CO 2 injection-induced fracturing processes under true tri-axial stress conditions. A systematic experimental procedure, consisting of active acoustic emission measurement, pressure decay, injection pressure and temperature monitoring, fracture coloring, and gas fracturing, is formulated to qualitatively and quantitatively characterize the injection-induced fracturing processes and fracture morphology. Occurrence of injection-induced fracturing can be directly identified from peaks of borehole pressure profiles as well as sharp drops on temperature profiles due to CO 2 expansion, but generally there was no fracture propagation plateau appearing for the 20 cm rock cubes with zero pore pressure. Acoustic wave signatures, including both waveform change and arrival time delay, can effectively capture the extension of induced fractures inside the opaque rock. Initiation and propagation of supercritical CO 2 injection-induced fractures are highly dominated by the tri-axial stresses, following the general trend of continuum mechanics at high stress levels with large stress difference. As the stress difference decreases, induced fractures branch off in relatively arbitrary directions. For these supercritical CO 2 injection-induced fracturing experiments, poroelastic mechanics model makes a decent fit with the measured breakdown pressure of the caprock analogs. Findings in this study are valuable for risk analysis and operation optimization of geological CO 2 sequestration and storage as well as for CO 2 fracturing design and implementation in shale and tight reservoirs.

42 ENGINEERING↗
DOE OSTI2016

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations. Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach has potential implications in many fields in which the governing equations are well understood but the model uncertainty comes from unresolved physical processes. - Highlights: • Proposed a physics–informed framework to quantify uncertainty in RANS simulations. • Framework incorporates physical prior knowledge and observation data. • Based on a rigorous Bayesian framework yet fully utilizes physical model. • Applicable for many complex physical systems beyond turbulent flows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4 °C for the cooling mode with a median of 23.7 °C, and between 20.5 and 24.9 °C for the heating mode with a median of 22.7 °C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2–3 °C lower in the cooling mode. Finally, the results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery

Methods that accelerate the evaluation of molecular properties are essential for chemical discovery. While some degree of ligand additivity has been established for transition metal complexes, it is underutilized in asymmetric complexes, such as the square pyramidal coordination geometries highly relevant to catalysis. To develop predictive methods beyond simple additivity, we apply a many-body expansion to octahedral and square pyramidal complexes and introduce a correction based on adjacent ligands (i.e., the cis interaction model). We first test the cis interaction model on adiabatic spin-splitting energies of octahedral Fe(II) complexes, predicting DFT-calculated values of unseen binary complexes to within an average of 1.4 kcal/mol. Uncertainty analysis reveals the optimal basis, comprising the homoleptic and mer symmetric complexes. We next show that the cis model (i.e., the cis interaction model solved for the optimal basis) infers both DFT- and CCSD(T)-calculated model catalytic reaction energies to within 1 kcal/mol on average. The cis model predicts low-symmetry complexes with reaction energies outside the range of binary complex reaction energies. We observe that trans interactions are unnecessary for most monodentate systems but can be important for some combinations of ligands, such as complexes containing a mixture of bidentate and monodentate ligands. Lastly, we demonstrate that the cis model may be combined with Δ-learning to predict CCSD(T) reaction energies from exhaustively calculated DFT reaction energies and the same fraction of CCSD(T) reaction energies needed for the cis model, achieving around 30% of the error from using the CCSD(T) reaction energies in the cis model alone.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Hardware-accelerated inference for real-time gravitational-wave astronomy

The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Overall, our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Benchmarking second and third-generation sequencing platforms for microbial metagenomics

Shotgun metagenomic sequencing is a common approach for studying the taxonomic diversity and metabolic potential of complex microbial communities. Current methods primarily use second generation short read sequencing, yet advances in third generation long read technologies provide opportunities to overcome some of the limitations of short read sequencing. Here, we compared seven platforms, encompassing second generation sequencers (Illumina HiSeq 300, MGI DNBSEQ-G400 and DNBSEQ-T7, ThermoFisher Ion GeneStudio S5 and Ion Proton P1) and third generation sequencers (Oxford Nanopore Technologies MinION R9 and Pacific Biosciences Sequel II). We constructed three uneven synthetic microbial communities composed of up to 87 genomic microbial strains DNAs per mock, spanning 29 bacterial and archaeal phyla, and representing the most complex and diverse synthetic communities used for sequencing technology comparisons. Our results demonstrate that third generation sequencing have advantages over second generation platforms in analyzing complex microbial communities, but require careful sequencing library preparation for optimal quantitative metagenomic analysis. Our sequencing data also provides a valuable resource for testing and benchmarking bioinformatics software for metagenomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

In situ self-assembled organic interface layers for the controlled growth of oligothiophene thin films on ferroelectric Pb(Zr{sub 0.2}Ti{sub 0.8})O{sub 3}

We introduce an in situ vacuum procedure for the optimal preparation and analysis of self-assembled monolayers (SAMs) as used in organic molecular electronics on ferroelectric lead zirconate titanate (PZT) substrates. Excellent ordering of oligothiophene semiconductor layers is heavily promoted through the presence of an interfacial bi-functional SAM layer that binds to both the oxidic PZT surface and the organic semiconductor molecules. The described method can be extended to other material combinations, featuring a variety of substrate materials and molecular functionalities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗