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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Optimal, centralized dynamic curbside parking space zoning

In this paper we formulate a dynamic mixed integer program for optimally zoning curbside parking spaces subject to transportation policy-inspired constraints and regularization terms. First, we illustrate how given some objective of curb zoning valuation as a function of zone type (paid parking, bus stop, etc.), dynamically rezoning involves unrolling this optimization program over a fixed time horizon. Second, we implement two different solution methods given an example curb zoning valuation. In the first method, we solve long horizon dynamic zoning problems via approximate dynamic programming. In the second method, we employ Dantzig-Wolfe decomposition to break-up the mixed-integer program into a master problem and several sub-problems that can be solved in parallel. This speeds up the computational solve-time of the MIP considerably. We present simulation results and comparisons of the different employed techniques on vehicle arrival-rate data obtained for a neighborhood in downtown Seattle, Washington, USA.

Nazir, Mohammad Nawaf↗
DOE OSTI2020

Building fault detection data to aid diagnostic algorithm creation and performance testing

It is estimated that approximately 4-5% of national energy consumption can be saved through corrections to existing commercial building controls infrastructure and resulting improvements to efficiency. Correspondingly, automated fault detection and diagnostics (FDD) algorithms are designed to identify the presence of operational faults and their root causes. A diversity of techniques is used for FDD spanning physical models, black box, and rule-based approaches. A persistent challenge has been the lack of common datasets and test methods to benchmark their performance accuracy. This article presents a first of its kind public dataset with ground-truth data on the presence and absence of building faults. This dataset spans a range of seasons and operational conditions and encompasses multiple building system types. It contains information on fault severity, as well as data points reflective of the measurements in building control systems that FDD algorithms typically have access to. The data were created using simulation models as well as experimental test facilities, and will be expanded over time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Neural MUSE Analysis

Researchers at Oak Ridge National Laboratory (ORNL) created data as part of the MUSE (Multi-Agency Urban Search Experiment Detector and Algorithm Test Bed) project simulating illicit nuclear materials located in various buildings along a road. In the simulation, a truck containing a radiation detector drives down the road gathering listmode data (counting the and energy of incident gamma radiation). Building materials, source shielding, driving speed, truck direction, truck location on the road, source type, and source placement are all varied between runs of the data set. This data was created using deterministic neutron transport and Monte Carlo methods through a combination of SCALE, MAVRIC, MCNP, and GADRAS. As part of a follow-on NA-22 project, two Kaggle competitions were created to determine the best algorithms for finding and identifying gamma sources in this simulated urban environment. The winning algorithm was neural network-based and had a test accuracy of 76.4% accuracy for source identification. This work seeks to build upon this work and improve the results through the application of novel machine learning techniques. As a first step, the data was classified by a simple Convolutional Neural Network (CNN) To accomplish this, the data was first preprocessed into “waterfall plots.” These plots are composed of energy vs count plots that are stacked vertically to show progression in time. The horizontal axis indicating the particle energy incorporated user defined bin spacing with options for in linear-, logarithmic-, square root-, and user-spaced bins. The z or color dimension showed the number of counts corresponding the energy-time combination. This data was then used to generate more data, by generating a local estimate of the mean of the distribution for a bin and then randomly re-sampling that bin from a Poisson distribution. Once all of this data was generated, it was fed into a well-known CNN architecture, ResNet50. The output layer of this model was removed and replaced with layers corresponding to the shape desired isotope outputs. The provided training data was used to train the classifier and the remaining testing data was used to evaluate the model. Results are soon to be forthcoming.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

A Confidence-Guided Technique for Tracking Time-Varying Features

Application scientists often employ feature tracking algorithms to capture the temporal evolution of various features in their simulation data. However, as the complexity of the scientific features is increasing with the advanced simulation modeling techniques, quantification of reliability of the feature tracking algorithms is becoming important. One of the desired requirements for any robust feature tracking algorithm is to estimate its confidence during each tracking step so that the results obtained can be interpreted without any ambiguity. To address this, we develop a confidence-guided feature tracking algorithm that allows reliable tracking of user-selected features and presents the tracking dynamics using a graph-based visualization along with the spatial visualization of the tracked feature. Here, the efficacy of the proposed method is demonstrated by applying it to two scientific datasets containing different types of time-varying features.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Economic assessment of seismic monitoring for underground hydrogen storage

Underground hydrogen storage (UHS) plays a key role in the energy landscape. However, like other subsurface engineering technologies, UHS may cause leakage into the groundwater or atmosphere and possibly induce local seismicity. To reduce these risks, seismic monitoring could be a viable technique to track the UHS plume, detect leakages, and locate induced seismicity events. Seismic monitoring has been proposed to safely monitor UHS, but research in this area is still new and requires field studies. Lab and theoretical studies have demonstrated the validity of seismic monitoring for UHS. Therefore, it is imperative to analyze the economic feasibility of seismic monitoring for UHS. Hence, we develop a cost model and open-source Python code for seismic monitoring that considers types of seismometers, comprehensive operational scenarios, detection thresholds, and long-term leakage monitoring. A case study is further provided to validate the cost model on reservoir simulations of UHS. We find that the levelized cost for a 10-year operating UHS site will range on the order of ∼0.003 $\$$/kg. The methods developed in this study could also be applied to the monitoring of groundwater, gas, and/or wastewater injection.

08 HYDROGEN↗
DOE OSTI2023

Investigation of the Role of the Role of Nuclear Physics in Heavy Element Nucleosynthesis, through the Study of Key Reactions, and the Improvement of Theoretical Reaction Rates (Final Report)

Neutrino-driven winds in core-collapse supernovae have been identified as a possible site for the production of elements heavier than iron. Traditionally, these neutrino-driven winds have been proposed as the site of the main r-process. Recent simulations fail to reproduce the conditions required for the main r-process. while they remain a promising site for producing the lightest elements beyond iron, e.g., Sr, Y, and Zr through the νp process. The efficiency of the νp process depends on the hydrodynamical conditions, the electron fraction (which is related to the neutrino properties), and the nuclear reactions on many short-lived nuclei with limited (if at all) experimental information. The reaction rates on these nuclei are based on theoretical predictions using the Hauser-Feshbach model. Recent sensitivity studies have highlighted the importance of neutron-induced reactions on these nuclei along the νp process path. This work aimed to experimentally constrain reaction rates that are known to play a key role in the neutrino-p process nucleosynthesis. A secondary subsequently-added objective was to start the implementation of techniques that improve the description of nuclear properties in the Hauser-Feshbach model by extending the microscopic nuclear level density description offered via the shell model to high excitation energies without using experiment-based renormalizations. The main objective of this work was the experimental constraint of the 56 Ni(n,p) 56 Co reaction rate via a measurement of the inverse reaction 56 Co(p,n) 56 Ni at the National Superconducting Cyclotron Laboratory (NSCL) and later the Facility for Rare Isotope Beams (FRIB). This reaction is considered the key one for determining the yields possible by the neutrino-p process. A technique for this type of measurement in inverse kinematics at low energies did not exist before this work. The work also had two secondary objectives. First, to contribute to efforts to measure the same reaction in direct kinematics using a radioactive target at Los Alamos National Laboratory (LANL), and second, to advance work to implement shell-model-deduced microscopic level densities in Hauser-Feshbach calculations. The project has resulted in the development of the first technique to perform (p,n) cross-section measurements in relevant-for-astrophysics low energies in inverse kinematics using a magnetic spectrometer or separator, and neutron detectors for neutron tagging. It has also resulted in the precise measurement of the cross-section of the 40 Ar(p,n) 40 K reaction in a proof-of-principle experiment realized by using a beam-line quadrupole of the ReA3 accelerator of NSCL/FRIB. As part of this project the technique was successfully adapted to make use of the superior acceptance of the Separator for Capture Reactions (SECAR) at FRIB. In this project, the required experimental setup simulations and beam optics were developed and tested with the measurement of the 58 Fe(p,n) 58 Cu reaction cross-section. Additionally, this project contributed with simulation work to the development of a technique to measure (n,p) reactions with radioactive targets at LANL, and which resulted in the measurement of the key 56 Ni(n,p) 56 Co reaction cross-section at neutron energies above ≈1 MeV. Last, the project initiated work in the development of shell model based level densities using the moments method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Near-infrared C-term MCD spectroscopy of octahedral uranium (V) complexes

C-term magnetic circular dichroism (MCD) spectroscopy is a powerful method for probing d–d and f–f transitions in paramagnetic metal complexes. However, this technique remains underdeveloped both experimentally and theoretically for studies of U(V) complexes of Oh symmetry, which have been of longstanding interest for probing electronic structure, bonding, and covalency in 5f systems. In this study, C-term NIR MCD of the Laporte forbidden f–f transitions of [UCl 6 ] - and [UF 6 ] - are reported, demonstrating the significant fine structure resolution possible with this technique including for the low energy Γ 7 → Γ 8 transitions in [UF 6 ] - . The experimental NIR MCD studies were further extended to [U(OC 6 F 5 ) 6 ] - , [U(CH 2 SiMe 3 ) 6 ] - , and [U(NC(tBu)(Ph)) 6 ] - to evaluate the effects of ligand-type on the f–f MCD fine structure features. Theoretical calculations were conducted to determine the Laporte forbidden f–f transitions and their MCD intensity experimentally observed in the NIR spectra of the U(V) hexahalide complexes, via the inclusion of vibronic coupling, to better understand the underlying spectral fine structure features for these complexes. These spectra and simulations provide an important platform for the application of MCD spectroscopy to this widely studied class of U(V) complexes and identify areas for continued theoretical development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

BISON: A Flexible Code for Advanced Simulation of the Performance of Multiple Nuclear Fuel Forms

BISON is a nuclear fuel performance application built using the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element library. One of its major goals is to have a great amount of flexibility in how it is used, including in the types of fuel it can analyze, the geometry of the fuel being modeled, the modeling approach employed, and the dimensionality and size of the models. Fuel forms that can be modeled include standard light water reactor fuel, emerging light water reactor fuels, tri-structural isotropic fuel particles, and metallic fuels. BISON is a platform for research in nuclear fuel performance modeling while simultaneously serving as a tool for the analysis of nuclear fuel designs. Recent research in BISON includes techniques such as the extended finite element method for fuel cracking, exploration of high-burnup light water reactor fuel behavior, swelling behavior of metallic fuels, and central void formation in mixed-oxide fuel. BISON includes integrated documentation for each of its capabilities, follows rigorous software quality assurance procedures, and has a growing set of rigorous verification and validation tests.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Machine learning prediction of the mechanical properties of refractory multicomponent alloys based on a dataset of phase and first principles simulation

In this work, a dataset including structural and mechanical properties of refractory multicomponent alloys was developed by fusing computations of phase diagram (CALPHAD) and density functional theory (DFT). The refractory multicomponent alloys, also named refractory complex concentrated alloys (CCAs) which contain 2–5 types of refractory elements were constructed based on Special Quasi-random Structure (SQS). The phase of alloys was predicted using CALPHAD and the mechanical property of alloys with stable and single body-centered cubic (BCC) at high temperature (over 1,500°C) was investigated using DFT-based simulation. As a result, a dataset with 393 refractory alloys and 12 features, including volume, melting temperature, density, energy, elastic constants, mechanical moduli, and hardness, were produced. To test the capability of the dataset on supporting machine learning (ML) study to investigate the property of CCAs, CALPHAD, and DFT calculations were compared with principal components analysis (PCA) technique and rule of mixture (ROM), respectively. It is demonstrated that the CALPHAD and DFT results are more in line with experimental observations for the alloy phase, structural and mechanical properties. Furthermore, the data were utilized to train a verity of ML models to predict the performance of certain CCAs with advanced mechanical properties, highlighting the usefulness of the dataset for ML technique on CCA property prediction.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Framework Short-Range Order Observed in a Spinel-Type Li Superionic Conductor

Solid-state superionic conductors are characterized by rich structural disorders. Though structural complexities are central to their functionalities, they often give rise to short-range order that eludes detection by conventional diffraction-based techniques and is thus overlooked in establishing precise structure-property relationships. In this work, we synthesized single crystals of a recently discovered lithium (Li) superionic conductor Li16.2(1)In9.00(2)Sn1.10(1)O23.8 (LISO) for in-depth characterizations of structural subtleties. LISO exhibits an unusual spinel-like phase with significant Li overstoichiometry and a face-sharing Li network. Single-crystal neutron diffraction confirms significant Li disorder, as manifested in Li site splitting and partial occupancy. More importantly, synchrotron diffuse scattering combined with 3D-ΔPDF analysis and Monte Carlo simulations reveal short-range order in the nonalkali framework that might contribute to the phase stability and ionic conductivity. This work showcases an example in which subtle local energetics can be directly visualized in structurally disordered ionic conductors.

Chen, Yu↗
DOE OSTI2021

Advances in constraining intrinsic alignment models with hydrodynamic simulations

We use galaxies from the illustristng, massiveblack-ii, and illustris-1 hydrodynamic simulations to investigate the behaviour of large scale galaxy intrinsic alignments. Our analysis spans four redshift slices over the approximate range of contemporary lensing surveys z = 0–1. We construct comparable weighted samples from the three simulations, which we then analyse using an alignment model that includes both linear and quadratic alignment contributions. Our data vector includes galaxy–galaxy, galaxy–shape, and shape–shape projected correlations, with the joint covariance matrix estimated analytically. In all of the simulations, we report non-zero IAs at the level of several σ. For a fixed lower mass threshold, we find a relatively strong redshift dependence in all three simulations, with the linear IA amplitude increasing by a factor of ~2 between redshifts z = 0 and z = 1. We report no significant evidence for non-zero values of the tidal torquing amplitude, A 2 , in TNG, above statistical uncertainties, although MBII favours a moderately negative A 2 ~ –2. Examining the properties of the TATT model as a function of colour, luminosity and galaxy type (satellite or central), our findings are consistent with the most recent measurements on real data. We also outline a novel method for constraining the TATT model parameters directly from the pixelized tidal field, alongside a proof-of-concept exercise using TNG. This technique is shown to be promising, although comparison with previous results obtained via other methods is non-trivial.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The first Hubble diagram and cosmological constraints using superluminous supernovae

In this work, we present the first Hubble diagram of superluminous supernovae (SLSNe) out to a redshift of two, together with constraints on the matter density, Ω M , and the dark energy equation-of-state parameter, w(≡p/ρ). We build a sample of 20 cosmologically useful SLSNe I based on light curve and spectroscopy quality cuts. We confirm the robustness of the peak–decline SLSN I standardization relation with a larger data set and improved fitting techniques than previous works. We then solve the SLSN model based on the above standardization via minimization of the χ2 computed from a covariance matrix that includes statistical and systematic uncertainties. For a spatially flat Λ cold dark matter (ΛCDM) cosmological model, we find $\Omega _{\rm M}=0.38^{+0.24}_{-0.19}$, with an rms of 0.27 mag for the residuals of the distance moduli. For a w 0 w a CDM cosmological model, the addition of SLSNe I to a ‘baseline’ measurement consisting of Planck temperature together with Type Ia supernovae, results in a small improvement in the constraints of w 0 and wa of 4 per cent. We present simulations of future surveys with 868 and 492 SLSNe I (depending on the configuration used) and show that such a sample can deliver cosmological constraints in a flat ΛCDM model with the same precision (considering only statistical uncertainties) as current surveys that use Type Ia supernovae, while providing a factor of 2–3 improvement in the precision of the constraints on the time variation of dark energy, w 0 and w a . This paper represents the proof of concept for superluminous supernova cosmology, and demonstrates they can provide an independent test of cosmology in the high-redshift (z > 1) universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Refinements to the Graves–Pitarka Kinematic Rupture Generator, Including a Dynamically Consistent Slip-Rate Function, Applied to the 2019 Mw 7.1 Ridgecrest Earthquake

The main objective of this study is to develop physics–based constraints on the spatiotemporal variation of the slip–rate function using a simplified dynamic rupture model. First, we performed dynamic rupture modeling of the 2019 M w 7.1 Ridgecrest, California, earthquake, to analyze the effects of depth–dependent stress and material friction on slip rate. Then, we used our modeling results to guide refinements to the slip–rate function that were implemented in the Graves–Pitarka kinematic rupture generation technique. The dynamic ruptures were computed on a surface–rupturing, planar strike–slip fault that includes a weak (negative to low–stress–drop) zone in the upper 4 km of the crust. Below the weak zone, we placed high–stress–drop patches designed to mirror the large–slip areas seen in various rupture model inversions of the event. The locations of the high–stress–drop patches and the hypocenter were varied in multiple realizations to investigate how changing the dynamic conditions affected the resulting rupture kinematics, in particular, the slip rate. From these simulations, we observed a systematic change in the shape of the slip–rate function from Kostrov type below the weak zone to a predominantly symmetric shape within the weak zone, along with a depth–dependent reduction of peak slip rate. We generalized these shallow rupture features into a depth–dependent parametric variation of the slip–rate function and implemented it in the Graves–Pitarka kinematic rupture model generator. The performance of the updated kinematic approach was then verified in 0–4 Hz simulations of the M w 7.1 Ridgecrest earthquake, which showed that incorporating the depth–dependent variation in the shape of the slip–rate function improves the fit to the observed near–fault ground motions in the 0.5–3 s period range.

58 GEOSCIENCES↗
DOE OSTI2024

Analysis of dislocation configurations in SiC crystals through X-ray topography aided by ray tracing simulations

Silicon carbide as a wide bandgap semiconductor is of great research interest for its widespread deployment in a range of electronic and optoelectronic devices, particularly in power electronics. However, defects in silicon carbide crystals are still major concerns that is hampering the development of high-performance devices. X-ray topography, particularly using the synchrotron beam has been instrumental in characterizing and analyzing defect configurations in silicon carbide crystals to optimize crystal growth as well as understand the effect of defects on device performance. Here, in recent years, the use of ray-tracing simulation technique based on the orientation contrast mechanism to simulate contrast of defects observed on actual X-ray topographs has proven to be an effective approach to investigate the nature of crystallographic defects in various semiconductors. This review discusses the principle of ray-tracing simulation and its application and modifications to incorporate the effects of surface relaxation and photoelectric absorption to better simulate different dislocations observed in 4H–SiC as well as 6H–SiC crystals of various orientations. The adaptation to weak beam topography and plane wave topography is also discussed. The application of ray-tracing simulation in dislocation characterization of silicon carbide of different polytypes is systematically reviewed including different types of dislocations observed in both off-axis wafers and axial-sliced samples through synchrotron X-ray topography under various beam conditions, recording geometries and reflections. The result of ray-tracing simulation is further utilized in other studies including the investigation of effective penetration depth of all types of dislocations lying on the basal plane on grazing-incidence X-ray topography.

Engineering↗
DOE OSTI2020

Retrieving functional pathways of biomolecules from single-particle snapshots

Abstract A primary reason for the intense interest in structural biology is the fact that knowledge of structure can elucidate macromolecular functions in living organisms. Sustained effort has resulted in an impressive arsenal of tools for determining the static structures. But under physiological conditions, macromolecules undergo continuous conformational changes, a subset of which are functionally important. Techniques for capturing the continuous conformational changes underlying function are essential for further progress. Here, we present chemically-detailed conformational movies of biological function, extracted data-analytically from experimental single-particle cryo-electron microscopy (cryo-EM) snapshots of ryanodine receptor type 1 (RyR1), a calcium-activated calcium channel engaged in the binding of ligands. The functional motions differ substantially from those inferred from static structures in the nature of conformationally active structural domains, the sequence and extent of conformational motions, and the way allosteric signals are transduced within and between domains. Our approach highlights the importance of combining experiment, advanced data analysis, and molecular simulations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

WEC fault modelling and condition monitoring: A graph-theoretic approach

The nature of wave resources usually requires wave energy converter (WEC) components to handle peak loads (i.e., torques, forces, and powers) that are many times greater than their average loads, accelerating equipment degradation. Moreover, due to their isolated nature and harsh operating environment, WEC systems are projected to possess high operations and maintenance (O&M) cost, i.e., around 27% of their leveled cost of energy. As such, developing techniques to mitigate these costs through the application of condition monitoring and fault tolerant control will significantly impact the economic feasibility of grid connected WEC power. Toward this goal, models of faulty components are developed in the open source modeling platform, WEC-Sim, to estimate the performance and measurable states of a WEC operating with likely device and sensor failures. Two types of faulty component models are then applied to a point absorber WEC model with basic controller damping and spring forces. Resulting changes in device behavior are recorded as a benchmark, and a graph-theoretic approach is proposed for fault detection and identification utilizing multivariate time series. Simulation results demonstrate that these faults can greatly affect the WEC performance, and that the proposed method can effectively detect and classify different types of faults.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Surface Wave Based Non-conductor-contact Reflectometry Method for Insulation and Jacket Damage Detection in Multi-Conductor Cables

This paper presents a non-conductor-contact surface wave reflectometry technique to locate insulation and jacket damage on multi-conductor cables at a distant location from the sensor. Simulation studies were conducted using Ansys high frequency structure simulator (HFSS). A GHz range surface wave launcher was employed such that maximum surface wave energies were confined in the insulation layer while minimizing attenuation. The wave launcher can potentially be mounted on the outer surface of live cables or cable conduits and requires no physical connection to the conductors. Multiple simulations involving a 4 GHz wave launcher and a three-conductor cable with an 8 mm outer diameter were carried out demonstrating the efficacy of the proposed method. Water leak and different types of insulation/jacket damage were simulated and detected.

reflectometry, multi-conductor cable, frequency do↗