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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 OSTI2024

Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology Using Large Language Models—A Case in Optimizing Intermodal Freight Transportation

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Engineering Ru@Pt Core-Shell Catalysts for Enhanced Electrochemical Oxygen Reduction Mass Activity and Stability

Improving the performance of oxygen reduction reaction (ORR) electrocatalysts is essential for the commercial efficacy of many renewable energy technologies, including low temperature polymer electrolyte fuel cells (PEFCs). Herein, we report highly active and stable carbon-supported Ru@Pt core-shell nanoparticles (Ru@Pt/C) prepared by a wet chemical synthesis technique. Through rotating disc electrode testing, the Ru@Pt/C achieves an ORR Pt mass-based activity of 0.50 A mg Pt -1 at 0.9 V versus the reversible hydrogen electrode (RHE), which exceeds the activity of the state-of-the-art commercial Pt/C catalyst as well as the Department of Energy 2020 PEFC electrocatalyst activity targets for transportation applications. The impact of various synthetic parameters, including Pt to Ru ratios and catalyst pretreatments (i.e., annealing) are thoroughly explored. Pt-based mass activity of all prepared Ru@Pt/C catalysts was found to exceed 0.4 mg Pt -1 across the range of compositions investigated, with the maximum activity catalyst having a Ru:Pt ratio of 1:1. This optimized composition of Ru@Pt/C catalyst demonstrated remarkable stability after 30,000 accelerated durability cycles (0.6 to 1.0 V vs. RHE at 125 mV s -1 ), maintaining 85% of its initial mass activity. Scanning transmission electron microscopy energy dispersive spectroscopy (STEM-EDS) analysis at various stages of electrochemical testing demonstrated that the Pt shell can provide sufficient protection against the dissolution of the otherwise unstable Ru core.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

SECARB Commercial Scale CO 2 Injection and Optimization of Storage Capacity in the Southeastern United States

The Commercial Scale Project is a US DOE/NETL funded initiative aimed at enhancing the knowledge-base and industry’s ability to geologically store vast quantities of anthropogenic carbon. In support of this goal, a large-scale, stacked reservoir geologic model was developed for Gulf Coast sediments centered on the Citronelle Dome in southwest Alabama, the site of the SECARB Phase III Anthropogenic Test. Characterization of regional geology to construct the model consists of an assessment of the entire stratigraphic continuum at Citronelle Dome, from surface to the depth of the Donovan oil-bearing formation. This project utilizes all available geologic data available, which includes: modern geophysical well logs from three new wells drilled for SECARB’s Anthropogenic Test; vintage logs from the Citronelle oilfield wells; porosity and permeability data from whole core and sidewall cores obtained from the injection and observation wells drilled for the Anthropogenic Test; core data obtained from the SECARB Phase II saline aquifer injection test; regional core data for relevant formations from the Geological Survey of Alabama archives. Cross sections, isopach maps, and structure maps were developed to validate the geometry and architecture of the Citronelle Dome for building the model, and assuring that no major structural defects exist in the area. A synthetic neural network approach was used to predict porosity using the available SP and resistivity log data for the storage reservoir formations. These data are validated and applied to extrapolate porosity data over the study area wells, and to interpolate permeability amongst these data points. Geostatistical assessments were conducted over the study area. In addition to geologic characterization of the region, a suite of core analyses was conducted to construct a depositional model and constrain caprock integrity. Petrographic assessment of core was conducted by OSU and analyzed to build a depositional framework for the region and provide modern day analogues. Stability of the caprock over several test parameters was conducted by UAB to yield comprehensive measurements on long term stability of caprocks. The detailed geologic model of the full earth volume from surface thru the Donovan oil reservoir is incorporated into a state-of-the-art reservoir simulation conducted by the University of Alabama at Birmingham (UAB) to explore optimization of CO 2 injection and storage under different characterizations of reservoir flow properties. The application of a scaled up geologic modeling and reservoir simulation provides a proof of concept for the large scale volumetric modeling of CO 2 injection and storage the subsurface.

58 GEOSCIENCES↗
DOE OSTI2016

Comparison of Fuel Loading Pattern Optimization Results using Exhaustive Search for Fresh Fuels and Local Search for Burned Fuels

Fuel loading pattern optimization is a combinatorial optimization problem from the mathematical point of view. However, exhaustive search is impractical because enormous computational cost is necessary due to huge search space. For this reason, various optimization methods have been developed so far, but no methods are able to guarantee to reach the optimum solution. In the present study, search space is divided into subspaces using key parameters that well represent characteristics of a loading pattern and then promising subspaces are extensively searched. In the present method, balance between division of a search space into subspaces and a search method used in a subspace is essential. For example, when the number of division is set to be one, it is equivalent to the exhaustive search thus is impractical. Similarly, when the division to subspaces is too coarse, an optimum (or near optimum) solution cannot be found through local search in each subspace since design space of a subspace is still too large. Contrary, if division to subspaces is too fine, number of subspaces becomes to large thus promising subspaces cannot be found. In the present study, search space is divided focusing on the location of fresh fuel assemblies with gadolinia since it has significant impact on core characteristics. Number of possible loading patterns of gadolinia bearing fresh fuel assemblies is not very large thus exhaustive search is possible. Local fine search is executed by considering location of burned fuels for each loading pattern of gadolinia bearing fresh fuel assembly. If the same solution is obtained by multiple local searches starting from different initial burnt fuel locations (but the same loading patterns for gadolinia bearing fresh fuel assemblies), the obtained solution is highly likely the optimum in the subspace. In more general, the present method is based on the 'divide and conquer' approach, which is familiar in the optimization field. Namely, after division of search space into subspaces and then promising subspace is searched. Once the promising subspace is found, the subspace is further subdivided into subspaces and repeat the above procedures. This method would be efficient when appropriate balance between search space division method and performance of local search is achieved. The previous works utilizes the similar approach. In reference [1], exhaustive search in small region in a core is carried out focusing on loading patterns of gadolinia bearing fresh fuel assemblies. In reference [2], rough exhaustive search based on the 'batch-wise' loading pattern is performed with the assist of the linear programming. By contrast, in the present study, exhaustive search is carried out for loading patterns of gadolinia bearing fresh fuel assemblies. The random Multi-start Local Search (MSLS) with the direct search or the exhaustive binary swap is used to find out optimal solutions in a subspace, which consists of loading patterns having the same arrangement of gadolinia bearing fresh fuel assemblies. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Controlled Experiments and Optimized Theory of Absorption Spectra of Li Metal and Salts

Investigation of Li metal and ionic compounds through experimental and theoretical spectroscopy has been of tremendous interest due to their prospective applications in Li-metal and Li-ion batteries. Li K-edge soft X-ray absorption spectroscopy (sXAS) provides the most direct spectroscopic characterization; unfortunately, due to the low core-level energy and the highly reactive surface, Li-K sXAS of Li metal has been extremely challenging, as evidenced by many controversial reports. Here, through controlled and ultra-high energy resolution experiments of two kinds of in situ prepared samples, we report the intrinsic Li-K sXAS of Li-metal that displays a prominent leading peak that has not been revealed before. Furthermore, theoretical simulations show that, due to the low number of valence electrons in Li, the Li-K sXAS is strongly affected by the response of the valence electrons to the core hole. We successfully reproduce the Li-K sXAS by state-of-the-art calculations with considerations of a number of relevant parameters such as temperature, energy resolution, and, especially, contributions from transitions which are forbidden in the single-particle treatment. Such a comparative experimental and theoretical investigation is further extended to a series of Li ionic compounds, which highlight the importance of considering the total and single-particle energies for obtaining an accurate alignment of the spectra. Furthermore, our work provides the first reliable Li-K sXAS of the Li metal surface with advanced theoretical calculations. The experimental and theoretical results provide a critical benchmark for studying Li chemistry in both metallic and ionic states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Study of grain boundary transparency in(Yb$_{1-x}$Ca$_x$)Ba$_2$Cu$_3$O bicrystal thin films over a wide temperature, field, and field orientation range

The residual low angle grain boundary (GB) network is still the most important current-limiting mechanism operating in bi-axially textured rare earth barium copper oxide (REBCO) coated conductors. While Ca-doping is well established to improve super-current flow across low angle GBs in weak fields at high temperatures, Ca-doping also depresses T c , making it so far impractical for high temperature applications of REBCO coated conductors. On the other hand, high field magnet applications of REBCO require low temperatures. Here we systematically evaluate the effectiveness of Ca-doping in improving the GB transparency, r GB = J c GB /J c grain , of low angle Yb 1-x Ca x BaCuO [001] tilt bi-crystal films down to 10K and with magnetic fields perpendicular and parallel to the film surfaces, while varying the Ca and oxygen doping level. Using Low Temperature Scanning Laser Microscopy (LTSLM) and Magneto-Optical Imaging (MOI), we found r GB to strongly depend on the angle between magnetic field and the GB plane and clearly identified regimes in which J c GB can exceed J c grain (rGB>1) where the GB pinning is optimized by the field being parallel to the GB dislocations. However, even in this favorable situation, we found that r GB became much smaller at lower temperatures. Calculations of the GB Ca segregation profile predict that the high J c channels between the GB dislocation cores are almost Ca-free. It may be therefore that the positive effects of Ca doping seen by many authors near T c are partly a consequence of the higher Tc of these Ca-free channels.

Li, Pei↗
DOE OSTI2025

Characterization and Optimization of the Fitting of Quantum Correlation Functions

This case study presents a characterization and optimization of an application code for extracting parton distribution functions from high energy electron-proton scattering data. Profiling this application code reveals that the phase-space density computation accounts for 93% of the overall execution time for a single iteration on a single core. When executing multiple iterations in parallel on a multicore system, the application spends 78% of its overall execution time idling due to load imbalance. We address these issues by first transforming the application code from Python to C++ and then tackling the application load imbalance via a hybrid scheduling strategy that combines dynamic and static scheduling. These techniques result in a 62% reduction in CPU idle time and a 2.46x speedup in overall execution time per node. In addition, the typically enabled power-management mechanisms in supercomputers (e.g., AMD Turbo Core, Intel Turbo Boost, and RAPL) can significantly impact intra-node scalability when more than 50% of the CPU cores are used. This finding underscores the importance of understanding system interactions with power management, as they can adversely impact application performance, and highlights the necessity of intra-node scaling tests to identify performance degradation that inter-node scaling tests might otherwise overlook.

Chuang, Pi-Yueh [Virginia Tech,Dept. of Computer S↗
DOE OSTI2018

Metal Amorphous Nanocomposite Soft Magnetic Material-Enabled High Power Density, Rare Earth Free Rotational Machines [Metal Amorphous Nanocomposite (MANC) Soft Magnetic Material (SMM) Enabled High Power Density, Rare Earth Free Rotational Machines]

Metal amorphous nanocomposites (MANCs) are promising soft magnetic materials (SMMs) for power electronic applications offering low power loss at high frequency and maintaining a relatively high flux density. While applications in certain motor designs have been recently modeled, their widespread application awaits scaled manufacturing of MANC materials and proliferation of new higher speed motor designs. A hybrid motor design based on permanent magnets and doubly salient stator and rotor is reported here to develop a compact (a factor of 10 smaller than currently possible in Si steels), high-speed (>1 kHz, electrical), high-power (>2.5 kW) motor by incorporating low loss (<10 W/kg at 1 kHz) MANCs such as recently reported Fe-Ni-based alloys. A feature of this motor design is flux focusing from the permanent magnet allowing use of lower energy permanent magnet chosen from among non-rare earth containing compositions and attractive due to constraints posed by rare earth criticality. A 2-D finite element analysis model reported here indicates that a 2.5 kW hybrid motor may be built with a permanent magnet with a 0.4 T remanence at a rotor speed of 6000 rpm. At a magnetic switching frequency of 1.4 kHz, the core loss may be limited to <3 W by selecting an appropriate MANC SMM. The projected efficiency exceeds 96% not including power loss in the controller. Under full load conditions, the flux density distributions for the SMM stay predominantly <1.3 T, the saturation magnetization of optimized FeNi-based MANC alloys. As a result, the maximum demagnetizing field in the permanent magnet is less than 2.2 × 10 5 A/m sustainable, for example, with a high-grade hard ferrite magnet.

42 ENGINEERING↗
DOE OSTI2021

MemXCT: Design, Optimization, Scaling, and Reproducibility of X-Ray Tomography Imaging

Here, this work extends our previous research entitled "MemXCT: Memory-centric X-ray CT Reconstruction with Massive Parallelization" that was originally published at SC19 conference (Hidayetoglu et al., 2019) with reproducibility of the computational imaging performance. X-ray computed tomography (XCT) is regularly used at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be in the order of terabytes. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use an exception rather than the rule. We propose a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a memory-centric iterative reconstruction system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures involving KNL and GPU. MemXCT can reconstruct a large (11Kx11K) mouse brain tomogram in 10 seconds using 4096 KNL nodes (256K cores). The results presented in our original article at the SC19 were based on large-scale supercomputing resources. The MemXCT application was selected for the Student Cluster Competition (SCC) Reproducibility Challenge and evaluated on a variety of cloud computing resources by universities around the world in the SC20 conference. We summarize the results of the top-ranked SCC Reproducibility Challenge teams and identify the most pertinent measures for ensuring the reproducibility of our experiments in this article.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

On the Training and Generalization of Deep Operator Networks

Here, we present a novel training method for deep operator networks (DeepONets), one of the most popular neural network models for operators. DeepONets are constructed by two subnetworks, namely the branch and trunk networks. Typically, the two subnetworks are trained simultaneously, which amounts to solving a complex optimization problem in a high dimensional space. In addition, the nonconvex and nonlinear nature makes training very challenging. To tackle such a challenge, we propose a two-step training method that trains the trunk network first and then sequentially trains the branch network. The core mechanism is motivated by the divide-and-conquer paradigm and is the decomposition of the entire complex training task into two subtasks with reduced complexity. Therein the Gram–Schmidt orthonormalization process is introduced which significantly improves stability and generalization ability. On the theoretical side, we establish a generalization error estimate in terms of the number of training data, the width of DeepONets, and the number of input and output sensors. Numerical examples are presented to demonstrate the effectiveness of the two-step training method, including Darcy flow in heterogeneous porous media.

deep operator networks↗
DOE OSTI2022

The fast neutron source at UTK: a project with Massimo Salvatores

This article is dedicated to the memory of Massimo Salvatores and offers an overview of the history of the design of the Fast Neutron Source (FNS) flexible experiment system at the University of Tennessee. Our collaboration with Massimo set much of the over-arching design goals and inspired the concept that we have today. The decision to design the FNS as a subcritical assembly rather than a critical one stems back to discussions with Massimo Salvatores and his advocacy for subcritical Accelerator Driven Systems (ADS) which coincides with lower licensing time, costs and greater safety for a university-operated nuclear experimental facility. The second contribution of Massimo to the design concept of the FNS was the direct instruction to design the FNS as an extremely flexible facility. This article provides an update on the current design concept and discusses the future innovations in design optimization of flexible, re-configurable nuclear experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Dynamical formation of the GW150914 binary black hole

We explore the possibility that GW150914, the binary black hole (BBH) merger recently detected by Advanced LIGO, was formed by gravitational interactions in the core of a dense star cluster. Using models of globular clusters (GCs) with detailed N-body dynamics and stellar evolution, we show that a typical cluster with a mass of 3×10{sup 5}M{sub ⊙} to 6×10{sup 5}M{sub ⊙} is optimal for forming GW150914-like BBHs that will merge in the local universe. We identify the most likely dynamical processes for forming GW150914 in such a cluster, and we show that the detection of GW150914 is consistent with the masses and merger rates expected for BBHs from GCs. Our results show that dynamical processes provide a significant and well-understood pathway for forming BBH mergers in the local universe. Understanding the contribution of dynamics to the BBH merger problem is a critical step in unlocking the full potential of gravitational-wave astronomy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Supernova neutrino detection in LZ

In the first 10 seconds of a core-collapse supernova, almost all of its progenitor's gravitational potential, O(10 53 ergs), is carried away in the form of neutrinos. These neutrinos, with O(10 MeV) kinetic energy, can interact via coherent elastic neutrino-nucleus scattering (CEνNS) depositing O(1 keV) in detectors. In this work we describe the performances of low-background dark matter detectors, such as LUX-ZEPLIN (LZ), optimized for detecting low-energy depositions, in detecting these neutrino interactions. For instance, a 27 M ⊙ supernova at 10 kpc is expected to produce ~350 neutrino interactions in the 7-tonne liquid xenon active volume of LZ. Based on the LS220 EoS neutrino flux model for a SN, the Noble Element Simulation Technique (NEST), and predicted CEνNS cross-sections for xenon, to study energy deposition and detection of SN neutrinos in LZ. Here we simulate the response of the LZ data acquisition system (DAQ) and demonstrate its capability and limitations in handling this interaction rate. We present an overview of the LZ detector, focusing on the benefits of liquid xenon for supernova neutrino detection. We discuss energy deposition and detector response simulations and their results. We present an analysis technique to reconstruct the total number of neutrinos and the time of the supernova core bounce.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Twisted A-D-A Type Acceptors with Thermally-Activated Delayed Crystallization Behavior for Efficient Nonfullerene Organic Solar Cells

We report that molecular aggregation and crystallization during film coating play a crucial role in the realization of high-performing organic photovoltaics. Strong intermolecular interactions and high solid-state crystallinity are beneficial for charge transport. However, fast crystallization during thin-film drying often limits the formation of the finely phase-separated morphology required for efficient charge generation. Herein, the authors show that twisted acceptor-donor-acceptor (A-D-A) type compounds, containing an indacenodithiophene (IDT) electron-rich core and two naphthalenediimide (NDI) electron-poor units, leads to formation of mostly amorphous phases in the as-cast film, which can be readily converted into more crystalline domains by means of thermal annealing. This design strategy solves the aforementioned conundrum, leading to an optimal morphology in terms of reduced donor/acceptor domain-separation sizes (ca. 13 nm) and increased packing order. Solar cells based on these acceptors with a PBDB-T polymer donor show a power conversion efficiency over 10% and stable morphology, which results from the combined properties of desirable excited-state dynamics, high charge mobility, and optimal aggregation/crystallization characteristics. These results demonstrate that the twisted A-D-A motif featuring thermally-induced crystallization behavior is indeed a promising alternative design approach toward more morphologically robust materials for efficient organic photovoltaics.

14 SOLAR ENERGY↗
DOE OSTI2019

Verification of Reduced Order Modeling Based Uncertainty/Sensitivity Estimator (ROMUSE)

This article presents a number of verification case studies for a recently developed sensitivity/uncertainty code package. The code package, ROMUSE (Reduced Order Modeling based Uncertainty/Sensitivity Estimator) is an effort to provide an analysis tool to be used in conjunction with reactor core simulators, in particular the Virtual Environment for Reactor Applications (VERA) core simulator. ROMUSE has been written in C++ and is currently capable of performing various types of parameter perturbations and associated sensitivity analysis, uncertainty quantification, surrogate model construction and subspace analysis. The current version 2.0 has the capability to interface with the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) code, which gives ROMUSE access to the various algorithms implemented within DAKOTA, most importantly model calibration. The verification study is performed via two basic problems and two reactor physics models. The first problem is used to verify the ROMUSE single physics gradient-based range finding algorithm capability using an abstract quadratic model. The second problem is the Brusselator problem, which is a coupled problem representative of multi-physics problems. This problem is used to test the capability of constructing surrogates via ROMUSE-DAKOTA. Finally, light water reactor pin cell and sodium-cooled fast reactor fuel assembly problems are simulated via SCALE 6.1 to test ROMUSE capability for uncertainty quantification and sensitivity analysis purposes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The Role and Value of Interregional Transmission in a Decarbonized U.S. Electricity System

Decarbonizing the U.S. energy system entails a significant expansion of wind and solar power and electrification of end-use applications. Achieving both is more efficient and less costly when interregional transmission capacity is expanded, but the fractured nature of grid planning in the United States is often a barrier to such expansion. Here, we explore the role of interregional transmission under a variety of decarbonization scenarios, using a capacity-expansion model to generate co-optimized portfolios of generation, storage, and transmission that meet decarbonization targets and electrification-driven demand. We explore portfolio and cost differences across 92 scenarios, from scenarios with limited transmission expansion to those that include a meshed high-voltage direct current (HVDC) network. In the core decarbonization scenarios, wind capacity expands by ~10x and solar by ~20x compared to 2020, hundreds of gigawatts of battery storage are deployed, and interregional transmission expands by 3-6x. Transmission expansion occurs nationwide but is concentrated between the central "wind belt" and eastern load centers. The HVDC scenarios result in hundreds of billions of dollars of savings in total system cost, demonstrating the economic benefits of interregional transmission in support of rapid decarbonization.

capacity expansion↗