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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Experimental Measurements of Soot and Soot Precursors of Sustainable Aviation Fuels

Sustainable Aviation Fuels (SAFs) are promising for decarbonizing the challenging-to-electrify aerospace sector and reducing yearly greenhouse gas emissions by thousands of tons. Developing predictive detailed chemical kinetic models for SAF (and traditional) fuel mixtures necessitates a robust experimental database to elucidate the intricate pyrolysis and oxidation chemistry of SAF fuel blend components. Two critical components of SAF surrogate mixtures are iso-dodecane isomers and trans-decalin. This report characterizes the structure of three slightly sooting, laminar, non-premixed, nitrogen-diluted Planar Mixing Layer Flames (PMLFs) fueled by either pure ethylene, ethylene doped with 1500ppm of either 2,2,4,6,6-pentamethyl-heptane or trans-decalin. The flames have the same stoichiometric mixture fraction and total mole fraction of fuels in the fuel stream (X F,F = X C2H4,F + X Dopant,F = 0.26), resulting in approximately the same maximum temperature (T max ≈ 1800 K) to highlight the effect of doping on soot and its gaseous precursors. The PMLF is unconfined and established between adjacent planar jets of the fuel and oxidizer streams surrounded by annular shielding nitrogen. The flow is stabilized by exhausting the hot buoyant flame products through a slot in a downstream plate onto which the cold flow impinges. Importantly, any horizontal cross-section of the PMLF has a self-similar thermochemical structure of a reactive boundary layer that grows at increasing Height Above the Burner (HAB). Therefore, the horizontal cross-sections of a PMLF can be modeled as a One-Dimensional Counterflow Flame (1D-CF) with very low strain rates which are unachievable in traditional CFs and yield a structure that is several millimeters thick. In brief, the PMLF is ideal for performing spatially resolved measurements with minimal effects of the sampling-induced perturbations, to develop and validate detailed chemical kinetic models on time scales of a few tens of milliseconds that are relevant in aviation applications. The horizontal profiles of C0-C18 gaseous species at HAB = 50 mm and soot volume fraction at 25 and 50 mm are measured using capillary sampling followed by GC-MS analyses and Laser Induced Emission Spectroscopy (LIES), respectively. Additionally, the Elastic Laser Light Scattering (E-LLS) coefficient is measured at HAB = 50 mm and 25 mm to determine the strain rates of the 1D-CFs equivalent to the characterized PMLF horizontal cross sections and the profile of the E-LLS equivalent diameter of soot. The comparison of the results in the base PMLF fueled only by ethylene with those in either doped PMLF in which 1500 ppm of ethylene are replaced with 2,2,4,6,6-pentamethyl-heptane or trans-decalin, reveals that the doping increases the mole fraction of several Polycyclic Aromatic Hydrocarbons (PAHs) at both HABs by a factor of approximately 2.5, and the soot volume fraction at both HABs by factors of ~1.5 for 2,2,4,6,6-pentamethyl-heptane and ~1.25 for trans-decalin doping, respectively. Conversely, doping causes a near doubling of the soot equivalent diameter only at HAB = 50 mm, without significant effects at 25 mm. The experimental results partially validate the tested state-of-the-art detailed chemical kinetic model and also point toward the further improvement of its predictive capabilities.

10 SYNTHETIC FUELS↗
NASA NTRS2023

Turbulence Model Validation Through Joint Experimental /Computational Studies of Separated Flow Over A Three-Dimensional Tapered Bump: Part I - Experimental Investigation

The results of a three-year experimental campaign aimed at comprehensively documenting the separated flow over a three-dimensional bump are presented with the purpose of generating a benchmark experimental database useful in validating computational fluid dynamics (CFD) flow simulations. The bump model geometry was designed to provide well-defined and repeatable smooth-body flow separation conditions that were suitable for both experiments and simulations. The bump had a Gaussian streamwise profile with a constant height equal to 8.5% of its width over the central 60% of the test section width. The remaining 40% were outboard spanwise portions that gradually taper to zero using an error function profile to minimize side-wall boundary layer effects. The model was immersed in a turbulent boundary layer that was developed on a suspended flat plate in the Notre Dame Mach 0.6 Wind Tunnel. In order to document the effect of the incoming boundary layer thickness on the flow separation, the bump model could be located at two streamwise positions. The mean velocity and turbulence intensity of the wind tunnel freestream flow field and approaching turbulent boundary layer were fully documented. The measurements of the flow separation region included surface visualization, wall shear stress using oil-film interferometry, mean and dynamic surface pressure, and planar and stereoscopic particle image velocimetry. The experiments were conducted over a range of Mach numbers from 0.05 to 0.2 corresponding to a range of Reynolds numbers based on the test section spanwise dimension (0.914m) of 1.0 × 10 6 ≤ Re L = U ∞ L/ν ≤ 4.0 × 10 6 . The bulk of the results are presented for the higher Mach number conditions of 0.1 and 0.2 with Re L = 2.0 × 10 6 and 4.0 × 10 6 , respectively. Extensive uncertainty analysis of the data was performed. The data is archived in the NASA Langley Turbulence Modeling Resource website at https://turbmodels.larc.nasa.gov/Other_exp_Data/speedbump_sep_exp.html. In addition to the experiments, a computational effort was made in parallel by the CFD group at Boeing Research & Technology highlighting the usefulness of the data set, which is outlined in the accompanying CFD report.

Flow separation↗
DOE OSTI2023

Systematically derived thermodynamic properties for alkane oxidation

Key combustion properties, such as ignition delay time, show strong sensitivity to the thermochemistry of the main species in the standard radical oxidation pathway at low temperatures (600-1000K). Significant uncertainties persist in current estimates of thermodynamic properties, particularly for the larger species of relevance to common practical fuels. In this article, we use advanced computational schemes to evaluate thermodynamic properties for the fuel, fuel radical, peroxy, hydroperoxy-alkyl radical, and hydroperoxide species for a set of 17 fuels containing up to 9 carbon atoms, and with various degrees of branching in the alkane backbone. The procedure, termed STAR-1D, combines conformer sampling to find the minimum geometry, B2PLYP-D3/cc-pVTZ harmonic frequency evaluations, and omega B97X-D/cc-pVTZ one-dimensional torsional mapping. It includes two physically-based scaling routines: a frequency-dependent scaling to B2PLYP-D3/cc-pVTZ anharmonic frequencies and a scaling of one-dimensional omega B97X-D/ccpVTZ torsional profiles to reproduce the product of the B2PLYP-D3/cc-pVTZ frequencies in the harmonic limit. Substantive comparisons with existing experimental databases, together with careful examinations of key theoretical assumptions, are used to explore the accuracy of the predictions. These computationally intensive explorations of 195 species were facilitated by automated thermochemistry software. Further, high accuracy 0 K heats of formation from a separate study are used with the STAR-1D computations to generate NASA polynomial representations. In addition to their intrinsic value, the present results also provide a reliable database for the optimization of group additivity or machine learning schemes for scaling to larger combustion systems. Towards this end a complementary extensive conformational analysis is carried out for the medium sized species and the thermodynamic properties of the lowest energy hydrogen-bonded and non-hydrogen bonded conformers are contrasted for larger hydroperoxy-alkyl radical species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Effect of spacer grids on high-burnup fuel fragmentation, relocation, and dispersal

Increasing the fuel burnup limit in light-water reactors to improve fuel cycle economics requires a strong technical foundation. Experimental observations from the Halden and Studsvik programs have revealed severe fuel fragmentation during loss-of-coolant accident (LOCA) conditions, highlighting the need for additional technical evaluation. Consequently, further LOCA test data are needed to complement existing findings and improve the understanding of fuel fragmentation, relocation, and dispersal (FFRD) behavior. Oak Ridge National Laboratory’s Severe Accident Test Station has played a significant role in advancing the understanding of high-burnup fuel fragmentation, relocation, and dispersal phenomena. One remaining gap in the available experimental database is the effect of fuel assembly structural features on cladding deformation behavior during a LOCA, and more specifically, their impact on the fuel’s ability to fragment, relocate, and disperse. Recent analyses using the BISON fuel performance code suggest that cladding deformation near grid spacers will remain below the 3% threshold that has been reported in the NRC Research Information Letter, indicating that the cladding could remain mechanically constrained during the LOCA event. This paper builds upon the BISON analyses to design and conduct a series of out-of-cell tests aimed at further evaluating cladding deformation in and around grid spacers. In addition, these tests were used to assess local cladding temperature conditions and compare them against analytical predictions in order to better replicate expected in-reactor behavior. Finally, an in-cell high-burnup LOCA test was designed and performed to evaluate the effects of a grid spacer, or cladding restraint, on fuel fragmentation, relocation, and dispersal susceptibility. The high-burnup test results differed from those of historical LOCA experiments, with a recorded rupture temperature of 861°C. Two ballooned regions and corresponding rupture openings were observed, with rupture widths of approximately 0.64 mm for both ruptures and rupture lengths of 4.8 mm and 5.6 mm, respectively.

Capps, Nathan [ORNL]↗
DOE OSTI2024

Machine learning for the redox potential prediction of molecules in organic redox flow battery

Here, organic redox flow batteries (ORFB) are recognized as an innovative technology for the large-scale storage of renewable energy. The redox potential of organic redox-active molecules plays a vital role in their performance. Advanced screening techniques like high-throughput experiment and machine learning (ML) have significantly enhanced organic material performance and transformed the field of ORFB. However, the scarcity of experimental data poses a considerable challenge for ML model development in this domain. In our study, we developed lightweight graph-based Gaussian process regression (GPR) models with GPU-accelerated marginalized graph kernel and hybrid kernel to predict the redox potentials of organic redox-active molecules for ORFBs, specifically focusing on small datasets. To evaluate model accuracy, we created a new experimental database of organic redox-active molecules by the data from hundreds of published papers and assembled previous computational datasets. We also considered some key parameters, such as pH conditions and solvent type, to assess their impact on redox potential prediction. Our GPR model predicted redox potentials with high accuracy across all datasets using minimal training data. The study provides powerful tools for molecule screening and design and delivers valuable guidance on designing training datasets for costly experiments.

25 ENERGY STORAGE↗
DOE OSTI2022

Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixtures containing 75 unique compounds. The presence of strong hydrogen bonding molecules may lead to clustering or dimerization resulting in non-linear diffusive behavior. To address this, self- and binary association energies were calculated for each molecule and mixture to provide information on intermolecular interaction strength and were used as input features to the ANN. An accurate, generalized ANN model was developed with an overall average absolute deviation of 4.1%. Forward input feature selection reveals the importance of critical properties and self-association energies along with other fluid properties. Additional ANNs were developed with subsets of the full input feature set to further investigate the impact of various properties on model performance. The results from two specific mixtures are discussed in additional detail: one providing an example of strong hydrogen bonding and the other an example of extreme pressure changes, with the ANN models predicting self-diffusion well in both cases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

193 nm Ultraviolet Photodissociation for the Characterization of Singly Charged Proteoforms Generated by MALDI

MALDI imaging allows for the near-cellular profiling of proteoforms directly from microbial, plant, and mammalian samples. Despite detecting hundreds of proteoforms, identification of unknowns with only intact mass information remains a distinct challenge, even with high mass resolving power and mass accuracy. To this end, many supplementary methods have been used to create experimental databases for accurate mass matching, including bulk or spatially resolved bottom-up and/or top-down proteomics. Herein, we describe the application of 193 nm ultraviolet photodissociation (UVPD) for fragmentation of quadrupole isolated singly charged ubiquitin (m/z 8565) by MALDI-UVPD on a UHMR HF Orbitrap. This platform permitted the high-resolution accurate mass measurement of not just terminal fragments but also large internal fragments. Finally, the outlined workflow demonstrates the feasibility of top-down analyses of isolated MALDI protein ions and the potential toward more comprehensive characterization of proteoforms in MALDI imaging applications.

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

Artificial neural network prediction of self-diffusion in pure compounds over multiple phase regimes

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largest impact on the ANN prediction of self-diffusion constants in pure compounds.

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

Structured illumination with thermal imaging (SI-TI): A dynamically reconfigurable metrology for parallelized thermal transport characterization

The recent push for the “materials by design” paradigm requires synergistic integration of scalable computation, synthesis, and characterization. Among these, techniques for efficient measurement of thermal transport can be a bottleneck limiting the experimental database size, especially for diverse materials with a range of roughness, porosity, and anisotropy. Traditional contact thermal measurements have challenges with throughput and the lack of spatially resolvable property mapping, while non-contact pump-probe laser methods generally need mirror smooth sample surfaces and also require serial raster scanning to achieve property mapping. Here, we present structured illumination with thermal imaging (SI-TI), a new thermal characterization tool based on parallelized all-optical heating and thermometry. Experiments on representative dense and porous bulk materials as well as a 3D printed thermoelectric thick film (~50 μm) demonstrate that SI-TI (1) enables paralleled measurement of multiple regions and samples without raster scanning; (2) can dynamically adjust the heating pattern purely in software, to optimize the measurement sensitivity in different directions for anisotropic materials; and (3) can tolerate rough (~3 μm) and scratched sample surfaces. Here, this work highlights a new avenue in adaptivity and throughput for thermal characterization of diverse materials.

42 ENGINEERING↗
DOE OSTI2024

The role of ion-scale micro-turbulence in pedestal width of the DIII-D wide-pedestal QH mode

The low-edge rotation, intrinsically ELM-free, and improved confinement wide-pedestal quiescent H-mode (QH-mode), discovered in DIII-D tokamak, has pedestal widths exceeding the EPED-kinetic-ballooning mode (KBM) model scaling typically by at least 25%. Ion-scale ($k_yp_s$ < 1) microturbulence and its role in setting the pedestal structure is investigated using the radially local δ$f$ gyrokinetic code CGYRO. The electromagnetic trapped electron mode (TEM) is unstable at the pedestal top, while plasma beta (β$_e$) is ∼60% below the KBM onset threshold and the electron temperature gradient mode is found to be unstable in the peak gradient region. Nonlinear simulation reveals that the ion-scale turbulence could produce electron energy flux consistent with the flux inferred from power balance at the pedestal top, with a reasonable variation of the local shearing rate; and the local neoclassical transport from NEO is dominant over the simulated turbulent transport in the ion energy flux channel. The simulated ion-scale turbulence produces much lower electron energy flux than inferred from experiment in the pedestal peak gradient region. A correction to the EPED-KBM pedestal width scaling is obtained based on the two-dimensional scan of pedestal top plasma beta (β$_e$) and normalized electron density and temperature scale lengths,${a}$/${L_n}_e$, ${a}$/${L_T}_n$using CGYRO linear simulations. Mode transitions among TEM, micro-tearing mode, ion-temperature gradient mode and KBM, are observed in the 2D scan at the pedestal top. A fixed normalized growth rate for these drift-type modes is taken to determine the pedestal width scaling, which shows good consistency with the QH experimental database on pedestal heights and widths. The onset of KBM instabilities and the local E × B shear suppression criterion set the lower and upper limit for the pedestal width of standard QH-mode, wide-pedestal QH-mode and type-I ELMy H mode. A potentially higher and wider pedestal is expected from the new scaling of pedestal width. This work presents an improved understanding of the ion-scale micro-turbulence of wide-pedestal QH-mode and sheds light on a promising scenario for future reactors, including ITER and beyond.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Polymer genome–based prediction of gas permeabilities in polymers

Abstract Predicting gas permeabilities of polymers a priori is a long-standing challenge within the membrane research community that has important applications for membrane process design and ultimately widespread adoption of membrane technology. From early attempts based on free volume and cohesive energy to more recent group contribution methods, the ability to predict membrane permeability has improved in terms of accuracy. However, these models usually stay “within the paper”, i.e. limited model details are provided to the wider community such that adoption of these predictive platforms is limited. In this work, we combined an advanced polymer chemical structure fingerprinting method with a large experimental database of gas permeabilities to provide unprecedented prediction precision over a large range of polymer classes. No prior knowledge of the polymer is needed for the prediction other than the repeating unit chemical formula. In addition, we have incorporated this model into the existing Polymer Genome project to make it open to the membrane research community.

Polymer Science↗
DOE OSTI2021

Excitation functions of some deuteron-induced nuclear reactions on Al

Here, excitation functions of the reactions 27 Al(d,αp) 24 Na, 27 Al(d,2p) 27 Mg and 27 Al(d,p) 28 Al were measured by the activation technique up to deuteron energies of 37 MeV. The available experimental databases of the reaction products 27 Mg and 28 Al were extended and compared with the nuclear model calculations based on the code TALYS-1.8. Our measured data are reproduced well by the model calculations after adjustment of a few free input parameters. The cross-section ratio of the (d,αp) to (d,2p) process as a function of projectile energy was deduced from the measured data, and the result is interpreted in terms of competition between a proton and an α-particle emission.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2024

AIACHNE's contribution for Nuclear Energy Agency Working Party on International Nuclear Data Evaluation Co-operation Subgroup 50

The AIACHNE (AI/ML Informed cAlifornium CHi Nuclear data Experiment) project aims at designing an experiment for the 252 Cf Prompt Fission Neutron Spectrum (PFNS) that explores systematic biases in an experimental database retrieved from the EXFOR databases. To that end, machine learning (ML) methods were applied to pint-point measurement features likely related to bias. From that information, we selected a feature that should be explored by the AIACHNE experiment. Measurement features are metadata encapsulating all pertinent information about the physical measurement and analysis techniques. Examples are, for instance, what neutron and fission detectors were used for the physical metadata, and what background reduction techniques were employed for analysis techniques. Such metadata were retrieved both from EXFOR entries as well as the literature of data sets described in detail in Reference 2 (at the end of the article).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2025

Enabling Low-Temperature (LTP) Ignition Technologies for Multi-Mode Engines through the Development of a Validated High-Fidelity LTP Model for Predicative Simulations Tools

The goal of multi-mode engine architectures is to extend current lean-burn dilution limits with renewable fuels, which requires spark plugs to deposit high energies (hundreds of mJ) in order to initiate ignition and complete combustion. At elevated energy deposition rates, spark plugs experience increased electrode erosion and thermal losses, which ultimately shortens the spark-plug lifetime and lowers ignition efficiency. As such, in order to safeguard the efficiency gains of multi-mode concepts, new and improved ignition technologies are required. Recently, non-equilibrium low-temperature plasmas (LTP) have been shown to promote energy-efficient ignition via quenching and transport of electronically excited atoms and molecules, selective radical production and fast heating of hydrocarbon/air mixtures [1-2]. Thus, LTP is seen as a technology that can potentially improve the energy extraction efficiency of fuels, while enabling kinetically controlled combustion modes towards fuel leaner conditions to realize current DOE VTO goals of improving the sustainability of future mobility [3]. Although many previous studies have demonstrated the efficacy of plasma-assisted ignition to enhance combustion, the detailed enhancement mechanisms remain largely unknown, especially for oxygenated fuels and at elevated pressures that are most relevant to practical engine conditions. These barriers hinder the development of accurate and comprehensive numerical models that seek to describe LTP-based ignition in existing engine design software tools and methods. Current state-of-the-art simulation capabilities for LTP ignition systems are in need of improvements since they deliver qualitative results only due to important limitations of existing approaches. Firstly, validated kinetic models with elementary steps for plasma discharges in oxygenated fuel/air mixtures of relevance to the transportation sector are required. Such kinetic models do not exist at present and will be developed and validated within this project. Secondly, plasma discharges and reactive mixture ignition are multi-scale, unsteady processes requiring high-performance numerical methods and software that execute efficiently on DOE supercomputers. Such software does not exist at present and will be developed and applied to practical LTP ignition scenarios as part of this project. Thirdly, experimental databases that are tailored to serve as benchmark in support of the development of predictive computational models of LTP ignition do not exist and will be part of this project.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Neural Network Solver for Small Quantum Clusters

Machine learning approaches have recently been applied to the study of various problems in physics. Most of these studies are focused on interpreting the data generated by conventional numerical methods or the data on an existing experimental database. An interesting question is whether it is possible to use a machine learning approach, in particular a neural network, for solving the many-body problem. In this paper, we present a neural network solver for the single impurity Anderson model, the paradigm of an interacting quantum problem in small clusters. We demonstrate that the neural-network-based solver provides quantitative accurate results for the spectral function as compared to the exact diagonalization method. This opens the possibility of utilizing the neural network approach as an impurity solver for other many-body numerical approaches, such as the dynamical mean field theory.

36 MATERIALS SCIENCE↗
NASA NTRS1987

Flowfield characteristics of an axisymmetric 180-degree turnaround duct

In an attempt to provide an experimental database for computational fluid dynamics turbulence code evaluation and development, an experiment was conducted using an annular axisymmetric 180-deg turnaround passage through which air flows at close to ambient conditions. Measurements made at a nominal gap Reynolds number of 100,000 are presented; these are the static and fluctuating wall pressure distributions and the one- and two-component velocity profiles at various stations upstream, through, and downstream of the bend. Because of the large favorable pressure gradients on the inner wall up to 51 degrees, the flow experiences a large acceleration close to the wall; enhanced mixing occurs close to the outer wall. Downstream of this location, the flow close to the inner wall experiences an adverse pressure gradient before separating at about 150 degrees.

Ostermier, B. J.↗