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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Prediction of Cooling Curves for Controlled Unidirectional Solidification Under the Influence of Shrinkage: A Semi-analytical Approach

Quality of casting products can be significantly improved by controlling the crystal growth rate. Both dendritic and equiaxed grain structures depend strongly on crystal growth rate along the crystallographic direction. Uncontrolled solidification processes may lead to the formation of porous and columnar material with an extremely non-homogeneous composition distribution. These undesirable micro-structures can be avoided by adopting an optimal cooling rate (K/s) during the crystal growth. The present work focuses on determining suitable cooling curves to obtain desired unidirectional crystal growth rates, by implementing a semi-analytical model. Here, we defined cooling curve as the time history of temperature evolution at the cold boundary. The proposed semi-analytical heat transfer model is diffusion driven and accounts for shrinkage effect during solidification. The proposed model is found capable of predicting cooling curves for prescribed interface velocities. The model is self-sufficient in validating itself by means of solving inverse and forward problems. The cooling curve prediction by the model is validated by using it as the transient boundary condition for an existing enthalpy updating scheme-based numerical model. The proposed model is also applied to empirical data reported in literature, to validate the model against experimental results. All the validations showcased excellent fit. The proposed model will facilitate controlled unidirectional crystal growth rate by providing cooling rate (K/s) as an input for the experiments involving directional solidification.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Resonant cavity mode dependence of anomalous and inverse spin Hall effect

The direct current electric voltage induced by the Inverse Spin Hall Effect (ISHE) and Anomalous Hall Effect (AHE) was investigated in the TE{sub 011} and TE{sub 102} cavities. The ISHE and AHE components were distinguishable through the fitting of the voltage spectrum. The unwanted AHE was minimized by placing the DUT (Device Under Test) at the center of both the TE{sub 011} and TE{sub 102} cavities. The voltage of ISHE in the TE{sub 011} cavity was larger than that in the TE{sub 102} cavity due to the higher quality factor of the former. Despite optimized centering, AHE voltage from TE{sub 011} cavity was also higher. The reason was attributed to the E-field distribution inside the cavity. In the case of the TE{sub 011} cavity, the DUT was easily exposed to the E-field in all directions. Therefore, the parasitic AHE voltage in the TE{sub 102} cavity was less sensitive than that in the TE{sub 011} cavity to decentering problem.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Hamiltonian parameter inference from resonant inelastic x-ray scattering with active learning

Identifying model Hamiltonians is a vital step toward creating predictive models of materials. Here, in this study, we combine Bayesian optimization with the EDRIXS numerical package to infer Hamiltonian parameters from resonant inelastic x-ray scattering (RIXS) spectra within the single atom approximation. To evaluate the efficacy of our method, we test it on experimental RIXS spectra of NiPS 3 , NiCl 2 , Ca 3 ⁢LiOsO 6 , and Fe 2⁢ O 3 , and demonstrate that it can reproduce results obtained from hand-fitted parameters to a precision similar to expert human analysis while providing a more systematic mapping of parameter space. Our work provides a key first step toward solving the inverse scattering problem to extract effective multi-orbital models from information-dense RIXS measurements, which can be applied to a host of quantum materials. We also propose atomic model parameter sets for two materials, Ca 3⁢ LiOsO 6 and Fe 2⁢ O 3 , that were previously missing from the literature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Inverse design of nanoporous crystalline reticular materials with deep generative models

Reticular frameworks are crystalline porous materials that form via the self-assembly of molecular building blocks in different topologies, with many having desirable properties for gas storage, separation, catalysis, biomedical applications and so on. The notable variety of building blocks makes reticular chemistry both promising and challenging for prospective materials design. Here we propose an automated nanoporous materials discovery platform powered by a supramolecular variational autoencoder for the generative design of reticular materials. We demonstrate the automated design process with a class of metal–organic framework (MOF) structures and the goal of separating carbon dioxide from natural gas or flue gas. Our model shows high fidelity in capturing MOF structural features. Here, we show that the autoencoder has a promising optimization capability when jointly trained with multiple top adsorbent candidates identified for superior gas separation. MOFs discovered here are strongly competitive against some of the best-performing MOFs/zeolites ever reported.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Inversion of infrared imaging bolometer based on one-dimensional and three-dimensional modeling in HL-2A

Linear regularization has been applied to the HL-2A infrared imaging bolometer to reconstruct local plasma emission with one-dimensional (1D) and three-dimensional (3D) modeling under the assumption of toroidal symmetry. In the 3D modeling, a new method to calculate the detector point response function is introduced. This method can be adapted to an arbitrarily shaped pinhole. With the full 3D treatment of the detector geometry, up to 50% of the mean-squared error is reduced compared with the 1D modeling. This is attributed to the effects of finite detector size being taken into account in the 3D modeling. Meanwhile, the number of the bolometer pixels has been optimized to 20 × 20 by making a trade-off between the number of bolometer pixels and the sensitivity of the system. The plasma radiated power density distributions have been calculated as a demonstration using 1D modeling and 3D modeling, respectively.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Simulation study of high-current 7 Li 3+ beam acceleration with laser ion source and RFQ linac using direct plasma injection

Accelerator-based neutron sources (ABNS) utilizing the 7 Li(p,n) 7 Be reaction in inverse kinematics offer strong forward-directed neutron emission, making them attractive for compact and clean neutron source with low background neutrons and unwanted radiation. However, practical use of such systems requires lithium ion beam currents that exceed the capability of conventional ion accelerators by two orders of magnitude. In this study, we numerically designed and evaluated a high-current acceleration system based on a direct plasma injection scheme (DPIS), combining a laser ion source (LIS) and a radio-frequency quadrupole (RFQ) linac. The ion extraction optics and RFQ entrance were optimized using IGUN, OPERA, and GPT, demonstrating that over 1.2 A-class beam current can be injected into the RFQ. The RFQ structure was designed under realistic engineering constraints including surface field strength (Kilpatrick factor) and RF power. GPT simulations showed that a 370 mA 7 Li 3+ beam can be successfully accelerated within a ±10 % energy spread. To validate downstream compatibility, we also designed a simplified medium-energy beam transport (MEBT) section and an interdigital H-type (IH) linac, confirming successful acceleration of a 320 mA beam to the total energy of 14 MeV. These results support the feasibility of the DPIS and RFQ approach as a promising solution for compact neutron sources and other high-intensity ion beam applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

SU-G-BRC-15: The Potential Clinical Significance of Dose Mapping Error for Intra- Fraction Dose Mapping for Lung Cancer Patients

Purpose: To evaluate the dose-mapping error (DME) inherent to conventional dose-mapping algorithms as a function of dose-matrix resolution. Methods: As DME has been reported to be greatest where dose-gradients overlap tissue-density gradients, non-clinical 66 Gy IMRT plans were generated for 11 lung patients with the target edge defined as the maximum 3D density gradient on the 0% (end of inhale) breathing phase. Post-optimization, Beams were copied to 9 breathing phases. Monte Carlo dose computed (with 2*2*2 mm{sup 3} resolution) on all 10 breathing phases was deformably mapped to phase 0% using the Monte Carlo energy-transfer method with congruent mass-mapping (EMCM); an externally implemented tri-linear interpolation method with voxel sub-division; Pinnacle’s internal (tri-linear) method; and a post-processing energy-mass voxel-warping method (dTransform). All methods used the same base displacement-vector-field (or it’s pseudo-inverse as appropriate) for the dose mapping. Mapping was also performed at 4*4*4 mm{sup 3} by merging adjacent dose voxels. Results: Using EMCM as the reference standard, no clinically significant (>1 Gy) DMEs were found for the mean lung dose (MLD), lung V20Gy, or esophagus dose-volume indices, although MLD and V20Gy were statistically different (2*2*2 mm{sup 3}). Pinnacle-to-EMCM target D98% DMEs of 4.4 and 1.2 Gy were observed ( 2*2*2 mm{sup 3}). However dTransform, which like EMCM conserves integral dose, had DME >1 Gy for one case. The root mean square RMS of the DME for the tri-linear-to- EMCM methods was lower for the smaller voxel volume for the tumor 4D-D98%, lung V20Gy, and cord D1%. Conclusion: When tissue gradients overlap with dose gradients, organs-at-risk DME was statistically significant but not clinically significant. Target-D98%-DME was deemed clinically significant for 2/11 patients (2*2*2 mm{sup 3}). Since tri-linear RMS-DME between EMCM and tri-linear was reduced at 2*2*2 mm{sup 3}, use of this resolution is recommended for dose mapping. Interpolative dose methods are sufficiently accurate for the majority of cases. J.V. Siebers receives funding support from Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Mechanisms of Dynamic Near-Wake Modulation of a Utility-Scale Wind Turbine

The current study uses large eddy simulations to investigate the transient response of a utility-scale wind turbine wake to dynamic changes in atmospheric and operational conditions, as observed in previous field-scale measurements. Most wind turbine wake investigations assume quasi-steady conditions, but real wind turbines operate in a highly stochastic atmosphere, and their operation (e.g. blade pitch, yaw angle) changes constantly in response. Furthermore, dynamic control strategies have been recently proposed to optimize wind farm power generation and longevity. Therefore, improved understanding of dynamic wake behaviours is essential. First, changes in blade pitch are investigated and the wake expansion response is found to display hysteresis as a result of flow inertia. The time scales of the wake response to different pitch rates are quantified. Next, changes in wind direction with different time scales are explored. Under short time scales, the wake deflection is in the opposite direction of that observed under quasi-steady conditions. Finally, yaw changes are implemented at different rates, and the maximum inverse wake deflection and time scale are quantified, showing a clear dependence on yaw rate. To gain further physical understanding of the mechanism behind the inverse wake deflection, the streamwise vorticity in different parts of the wake is quantified. The results of this study provide guidance for the design of advanced wake flow control algorithms. The lag in wake response observed for both blade pitch and yaw changes shows that proposed dynamic control strategies must implement turbine operational changes with a time scale of the order of the rotor time scale or slower.

flow-structure interactions↗
DOE OSTI2016

Competing Structural Instabilities in the Ruddlesden–Popper Derivatives HRTiO 4 (R = Rare Earths): Oxygen Octahedral Rotations Inducing Noncentrosymmetricity and Layer Sliding Retaining Centrosymmetricity

We report the discovery of noncentrosymmetry in the family of HRTiO 4 (R = Eu, Gd, Dy) layered oxides possessing a Ruddlesden-Popper derivative structure, by second harmonic generation and synchrotron x-ray diffraction with the support of density functional theory calculations. These oxides were previously thought to possess inversion symmetry. Here, inversion symmetry is broken by oxygen octahedral rotations, a mechanism that is not active in simple perovskites. We discover a competition between oxygen octahedral rotations and sliding of the octahedral perovskite blocks at the OH layers. For the smaller rare earth ions, R = Eu, Gd, Dy, which favor the octahedral rotations, noncentrosymmetry is present but the sliding at the OH layer is absent. For the larger rare earth ions, R = Nd and Sm, the octahe-dral rotations are absent, but sliding of the octahedral blocks at the OH layer is present, likely to optimize the hydrogen bond length arising from the directional nature of these bonds in the crystal structure. The study reveals a new mechanism for inducing noncentrosymmetry in layered oxides, and chemical-structural effects related to rare earth ion size and hydrogen bonding that can turn this mechanism on and off. In conclusion, we construct a complete phase diagram of temperature versus rare earth ionic radius for the HRTiO 4 family.

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

Investigation of the N 2 O emission strength in the U. S. Corn Belt

Nitrous oxide (N 2 O) has a high global warming potential and depletes stratospheric ozone. The U. S. Corn Belt plays an important role in the global anthropogenic N 2 O budget. To date, studies on local surface N 2 O emissions and the atmospheric N 2 O budget have commonly used Lagrangian models. Here, in the present study, we used an Eulerian model - Weather Research and Forecasting Chemistry (WRF-Chem) model to investigate the relationships between N 2 O emissions in the Corn Belt and observed atmospheric N 2 O mixing ratios. We derived a simple equation to relate the emission strengths to atmospheric N 2 O mixing ratios, and used the derived equation and hourly atmospheric N 2 O measurements at the KCMP tall tower in Minnesota to constrain agricultural N 2 O emissions. The modeled spatial patterns of atmospheric N 2 O were evaluated against discrete observations at multiple tall towers in the NOAA flask network. After optimization of the surface flux, the model reproduced reasonably well the hourly N 2 O mixing ratios monitored at the KCMP tower. Agricultural N 2 O emissions in the EDGAR42 database needed to be scaled up by 19.0 to 28.1 fold to represent the true emissions in the Corn Belt for June 1–20, 2010 - a peak emission period. Optimized mean N 2 O emissions were 3.00–4.38, 1.52–2.08, 0.61–0.81 and 0.56–0.75 nmol m -2 s -1 for June 1–20, August 1–20, October 1–20 and December 1–20, 2010, respectively. The simulated spatial patterns of atmospheric N 2 O mixing ratios after optimization were in good agreement with the NOAA discrete observations during the strong emission peak in June. Such spatial patterns suggest that the underestimate of emissions using IPCC (Inter-governmental Panel on Climate Change) inventory methodology is not dependent on tower measurement location.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Extinction-sedimentation inversion technique for measuring size distribution of artificial fogs

In measuring the size distribution of artificial fog particles, it is important that the natural state of the particles not be disturbed by the measuring device, such as occurs when samples are drawn through tubes. This paper describes a method for carrying out such a measurement by allowing the fog particles to settle in quiet air inside an enclosure through which traverses a parallel beam of light for measuring the optical depth as a function of time. An analytic function fit to the optical depth time decay curve can be directly inverted to yield the size distribution. Results of one such experiment performed on artificial fogs are shown as an example. The forwardscattering corrections to the measured extinction coefficient are also discussed with the aim of optimizing the experimental design so that the error due to forwardscattering is minimized.

Deepak, A.↗
DOE OSTI2020

An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics

Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this paper, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.

36 MATERIALS SCIENCE↗
DOE OSTI2018

High-Throughput Material Characterization using the Virtual Fields Method

Modeling material and component behavior using finite element analysis (FEA) is critical for modern engineering. One key to a credible model is having an accurate material model, with calibrated model parameters, which describes the constitutive relationship between the deformation and the resulting stress in the material. As such, identifying material model parameters is critical to accurate and predictive FEA. Traditional calibration approaches use only global data (e.g. extensometers and resultant force) and simplified geometries to find the parameters. However, the utilization of rapidly maturing full-field characterization techniques (e.g. Digital Image Correlation (DIC)) with inverse techniques (e.g. the Virtual Feilds Method (VFM)) provide a new, novel and improved method for parameter identification. This LDRD tested that idea: in particular, whether more parameters could be identified per test when using full-field data. The research described in this report successfully proves this hypothesis by comparing the VFM results with traditional calibration methods. Important products of the research include: verified VFM codes for identifying model parameters, a new look at parameter covariance in material model parameter estimation, new validation techniques to better utilize full-field measurements, and an exploration of optimized specimen design for improved data richness.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Site B - NREL ASSIST (SN11) Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 11) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.

17 WIND ENERGY↗
DOE OSTI2025

Site G - NREL ASSIST (SN10) Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 10) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.

17 WIND ENERGY↗
DOE OSTI2025

Site C1a - NREL ASSIST (SN12) Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 12) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.

17 WIND ENERGY↗
NASA NTRS1991

Application of waverider-based configurations to hypersonic vehicle design

The hypersonic waverider concept is explored in the context of aircraft design and optimization. Waveriders are vehicles which provide the highest known values of lift-to-drag ratio, suggesting that they are ideal for hypersonic cruise applications. However, these high lift-to-drag ratios are not retained with engine integration. It is shown that waveriders may have applications to the design of accelerating vehicles, even though high L/D is no longer a priority, because they exhibit relatively low drag, and have highly desirable properties for engine/airframe integration. Along these lines, waveriders provide highly uniform flowfields, making them ideal for forebodies on engine-airframe integrated configurations. The inverse design methodology of a waverider lends itself readily to the vehicle design process. Characteristics which govern the performance of a hypersonic vehicle for cruise and accelerating flight are examined, and applied to waverider vehicle design. Finally, it is shown that waveriders, though configured for the specific shock flowfield of a single design point, have acceptable off-design performance characteristics.

Lewis, Mark J.↗
NASA NTRS2005

Constraining Oceanic Sources and Sinks of CO2 by Inverse Modeling: A First Step Toward Assimilation of In Situ and Remote Data

The primary goal of this collaborative project, lead by Nicholas Gruber at UCLA, is to better quantify the air-sea fluxes of pre-industrial and anthropogenic carbon dioxide CO2 for the global ocean with a newly developed inverse method. The method involves using ocean general circulation models (OGCMs) to characterize the subsurface distribution of tracers injected for different regions of the ocean surface. By linearly combining a series of these basis (or Greens) functions, one should be able to recreate any arbitrary ocean tracer field. In our case, we are using the global database of ocean dissolved inorganic carbon (DIC) from the WOCE/JGOFS global CO2 survey from the 1990s. The observed DIC field has been partitioned into pre-industrial and anthropogenic components using the empirical C* technique. We then solve for an optimal combination of the regional basis functions that best match the two sets observations in a least-squares sense with singular value decomposition. This in turn provides solutions for the regional air-sea CO2 fluxes (pre-industrial and anthropogenic) that are independent of more traditional estimates derived from surface water disequilibria in the partial pressure of CO2 and wind speed gas exchange parameterizations. We are apply the method to a suite of nine different OGCMs to test the robustness of the derived surface fluxes fields to differences in ocean circulation. Together with a collegue Keith Lindsay at NCAR, S. Doney's role in this project was to create the appropriate steady-state and temporally evolving basis functions from the NCAR CSM-1 ocean circulation model. Because of a slow start across the project in defining a common set of experimental protocols, we requested and were given a one year, no cost extension. Both the steady-state and temporally evolving basis functions were delivered to the UCLA group for further analysis. Additional information is included in the original extended abstract.

Doney, Scott↗