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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Implementation of higher-order velocity mapping between marker particles and grid in the particle-in-cell code XGC

The global total-f gyrokinetic particle-in-cell code XGC, used to study transport in magnetic fusion plasmas or to couple with a core gyrokinetic code while functioning as an edge gyrokinetic code, implements a 5-dimensional (5D) continuum grid to perform the dissipative operations, such as plasma collisions, or to exchange the particle distribution function information with a core code. To transfer the distribution function between marker particles and a rectangular 2D velocity-space grid, XGC employs a bilinear mapping. The conservation of particle density and momentum is accurate enough in this bilinear operation, but the error in the particle energy conservation can become undesirably large and cause non-negligible numerical heating in a steep edge pedestal. In the present work we update XGC to use a novel mapping technique, based on the calculation of a pseudo-inverse, to exactly preserve moments up to the order of the discretization space. We describe the details of the implementation and we demonstrate the reduced interpolation error for a tokamak test plasma by using 1st- and 2nd-order elements with the pseudo-inverse method and comparing to the bilinear mapping.

Fusion Plasma↗
DOE OSTI2020

XRD-PUAT (X-Ray Diffraction - Parameter Uncertainty Analysis Toolkit)

This XRD uncertainty toolkit was built to investigate uncertainty and local least squares topology over a specified parameter space on a single histogram in a gpx GSAS-II file. The parameter uncertainty can be investigated using either a frequentist F-test approach or a Bayesian Inference statistical inversion method on a weighted least squares or peak fit refinements.SAND Number: SAND2020-12227 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Moore, Alexander↗
DOE OSTI2023

Membrane Adsorbents Comprising Self-Assembled Inorganic Nanocages (SINCs) for Super-fast Direct Air Capture Enabled by Passive Cooling

The objectives of the proposed project were to develop highly porous membrane adsorbents comprising CO 2 -philic polymers and self-assembled inorganic nanocages (SINCs) for rapid temperature swing adsorption using electricity-free solar heating and radiative cooling, enabling an economically viable approach for direct air capture (DAC). Our core technical activities combine three key innovations. (1) Highly porous flat-sheet membrane adsorbents contain CO 2 -philic amines that can be easily produced using a phase inversion method. (2) CO 2 -philic SINCs can be easily dispersed in the polymers with great stability (compared with the metal-organic frameworks or MOFs). (3) The adsorption and desorption are integrated with solar heating and radiative cooling for rapid continuous operation, in contrast to traditional long-cycle separate operation. The membrane adsorbents containing amines, polymers, and SINCs were produced using a one-step industrial process. The porous membranes coupled with porous SINCs offer low resistance for gas flow and fast CO 2 sorption/desorption cycles, while the incorporation of the additional amine groups provides high CO 2 sorption capacity. The key achievements are summarized below. (1) Membrane adsorbents with high PEI loading (>40%), high porosity of >80%, and low gasflow resistance were prepared in one step using commercially available, low-cost materials. (2) Membrane adsorbents based on Solupor and PEI show CO 2 sorption capacity of >1.5 mmol/g using air containing 400 ppm at a relative humidity of 15%. (3) Effect of the adsorbent compositions (such as PEI type, PEI content, SINC content, porosity) on the CO 2 sorption was systematically investigated. (4) Effect of the processing conditions (such as CO 2 content, temperature, and relative humidity) on the CO 2 sorption was systematically investigated; (5) The stability of the membrane adsorption against many cycles of sorption and desorption was studied. The higher molecular weight of PEI (PEI25k) shows better stability than PEI800. (6) Advanced materials with radiative cooling were developed, which can decrease the temperature by 5-7 °C compared to the ambient temperature. (7) Preliminary techno-economic analysis shows that our process may achieve a capture cost of $1,343/tonne CO 2 with a total OPEX cost of $1,112/tonne CO 2 . The adsorbent replacement cost accounted for 52% of the total OPEX cost. Membrane adsorbents with lower costs and longer operation life can significantly decrease the cost. The proposed project directly addresses the requirement of DE-FOA-0002188, i.e., novel materials with CO 2 adsorption capacity for direct air capture with integrated solar heating and radiative cooling to reduce the cost of the DAC. Our future work will focus on the development of low-cost adsorbents that can be stable at the sorption and desorption conditions for long term.

14 SOLAR ENERGY↗
DOE OSTI2022

A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data

Infrared hyperspectral remote sensing has been widely used in the field of meteorology. Many scientists have carried out research on inversion methods of meteorological elements such as thermodynamic profile, boundary layer height, cloud base height, etc. In this study, a method based on machine learning for cloud detection using ground-based infrared hyperspectral radiation data is proposed. The features of outliers, the cloudy and cloud-free data of Atmospheric Emitted Radiance Interferometer (AERI) radiation are extracted. The “reference values” of cloudy and cloud-free are determined based on the observation data of Vaisala CL31 ceilometer within the time range of 8 min before the corresponding time of AERI. A support vector machine (SVM) algorithm is used for training. The dataset comes from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and North Slope Alaska (NSA) site from 2015 to 2017, and the ARM West Antarctic Radiation Experiment (AWARE) site in 2016 is also analyzed. The instruments used in this paper include AERI, ceilometer, etc. The experimental results reveal that the agreement of cloud detection results between the proposed algorithm and ceilometer is about 93% at each site. However, for high clouds or optically thin clouds, the agreement will decrease.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

SuperScreen: An open-source package for simulating the magnetic response of two-dimensional superconducting devices

Quantitative understanding of the spatial distribution of magnetic fields and Meissner screening currents in two-dimensional (2D) superconductors and mesoscopic thin film superconducting devices is critical to interpreting the results of magnetic measurements of such systems. Here, we introduce SuperScreen, an open-source Python package for simulating the response of 2D superconductors to trapped flux and applied time-independent or quasi-DC magnetic fields for any value of the effective magnetic penetration depth, Λ. Given an applied magnetic field, SuperScreen solves the 2D London equation using an efficient matrix inversion method to obtain the Meissner currents and magnetic fields in and around structures composed of one or more superconducting thin films of arbitrary geometry with spatially nonuniform magnetic penetration depth. SuperScreen can be used to model screening effects and calculate self- and mutual-inductance in superconducting devices, and simulate the magnetic response of inhomogeneous 2D superconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Recent Progress in U-10Mo Mechanical and Thermophysical Property Characterization

Results are presented on the mechanical and thermophysical property characterization of U-10Mo as part of the MP-1 qualification campaign. The thermal diffusivity, specific heat and thermal conductivity of as fabricated U-10Mo fuel foils and plates were measured using the thermal conductivity microscope (TCM), differential scanning calorimeter (DSC) and laser flash analyzer (LFA) all of which located in a shielded glove box – thermal property cell (TPC). A novel inverse method based on finite element analysis was developed to evaluate the thermal diffusivity of composite/layered materials via the LFA technique such as the 5-lyered U-10Mo monolithic fuel mini-plates.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Recent Progress in U-10Mo Mechanical and Thermophysical Property Characterization -- Poster

Results are presented on the mechanical and thermophysical property characterization of U-10Mo as part of the MP-1 qualification campaign. The thermal diffusivity, specific heat and thermal conductivity of as fabricated U-10Mo fuel foils and plates were measured using the thermal conductivity microscope (TCM), differential scanning calorimeter (DSC) and laser flash analyzer (LFA) all of which located in a shielded glove box – thermal property cell (TPC). A novel inverse method based on finite element analysis was developed to evaluate the thermal diffusivity of composite/layered materials via the LFA technique such as the 5-lyered U-10Mo monolithic fuel mini-plates.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Calculation of Critical Heat Flux Using an Inverse Heat Transfer Method to Support TREAT Experiment Analysis

Heat transfer between cladding and coolant during transient scenarios remains a critical area of uncertainty in understanding nuclear reactor safety. To advance the understanding of transient and accident scenarios involving critical heat flux (CHF), an in-pile experiment for the Transient Reactor Test (TREAT) facility at Idaho National Laboratory was developed. The experiment named CHF-Static Environment Rodlet Transient Test Apparatus (CHF-SERTTA), consists of a hollow borated stainless steel heater rod submerged in a static water pool which is heated via the (n, a) reaction in boron-10. This paper presents a novel inverse heat transfer method to determine CHF by using the optimization and uncertainty software DAKOTA to calibrate a RELAP5-3D model of CHF-SERTTA to temperature measurements obtained from a thermocouple welded to the surface of the rod.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Inverse deep learning methods and benchmarks for artificial electromagnetic material design

In this work we investigate the use of deep inverse models (DIMs) for designing artificial electromagnetic materials (AEMs) – such as metamaterials, photonic crystals, and plasmonics – to achieve some desired scattering properties (e.g., transmission or reflection spectrum). DIMs are deep neural networks (i.e., deep learning models) that are specially-designed to solve ill-posed inverse problems. There has recently been tremendous growth in the use of DIMs for solving AEM design problems however there has been little comparison of these approaches to examine their absolute and relative performance capabilities. In this work we compare eight state-of-the-art DIMs on three unique AEM design problems, including two models that are novel to the AEM community. Our results indicate that DIMs can rapidly produce accurate designs to achieve a custom desired scattering on all three problems. Although no single model always performs best, the Neural-Adjoint approach achieves the best overall performance across all problem settings. As a final contribution we show that not all AEM design problems are ill-posed, and in such cases a conventional deep neural network can perform better than DIMs. We recommend that a deep neural network is always employed as a simple baseline approach when addressing AEM design problems. Furthermore, we publish python code for our AEM simulators and our DIMs to enable easy replication of our results, and benchmarking of new DIMs by the AEM community.

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

Neural-adjoint method for the inverse design of all-dielectric metasurfaces

All-dielectric metasurfaces exhibit exotic electromagnetic responses, similar to those obtained with metal-based metamaterials. Research in all-dielectric metasurfaces currently uses relatively simple unit-cell designs, but increased geometrical complexity may yield even greater scattering states. Although machine learning has recently been applied to the design of metasurfaces with impressive results, the much more challenging task of finding a geometry that yields a desired spectra remains largely unsolved. We propose and demonstrate a method capable of finding accurate solutions to ill-posed inverse problems, where the conditions of existence and uniqueness are violated. A specific example of finding the metasurface geometry which yields a radiant exitance matching the external quantum efficiency of gallium antimonide is demonstrated. We also show how the neural-adjoint method can intelligently grow the design search space to include designs that increasingly and accurately approximate the desired scattering response. The neural-adjoint method is not restricted to the case demonstrated and may be applied to plasmonics, photonic crystal, and other artificial electromagnetic materials.

42 ENGINEERING↗
DOE OSTI2024

A New Galerkin Quadrature Method Not Requiring a Matrix Inverse

We derive a new Galerkin quadrature (GQ) method for S 𝑛 calculations that differs from the two methods preceding it in that a matrix inverse for an 𝑁 𝑑 × 𝑁 𝑑 matrix, where 𝑁𝑑 is the number of directions in the quadrature set, is no longer required. Galerkin quadrature methods are designed for calculations with highly anisotropic scattering. Such methods are not simply special angular quadratures but also are methods for representing the S 𝑛 scattering source that offers several advantages relative to the standard scattering source representation when highly truncated Legendre cross-section expansions must be used. Galerkin quadrature methods are also useful when the scattering is moderately anisotropic, but the quadrature being used is not sufficiently accurate for the order of the scattering source expansion that is required. Furthermore, we derive the new method and present computational results showing that its performance for two challenging problems is comparable to those of the two GQ methods that preceded it.

Galerkin quadrature↗
DOE OSTI2025

Using DAPPER to extract the photon strength function of 58 Fe using the inverse Oslo and shape methods

The photon strength function of 58 Fe has been extracted using both the Oslo and Shape methods from particle–γ coincidence data measured using the Detector Array for Photons, Protons, and Exotic Residues, which probes nuclei utilizing (d,p) reactions in inverse kinematics. Four particle–γ coincidence matrices, each constructed with different treatments of the γ–ray energies, are explored in order to observe the impact on the resulting nuclear level density and photon strength. The final photon strength function reported is found to agree well with previous Oslo measurements of other iron isotopes. Systematic uncertainties are included, using different model parameters and their reported errors to perform the Oslo method normalization. The model-independent Shape method is explored and the functional form of the photon strength function obtained is in agreement with the Oslo method results. A low-energy enhancement is not reported for 58 Fe in this work given possible subtraction issues originating from strongly populated states.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Using DAPPER to extract the photon strength function of 58Fe using the inverse Oslo and shape methods

The photon strength function of 58 Fe has been extracted using both the Oslo and Shape methods from particle-γ coincidence data measured using the Detector Array for Photons, Protons, and Exotic Residues, which probes nuclei utilizing (d,p) reactions in inverse kinematics. Four particle-γ coincidence matrices, each constructed with different treatments of the γ-ray energies, are explored in order to observe the impact on the resulting nuclear level density and photon strength. The final photon strength function reported is found to agree well with previous Oslo measurements of other iron isotopes. Systematic uncertainties are included, using different model parameters and their reported errors to perform the Oslo method normalization. The model-independent Shape method is explored and the functional form of the photon strength function obtained is in agreement with the Oslo method results. A low-energy enhancement is not reported for 58 Fe in this work given possible subtraction issues originating from strongly populated states.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Grand canonical inverse design of multicomponent colloidal crystals

Inverse design methods are powerful computational approaches for creating colloidal systems which self-assemble into a target morphology by reverse engineering the Hamiltonian of the system. Despite this, these optimization procedures tend to yield Hamiltonians which are too complex to be experimentally realized. An alternative route to complex structures involves the use of several different components, however, conventional inverse design methods do not explicitly account for the possibility of phase separation into compositionally distinct structures. We present an inverse design scheme for multicomponent colloidal systems by combining active learning with a method to directly compute their ground state phase diagrams. This explicitly accounts for phase separation and can locate stable regions of Hamiltonian parameter space which grid-based surveys are prone to miss. Using this we design low-density, binary structures with Lennard-Jones-like pairwise interactions that are simpler than in the single component case and potentially realizable in an experimental setting. This reinforces the concept that ground states of simple, multicomponent systems might be rich with previously unappreciated diversity, enabling the assembly of non-trivial structures with only few simple components instead of a single complex one.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Application of Markov Chain Monte Carlo Methods for Uncertainty Quantification in Inverse Transport Problems

Determination of the components of a radioactive source/shield system using the system’s radiation signature is of great importance in homeland security, material safeguards, and waste management. Although significant progress has been made toward solving this inverse transport problem in recent years, work remains to be done to quantify the uncertainty in reconstructed results. In this article we apply two Markov chain Monte Carlo (MCMC) approaches, the delayed rejection adaptive metropolis (DRAM) and differential evolution adaptive metropolis (DREAM) methods, to solve inverse problems and quantify uncertainty. The DRAM method uses delayed rejection combined with global adaptation of the proposal covariance matrix. Furthermore, the DREAM method hybridizes MCMC sampling with the differential evolution (DE) algorithm. In numerical test cases, the DRAM and DREAM methods are shown to be superior to a first-order inverse Hessian approach for problems with noisy data and multiple unknown quantities, with DREAM converging to the posterior distribution more quickly than DRAM. The DREAM and DRAM results indicate that a full posterior distribution is required to quantify uncertainty in many inverse transport problems.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗