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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

HighNESS conceptual design report: Volume II. The NNBAR experiment.

A key aim of the HighNESS project for the European Spallation Source is to enable cutting-edge particle physics experiments. This volume presents a conceptual design report for the NNBAR experiment. NNBAR would exploit a new cold lower moderator to make the first search in over thirty years for free neutrons converting to anti-neutrons. The observation of such a baryon-number-violating signature would be of fundamental significance and tackle open questions in modern physics, including the origin of the matter-antimatter asymmetry. This report shows the design of the beamline, supermirror focusing system, magnetic and radiation shielding, and anti-neutron detector necessary for the experiment. A range of simulation programs are employed to quantify the performance of the experiment and show how background can be suppressed. For a search with full background suppression, a sensitivity improvement of three orders of magnitude is expected, as compared with the previous search. Civil engineering studies for the NNBAR beamline are also shown, as is a costing model for the experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Mu2e experiment: Simulation of the Production Target

The Mu2e experiment will search for coherent, neutrinoless conversion of muons to elections in an aluminum stopping target. A simulation program is used to experiment and design the production target of the Mu2e experiment. The module uses programing languages and libraries to work. The program called the Offline uses C++ as the programing language in the module. To construct the simulation, the Geant4 library constructs shapes and uses them in the simulation program, ROOT. The offline program simplifies the creation of the tube by organizing them into parameter objects and nesting them with other variables required to construct the tube-like center point, material, or if it is allowed to conflict with other solids. The parameter varies for every solid tube. The geometry file uploads the parameters by declaring and organizing the parameters in C++ using the configuration tools. The author describes the development and the future of this project.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Mu2e experiment: Simulation of the Production Target

The Mu2e experiment will search for coherent, neutrinoless conversion of muons to elections in an aluminum stopping target. A simulation program is used to experiment and design the production target of the Mu2e experiment. The module uses programing languages and libraries to work. The program called the Offline uses C++ as the programing language in the module. To construct the simulation, the Geant4 library constructs shapes and uses them in the simulation program, ROOT. The offline program simplifies the creation of the tube by organizing them into parameter objects and nesting them with other variables required to construct the tube-like center point, material, or if it is allowed to conflict with other solids. The parameter varies for every solid tube. The geometry file uploads the parameters by declaring and organizing the parameters in C++ using the configuration tools. The author describes the development and the future of this project.

Szewczyk, Albert↗
DOE OSTI2022

Enabling High-Energy-Density Physics Using X-Ray Free Electron Lasers

New High-Energy-Density (HED) Physics research facilities, such as x-ray free electron lasers (XFEL) are becoming available to the research community. These new tools can be used to address stockpile stewardship science questions in the HED regime with greater accuracy and in parameter ranges not previously accessible. Two important challenges that must be overcome before using the XFEL are developing simulation tools needed to design the experiment and developing large quantities of inexpensive targets from which the HED state is created. We developed two-dimensional simulations in a new geometry (planar) to determine basic plasma parameters that could be expected in a notional experiment. We developed a postprocessor that used the results of the simulations to predict the x-ray Thomson scattering (XRTS) signal that yield the plasma density and pressure at a specified location and time. Finally, we developed several possible target geometries that are inexpensive and easy to use. With these tools in hand, the risk in designing an experiment for an XFEL is significantly reduced.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The Material Plasma Exposure eXperiment: Mission and conceptual design

Mastering Plasma Material Interactions (PMI) is key for obtaining a high performance, high duty-cycle and safe operating fusion reactor. Numerous gaps exist in PMI which have to be addressed before a reactor can be built. In particular the lack of data at high ion fluence, fusion reactor divertor relevant plasma conditions and neutron displacement damage requires new experimental devices to be able to develop plasma facing materials and components. This has been recognized in the community and the U.S. fusion program is addressing this need with a new linear plasma device—the Material Plasma Exposure eXperiment (MPEX). MPEX will be a superconducting linear plasma device with magnetic fields of up to 2.5 T. The plasma source is a high-power helicon source (200 kW, 13.56 MHz). The electrons will be heated via Electron Bernstein Waves with microwaves using multiple 70 GHz gyrotrons (up to 600 kW in total). Ions will be heated via ion cyclotron heating in the so-called “magnetic beach heating” scheme in the frequency range of 6–9 MHz (up to 400 kW in total). An overview of the conceptual design and the project/design requirements is given in this paper.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Comprehensive symmetric-hybrid ring design for a proton EDM experiment at below 10 –29 e · cm

A concise demonstrative summary of the Symmetric Hybrid ring design for the storage ring proton electric dipole moment experiment is presented. In this paper, critical issues such as lattice design, background electrical fields, geometrical phase, general relativity, spin coherence time and polarimeter systematics are presented. Overall, we find that with the currently proposed design iteration, systematic error sources are reduced by orders of magnitude and that the ring alignment requirements are within the currently available technology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Conceptual design of HFIR irradiation experiment for material compatibility study on liquid Sn divertor

Liquid Sn is one of the promising coolants for liquid surface divertor concept of fusion reactors. However, the compatibility between liquid Sn and structural materials is an important issue that has to be addressed, because liquid Sn is extremely corrosive to steels at high temperatures. The corrosion may be mitigated when a protective Al2O 3 layer is formed on the surface of alumina forming steels. However, the effect of neutron irradiation on the integrity of protective layer is not made clear so far. Japan and US joint research project “FRONTIER” started in 2019 to investigate the material compatibility under neutron irradiation. The purpose of the present study is to develop the conceptual design of the irradiation test capsule which enables material compatibility tests for the alumina forming steels - liquid metal systems under neutron irradiation in the High Flux Isotope Reactor at Oak Ridge National Laboratory, TN, USA. The three dimensional drawing of capsule structure was then developed. The validity of the material selections for the capsule design was investigated by means of corrosion tests of SiC, Si3N4, Ti, and Mo in liquid Sn at 773 K for 262 hr.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.

Renganathan, S. Ashwin↗
DOE OSTI2025

Design Optimization of a Criticality Experiment for the Molten Chloride Reactor Experiment Facility

Neutronics simulations of Molten Chloride Fast Reactors have quantifiable biases that arise from nuclear data, modeling choices, or numerical methods. The multiphysics nature of molten salt reactors makes it challenging to disentangle neutronics modeling biases from biases originating from other physical phenomena. In comparison to a mock-up reactor, criticality experiments can specifically assess the neutronics modeling bias while limiting multiphysics effects. The criticality experiment must be neutronically representative of the full-scale reactor to be valuable. Here, in this paper, we describe the design of a criticality experiment to validate only the neutronics of TerraPower’s Molten Chloride Reactor Experiment (MCRE) and its criticality safety upset scenarios. The proposed experiment uses different chlorine-containing materials to maximize its similarity to the MCRE. The design process uses a constrained Bayesian optimization algorithm to investigate different objective functions that use covariance information for 35 Cl nuclear data. The experiments could reduce the nuclear data–induced uncertainty in k eff of the MCRE from 2161 to 886 pcm. They would also increase the upper subcritical limit of the MCRE criticality safety upset scenario from 0.94101 to 0.94476 when using the WHISPER analysis framework.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Optimizing enzymes for plastic upcycling using machine learning design and high throughput experiments

Plastic use is ubiquitous in the modern world, and polyethylene terephthalate (PET) is one of the most abundantly produced plastics (and the most highly produced polyester), with ~65 million metric tons manufactured annually. To the consumer, PET is likely most recognizable as the plastic used to make beverage bottles. Like many plastics, traditional mechanical or chemical means of PET deconstruction and upcycling are costly and inefficient. Because of these challenges, recycled plastic is generally of lower quality and is more expensive to produce than virgin plastic derived from petroleum. Ultimately, this results in most plastic ending up as waste. We view plastic waste as an underutilized resource which, with the development of more efficient and high-quality recycling processes, could (1) generate significant economic value while (2) decreasing petroleum usage and greenhouse gas emissions, as well as (3) minimizing its negative environmental and health impacts. Biocatalytic recycling, or biomanufacturing the basic building blocks of new plastic from plastic waste, is a promising approach to plastic reuse that complements existing recycling technologies. Recently, biological enzymes capable of breaking down PET have garnered significant attention as an attractive means of dealing with the plastic problem. These enzymes are currently undergoing pilot studies for implementation in industrial-scale enzyme-based recycling. However, there are significant limitations to current enzymes, including the need to perform costly pre-processing of the plastic waste before the enzymes are able to work. Further optimization of these enzymes is necessary to make these technologies competitive, and ultimately incentivise industry-wide adoption of this biology-based green recycling technology. n this work we demonstrate a means to design and generate performant biological enzymes, capable of efficiently deconstructing plastic waste. Specifically, we applied recent advances in artificial intelligence, machine learning, and statistical analysis to design new versions and discover natural enzymes capable of breaking down PET. We focused on optimizing key properties that are important for industrial-scale enzymatic recycling such as pH and thermotolerance. Normal testing of enzymatic plastic-deconstruction is extremely labor intensive and so through this work we also developed a robotic-assisted experimental pipeline capable of characterizing thousands of candidate enzymes. The results of this iterative, AI-guided, multi-discipline approach have led to increases in enzymatic breakdown of over 150X over starting enzymes. This work supports the rapidly developing and transformative field of biocatalytic solutions to environmental problems beyond the discovery and predictive understanding of enzymes for polymer recycling, and has wide implications for tackling numerous energy problems such as carbon capture and fixation (e.g., engineering carbon monoxide dehydrogenase and the rubisco-pathway), biomining (e.g., design of lanthanide-binding proteins) and biomanufacturing (e.g., lignin-deconstruction enzymes).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Effects of groundwater pumping on ground surface temperature: A regional modeling study in the North China Plain

Over-exploitation of groundwater (GW) in the North China Plain (NCP) since the 1960s has many environmental consequences. However, understanding of the dominant mechanisms remains limited, particularly at regional scale. In this study, the coupled ParFlow.CLM model representing subsurface and land-surface processes and their interactions was applied in the NCP at high spatio-temporal resolutions. The model was validated using the water and energy fluxes reported in previous studies and from the JRA-55 reanalysis. Numerical experiments were designed to examine the relative impacts of GW pumping and irrigation on the ground surface temperature (GST). Results show significant effects of GW pumping on GST in the NCP. Generally, the subsurface acts as a buffer to temporal variations in heat fluxes at the land-surface, but long-term pumping can gradually weaken this buffer, resulting in increases in the spatio-temporal variability of GST, as exemplified by hotter summers and colder winters. Considering that changes of water table depth (WTD) can significantly affect land surface heat fluxes when WTD ranges roughly between 0.01–10 m, the 0.5 m/year increase of WTD simulated by the model due to pumping can continue to increase the average WTD and hence, disturb GST, for about 20 years from the pre-pumping WTD in the NCP, before the WTD exceeds 10 m. The variations of GST are expected to be faster initially and gradually slow down due to the nonlinear behaviors of GST with WTD. The findings from this study in the NCP may also have implications for other regions with GW depletion.

Yang, Chen↗
DOE OSTI2022

Sensing depths in frequency domain thermoreflectance

In this work, a method is developed to calculate the length into a sample to which a Frequency Domain Thermoreflectance (FDTR) measurement is sensitive. Sensing depth and sensing radius are defined as limiting cases for the spherically spreading FDTR measurement. A finite element model for FDTR measurements is developed in COMSOL multiphysics and used to calculate sensing depth and sensing radius for silicon and silicon dioxide samples for a variety of frequencies and laser spot sizes. The model is compared to experimental FDTR measurements. Design recommendations for sample thickness are made for experiments where semi-infinite sample depth is desirable. For measurements using a metal transducer layer, the recommended sample thickness is three thermal penetration depths, as calculated from the lowest measurement frequency.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Novel Hot-Spot Ignition Designs for Inertial Confinement Fusion with Liquid-Deuterium-Tritium Spheres

A new class of ignition designs is proposed for inertial confinement fusion (ICF) experiments. These designs are based on the hot-spot-ignition approach, but instead of conventional targets that comprise of spherical shells with thin frozen deuterium{tritium (DT) layers, homogeneous-density (solid or liquid) DT spheres are used where the lower-density central region and higher-density shell are created dynamically by appropriately shaping the laser pulse. These offer several advantages, including simplicity in target production and lower sensitivity to both laser imprint and physics uncertainty in shock interaction with the ice-vapor interface. The design evolution starts by launching an ~1-Mbar shock into a homogeneous DT sphere. After bouncing from the center, the reflected shock reaches the outer surface of the sphere and the shocked material starts to expand outward until its pressure drops below the ablation pressure. At this point, an adjustment shock is launched inward by supporting ablation pressure. This shock compresses the ablator and fuel, forming a shell. The shell is then accelerated and compressed by appropriately shaping the drive laser pulse, similar to the conventional thin-shell, hot-spot designs. Here, this paper demonstrates the feasibility of the new concept using hydrodynamic simulations and discusses the advantages and disadvantages of the concept compared with more-traditional ICF designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗