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All sources NASA NTRS DOE OSTI

DOE OSTI2019

ASCR Workshop on In Situ Data Management: Enabling Scientific Discovery from Diverse Data Sources

In January 2019, the U.S. Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions for in situ data management (ISDM). The workshop defined ISDM as the practices, capabilities, and procedures to control the organization of data and enable the coordination and communication among heterogeneous tasks, executing simultaneously in a high-performance computing system, cooperating toward a common objective. The workshop revealed two primary, interdependent motivations for processing and managing data in situ. The first motivation is that the in situ methodology enables scientific discovery from a broad range of data sources over a wide scale of computing platforms: leadership-class systems, clusters, clouds, workstations, and embedded devices at the edge. The successful development of ISDM capabilities will benefit real-time decision-making, design optimization, and data-driven scientific discovery. The second motivation is the need to decrease data volumes. ISDM can make critical contributions to managing large data volumes from computations and experiments to minimize data movement, save storage space, and boost resource efficiency, often while simultaneously increasing scientific precision. A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery across the Office of Science mission space. Applications wanting to use the in situ capabilities include those where data analysis feeds back to the simulation, decisions are made autonomously, big data or machine learning is among the tasks to be coordinated, and computations need to be completed in real time. The workshop identified six priority research directions that highlight the components and capabilities needed for ISDM to be successful for the wide variety of applications discussed: making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. Here we introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms and supports both automated an d human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. Here, the predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Implementation of cross correlation for energy discrimination on the time-of-flight spectrometer CORELLI

The CORELLI instrument at Oak Ridge National Laboratory is a statistical chopper spectrometer designed and optimized to probe complex disorder in crystalline materials through diffuse scattering experiments. On CORELLI, the high efficiency of white-beam Laue diffraction combined with elastic discrimination have enabled an unprecedented data collection rate to obtain both the total and the elastic-only scattering over a large volume of reciprocal space from a single measurement. To achieve this, CORELLI is equipped with a statistical chopper to modulate the incoming neutron beam quasi-randomly, and then the cross-correlation method is applied to reconstruct the elastic component from the scattering data. Details of the implementation of the cross-correlation method on CORELLI are given and its performance is discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Performance of a second generation of the Fermilab Constant Fraction Discriminator ASIC for time-stamping long-strip AC-LGAD sensors.

We present the design and performance characterization results of the second generation of the novel Fermilab Constant Fraction Discriminator ASIC (FCFD) developed to readout AC-coupled low gain avalanche detector (AC-LGAD) sensors. This study presents the performance of the ASIC which was optimized specifically for reading out strip AC-LGAD sensors designed for ePIC experiment at EiC. Performance was measured using charge injection and particle beams with prototype AC-LGAD sensors wirebonded to the FCFD ASIC.

Apresyan, A.↗

DOE OSTI2018

Design and demonstration of tunable soft x-ray lateral shearing and Hartmann wavefront sensors

We describe design guidelines for soft x-ray wavefront sensors and experimentally demonstrate their performance, comparing grating-based lateral shearing interferometry and Hartmann wavefront sensing. We created a compact shearing interferometer concept with a dense array of binary amplitude gratings in a single membrane to support one-dimensional wavefront measurements across a wide wavelength range without the need for longitudinal position adjustment. We find that a common scaling parameter based on wavelength and the distance to the measurement plane guides the design of both systems toward optimal sensitivity. We show preliminary results from recent experiments demonstrating one and two-dimensional wavefront sensing below the Marechal criterion.

Chao, Weilun↗

NASA NTRS1991

Operational perspectives for performing microgravity science in orbit

The combination of automation and operator-based technologies is discussed with respect to orbital microgravity experiments in order to optimize the performance and results. A list of five design objectives is presented, and each item is examined in terms of on-orbit flight history to identify relevant examples. It is suggested that human factors be considered in hardware design and that in-flight maintenance operations be considered an integral part of a program. The operational design can produce more effective results when it is established for 'failures' and mission success operations. The design must also be approached in terms of both the limitations and advantages of microgravity. The overarching theme of the investigation is that the mix of automation and operator interaction should be optimized by automating routine tasks and using the operator for critical assessments.

Dunbar, Bonnie J.↗

NASA NTRS2003

The Role of Design-of-Experiments in Managing Flow in Compact Air Vehicle Inlets

It is the purpose of this study to demonstrate the viability and economy of Design-of-Experiments methodologies to arrive at microscale secondary flow control array designs that maintain optimal inlet performance over a wide range of the mission variables and to explore how these statistical methods provide a better understanding of the management of flow in compact air vehicle inlets. These statistical design concepts were used to investigate the robustness properties of low unit strength micro-effector arrays. Low unit strength micro-effectors are micro-vanes set at very low angles-of-incidence with very long chord lengths. They were designed to influence the near wall inlet flow over an extended streamwise distance, and their advantage lies in low total pressure loss and high effectiveness in managing engine face distortion. The term robustness is used in this paper in the same sense as it is used in the industrial problem solving community. It refers to minimizing the effects of the hard-to-control factors that influence the development of a product or process. In Robustness Engineering, the effects of the hard-to-control factors are often called noise , and the hard-to-control factors themselves are referred to as the environmental variables or sometimes as the Taguchi noise variables. Hence Robust Optimization refers to minimizing the effects of the environmental or noise variables on the development (design) of a product or process. In the management of flow in compact inlets, the environmental or noise variables can be identified with the mission variables. Therefore this paper formulates a statistical design methodology that minimizes the impact of variations in the mission variables on inlet performance and demonstrates that these statistical design concepts can lead to simpler inlet flow management systems.

Anderson, Bernhard H.↗

NASA NTRS2000

SHIVA (Spaceflight Holography Investigation in a Virtual Apparatus)

This paper provides the description and results of a ground-based experiment designed to support the spaceflight program SHIVA, which started its flight definition in 1998. SHIVA will apply new diagnostic tools and experimental techniques to test the validity of a newly discovered analytical solution to the general equation of motion of a particle in a fluid. We have designed a spaceflight experiment with the help of the theoretical model that is optimized for testing the model, and for measuring g, g-jitter, and other microgravity phenomena. Our ongoing, ground-based particle/fluid experiment supports both the experimental and theoretical aspects of the project. The ultimate spaceflight experiment will be similar to the ground-based experiment. The "virtual spaceflight chamber" concept asserts that certain spaceflight experiments can be recorded in holograms in such a manner that having the holograms on earth is optically equivalent to being back in space with unlimited time to conduct the experiment. Properly exploited, this concept can save a significant amount of experiment time in space by effectively bringing the. experiment optically back to earth. SHIVA will accomplish the following: record a large number of holograms of particle fields in space under controlled conditions, extract the precise, three-dimensional position of all of the particles as a function of time, examine the effects of all parameters on the motion of the particles, and test these against predictions of the Coimbra-Range1 solution to the general equation of motion. Particle sizes will range from hundreds of microns up to about 2 mm in diameter and will cover a range of densities and fluid viscosities. Forcing functions will be introduced onto the particle field, including at least the following: a) No isolation in the Spacelab vibration environment. b) Isolation from Spacelab. c) Oscillatory motion from 1 to 100 Hz with amplitudes of a few millimeters. d) Convective fields to be introduced mechanically. The first objective of this research is to understand the physics of particle interactions with fluids and other particles in low Reynolds number flows in microgravity. Secondary objectives are to: (1) observe and quantify g-jitter effects and microconvection on particles in fluids, (2) validate an exact solution to the general equation of motion of a particle in a fluid, and (3) to characterize'the ability of isolating platforms to isolate experiments containing particles in liquids.

Trolinger, James D.↗

DOE OSTI2015

Plans for Deployment of Hollow Electron Lenses at the LHC for Enhanced Beam Collimation

Hollow electron lenses are considered as a possible means to improve the LHC beam collimation system, providing active control of halo diffusion rates and suppressing the population of transverse halos. After a very successful experience at the Tevatron, a conceptual design of a hollow e-lens optimized for the LHC was produced. Recent further studies have led to a mature preliminary technical design. In this paper, possible scenarios for the deployment of this technology at the LHC are elaborated in the context of the scheduled LHC long shutdowns until the full implementation of the HL-LHC upgrade in 2023. Possible setups of electron beam test stands at CERN and synergies with other relevant electron beam programmes are also discussed.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Design considerations for optimizing the transient CHI injector on QUEST

Transient coaxial helicity injection (transient CHI) on the QUEST (Q-shu University Experiment with Steady-State ST) spherical tokamak (ST) has recently validated the floating biased electrode configuration for solenoid-free plasma startup. In support of a significant divertor upgrade on QUEST, the details of the transient CHI injector geometry on QUEST have been examined more closely using tokamak simulation code (TSC) simulations. QUEST uses a HIT-II-like (Helicity Injected Torus) injector configuration in which the injector region is comprised of coaxial injector electrodes located in the lower part of the machine. TSC simulations indicate high injector flux operation may benefit from an increased gap between the coaxial electrodes. This is the first study to examine the differences between an open and closed electrode configuration for solenoid-free plasma startup using transient CHI. Results show that both configurations can generate similar levels of closed flux, but the closed configuration may be easier to implement in some ST designs as a much smaller portion of the injector needs to be close to the injector flux coil. Results show that increasing the electrode gap width from the present 10.8 cm to about 15–20 cm would increase the closed flux fraction by about 40%. The results presented in this paper are generally applicable to the CHI design for other STs.

coaxial helicity injection↗

DOE OSTI2021

Machine Learning Tools Set for Natural Gas Fuel Cell System Design

This study is focusing on leveraging the system design tools set for the next-generation solid oxide fuel cell (SOFC) based natural gas fuel cell (NGFC) system. Conventionally, system design and optimization of NGFC systems rely heavily on traditional reduced order model (ROM) techniques and designers’ experience level. For overcoming the technical barriers of system design, multiple multi-physics models and machine learning (ML) tools have been utilized to automate the conceptual design process and enhance the reliability of solutions for the NGFC system. The proposed tools set includes a physics-informed ML tool for automated ROM construction that leverages advances in deep neural networks to significantly reduce ROM prediction error for the NGFC power island compared to traditional approaches. The constructed physics-informed ML ROM can be used in system design, and optimization tools set Institute for the Design of Advanced Energy Systems (IDAES) Process Systems Engineering (PSE) framework. The tools set also provides a user-friendly graphic user interface built within Jupyter Notebooks, and the complete tools set is open-source public available.

Wang, Dewei↗

DOE OSTI2025

Accelerating detector simulations with Celeritas: profiling and performance optimizations

Celeritas is a GPU-optimized MC particle transport code designed to meet the growing computational demands of next-generation HEP experiments. It provides efficient simulation of EM physics processes in complex geometries with magnetic fields, detector hit scoring, and seamless integration into Geant4-driven applications to offload EM physics to GPUs. Recent efforts have focused on performance optimizations and expanding profiling capabilities. This paper presents some key advancements, including the integration of the Perfetto system profiling tool for detailed performance analysis and the development of track-sorting methods to improve computational efficiency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Post-assembly magnetization of a 100 kW high speed permanent magnet rotor

A post-assembly magnetizing fixture has been designed and successfully used to magnetize the rotor of a 100 kW high speed permanent magnet synchronous motor. The rotor is a solid cylinder with outer diameter of 80 mm and total length of 515 mm. The permanent magnet material is samarium-cobalt (Sm{sub 2}Co{sub 17}) with saturation magnetizing field of 6 T. The mechanical stability of the magnetizing fixture has been studied as well as the general design methodology. The magnetizing coil is subdivided in order to reduce the electromagnetic force, and the coils are separately reinforced in different ways. The electromagnetic and structural optimization is performed by finite element analysis and verified by experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

A New Shape Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing

Minimizing the mass in electric machines while maintaining superior performance is a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization (TO) for lightweighting electric machines using traditional approaches typically explores a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods, such as cell-based density approaches, suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary optimization where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning-based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque, and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the International Energy Agency (IEA) 15-MW radial flux direct-drive wind turbine generator. The new approach to shape optimization resulted in smooth and concise shapes that can be easily additively manufactured with up to a 20-ton reduction in electrical steel mass.

additive manufacturing↗

DOE OSTI2016

Multi-species Modeling of Molten-Salt Electrochemical Process

Spent nuclear fuel has been produced and stored in over 30 different countries which are operating commercial nuclear power plants. Molten-salt pyroprocessing has been developed for treating both oxide and metal spent nuclear fuels. This process separates long-lived actinide isotopes together as a group from fission products for burning the long-lived isotopes in fast reactors. Main processes of pyroprocessing such as oxide reduction, electrorefining, and electrowinning are based on a concept of electrolytic cell consuming energy to proceed electrochemical reactions. To scale-up the electrochemical processes, the design optimization of the key steps is required to reach high throughput that improves economic competitiveness and reduces an environmental impact. For the optimization, it is significant to compare different unit process designs and find optimum designs and operating conditions. However, this optimization process is very costly if we need to conduct experiments on every case with spent nuclear fuel. Instead, a validated simulation model can help to rapidly calculate and evaluate different case at significantly reduced costs. This paper presents recent development of multi-physics simulation of molten salt electrochemistry for partitioning spent nuclear fuel with limiting factors. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

Characterization of Oxide + TiN Inclusions in Fe-16 Mass Pct Cr Ferritic Alloy Using Automatic SEM-EDS Analysis

The assessment of automatic SEM-EDS results is significant to reveal the possible inaccuracy of the results and the limitation of this characterization technique in analyzing inclusions. In current research, the oxide + TiN inclusion in Fe-16 mass pct Cr ferritic alloy is characterized by emphasizing the effects of Mg addition and N content on the composition, morphology, homogeneity of dispersion, and size distribution of inclusions; the assessment of automatic SEM-EDS results in rapid analysis concerning its limitation and the utilization of its advantage to clarify the formation mechanism of inclusions are conducted. The nature of inclusions is revealed. Mg addition makes the number density of inclusions larger and the size of inclusions finer. The effect of N content on the size of oxde + TiN was not obvious. Automatic SEM-EDS analysis is deficient in detecting light elements and low content elements in the inclusions and distinguishing between TiN and oxide + TiN. Optimizing the experimental design and combining automatic SEM-EDS results with manual SEM-EDS and optical microscopy results can contribute to delivering accurate and speedy results.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A high-volume resonator for L-band DNP-NMR

DNP-NMR and EPR experiments that operate at or greater than L-band (i.e., ν 0 (e – ) = 1–2 GHz) are typically limited to maximum sample volumes of several hundred µL. These experiments rely on well-known resonator designs for DNP/EPR irradiation such as the loop-gap resonator and Alderman-Grant coil, where their maximum volumes limit further application to imaging experiments and high-throughput screening beyond L-band. Herein, we demonstrate a birdcage (BC) resonator design that can accommodate several mL of sample while operating around 1.5 GHz. The sample volume is maximized by using two identical BC resonators in a stacked configuration. Simulations are used to optimize the BC design and the performance is validated experimentally with liquid-state Overhauser-DNP-NMR experiments. This BC design exploits just the parasitic capacitance of conductive rings and features no fixed tuning capacitors. An enhancement of –77 is achieved on a 10 mM 4-Amino-TEMPO in H 2 O sample for a 5 mL sample volume. Finally, the associated sample heating is minimal due to the low-E-fields generated and the large sample mass with +3.4 K when driving 100 W for several seconds.

47 OTHER INSTRUMENTATION↗