Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “field optimization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

CATS: The Hubble Constant from Standardized TRGB and Type Ia Supernova Measurements

The tip of the red giant branch (TRGB) provides a luminous standard candle for constructing distance ladders to measure the Hubble constant. In practice, its measurements via edge-detection response (EDR) are complicated by the apparent fuzziness of the tip and the multipeak landscape of the EDR. Previously, we optimized an unsupervised algorithm, Comparative Analysis of TRGBs, to minimize the variance among multiple halo fields per host without relying on individualized choices, achieving state-of-the-art ~<0.05 mag distance measures for optimal data. Here we apply this algorithm to an expanded sample of SN Ia hosts to standardize these to multiple fields in the geometric anchor, NGC 4258. In concert with the Pantheon+ SN Ia sample, this analysis produces a (baseline) result of H 0 = 73.22 ± 2.06 km s -1 Mpc -1 . The largest difference in H 0 between this and similar studies employing the TRGB derives from corrections for SN survey differences and local flows used in the most recent SN Ia compilations that were absent in earlier studies. The SN-related differences total ~2.0 km s -1 Mpc -1 . A smaller share, ~1.4 km s -1 Mpc -1 , results from the inhomogeneity of the TRGB calibration across the distance ladder. We employ a grid of 108 variants around the optimal TRGB algorithm and find that the median of the variants is 72.94 ± 1.98 km s -1 Mpc -1 with an additional uncertainty due to algorithm choices of 0.83 km s -1 Mpc -1 . None of these TRGB variants result in an H 0 of less than 71.6 km s -1 Mpc -1 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

High-Throughput Computing: Case Study of Medical Image Processing Applications

HPC is designed for large-scale simulations using monolithic codes of tightly coupled processes highly optimized to deliver decreased time to solution. Medical image processing is not a traditional field of HPC. Similar to AI applications, medical image processing parses large datasets, typically multiple times, to support a variety of studies for classification, diagnosis or monitoring purposes. The convergence of AI, HPC and Big Data encouraged more fields using image processing to transition to HPC. However, not all applications benefit from the same optimizations. In this paper we focus on high throughput medical image processing applications that analyze a huge dataset of small MRI images and that require HPC systems to decrease the time of parsing the entire dataset and not individual MRIs. We show in this research the performance of running SLANT, an image processing application for a whole brain segmentation, on large-scale systems and highlight performance limitations. We present optimizations prioritizing throughput that exhibit a 3.5x speed-up on the Summit Supercomputer that can be used as a baseline for building a high-throughput execution framework for other HPC systems.

Predescu, Maria↗
NASA NTRS2013

Magnetic Field Dependence of the Critical Current in S-N Bilayer Thin Films

Here we investigate the effects a non-uniform applied magnetic field has on superconducting transition-edge sensors (TESs) critical current. This has implications on TES optimization. It has been shown that TESs resistive transition can be altered by magnetic fields. We have observed critical current rectification effects and explained these effects in terms of a magnetic self-field arising from asymmetric current injection into the sensor. Our TES physical model shows that this magnetic self-field can result in significantly degraded or improved TES performance. In order for this magnetically tuned TES strategy to reach its full potential we are investigating the effect a non-uniform applied magnetic field has on the critical current.

Field Dependence↗
DOE OSTI2022

Design, Construction, and Testing of 0.5-m, 18-mm Period Nb 3 Sn Superconducting Undulator Magnets

We report design and fabrication of a new Nb 3 Sn-based superconducting undulator (SCU) are underway at the Advanced Photon Source (APS) of Argonne National Laboratory in collaboration with Fermilab and Lawrence Berkeley National Laboratory. To develop a robust and reliable fabrication process, the magnet development consists of several steps. First, magnetic and mechanical simulations were performed to optimize the magnet design; then, the design matured further by fabricating and testing a series of very short prototypes, ~8 cm long with a period length of 18 mm. These short prototype studies were previously reported. Second, the design was scaled to an intermediate length of ~0.5 m. These two steps led to the final design of ~1.1–m-long magnets, which are currently being fabricated. The quench behavior of each 0.5-m-long undulator magnet, as well as undulator assemblies from these magnets, was studied. The first SCU magnet assembly did not meet the design specifications due to the breakdown of the insulation. With an improved design and fabrication process based on lessons learned, the second SCU assembly achieved the design undulator field of 1.2 T. The design was further optimized, and the third set of magnets was fabricated and successfully tested.

43 PARTICLE ACCELERATORS↗
NASA NTRS1977

Multipole gas thruster design

The development of a low field strength multipole thruster operating on both argon and xenon is described. Experimental results were obtained with a 15-cm diameter multipole thruster and are presented for a wide range of discharge-chamber configurations. Minimum discharge losses were 300-350 eV/ion for argon and 200-250 eV/ion for xenon. Ion beam flatness parameters in the plane of the accelerator grid ranged from 0.85 to 0.93 for both propellants. Thruster performance is correlated for a range of ion chamber sizes and operating conditions as well as propellant type and accelerator system open area. A 30-cm diameter ion source designed and built using the procedure and theory presented here-in is shown capable of low discharge losses and flat ion-beam profiles without optimization. This indicates that by using the low field strength multipole design, as well as general performance correlation information provided herein, it should be possible to rapidly translate initial performance specifications into easily fabricated, high performance prototypes.

Isaacson, G. C.↗
NASA NTRS1972

Optimization of multimagnetometer systems on a spacecraft

Magnetometers on a radial boom may be employed to determine the first N-1 multipole contributions to the spacecraft field and improve the total accuracy of a magnetic field experiment. The total error for systems of one to four magnetometers is investigated. The optimal magnetometer locations, for which the total error is a minimum, are found for given boom-length, instrument errors and magnetic field models characteristic for spacecraft with only a restricted or ineffective magnetic cleanliness program.

Neubauer, F. M.↗
NASA NTRS1987

Optimized retrievals of precipitable water from the VAS 'split window'

Precipitable water fields have been retrieved from the VISSR Atmospheric Sounder (VAS) using a radiation transfer model for the differential water vapor absorption between the 11- and 12-micron 'split window' channels. Previous moisture retrievals using only the split window channels provided very good space-time continuity but poor absolute accuracy. This note describes how retrieval errors can be significantly reduced from plus or minus 0.9 to plus or minus 0.6 gm/sq cm by empirically optimizing the effective air temperature and absorption coefficients used in the two-channel model. The differential absorption between the VAS 11- and 12-micron channels, empirically estimated from 135 colocated VAS-RAOB observations, is found to be approximately 50 percent smaller than the theoretical estimates. Similar discrepancies have been noted previously between theoretical and empirical absorption coefficients applied to the retrieval of sea surface temperatures using radiances observed by VAS and polar-orbiting satellites. These discrepancies indicate that radiation transfer models for the 11-micron window appear to be less accurate than the satellite observations.

Chesters, Dennis↗
DOE OSTI2025

Quantum annealing-assisted lattice optimization

High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging. In this work, we proposed a quantum annealing-assisted lattice optimization (QALO) algorithm, which is an active learning framework that integrates the Field-aware Factorization Machine (FFM) as the surrogate model for lattice energy prediction, Quantum Annealing (QA) as an optimizer and Machine Learning Potential (MLP) for ground truth energy calculation. By applying our algorithm to the NbMoTaW alloy, we reproduced the Nb depletion and W enrichment observed in bulk HEA. We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations. Our algorithm highlights the potential of quantum computing in materials design and discovery, laying a foundation for further exploring and optimizing structure-property relationships.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator

Quantum computers and simulators may offer significant advantages over their classical counterparts, providing insights into quantum many-body systems and possibly improving performance for solving exponentially hard problems, such as optimization and satisfiability. Here, we report the implementation of a low-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator. We estimate the ground-state energy of the Transverse Field Ising Model with long-range interactions with tunable range, and we optimize the corresponding combinatorial classical problem by sampling the QAOA output with high-fidelity, single-shot, individual qubit measurements. We execute the algorithm with both an exhaustive search and closed-loop optimization of the variational parameters, approximating the ground-state energy with up to 40 trapped-ion qubits. We benchmark the experiment with bootstrapping heuristic methods scaling polynomially with the system size. We observe, in agreement with numerics, that the QAOA performance does not degrade significantly as we scale up the system size and that the runtime is approximately independent from the number of qubits. We finally give a comprehensive analysis of the errors occurring in our system, a crucial step in the path forward toward the application of the QAOA to more general problem instances.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2000

Comparison of Adsorbed Mercury Screening Method With Cold-Vapor Atomic Absorption Spectrophotometry for Determination of Mercury in Soil

A field screening method for the determination of elemental mercury in environmental soil samples involves the thermal desorption of the mercury from the sample onto gold and then the thermal desorption from the gold to a gold-film mercury vapor analyzer. This field screening method contains a large number of conditions that could be optimized for the various types of soils encountered. In this study, the conditions were optimized for the determination of mercury in silty clay materials, and the results were comparable to the cold-vapor atomic absorption spectrophotometric method of determination. This paper discusses the benefits and disadvantages of employing the field screening method and provides the sequence of conditions that must be optimized to employ this method of determination on other soil types.

Easterling, Donald F.↗
DOE OSTI2024

Advocating Feedback Control for Human-Earth System Applications

This paper proposes a feedback control perspective for Human-Earth Systems (HESs) which essentially are complex systems that capture the interactions between humans and nature. Recent attention in HES research has been directed towards devising strategies for climate change mitigation and adaptation, aimed at achieving environmental and societal objectives. However, existing approaches heavily rely on HES models, which inherently suffer from inaccuracies due to the complexity of the system. Moreover, overly detailed models often prove impractical for optimization tasks. We propose a framework inheriting from feedback control strategies the robustness against model errors, because inaccuracies are mitigated using measurements retrieved from the field. The framework comprises two nested control loops. The outer loop computes the optimal inputs to the HES, which are then implemented by actuators controlled in the inner loop. Potential fields of applications are also identified and a numerical example is provided.

biological system modeling↗
NASA NTRS1990

Optimal impulsive time-fixed orbital rendezvous and interception with path constraints

Minimum-fuel, impulsive, time-fixed solutions are obtained for the problem of orbital rendezvous and interception with interior path constraints. Transfers between coplanar circular orbits in an inverse-square gravitational field are considered, subject to a circular path constraint representing a minimum or maximum permissible orbital radius. Primer vector theory is extended to incorporate path constraints. The optimal number of impulses, their times and positions, and the presence of initial or final coasting arcs are determined. The existence of constraint boundary arcs and boundary points is investigated as well as the optimality of a class of singular arc solutions. To illustrate the complexities introduced by path constraints, an analysis is made of optimal rendezvous in field-free space subject to a minimum radius constraint.

Taur, D.-R.↗
DOE OSTI2020

Optimal Heliostat Assignment Strategy for Multiple-Receiver Systems

Next-generation concentrating solar power concepts currently being developed under the U.S. Department of Energy's Gen3 program include several novel heliostat field and receiver arrangements. Among these concepts are solar fields utilizing multiple receivers or several distinct receiver flux control zones, and each of which has particular power or peak flux constraints. In this paper, we present a linear program that identifies optimal heliostat-to-receiver assignment strategies for multi-receiver or multi-zone solar fields based on detailed calculations made by an optics modeling package. Two distinct problems are addressed: namely, (1) a design problem that chooses the optimal set of heliostats to include in a final layout given receiver constraints and performance, and (2) an operations problem that assigns existing heliostats to a receiver, maximizing field power output while maintaining receiver power constraints. The optimization methodology is integrated into the concentrating solar power tower modeling package SolarPILOTTM, and results are presented showing that heliostat assignment strategies can impact both initial heliostat field layout characteristics and the cost of thermal energy produced by the field. The methodology succeeds in maintaining power requirements but at the expense of producing different flux profiles on each receiver surface. Consequently, the paper also discusses additional measures that are applied to ensure that desirable flux distribution properties are maintained, reducing local flux mean-absolute-deviation values from a baseline of 17% or greater to less than 5% compared to a desired reference flux profile.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Variational Monte Carlo Calculations of A ≤ 4 Nuclei with an Artificial Neural-Network Correlator Ansatz

Here, the complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where nonperturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum many-body states in condensed matter and chemistry problems. In this work we introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei, and approximately solve the nuclear many-body Schrodinger equation. Using efficient stochastic sampling and optimization schemes, our approach extends pioneering applications of ANNs in the field, which present exponentially scaling algorithmic complexity. We compute the binding energies and point-nucleon densities of A ≤ 4 nuclei as emerging from a leading-order pionless effective field theory Hamiltonian. We successfully benchmark the ANN wave function against more conventional parametrizations based on two- and three-body Jastrow functions, and virtually exact Green's function Monte Carlo results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Optimization with the OpenACC-to-FPGA framework on the Arria 10 and Stratix 10 FPGAs

The reconfigurable computing paradigm with field programmable gate arrays (FPGAs) has received renewed interest in the high-performance computing field due to FPGAs’ unique combination of performance and energy efficiency. However, difficulties in programming and optimizing FPGAs have prevented them from being widely accepted as general-purpose computing devices. In accelerator-based heterogeneous computing, portability across diverse heterogeneous devices is also an important issue, but the unique architectural features in FPGAs make this difficult to achieve. To address these issues, a directive-based, high-level FPGA programming and optimization framework was previously developed. In this work, developed optimizations were combined holistically using the directive-based approach to show that each individual benchmark requires a unique set of optimizations to maximize performance. We perform this exploration on Intel Arria 10 and Stratix 10 FPGAs. We also explored the relationships between performance, resource usages, and compilation times, and investigated implications for performance portability. Finally, we present an initial evaluation of a real-world proxy application, LULESH.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine-Learned Force Field for Molecular Dynamics Simulations of Nonequilibrium Ammonia Synthesis on Iron Catalysts

Ammonia (NH 3 ) is one of the most important industrial chemicals. The conventional NH 3 synthesis method-the Haber–Bosch process-converts atmospheric nitrogen (N 2 ) into NH 3 using H 2 with an iron (Fe) catalyst. However, this process requires high pressures (100–200 atm) and temperatures (700–800 K) near thermal equilibrium. Recently, Fe-based nanocatalysts have been reported to produce promising NH 3 yields under atmospheric pressures and temperature-modulated nonequilibrium conditions. Understanding the mechanism of nonequilibrium catalysis with programmed temperature variation could help to optimize this fully electrified and less energy-intensive process. Although reactive molecular dynamics (RMD) simulations can be a useful tool to model nonequilibrium catalytic processes, they require the development of accurate force fields (i.e., interatomic potentials). Here, we present a machine-learned (ML) force field within the Deep Potential MD (DPMD) framework, trained using periodic density functional theory (DFT) calculations, to model NH 3 synthesis on Fe catalysts with various surface adsorbates such as *N, *H, *N 2 , *H 2 , *NH, *NH 2 , and *NH 3 . Here, we generated the DFT data from static models of elementary reactions on the most stable (110) surface of body-centered cubic Fe, which then were augmented by data from constant number of particles–volume–temperature (NVT) DFT-MD trajectories at various temperatures. Finally, we utilized the fully optimized ML force field to investigate reaction dynamics at an Fe(110) surface at linearly increasing temperatures using NVT-DPMD simulations. Our simulations indicate that pulsed temperature ramping could prove favorable for NH3 synthesis. For example, we conducted ramping under multiple sets of conditions: (i) from 900 to 1200 K over periods of 0.1–0.3 ns for Fe surfaces precovered with N or NH along with H; and (ii) from 300 to 600 K over 0.1–0.3 ns for Fe surfaces precovered with NH 3 . While our simulations so far are limited to short time scales (very rapid heating), these observations shed light on the mechanism of the high NH 3 synthesis rate achieved in a novel temperature-modulated nonequilibrium catalytic reactor using pulsed heating and cooling.

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

Scaling laser preheat for MagLIF with the Z-Beamlet laser

Optimizing the performance of the Magnetized Liner Inertial Fusion (MagLIF) platform on the Z pulsed power facility requires coupling greater than 2 kJ of preheat energy to an underdense fuel in the presence of an applied axial magnetic field ranging from 10 to 30 T. Achieving the suggested optimal preheat energies has not been experimentally achieved so far. In this work, we explore the preheat design space for cryogenically cooled MagLIF targets, which represent a viable candidate for increasing preheat energies. Using 2D and 3D HYDRA MHD simulations, we first discuss the various physical effects that occur during laser preheat, such as laser energy deposition, self-focusing, and filamentation. After identifying the changes that different phase plates, gas-fill densities, and magnetic fields bring to the aforementioned physical effects, we, then, consider higher laser energies that are achievable with modest upgrades to the Z Beamlet laser. Lastly, with a 6.0-kJ upgraded laser, 3D calculations suggest that it is possible to deliver 4.25 kJ into the MagLIF fuel, resulting in an expected deuterium neutron yield of Y DD ≃ 1.5 × 10 14 , or roughly 50 kJ of DT equivalent yield, at 20-MA current drive. This represents a 10-fold increase in the currently achieved yields for MagLIF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

GOOML - Real World Applications of Machine Learning in Geothermal Operations

GOOML (Geothermal Operational Optimization with Machine Learning) is a machine-learning based framework that enables geothermal power plant operators to explore optimization opportunities for their assets in an efficient and robust digital environment. Backed by real-world data sources, thermodynamic constraints and steamfield intelligence, the GOOML environment provides new tools to explore how to best operate steamfields as well as test new scenarios and configurations prior to implementation in the field. To prove the effectiveness of GOOML, we have undertaken optimization experiments using reinforcement learning (RL) to generate operational suggestions using a balance of mass-take targets, sustainability considerations and net generation. Our experiments use the GOOML construct to explore different field parameters and perform multiple reinforcement learning experiments. Like a comprehensive laboratory workbench, we can change out components of a steamfield to perform testing under a variety of conditions (restrict mass, increase pressure, reroute steam, etc.). This flexibility allows us to explore conditions that would require significant infrastructure changes in a real-world setting at a fraction of the cost and time in a digital environment. The results highlight the benefits of using digital twins and advanced data analytics for the geothermal industry.

forecasting↗