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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Monte Carlo Simulations in Support of NorthStar Irradiation Facility Commissioning Procedure

NorthStar Medical Radioisotopes LLC is constructing an irradiation facility at which electron accelerators will induce photo-transmutation (i.e., neutron knockout) of molybdenum-100 (Mo-100) to produce medically useful Mo-99. The facility will house two high-intensity electron RhodotronTM accelerators developed by IBA Industrial. The nominal power of each Rhodotron is 125kW at 40 MeV beam energy. The production target is made of metallic molybdenum enriched with Mo-100 isotope. It will be irradiated by electron beams and will itself play a role in electron conversion to intense X-rays. A full description of the NorthStar target as well as the concept of radiation shielding of the facility can be found in our previous report. The NorthStar irradiation facility is under commissioning now. The goal of the present work is to support the commissioning process by: (1) Studying residual radiation and activation to minimize personnel exposure and inform radiation waste management; (2) Using computational fluid dynamics (CFD) of the irradiated target to ensure the facility design is safe and reliable; and (3) Helping to determine radiation protection requirements during operation. Monte Carlo simulations are used for the analyses. Most of the results are presented as 3D arrays representing spatial distribution of values across simulated geometries: radiation energy depositions, ambient doses, residual isotopes accumulated, and residual doses.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2026

Improving ideal MHD equilibrium accuracy with physics-informed neural networks

We present a novel approach to compute three-dimensional magnetohydrodynamic equilibria with isotropic pressure profiles and nested surfaces by parametrizing Fourier modes with artificial neural networks (NNs). The full nonlinear global force residual of single equilibria across the volume in real space is then minimized with first order optimizers and compared to equilibria computed by conventional solvers. Already, we observe competitive computational cost to arrive at the same minimum residuals computable with existing codes. With increased computational cost, lower minima of the residual are computable with the NNs than with any other tested solver, establishing a new lower bound for the force residual. We use minimally complex NNs, and we expect significant improvements for solving not only single equilibria with NNs, but also for creating NN models valid over continuous distributions of equilibria.

ideal magnetohydrodynamics↗
DOE OSTI2020

Alignment of the ATLAS Inner Detector in Run 2

The performance of the ATLAS Inner Detector alignment has been studied using pp collision data at s√=13TeV collected by the ATLAS experiment during Run 2 (2015–2018) of the Large Hadron Collider (LHC). The goal of the detector alignment is to determine the detector geometry as accurately as possible and correct for time-dependent movements. The Inner Detector alignment is based on the minimization of track-hit residuals in a sequence of hierarchical levels, from global mechanical assembly structures to local sensors. Subsequent levels have increasing numbers of degrees of freedom; in total there are almost 750,000. The alignment determines detector geometry on both short and long timescales, where short timescales describe movements within an LHC fill. The performance and possible track parameter biases originating from systematic detector deformations are evaluated. Momentum biases are studied using resonances decaying to muons or to electrons. The residual sagitta bias and momentum scale bias after alignment are reduced to less than ~0.1 TeV -1 and 0.9×10 -3 , respectively. Impact parameter biases are also evaluated using tracks within jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Retrieval of the sea spray aerosol mode from submicron particle size distributions and supermicron scattering during LASIC

Abstract. Improved quantification of sea spray aerosol concentration and size is important for determining aerosol effects on clouds and the climate, though attempts to accurately capture the size distribution of the sea spray mode remain limited by the availability of supermicron size distributions. In this work, we introduce a new approach to retrieving lognormal mode fit parameters for a sea spray aerosol mode by combining submicron size distributions with supermicron scattering measurements using a Mie inversion. Submicron size distributions were measured by an ultra-high-sensitivity aerosol spectrometer (UHSAS), and supermicron scattering was taken as the difference between <10 µm and <1 µm three-wavelength integrating nephelometer measurements (NEPH). This UHSAS-NEPH method was applied during background marine periods of the Department of Energy Atmospheric Radiation Measurement Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign on Ascension Island (November 2016–May 2017), when the contribution of sea spray aerosol was expected to represent a large fraction of the aerosol mass and total scattering. Lognormal sea spray modal parameters were retrieved from comparisons between nephelometer measurements and a lookup table of Mie theory-simulated scattering coefficients for low-error solutions that minimized the 0.4–1 µm residual in the UHSAS size distribution. We evaluated the UHSAS-NEPH method with a set of clean marine measurements in the North Atlantic that included supermicron size and chemical measurements, showing that measured supermicron size distributions are needed to constrain the sea spray number concentration but that mass concentration was reasonably characterized using supermicron scattering. For LASIC, the UHSAS-NEPH method retrieved sea spray mode properties for approximately 88 % of the background marine times when the scattering variability and total particle concentration were low (<± 5 Mm−1 and <400 cm−3, respectively), with mass mean diameter ranging from 0.6 to 1.9 µm (1.47 ± 0.17 µm), modal width ranging from 1.1 to 3.97 (2.4±0.3), and mass concentration ranging from 0.18 to 23.0 µg m−3 (8.37. ± 4.1 µg m−3). The measured nephelometer scattering at three wavelengths was found to constrain the mode width marginally at the largest particle sizes in the absence of additional size and chemical measurements for defining parameters for the Mie solutions. Comparing UHSAS-NEPH retrievals to those of a fitting algorithm applied only to the submicron UHSAS number size distribution showed that correlations between retrieved mass concentration and the available mass-based sea spray tracers (coarse scattering, wind speed, and chloride) are low when supermicron measurements are not considered. This work demonstrates the added value of supermicron scattering measurements for retrieving reasonable sea spray mass concentrations, providing the best-available observationally constrained estimate of the sea spray mode properties when supermicron size distribution measurements are not available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Compensation of residual amplitude modulation fluctuations in an optoelectronic system for laser radiation frequency stabilisation

Fluctuations of residual amplitude modulation in a scheme of laser frequency stabilisation by a Fabry – Perot cavity reduce the frequency stability of the laser systems intended for interrogating clock transitions of atoms and ions. The dependences of the residual amplitude modulation on a temperature of the waveguide-based electro-optical modulator used for modulating the radiation phase and on polarisation of the radiation are measured. The parameters are found, at which the influence of the residual amplitude modulation on the frequency stability is minimal. A system for active compensation is created, which reduces the contribution of fluctuations of residual amplitude modulation to the instability down to 2.1 × 10{sup −16}, which makes it possible to reach the thermal noise limit of a silicon cryogenic cavity. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets

A novel extension of the Probabilistic Learning on Manifolds (PLoM) is presented. It makes it possible to synthesize solutions to a wide range of nonlinear stochastic boundary value problems described by partial differential equations (PDEs) for which a stochastic computational model (SCM) is available and which depend on a vector-valued random control parameter. The cost of a single numerical evaluation of this SCM is assumed to be such that only a limited number of points can be computed for constructing the training dataset (small data). Each point of the training dataset is made up of realizations from a vector-valued stochastic process (the stochastic solution) and the associated random control parameter on which it depends. The presented PLoM constrained by PDE allows for generating a large number of learned realizations of the stochastic process and its corresponding random control parameter. These learned realizations are generated so as to minimize the vector-valued random residual of the PDE in the mean-square sense. Appropriate novel methods are developed to solve this challenging problem. Three applications are presented. The first one is a simple uncertain nonlinear dynamical system with a nonstationary stochastic excitation. The second one concerns the 2D nonlinear unsteady Navier–Stokes equations for incompressible flows in which the Reynolds number is the random control parameter. Here, the last one deals with the nonlinear dynamics of a 3D elastic structure with uncertainties. The results obtained make it possible to validate the PLoM constrained by stochastic PDE but also provide further validation of the PLoM without constraint.

Machine learning↗
DOE OSTI2022

A Multi-Objective Approach for Optimizing Edge-Based Resource Allocation Using TOPSIS

Existing approaches for allocating resources on edge environments are inefficient and lack the support of heterogeneous edge devices, which in turn fail to optimize the dependency on cloud infrastructures or datacenters. To this extent, we propose in this paper OpERA, a multi-layered edge-based resource allocation optimization framework that supports heterogeneous and seamless execution of offloadable tasks across edge, fog, and cloud computing layers and architectures. By capturing offloadable task requirements, OpERA is capable of identifying suitable resources within nearby edge or fog layers, thus optimizing the execution process. Throughout the paper, we present results which show the effectiveness of our proposed optimization strategy in terms of reducing costs, minimizing energy consumption, and promoting other residual gains in terms of processing computations, network bandwidth, and task execution time. We also demonstrate that by optimizing resource allocation in computation offloading, it is then possible to increase the likelihood of successful task offloading, particularly for computationally intensive tasks that are becoming integral as part of many IoT applications such robotic surgery, autonomous driving, smart city monitoring device grids, and deep learning tasks. The evaluation of our OpERA optimization algorithm reveals that the TOPSIS MCDM technique effectively identifies optimal compute resources for processing offloadable tasks, with a 96% success rate. Moreover, the results from our experiments with a diverse range of use cases show that our OpERA optimization strategy can effectively reduce energy consumption by up to 88%, and operational costs by 76%, by identifying relevant compute resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Residual Dose and Environmental Monitoring for the Fermilab Main Injector Tunnel Using the Data Acquisition Logging Engine (Dale)

The Recycler and the Main Injector are part of the Fermilab Accelerator complex used to deliver proton beam to the different experiments. It is very important to control and minimize losses in both machines during operation, to reduce personnel dose from residual activation and to preserve component lifetime. To minimize losses, we need to identify the loss points and adjust the components accordingly. The Data Acquisition Loss Engine (DALE) platform has been developed within the Main Injector department and upgraded throughout the years. DALE is used to survey the entire enclosure for residual dose rates and environmental readings when unrestricted access to the enclosure is possible. Currently DALE has two radiation meters, which are aligned along each machine, so loss points can be identified for both at the same time. DALE attaches to the enclosure carts and is continuously in motion monitoring dose rates and other environmental readings. In this paper we will describe how DALE is used to provide radiation maps of the residual dose rates in the enclosure. We will also compare the loss points with the Beam Loss monitor data.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Learning electric vehicle driver range anxiety with an initial state of charge-oriented gradient boosting approach

This manuscript focuses on the modeling of electric vehicle (EV) driver’s range anxiety, a fear that a vehicle does not have sufficient range, or state of charge (SOC) of the battery pack, to reach its destination and would strand its occupants. Despite numerous research studies on the modeling of charging behaviors, modeling efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To this end, an ensemble learning model based on gradient boosting is developed. The model sequentially fits new predictors to new residuals of the previous prediction and, then, minimizes the loss when adding the latest prediction. A total of 18 features are defined and extracted from the multisource data, which cover information on driver, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5-year’s charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. Here, the result shows the proposed model achieved a satisfactory result with a R square value of 0.54 and root mean square error of 0.14, both better than multiple linear regression model and random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users or that are further from residential areas.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Deployable Element Ultra High Vacuum Pump

Most existing vacuum pump technologies remain stationary, limiting their effectiveness in a molecular environment. In low vacuum environments, molecules move in a particular path towards the source of pumping. As the mean-free-path increases, the system’s ability to capture molecules diminishes and pump-down time increases. To address this, the team explored various designs that would deploy the pump surface closer to the molecules. After several iterations, a “windmill” shape for the final design. This design employs titanium sublimation technology in a deployable configuration, effectively reducing the distance between residual molecules and the pump surfaces, thereby minimizing randomness and improving particle capture.

Etayem, Brandon [Northern Illinois U.]↗
DOE OSTI2020

Robust Matrix Completion State Estimation in Distribution Systems

Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Predicting metal-binding proteins and structures through integration of evolutionary-scale and physics-based modeling

Metals are essential elements in all living organisms, binding to approximately 50% of proteins. They serve to stabilize proteins, catalyze reactions, regulate activities, and fulfill various physiological and pathological functions. While there have been many advancements in determining the structures of protein-metal complexes, numerous metal-binding proteins still need to be identified through computational methods and validated through experiments. Here, to address this need, we have developed the ESMBind workflow, which combines evolutionary scale modeling (ESM) for metal-binding prediction and physics-based protein-metal modeling. Our approach utilizes the ESM-2 and ESM-IF models to predict metal-binding probability at the residue level. In addition, we have designed a metal-placement method and energy minimization technique to generate detailed 3D structures of protein-metal complexes. Our workflow outperforms other models in terms of residue and 3D-level predictions. To demonstrate its effectiveness, we applied the workflow to 142 uncharacterized fungal pathogen proteins and predicted metal-binding proteins involved in fungal infection and virulence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Residual symmetries and scalar multiplet vacuum alignment in non-Abelian flavour models

We demonstrate that, upon minimizing a renormalizable, single-scalar potential invariant under a non-Abelian symmetry, special orientations in the associated vacuum alignment of the scalar multiplet correspond to the preservation of a discrete residual flavour symmetry in the broken phase of the theory. Conversely, we show that these special scalar alignments are perturbed when additional Lagrangian operators (e.g. renormalizable, multi-flavon operators and/or effective, higher-dimensional operators) are present that break said residual symmetry, leading to a vacuum reorientation and phenomenological consequences. We therefore construct a one-to-one correspondence principle between broken residual symmetries and vacuum alignment corrections, providing a mechanism to identify (and correct) a subtle but persistent form of phenomenologically relevant fine-tuning embedded in — but often ignored by — most successful non-Abelian flavour models. We first establish this correspondence in a set of toy models based on the S4 permutation symmetry, and then apply the lessons learned to the more realistic A4 Altarelli-Feruglio and ∆(27) Universal Texture Zero models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Automated shaker placement and regularized input estimation for MIMO testing.

Multi-input, multi-output (MIMO) testing is used in component qualification to reproduce operational responses in the laboratory. It is often preferred to single-input and base-shake testing because of the potential for equivalent or better tests using smaller actuators and shorter test suites. Given a target response, two key steps in MIMO test design are selecting actuator locations and solving for input loads. Actuator locations are often manually selected using expert judgment. If an automatic method is used, locations are usually determined by simulating the vibration control problem and minimizing a combination of the input energy and control residuals. To select a configuration, the relative importance of input energy and residuals must be specified. Specifying relative weights is, in general, a manual and subjective process. This paper develops an objective function that compares actuator configurations based on control accuracy and required input energy without any manual parameter tuning. The objective function uses an optimally selected tradeoff parameter for each candidate configuration. To choose actuator locations using the new objective function, a pivoting algorithm for integer programming problems is developed. Starting with an initial configuration (such as the one generated by a greedy algorithm), the pivoting algorithm guarantees an objective function decrease in each iteration until convergence is reached. In a simulation featuring a structure excited by a diffuse acoustic field, electrodynamic shaker locations and regularized inputs are solved for without any analyst-specified parameters. Simulations are performed in MIMO configurations where the number of target responses is less than, equal to, and greater than the number of actuators.

Multi-input multi-output↗
DOE OSTI2020

Pilot plant reliability metrics for grinding and fast pyrolysis of woody residues

Here we report on the effects of loblolly pine residue variability on material throughput, pilot plant uptime, operator intervention, product yield, and product quality for grinding, fast pyrolysis, and hydrotreating operations. Preprocessing throughput using a hammer mill varied between 31-48% of nameplate capacity (5 tons/hr). Grinder overloads in the size reduction step were more prevalent for lower ash and higher moisture materials. Fast pyrolysis throughput varied between 57-72% of nameplate capacity (20 kg/hr) and bio-oil yields varied between 46-53% (feedstock carbon to oil, dry basis). During fast pyrolysis operations, downtime was caused by bridging in the feed and char removal systems and plugging in the condensation system. Cohesion of feedstock and char leading to system plugging was less frequent for higher ash feedstocks, and differences in condenser plugging behavior between high and low ash feedstocks were observed. The catalyst stability of the bio-oil stabilization step was strongly dependent on the sulfur content in the bio-oil, which was higher for the high-ash residue oils. Lower moisture content in the starting biomass was consistent with lower sulfur content in bio-oil. Yields and properties of hydrotreated fuel products showed minimal deference among the bio-oils.

Klinger, Jordan↗
DOE OSTI2021

Development of Standardized Property Requirements, Measurement Methods, and Reporting Guidance for Coatings

The diverse research programs presently in progress around the world have advanced a large number of coated cladding concepts to various stages of maturity. Industry teams have developed leading coating compositions and geometries, but significant disparity exists in the importance placed on different coating properties, as well as the applicability and standardization of the various methods used to measure these properties. This milestone report provides an initial assessment of the most important coating properties and the experimental methods available for their determination to lay the foundation for standardized reporting and development of comprehensive datasets for modeling efforts. This report has determined that the residual stress of the coating is the most crucial property to investigate, yet it has received minimal attention to date due to a knowledge gap about the growth and formation of the brittle Laves phase under reactor conditions. The significance of residual stress of the coating is further enhanced by its connection and interdependence with other important properties like creep and coating failure mechanisms. This milestone proposes an importance rating for residual stress properties and their associated measurement techniques to fill in knowledge. Furthermore, techniques to investigate the outlined properties are discussed, and their capabilities and limitations are illustrated to provide guidance on their use.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗