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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Fragmentation analysis of a bar with the Lip-field approach

The Lip-field approach was introduced in Moës and Chevaugeon (2021) as a new way to regularize softening material models. It was tested in 1D quasistatic in Moës and Chevaugeon (2021) and 2D quasistatic in Chevaugeon and Moës (2021): this paper extends it to 1D dynamics, on the challenging problem of dynamic fragmentation. The Lip-field approach formulates the mechanical problem to be solved as an optimization problem, where the incremental potential to be minimized is the non-regularized one. Spurious localization is prevented by imposing a Lipschitz constraint on the damage field. Here, the displacement and damage field at each time step are obtained by a staggered algorithm, that is the displacement field is computed for a fixed damage field, then the damage field is computed for a fixed displacement field. Indeed, these two problems are convex, which is not the case of the global problem where the displacement and damage fields are sought at the same time. The incremental potential is obtained by equivalence with a cohesive zone model, which makes material parameters calibration simple. A non-regularized local damage equivalent to a cohesive zone model is also proposed. It is used as a reference for the Lip-field approach, without the need to implement displacement jumps. These approaches are applied to the brittle fragmentation of a 1D bar with randomly perturbed material properties to accelerate spatial convergence. Both explicit and implicit dynamic implementations are compared. Favorable comparison to several analytical, numerical and experimental references serves to validate the modeling approach.

36 MATERIALS SCIENCE↗
DOE OSTI2019

TENDL: Complete Nuclear Data Library for Innovative Nuclear Science and Technology

The TENDL library is now established as one of the major nuclear data libraries in the world, striving for completeness and quality of nuclear data files for all isotopes, evaluation methods, processing and applied performance. To reach this status, some basic principles have been applied which sets it apart from other libraries: reproducible dedicated evaluations when differential data are available, through determination of nuclear models implemented in TALYS and their parameters, completeness (with or without experimental data), format and processing standardization, automation of production and reproducibility. In this paper, we will outline how such an approach has become a reality, and recall some of the past successes since the first TENDL release in 2008. Next, we will demonstrate the performance of the latest TENDL releases for different application fields, as well as new approaches for uncertainty quantification based on Bayesian inference methods and possible differential and integral adjustments. Also, current limitations of the library performances due to modelling and needs for new and more precise experimental data will be outlined.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Kinematic and dynamical origins of mean-p T fluctuations in heavy-ion collisions

Event-by-event fluctuations of the mean transverse momentum (mean-p T ) provide a sensitive probe of collective dynamics beyond single-particle spectra and anisotropic flow. We present a systematic study of mean-p T fluctuation observables using a Bayesian-calibrated multistage hydrodynamic framework, including quantitative comparisons to RHIC measurements and model-based investigations of beam-energy and kinematic-acceptance effects. The experimental definitions employed by the STAR and ALICE Collaborations are implemented explicitly and found to yield consistent results within controlled limits. We study the centrality and beam-energy dependence of the observable, its sensitivity to key soft-sector ingredients, and the impact of the kinematic p T acceptance. By introducing scaled-p T cuts, we demonstrate that a part of the apparent energy dependence arises from kinematic projection effects, while the remaining trends reflect genuine collective dynamics. Our results establish mean-p T fluctuations as a nontrivial and independent validation of calibrated hydrodynamic descriptions of the quark–gluon plasma.

Event-by-event correlations↗
DOE OSTI2024

High-Pressure and Temperature Effects on the Clustering Ability of Monohydroxy Alcohols

This study examined the clustering behavior of monohydroxy alcohols, where hydrogen-bonded clusters of up to a hundred molecules on the nanoscale can form. By performing X-ray diffraction experiments at different temperatures and under high pressure, we investigated how these conditions affect the ability of alcohols to form clusters. The pioneering high-pressure experiment performed on liquid alcohols contributes to the emerging knowledge in this field. Implementation of molecular dynamics simulations yielded excellent agreement with the experimental results, enabling the analysis of theoretical models. Here we show that at the same global density achieved either by alteration of pressure or temperature, the local aggregation of molecules at the nanoscale may significantly differ. Surprisingly, high pressure not only promotes the formation of hydrogen-bonded clusters but also induces the serious reorganization of molecules. This research represents a milestone in understanding association under extreme thermodynamic conditions in other hydrogen bonding systems such as water.

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

Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data

Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments benefit from the use of machine learning. Incorporating these computational tools in the experimental framework presents new challenges. This paper reports on the implementation of the end-to-end deep learning with the CMS software framework and the scaling of the end-to-end deep learning with multiple GPUs. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation for particle and event identification. We demonstrate the end-to-end implementation on a top quark benchmark and perform studies with various hardware architectures including single and multiple GPUs and Google TPU.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Perspective on machine learning for advancing fluid mechanics

In this paper, a perspective is presented on how machine learning (ML), with its burgeoning popularity and the increasing availability of portable implementations, might advance fluid mechanics. As with any numerical or experimental method, ML methods have strengths and limitations, which are acknowledged. Their potential impact is high so long as outcomes are held to the long-standing critical standards that should guide studies of flow physics.

42 ENGINEERING↗
DOE OSTI2021

Local rules for fabricating allosteric networks

Mechanical properties of disordered networks can be significantly tailored by modifying a small fraction of their bonds. This procedure has been used to design and build mechanical metamaterials with a variety of responses. A long-range “allosteric” response, where a localized input strain at one site gives rise to a localized output strain at a distant site, has been of particular interest. This work presents an approach to incorporating allosteric responses in experimental systems by pruning disordered networks in situ. Previous work has relied on computer simulations to design and predict the response of such systems using a cost function where the response of the entire network to each bond removal is used at each step to determine which bond to prune. It is not feasible to follow such a design protocol in experiments where one has access only to local response at each site. This paper presents design algorithms that allow determination of what bonds to prune based purely on the local forces in the network without employing a cost function; using only local information, allosteric networks are designed in simulations and then built out of real materials. The results show that some pruning strategies work better than others when translated into an experimental system. A method is presented to measure local stresses experimentally in disordered networks. This approach is then used to implement pruning methods to design desired responses in situ. Results from these experiments confirm that the pruning methods are robust and work in a real laboratory material.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Wireless Temperature Sensing Using Permanent Magnets for Nonlinear Feedback Control of Exothermic Polymers

Epoxies and resins can require careful temperature sensing and control in order to monitor and prevent degradation. To sense the temperature inside a mold, it is desirable to utilize a small, wireless sensing element. In this paper, we describe a new architecture for wireless temperature sensing and closed-loop temperature control of exothermic polymers. This architecture is the first to utilize magnetic field estimates of the temperature of permanent magnets within a temperature feedback control loop. We further improve performance and applicability by demonstrating sensing performance at relevant temperatures, incorporating a cure estimator, and implementing a nonlinear temperature controller. This novel architecture enables unique experimental results featuring closed-loop control of an exothermic resin without any physical connection to the inside of the mold. In this paper we describe each of the unique features of this approach including magnetic field-based temperature sensing, Extended Kalman Filtering (EKF) for cure state estimation, and nonlinear feedback control over time-varying temperature trajectories. We use experimental results to demonstrate how low-cost permanent magnets can provide wireless temperature sensing up to ~90°C. In addition, we use a polymer curecontrol test-bed to illustrate how internal temperature sensing can provide improved temperature control over both short and long time-scales. In conclusion, this wireless temperature sensing and control architecture holds value for a range of manufacturing applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Quantitative Study of the Effect of Non-Uniform Irradiance on Module Performance Combining EL and DLIT Imaging with Circuit Modeling

This paper presents a quantitative study of how the non-uniform irradiance from solar simulators and the often overlooked non-uniformities in modules (e.g. mismatched cell performance in a module, connection of cell strings, bypass diodes) affect the measurement accuracy in Si module power calibrations. By implementing spatial non-uniformity maps of solar simulators and experimental diode parameters of individual cells in a module to our circuit model, it enables a better understanding of the interaction of simulator irradiance non-uniformity with module imperfections in module power measurements. The comparison between simulation and experiment shows that with a nonuniform irradiance of 3%, the P MAX deviations could be as low as ~0.5 % for good-quality Si modules, and ~2% for poor-quality Si modules, confirming the sensitivity of module power calibrations to module properties.

14 SOLAR ENERGY↗
DOE OSTI2022

Switching Dynamics in Vanadium Dioxide-Based Stochastic Thermal Neurons

We report on switching dynamics of individual and coupled vanadium dioxide (VO 2 ) devices subject to voltage pulses as the temperature is systematically varied from room temperature spanning the insulator–metal transition (IMT) temperature. The switching voltage of single devices has a strong relationship with both temperature and voltage pulsewidth. Two-step switching in connected VO 2 devices has been noted in current transient plots and was found to depend on temperature, pulsewidth, and pulse amplitude. Experimental switching behavior measured from VO 2 artificial neurons was implemented into a spiking neural network (SNN). During training, modulating the switching voltage via temperature affords a novel method to implement homeostasis with the coupled devices. Simulation results show the efficacy of the stochastic neuronal characteristics and the proposed homeostasis mechanism on a standard digit recognition task. As a result, these studies contribute to ongoing efforts in neuromorphic computing exploiting collective phase transitions.

42 ENGINEERING↗
DOE OSTI2020

A Distributed Control Architecture for Cascaded H-Bridge Converter with Integrated Battery Energy Storage

Cascaded H-bridge topology has been used in grid-tied converters for battery energy storage system due to its modular structure. To fully utilize the converter’s modularity, this paper proposes a hierarchical distributed control architecture that consists of primary control, secondary control and battery state of charge (SOC) balancing control. Primary control ensures accurate current tracking while a distributed secondary control based on consensus algorithm is presented to regulate power sharing among modules and is proved to be stable theoretically. Here, a distributed SOC balancing control is further introduced to improve energy efficiency of battery energy storage system. Finally, the hierarchical distributed control strategy is implemented using hardware controllers and a software platform. Besides, a carrier phase shift control is also implemented to achieve multilevel output voltage and harmonic reduction. The experimental results demonstrate the performance of the proposed control scheme effectively.

25 ENERGY STORAGE↗
DOE OSTI2023

An Effective Current Balancing Method for Inverters With Paralleled Silicon Carbide Power Modules

Silicon carbide (SiC) MOSFET has many superior characteristics in high power applications. Paralleling the SiC devices is an effective approach to increase the current capacity of the system, which also leads to current sharing issues. In this article, both transient and steady-state current sharing issues are studied for the high-power grid-tied inverters with paralleled SiC modules with an effective current balancing strategy proposed to address both issues simultaneously. The steady-state imbalance current due to the parameter mismatch is suppressed by the self-inductance of power cables used for paralleling connection. With the elimination of steady-state imbalance current, the transient imbalance current sensing is much simplified and can be realized by using low-cost current sensors. With the sensed imbalance current, a closed-loop control approach is proposed to adjust the modulation reference signal for each module to mitigate the remaining transient imbalance current. Furthermore, the proposed method is straightforward to implement with enhanced compatibility and flexibility compared to existing methods. Experimental studies are performed using inverters with 2 and 4 power modules paralleled in each phase to demonstrate the effectiveness of the proposed method.

42 ENGINEERING↗
DOE OSTI2020

Decentralized Carrier Phase Shifting for Optimal Harmonic Minimization in Asymmetric Parallel-Connected Inverters

This paper presents a carrier phase shifting technique for minimizing the aggregate harmonics in networks of asymmetric parallel-connected inverters for distributed power generation system applications. The proposed technique is: 1) implemented in a decentralized manner, relying only on local voltage and current measurements, and 2) optimal in the sense that it minimizes a cost function representing the carrier-frequency current harmonics. The analysis indicates that the proposed optimal carrier phase shifting technique can enable order-of-magnitude reductions in harmonic power, and also universal improvements compared to symmetric carrier interleaving for asymmetric inverter networks. Moreover, compared to existing methods that require either centralized communication or information exchange between inverters to coordinate carriers, the proposed technique is completely decentralized, which provides important practical benefits for implementation, including improved robustness and reduced cost. The technique is experimentally validated on a network of three single-phase 2-kW inverters and demonstrates a 36.5% reduction in the weighted total harmonic distortion factor of the aggregate inverter current, and the ability to converge to the optimal carrier phase spacing dynamically in less than one line frequency cycle (16.7 ms) in steady state and transient operating conditions.

42 ENGINEERING↗
DOE OSTI2025

End-to-End Workflow for Machine-Learning-Based Qubit Readout With QICK and hls4ml

In this article, we present an end-to-end workflow for superconducting qubit readout that embeds codesigned neural networks into the quantum instrumentation control kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx radiofrequency system-on-chip field-programmable gate arrays (FPGAs), we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes the hls4ml package and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python application programming interfaces. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32.25 ns and less than 16% FPGA lookup table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.

42 ENGINEERING↗
DOE OSTI2017

Improvement of the RELAP5-3D Model of Condensation in the Presence of Noncondensables

Condensation of steam on the primary side of a steam generator in a pressurized water reactor (PWR) is one means of removing decay heat during some accident scenarios, including small break loss of coolant accident (SBLOCA). However, when noncondensable gasses mix with steam, it impairs condensation. To correctly predict plant behavior, nuclear power plant (NPP) safety analysis codes such as RELAP5-3D must model the effect of condensation in the presence of noncondensables properly. A potential error in the RELAP5-3D code was reported in the condensation model in the presence of noncondensables by the University of Wisconsin (Fu 2015). The report indicated that the calculated condensation heat flux was under-predicted due to the modeling of the mass transfer in the gas mixture. The original documentation describing the implementation of the model was reviewed and compared with alternative formulations. An alternative that uses saturation vapor density at temperature of total pressure instead of the saturation vapor density at vapor partial pressure for calculating vapor mass flux was implemented. Comparison of the alternative method with the original against experimental data for several test cases showed improvement for most test cases.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Half-Metal Spin Filter for Highly Polarized Emission from GaAs Photocathodes

GaAs-based photocathodes are one of the major sources of spin-polarized electrons and are crucial for the upcoming Electron-Ion collider experiments which includes study of proton spin and spin parity violation in the standard model. The theoretical polarization limit in unstrained GaAs photocathodes is 50 % but only 35 % is routinely achieved in experiments. Spin selective filtering allows to boost the spin polarization beyond the 50 % theoretical limit. In this work, first-principle electronic calculations using standard Density Functional Theory are performed to predict possible Heusler alloy half-metal candidates to be used as spin-filter. Simulations are also performed to investigate the half-metallicity as function of the magnetic spin direction. Several devices are experimentally fabricated using dedicated Molecular Beam Epitaxy growth system. We implemented Quantum Efficiency and Polarization testing of these half-metal/GaAs heterostructures using a dedicated Mott polarimeter system. Photoemission can also be seen on magnetically switching the spin-filter direction accompanied by a change in sign of the asymmetry which is a qualitative proof of the spin-filtering effect.

Poddar, S.↗
DOE OSTI2020

Reinterpretation of LHC Results for New Physics: Status and recommendations after Run 2

We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

WSEAT Shock Testing Margin Assessment Using Energy Spectra Final Report

Several programs at Sandia National Laboratories have adopted energy spectra as a metric to relate the severity of mechanical insults to structural capacity. The purpose being to gain insight into the system's capability, reliability, and to quantify the ultimate margin between the normal operating envelope and the likely system failure point -- a system margin assessment. The fundamental concern with the use of energy metrics was that the applicability domain and implementation details were not completely defined for many problems of interest. The goal of this WSEAT project was to examine that domain of applicability and work out the necessary implementation details. The goal of this project was to provide experimental validation for the energy spectra based methods in the context of margin assessment as they relate to shock environments. The extensive test results concluded that failure predictions using energy methods did not agree with failure predictions using S-N data. As a result, a modification to the energy methods was developed following the form of Basquin's equation to incorporate the power law exponent for fatigue damage. This update to the energy-based framework brings the energy based metrics into agreement with experimental data and historical S-N data.

47 OTHER INSTRUMENTATION↗