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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Asymptotic Freedom at the Berezinskii-Kosterlitz-Thouless Transition without Fine-Tuning Using a Qubit Regularization

We propose a two-dimensional hard-core loop-gas model as a way to regularize the asymptotically free massive continuum quantum field theory that emerges at the Berezinskii-Kosterlitz-Thouless transition. Without fine-tuning, our model can reproduce the universal step-scaling function of the classical lattice X Y model in the massive phase as we approach the phase transition. This is achieved by lowering the fugacity of Fock-vacuum sites in the loop-gas configuration space to zero in the thermodynamic limit. Some of the universal quantities at the Berezinskii-Kosterlitz-Thouless transition show smaller finite size effects in our model as compared to the traditional X Y model. Our model is a prime example of qubit regularization of an asymptotically free massive quantum field theory in Euclidean space-time and helps understand how asymptotic freedom can arise as a relevant perturbation at a decoupled fixed point without fine-tuning. Published by the American Physical Society 2024

Physics↗
DOE OSTI2020

Tuned and screened range-separated hybrid density functional theory for describing electronic and optical properties of defective gallium nitride

We apply a hybrid density functional theory approach, based on a tuned and screened range-separated hybrid (SRSH) exchange-correlation functional, to describe the optoelectronic properties of defective gallium nitride (GaN). SRSH and time-dependent SRSH (TDSRSH) are tuned to produce accurate energetics for the pristine material and applied to the study of a series of point defects in bulk GaN, a blue light-emitting material that degrades in the presence of defects. We first establish the accuracy of the method by comparing the predicted quasiparticle gap and low-energy excitation spectra of (TD)SRSH and many-body perturbation theory for both pristine GaN and GaN containing a single nitrogen vacancy. Aided by the reduced computational cost of (TD)SRSH, we then report on three additional technologically relevant point defects and defect complexes in GaN: the gallium vacancy, the carbon interstitial, and the carbon-silicon complex. We compute the low-energy optical absorption spectra for these defects and show the presence of defect-centered transitions. Furthermore, by estimating the Stokes shift, we predict, in agreement with previous studies, that the carbon substitutional defect is a candidate for the detrimental yellow luminescence in GaN. Furthermore, this study indicates that TDSRSH is a promising and computationally feasible approach for quantitatively accurate, first-principles modeling of defective semiconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Optimally tuned starting point for single-shot GW calculations of solids

The dependence of ab initio many-body perturbation theory within the $\textit{GW}$ approximation on the eigensystem used in calculating quasiparticle corrections limits this method's predictive power. Here, we investigate the accuracy of the recently developed Wannier-localized optimally tuned screened range-separated hybrid (WOT-SRSH) functional as a generalized Kohn-Sham starting point for single-shot $\textit{GW}$ ($G_0W_0$) calculations for a range of semiconductors and insulators. Comparison to calculations based on well-established functionals, namely, PBE, PBE0, and HSE, as well as to self-consistent GW schemes and to experiment, shows that band gaps computed via $G_0W_0$@WOT-SRSH have a level of precision and accuracy that is comparable to that of more advanced methods such as quasiparticle self-consistent $\textit{GW}$ and eigenvalue self-consistent $\textit{GW}$. In this work, we also find that $G_0W_0$@WOT-SRSH improves the description of states deeper in the valence band manifold. Finally, we show that $G_0W_0$@WOT-SRSH significantly reduces the sensitivity of computed band gaps to ambiguities in the underlying WOT-SRSH tuning procedure.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optical tuning of the diamond Fermi level measured by correlated scanning probe microscopy and quantum defect spectroscopy

Quantum technologies based on quantum point defects in crystals require control over the defect charge state. Here we tune the charge state of shallow nitrogen-vacancy and silicon-vacancy centers by locally oxidizing a hydrogenated surface with moderate optical excitation and simultaneous spectral monitoring. The loss of conductivity and change in work function due to oxidation are measured in atmosphere using conductive atomic force microscopy and Kelvin probe force microscopy (KPFM). We correlate these scanning probe measurements with optical spectroscopy of the nitrogen-vacancy and silicon-vacancy centers created via implantation 15–25 nm beneath the diamond surface and annealing. The observed charge state of the defects as a function of optical exposure demonstrates that laser oxidation provides a way to precisely tune the Fermi level over a range of at least 2.00 eV. We also observe a significantly larger oxidation rate for implanted surfaces compared to unimplanted surfaces under ambient conditions. Here, combined with knowledge of the electron affinity of a surface, these results suggest KPFM is a powerful, high-spatial-resolution technique to advance surface Fermi level engineering for charge stabilization of quantum defects.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Magnetically tuned metal-insulator transition in LaAlO 3 /SrTiO 3 nanowire arrays

A wide family of two-dimensional (2D) systems, including stripe-phase superconductors, sliding Luttinger liquids, and anisotropic 2D materials, can be modeled by an array of coupled one-dimensional (1D) electron channels or nanowire arrays. Here we report experiments in arrays of conducting nanowires with gate and field tunable interwire coupling, that are programmed at the LaAlO 3 /SrTiO 3 interface. We find a magnetically tuned metal-to-insulator transition in which the transverse resistance of the nanowire array increases by up to four orders of magnitude, which can be further tuned with a gate voltage. To explain this behavior, we develop a minimal model of a coupled two-wire system where a Wenzel-Kramers-Brillouin-based approach is used to estimate the transverse tunneling conductance. We demonstrate the existence of distinct conductance features and highlight the crucial role played by the field dependence of the interwire potential barrier on transport properties. Since our model makes minimal assumptions, we expect our predictions to hold for a wide class of coupled 1D systems. The nanowire arrays can serve as model systems to understand the origin of exotic behavior in correlated materials via analog quantum simulation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Performance Profile of Transformer Fine-Tuning in Multi-GPU Cloud Environments

The study presented here focuses on performance characteristics and trade-offs associated with running machine-learning tasks in multi-GPU environments on both on-site cloud computing resources and commercial cloud services (Azure). Specifically, this study examines these tradeoffs by examining the performance of training and fine-tuning of transformer-based deep-learning (DL) networks on clinical notes and data, a task of critical importance in the medical domain. To this end, we perform DL-related experiments on the widely deployed NVIDIA V100 GPUs and on the newer A100 GPUs connected via NVLink or PCIe. This study analyzes the execution time of major operations to train DL models and investigate popular options to optimize each of them. We examine and present the findings on the impacts that various operations (e.g. data loading into GPUs, training, fine-tuning), optimizations, and system configurations (single vs. multi-GPU, NVLink vs. PCIe) have on the overall training performance.

Begoli, Edmon↗
DOE OSTI2017

Load Power Agnostic 6.6 kW Wireless EV Charger with LCL Tuned Primary and Secondary Side Regulation

Wireless charging of electric vehicles (EVs) is going to play a major role in electrification of transportation. This paper proposes to utilize an LCL tuned primary and series tuned secondary with a secondary buck regulator to achieve power transfer control by secondary side control only. Analytical expressions for base (primary) coil current and transformer turns ratio N required to transfer the required power at a certain input voltage is calculated for a given coil set. The required parameters are calculated for a 6.6 kW wireless charging system with a matched circular coil set. The proposed control scheme and analysis were validated through Saber Sketch simulations and by a 6.6 kW laboratory prototype. Experimental results for different load power settings at voltages of 290 V, 340 V, and 370 V are presented.

Galigekere, Veda Prakash N.↗
DOE OSTI2023

Tuning Phase Lock Loop Controller of Grid Following Inverters by Reinforcement Learning to Support Networked Microgrid Operations

The dynamic operation of networked microgrids leads to varying topological configurations and generator commitments and dispatches. These variations correspond to systems with different electrical characteristics. The fixed control gains of high-speed power electronic devices may result in undesirable system performance when the electrical characteristics change significantly. As such, it is necessary to tune the control gains of power electronics devices to adapt to the changing system characteristics. This paper uses observer-based reinforcement learning to automatically tune the proportional-integral (PI) gains of phase lock loop (PLL) controller of grid-following (GFL) inverters to adapt to the changing system strengths, that would be seen in networked microgrid operations. Simulation results using an operational electric distribution system, modeled as networked microgrids, are presented to demonstrate the need and effectiveness of the proposed adaptive controls.

networked microgrids, reinforcement learning, grid↗
DOE OSTI2020

Multi-objective Optimization Paradigm for Toroidal Inductors with Spatially Tuned Permeability

Spatially tuning core permeability of an electromagnetic device enables superior performance. A permeability profile can be heuristically selected to improve the flux distribution in a device with a given geometry, but in order to fully leverage the capacity of spatial dependent permeability engineering, the geometry and the permeability should be optimized simultaneously. The work herein presented sets forth a multi-physics design optimization paradigm that includes the permeability profile tuning in the context of an inductor design. This approach enables the determination of Pareto optimal fronts consisting of a set of optimal solutions against competing objectives (e.g. mass and loss) under imposed constraints. To this end, analytical solutions of the heat transfer and electromagnetic formulations are derived for toroidal inductors. The software implemented in Matlab 2018b is available online as an attachment to this paper.

42 ENGINEERING↗
DOE OSTI2023

Fast Tuning-Free Distributed Algorithm for Solving the Network-Constrained Economic Dispatch

With the increasing penetration of distributed energy resources (DERs) and their participation in the electricity market, it becomes more desirable to apply distributed algorithms for resource allocation in order to address the resulting computational and communicational challenges. Most of the existing distributed algorithms for solving the network-constrained economic dispatch (NCED) problem require the tuning of certain auxiliary parameters. As a result, the robustness of these algorithms against the varieties in DERs is greatly undermined. In this paper, a new distributed algorithm, optimality condition consensus (OCC), is proposed to solve the NCED problem by using distributed power flow (DPF) and ratio consensus as fundamental tools. It inherits the advantages of existing distributed algorithms for the NCED problem but removes the need for parameter tuning to improve performance in practice. In conclusion, the effectiveness of the proposed distributed algorithm in terms of efficiency, scalability, and robustness is demonstrated through detailed case studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Shape tuning of large area silicon nanotip arrays through reactive ion etching

Nanostructures formed in silicon form an important class of structures that span a broad spectrum of application areas. Of these, columnar structures of silicon featuring tiplike apexes have their own niche applications. Here, the ability to afford shape tunability for these structures further enhances their application potential. In this paper, we present our findings on the large area fabrication of silicon nanotips defined through microsphere lithography and shape tuned through a combination of different reactive ion etching (RIE) techniques. The self-sharpening mechanism of the tips when using nonplanar etch masks (microspheres) under anisotropic etching conditions is elucidated. We further show that depending on the manner of etching (continuous versus discrete multistep etch), identical anisotropic etching recipes produce vastly different tip morphologies. Hourglass-shaped silicon tips were obtained when silicon was subjected to anisotropic followed by isotropic etching conditions. Sharp silicon tips with tip apex radii on the order of 2 nm have been successfully realized when the RIE shape tuned tips were subjected to a series of oxidative sharpening steps.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2016

Fast ultrasonic wavelength tuning in X-ray experiment

A method of tuning (scanning) X-ray beam wavelength based on modulation of the lattice parameter of X-ray optical crystal by an ultrasonic standing wave excited in it has been proposed and experimentally implemented. The double-crystal antiparallel scheme of X-ray diffraction, in which an ultrasonic wave is excited in the second crystal, is used in the experiment. The profile of characteristic line k{sub α1} of an X-ray tube with a molybdenum anode is recorded using both the proposed tuning scheme and conventional mechanical rotation of crystal. The results obtained by both techniques are in good agreement.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Event generator tunes obtained from underlying event and multiparton scattering measurements

Here, new sets of parameters (“tunes”) for the underlying-event (UE) modelling of the pythia8, pythia6 and herwig++ Monte Carlo event generators are constructed using different parton distribution functions. Combined fits to CMS UE proton–proton ( $\mathrm {p}\mathrm {p}$ ) data at $\sqrt{s} = 7\,\text {TeV} $ and to UE proton–antiproton ( $\mathrm {p}\overline{\mathrm{p}} $ ) data from the CDF experiment at lower $\sqrt{s}$ , are used to study the UE models and constrain their parameters, providing thereby improved predictions for proton–proton collisions at 13 $\,\text {TeV}$ . In addition, it is investigated whether the values of the parameters obtained from fits to UE observables are consistent with the values determined from fitting observables sensitive to double-parton scattering processes. Finally, comparisons are presented of the UE tunes to “minimum bias” (MB) events, multijet, and Drell–Yan ( $ \mathrm{q} \overline{\mathrm{q}} \rightarrow \mathrm{Z}/ \gamma ^* \rightarrow $ lepton-antilepton+jets) observables at 7 and 8 $\,\text {TeV}$ , as well as predictions for MB and UE observables at 13 $\,\text {TeV}$ .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Tuning Fluorination of Linear Carbonate for Lithium-Ion Batteries

Liquid electrolyte engineering plays a critical role in modern lithium-ion batteries. However, the existing electrolytes fall short when used with some trending battery chemistries such as high-voltage and high-energy-density electrodes. Fluorination of electrolyte solvents has been identified as an effective approach for improved cyclability, but few works systematically studied the effects of fluorination extent of carbonate solvents on battery performance. Here we design and synthesize a family of fluorinated ethyl methyl carbonates. Different numbers of F atoms are finely tuned to yield monofluoroethyl methyl carbonate (F1EMC), difluoroethyl methyl carbonate (F2EMC) and trifluoroethyl methyl carbonate (F3EMC). The cycling behavior of several types of lithium-ion pouch cells, including graphite (Gr)/single-crystalline LiNi 0.8 Mn 0.1 Co 0.1 O 2 (SC-NMC811), Gr-SiO x /LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622), high-voltage Gr/LiNi 0.5 Mn 1.5 O 4 (LNMO), Gr/layered Li-rich Mn-based oxide (LLMO) and fast-charging Gr/NMC622, were systematically investigated to understand the impact of fluorination degree. Compared to the commercially available F3EMC, we found that the partially-fluorinated F1EMC and F2EMC in some cases showed improved cycling stability, which we attribute to their locally-polar –CH 2 F and –CHF 2 groups and thus fast ion conduction than –CF 3 . This work suggests that highly or fully fluorinated solvents are not necessarily desirable; instead, fluorination degree needs to be rationally and finely tuned for optimized lithium-ion cell performance.

25 ENERGY STORAGE↗
DOE OSTI2017

Adaptive Self Tuning

The AST software includes numeric methods to 1) adjust STA/LTA signal detector trigger level (TL) values and 2) filter detections for a network of sensors. AST adapts TL values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: TL values are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The AST algorithm adapts in near real-time to changing conditions in an attempt to automatically self-tune a signal detector to identify (detect) only signals from events of interest.

Peterson, Matthew↗
DOE OSTI2023

Building Re-tuning Simulator

The Building Re-tuning Simulator (BRS) is a Re-tuning focused modeling and building automation system simulation environment.

Fernandez, Nick↗
DOE OSTI2012

Multi-core and many-core shared-memory parallel raycasting volume rendering optimization and tuning

Given the computing industry trend of increasing processing capacity by adding more cores to a chip, the focus of this work is tuning the performance of a staple visualization algorithm, raycasting volume rendering, for shared-memory parallelism on multi-core CPUs and many-core GPUs. Our approach is to vary tunable algorithmic settings, along with known algorithmic optimizations and two different memory layouts, and measure performance in terms of absolute runtime and L2 memory cache misses. Our results indicate there is a wide variation in runtime performance on all platforms, as much as 254% for the tunable parameters we test on multi-core CPUs and 265% on many-core GPUs, and the optimal configurations vary across platforms, often in a non-obvious way. For example, our results indicate the optimal configurations on the GPU occur at a crossover point between those that maintain good cache utilization and those that saturate computational throughput. This result is likely to be extremely difficult to predict with an empirical performance model for this particular algorithm because it has an unstructured memory access pattern that varies locally for individual rays and globally for the selected viewpoint. Our results also show that optimal parameters on modern architectures are markedly different from those in previous studies run on older architectures. In addition, given the dramatic performance variation across platforms for both optimal algorithm settings and performance results, there is a clear benefit for production visualization and analysis codes to adopt a strategy for performance optimization through auto-tuning. These benefits will likely become more pronounced in the future as the number of cores per chip and the cost of moving data through the memory hierarchy both increase.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

Auto-Tuning Memory-Intensive Kernels for Multicore

In this, chapter, we discuss the optimization of three memory-intensive computational kernels — sparse matrix-vector multiplication, the Laplacian differential operator applied to structured grids, and the collision() operator with the lattice Boltzmann magnetohydrodynamics (LBMHD) application. They are all implemented using a single-process, (POSIX) threaded, SPMD model. Unlike their computationally-intense dense linear algebra cousins, performance is ultimately limited by DRAM bandwidth and the volume of data that must be transfered. To provide performance portability across current and future multicore architectures, we utilize automatic performance tuning, or auto-tuning.

97 MATHEMATICS AND COMPUTING↗