Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Performance Gap”

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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints

A novel two-phase bounding and decomposition approach to compute optimal and near-optimal solutions to large-scale mixed-integer investment planning problems is proposed and it considers a large number of operating subproblems, each of which is a convex optimization. Our motivating application is the planning of power transmission and generation in which policy constraints are designed to incentivize high amounts of intermittent generation in electric power systems. The bounding phase exploits Jensen’s inequality to define a lower bound, which we extend to stochastic programs that use expected-value constraints to enforce policy objectives. The decomposition phase, in which the bounds are tightened, improves upon the standard Benders’ algorithm by accelerating the convergence of the bounds. The lower bound is tightened by using a Jensen’s inequality-based approach to introduce an auxiliary lower bound into the Benders master problem. Upper bounds for both phases are computed using a sub-sampling approach executed on a parallel computer system. Numerical results show that only the bounding phase is necessary if loose optimality gaps are acceptable. But, the decomposition phase is required to attain optimality gaps. Moreover, use of both phases performs better, in terms of convergence speed, than attempting to solve the problem using just the bounding phase or regular Benders decomposition separately.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Efficient distributed continual learning for steering experiments in real-time

Deep learning has emerged as a powerful method for extracting valuable information from large volumes of data. However, when new training data arrives continuously (i.e., is not fully available from the beginning), incremental training suffers from catastrophic forgetting (i.e., new patterns are reinforced at the expense of previously acquired knowledge). Training from scratch each time new training data becomes available would result in extremely long training times and massive data accumulation. Rehearsal-based continual learning has shown promise for addressing the catastrophic forgetting challenge, but research to date has not addressed performance and scalability. To fill this gap, we propose an approach based on a distributed rehearsal buffer that efficiently complements data-parallel training on multiple GPUs to achieve high accuracy, short runtime, and scalability. It leverages a set of buffers (local to each GPU) and uses several asynchronous techniques for updating these local buffers in an embarrassingly parallel fashion, all while handling the communication overheads necessary to augment input minibatches using unbiased, global sampling. We further propose a generalization of rehearsal buffers to support both classification and generative learning tasks, as well as more advanced rehearsal strategies (notably Dark Experience Replay, leveraging knowledge distillation). We illustrate this approach with a real-life HPC streaming application from the domain of ptychographic image reconstruction. Furthermore, we run extensive experiments on up to 128 GPUs of the ThetaGPU supercomputer to compare our approach with baselines representative of training-from-scratch (the upper bound in terms of accuracy) and incremental training (the lower bound). Results show that rehearsal-based continual learning achieves a top-5 validation accuracy close to the upper bound, while simultaneously exhibiting a runtime close to the lower bound.

Asynchronous data management↗
DOE OSTI2017

Lessons learned from a comparison study of charcoal stoves for Haiti

Biomass is the predominant cooking fuel in Haiti, where it creates burdens on both the environment and the Haitian people. Following the 2010 earthquake in Port-au-Prince, the need for fuel-efficient cookstoves was acute. Although several organizations were quite interested in dissemination of fuel-efficient stoves in the relief effort, there was little knowledge about the performance and usability of the proposed stoves. To help fill the knowledge gap, stove researchers from Lawrence Berkeley National Laboratory evaluated and compared the performance of several cookstoves intended for dissemination in Haiti. This paper discusses the decisions made throughout the course of that work, from project identification and approach through the dissemination of results. It identifies the challenges faced and how they were addressed, while briefly presenting the data from stove performance evaluated using Water Boiling and Controlled Cooking Tests. Furthermore, it also highlights the importance and benefits of evaluating technologies such as cookstoves prior to dissemination, even in urgent disaster relief situations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Atmospheric Turbulent Intermittency Over the Arctic Sea‐Ice Surface During the MOSAiC Expedition

Turbulent motions in the Arctic stable boundary layer are characterized by intermittency, but they are rarely investigated due to limited observations, in particular over the sea‐ice surface. In the present study, we explore the characteristics of turbulent intermittency over the Arctic sea‐ice surface using data collected during the Multidisciplinary drifting Observation for the Study of Arctic Climate expedition from October 2019 to September 2020. We first develop a new algorithm, which performs well in identifying the spectral gap over the Arctic sea‐ice surface. Then the characteristics of intermittency are investigated. It is found that the strength of intermittency increases under the conditions of light surface wind speed, small surface wind speed gradient, and strong surface air temperature gradient. The momentum flux, sensible heat flux, and latent heat flux calculated by raw eddy‐covariance fluctuations are overestimated by 3%, 10%, and 24%, respectively, because submesoscale motions are included. Furthermore, the characteristics of the atmospheric boundary layer structure under various intermittency conditions reveal that strong low‐level jets are favorable to surface turbulent motions that result in weak intermittency, while strong temperature inversions above the surface layer suppress surface turbulent motions and lead to strong intermittency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Active learning for accelerated design of layered materials

Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exploring cycling induced crystallographic change in NMC with X-ray diffraction computed tomography

This study presents the application of X-ray diffraction computed tomography for the first time to analyze the crystal dimensions of LiNi 0.33 Mn 0.33 Co 0.33 O 2 electrodes cycled to 4.2 and 4.7 V in full cells with graphite as negative electrodes at 1 μm spatial resolution to determine the change in unit cell dimensions as a result of electrochemical cycling. The nature of the technique permits the spatial localization of the diffraction information in 3D and mapping of heterogeneities from the electrode to the particle level. An overall decrease of 0.4% and 0.6% was observed for the unit cell volume after 100 cycles for the electrodes cycled to 4.2 and 4.7 V. Additionally, focused ion beam-scanning electron microscope cross-sections indicate extensive particle cracking as a function of upper cut-off voltage, further confirming that severe cycling stresses exacerbate degradation. Finally, the technique facilitates the detection of parts of the electrode that have inhomogeneous lattice parameters that deviate from the bulk of the sample, further highlighting the effectiveness of the technique as a diagnostic tool, bridging the gap between crystal structure and electrochemical performance.

25 ENERGY STORAGE↗
DOE OSTI2021

Doping-modulated strain control of bifunctional electrocatalysis for rechargeable zinc–air batteries

Changes in the local atomic arrangement in a crystal caused by lattice-mismatch-induced strain can efficiently regulate the performance of electrocatalysts for zinc–air batteries (ZABs) in many manners, mainly due to modulated electronic structure configurations that affect the adsorption energies for oxygen-intermediates formed during oxygen reduction and evolution reactions (ORR and OER). However, the application of strain engineering in electrocatalysis has been limited by the strain relaxation caused by structural instability such as dissolution and destruction, leading to insufficient durability towards the ORR/OER. In this work, we propose a doping strategy to modulate the phase transition and formation of self-supported cobalt fluoride–sulfide (CoFS) nanoporous films using a low amount of copper (Cu) as a dopant. This well-defined Cu–CoFS heterostructure overcomes the obstacle of structural instability. Our study of the proposed Cu–CoFS also helps establish the structure–property relationship of strained electrocatalysts by unraveling the role of local strain in regulating the electronic structure of the catalyst. As a proof-of-concept, the Cu–CoFS electrocatalyst with doping-modulated strain exhibited superior onset potentials of 0.91 V and 1.49 V for the ORR and OER, respectively, surpassing commercial Pt/C@RuO 2 and benchmarking non-platinum group metal (non-PGM) catalysts. ZABs with the Cu–CoFS catalyst delivered excellent charge/discharge cycling performance with an extremely low voltage gap of 0.5 V at a current density of 10 mA cm –2 and successively 0.93 V at a high current density of 100 mA cm –2 and afforded an outstanding peak power density of 255 mW cm –2 .

25 ENERGY STORAGE↗
DOE OSTI2014

Identification of nitrogen- and host-related deep-level traps in n-type GaNAs and their evolution upon annealing

Deep level traps in as-grown and annealed n-GaNAs layers (doped with Si) of various nitrogen concentrations (N=0.2%, 0.4%, 0.8%, and 1.2%) were investigated by deep level transient spectroscopy. In addition, optical properties of GaNAs layers were studied by photoluminescence and contactless electroreflectance. The identification of N- and host-related traps has been performed on the basis of band gap diagram [Kudrawiec, Appl. Phys. Lett. 101, 082109 (2012)], which assumes that the activation energy of electron traps of the same microscopic nature decreases with the rise of nitrogen concentration in accordance with the N-related shift of the conduction band towards trap levels. The application of this diagram has allowed to investigate the evolution of donor traps in GaNAs upon annealing. In general, it was observed that the concentration of N- and host-related traps decreases after annealing and PL improves very significantly. However, it was also observed that some traps are generated due to annealing. It explains why the annealing conditions have to be carefully optimized for this material system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Enhancing the thermoelectric figure of merit through the reduction of bipolar thermal conductivity with heterostructure barriers

In this paper, we present theoretically that the thermoelectric figure of merit for a semiconductor material with a small band gap can be significantly enhanced near the intrinsic doping regime at high temperatures via the suppression of bipolar thermal conductivity when the minority carriers are selectively blocked by heterostructure barriers. This scheme is particularly effective in nanostructured materials where the lattice thermal conductivity is lowered by increased phonon scatterings at the boundaries, so that the electronic thermal conductivity including the bipolar term is limiting the figure of merit $zT$. We show that $zT$ can be enhanced to above 3 for p-type PbTe, and above 2 for n-type PbTe at 900 K with minority carrier blocking, when the lattice thermal conductivity is as low as 0.3 W/m K.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Electrical/thermal transport and electronic structure of the binary cobalt pnictides CoPn{sub 2} (Pn = As and Sb)

We demonstrate the electrical and thermal transport properties of polycrystalline CoPn{sub 2} (Pn = As and Sb) between 300 and 900 K. CoAs{sub 2} shows semiconducting electrical transport up to 900 K, while CoSb{sub 2} exhibits degenerate conduction. Sign inversion of the Seebeck coefficient is observed at ∼310 and ∼400 K for CoAs{sub 2} and CoSb{sub 2}, respectively. Thermal conductivity at 300 K is 11.7 Wm{sup −1}K{sup −1} for CoAs{sub 2} and 9.4 Wm{sup −1}K{sup −1} for CoSb{sub 2}. The thermoelectric power factor of CoAs{sub 2} is ∼10 μWcm{sup −1}K{sup −2}, although the dimensionless figure of merit is limited to ∼0.1 due to relatively high thermal conductivity. Using electronic structure calculations, the band gap value is calculated to be 0.55 eV for CoAs{sub 2} and 0.26 eV for CoSb{sub 2}.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Requirements and sensitivity analysis for temporally- and spatially-resolved thermometry using neutron resonance spectroscopy

Neutron resonance spectroscopy (NRS) has been used extensively to make temperature measurements that are accurate, absolute, and nonperturbative within the interior of material samples under extreme conditions applied quasistatically. Yet NRS has seldom been used in dynamic experiments. There is a compelling incentive to do so because of the significant shortcomings of alternative techniques. An important barrier to adopting dynamic NRS thermometry is the difficulty in fielding it with conventional spallation neutron sources. To enable time-dependent and spatially resolved temperature measurements in dynamic environments, more compact neutron sources that can be used at user facilities in conjunction with other diagnostic probes (such as x-ray light sources) are required. Such sources may be available using ultrafast high-intensity optical lasers. We evaluate such possibilities by determining the sensitivities of the temperature estimate on neutron-beam and diagnostic parameters. Based on that evaluation, requirements are set on a pulsed neutron-source and diagnostics to make a meaningful dynamic temperature measurement. Dynamic thermometry measurements are examined in this context when driven by two alternative fast-neutron sources: the Los Alamos Neutron Scattering Center (LANSCE) proton accelerator driving isotropic spallation neutrons as a baseline and a laser-plasma ion accelerator driving a neutron beam from deuterium breakup. Strategies to close the gap between the required and demonstrated performance of laser-based fast-neutron sources are presented. A short-pulse high-intensity laser with state-of-the-art pulse contrast and an energy of a few hundred Joules would drive a compact neutron source suitable for NRS thermometry that could transform the dynamic study of materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Experimental and theoretical study of energy transfer in a chromophore triad: What makes modeling dynamics successful?

Simulation of electronic dynamics in realistically large molecular systems is a demanding task that has not yet achieved the same level of quantitative prediction already realized for its static counterpart. This is particularly true for processes occurring beyond the Born–Oppenheimer regime. Non-adiabatic molecular dynamics (NAMD) simulations suffer from two convoluted sources of error: numerical algorithms for dynamics and electronic structure calculations. While the former has gained increasing attention, particularly addressing the validity of ad hoc methodologies, the effect of the latter remains relatively unexplored. Indeed, the required accuracy for electronic structure calculations to reach quantitative agreement with experiment in dynamics may be even more strict than that required for static simulations. In this study, we address this issue by modeling the electronic energy transfer in a donor–acceptor–donor (D–A–D) molecular light harvesting system using fewest switches surface hopping NAMD simulations. In the studied system, time-resolved experimental measurements deliver complete information on spectra and energy transfer rates. Subsequent modeling shows that the calculated electronic transition energies are “sufficiently good” to reproduce experimental spectra but produce over an order of magnitude error in simulated dynamical rates. We further perform simulations using artificially shifted energy gaps to investigate the complex relationship between transition energies and modeled dynamics to understand factors affecting non-radiative relaxation and energy transfer rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Stability-driven nonnegative matrix factorization to interpret spatial gene expression and build local gene networks

Spatial gene expression patterns enable the detection of local covariability and are extremely useful for identifying local gene interactions during normal development. The abundance of spatial expression data in recent years has led to the modeling and analysis of regulatory networks. The inherent complexity of such data makes it a challenge to extract biological information. We developed staNMF, a method that combines a scalable implementation of nonnegative matrix factorization (NMF) with a new stability-driven model selection criterion. When applied to a set of Drosophila early embryonic spatial gene expression images, one of the largest datasets of its kind, staNMF identified 21 principal patterns (PP). Providing a compact yet biologically interpretable representation of Drosophila expression patterns, PP are comparable to a fate map generated experimentally by laser ablation and show exceptional promise as a data-driven alternative to manual annotations. Our analysis mapped genes to cell-fate programs and assigned putative biological roles to uncharacterized genes. Finally, we used the PP to generate local transcription factor regulatory networks. Spatially local correlation networks were constructed for six PP that span along the embryonic anterior-posterior axis. Using a two-tail 5% cutoff on correlation, we reproduced 10 of the 11 links in the well-studied gap gene network. In conclusion, the performance of PP with the Drosophila data suggests that staNMF provides informative decompositions and constitutes a useful computational lens through which to extract biological insight from complex and often noisy gene expression data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

OzDES Reverberation Mapping Program: Stacking analysis with Hβ, Mg ii , and C iv

ABSTRACT Reverberation mapping is the leading technique used to measure direct black hole masses outside of the local Universe. Additionally, reverberation measurements calibrate secondary mass-scaling relations used to estimate single-epoch virial black hole masses. The Australian Dark Energy Survey (OzDES) conducted one of the first multi-object reverberation mapping surveys, monitoring 735 AGN up to z ∼ 4, over 6 years. The limited temporal coverage of the OzDES data has hindered recovery of individual measurements for some classes of sources, particularly those with shorter reverberation lags or lags that fall within campaign season gaps. To alleviate this limitation, we perform a stacking analysis of the cross-correlation functions of sources with similar intrinsic properties to recover average composite reverberation lags. This analysis leads to the recovery of average lags in each redshift-luminosity bin across our sample. We present the average lags recovered for the Hβ, Mg ii, and C iv samples, as well as multiline measurements for redshift bins where two lines are accessible. The stacking analysis is consistent with the Radius–Luminosity relations for each line. Our results for the Hβ sample demonstrate that stacking has the potential to improve upon constraints on the R–L relation, which have been derived only from individual source measurements until now.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Nonstoichiometric Salt Intercalation as a Means to Stabilize Alkali Doping of 2D Materials

Although doping with alkali atoms is a powerful technique for introducing charge carriers into physical systems, the resulting charge-transfer systems are generally not air stable. Here, in this paper, we describe computationally a strategy towards increasing the stability of alkali-doped materials that employs stoichiometrically unbalanced salt crystals with excess cations (which could be deposited during, e.g., in situ gating) to achieve doping levels similar to those attained by pure alkali metal doping. The crystalline interior of the salt crystal acts as a template to stabilize the excess dopant atoms against oxidation and deintercalation, which otherwise would be highly favorable. We characterize this doping method for graphene, NbSe 2 , and Bi 2 Se 3 and its effect on direct-to-indirect band gap transitions, 2D superconductivity, and thermoelectric performance. Salt intercalation should be generally applicable to systems which can accommodate this “ionic crystal” doping (and particularly favorable when geometrical packing constraints favor nonstoichiometry).

2-dimensional systems↗
DOE OSTI2021

Parallel I/O Evaluation Techniques and Emerging HPC Workloads: A Perspective

Emerging workloads such as artificial intelligence, big data analytics and complex multi-step workflows alongside future exascale applications are anticipated future HPC workloads, which will result in a more diverse I/O system workload and even less predictable I/O behavior and access patterns. Along with the ever increasing gap between the compute and storage performance capabilities, the in-depth understanding of extreme-scale I/O behavior and the I/O performance modeling and prediction are essential tools of the large-scale I/O evaluation process for addressing the needs of extreme-scale hybrid workloads. In this survey article, we focus on the state-of-the-art of the I/O behavior and performance analysis process for HPC systems in a 5-year time window and identify future research challenges.

Neuwirth, Sarah↗
DOE OSTI2025

A Neural Optimizer With Decision-Focused Learning for Optimal Energy Storage Operation

Here, this article introduces a neural optimizer-based framework for optimizing battery energy storage system (BESS) control for grid services, including demand charge and energy cost reduction. By leveraging decision-focused learning (DFL), the proposed framework ensures seamless integration and adaptation, significantly enhancing control performance. A patch time-series transformer is employed for peak load forecasting, incorporating aleatoric uncertainty quantification to account for forecasting uncertainties within the decision-making process. The framework utilizes a solver-in-the-loop approach to generate optimal BESS actions, which are then used to train the neural optimizer-based agent. By co-optimizing both BESS operational modes and output power within the NN, the system achieves improved performance and robustness. After initial training, the forecasting and control models are jointly fine-tuned to account for forecasting errors, further improving decision precision and efficiency through DFL. Case studies are performed to validate the performance of the framework using multiple real-world datasets, demonstrating superior performance in monthly peak load forecasting compared to state-of-the-art models. In addition, the results are compared against existing decision-making approaches. The results demonstrate a reduction in monthly peak forecasting error by approximately 15% across various performance measures and achieve an optimization gap for BESS operation that is about three times smaller compared to existing methods.

Kim, Hyeonjin [Pacific Northwest National Laborato↗