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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Inference-Engine v0.1.0

Given a pre-trained neural network, Inference-Engine performs maps network inputs to outputs by executing the forward pass through the provided network. Although the predominant programming language for machine-learning is Python, most high-performance computing (HPC) applications are written in Fortran, C, or C++. Inference-Engine aims to support HPC programs and is written in Fortran, a language with a large feature set supporting interoperability with C. This software exposes concurrency in a portable way by using standard language features that some modern Fortran compilers can exploit with various optimizations, including offloading computation to a Graphics Processing Unit (GPU). In particular, this software makes extensive use of Fortran's "do concurrent" parallel loop construct, implicitly parallel array statements, and pure procedures that can be invoked inside "do concurrent" blocks. Inference-Engine also supports dynamic choice of inference methods at runtime. Two current options include one method that uses Fortran's "dot_product" intrinsic function inside "do concurrent" blocks and another method that instead uses Fortran' "matmul" array intrinsic function. We plan to investigate automatic compiler offloading of "do concurrent" calculations to GPUs and compile-time substitution of optimized libraries such as the Basic Linear Algebra Library (BLAS) for "matmul" invocations. We also envision the potential for the choice of which method to use could happen at program launch based on in situ performance measurements on any given platform.

Rouson, Damian↗
DOE OSTI2018

A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC). The outputs (targets) of interest from these simulations included five metrics related to engine performance and emissions. Over 2000 samples generated from CFD were then used to train an ML model that could predict these five targets based on the nine input features. A robust super learner approach was employed to build the ML model, where results from a collection of different ML algorithms were pooled together. Thereafter, a stochastic global optimization genetic algorithm (GA) was used, with the ML model as the objective function, to optimize the input parameters based on a merit function so as to minimize fuel consumption while satisfying CO and NOx emissions constraints. The optimized configuration from ML-GA was found to be very close to that obtained from a sequentially performed CFD-GA approach, where a CFD simulation served as the objective function. In addition, the overall turnaround time was (at least) 75% lower with the ML-GA approach, as the training data was generated from concurrent CFD simulations and employing the ML model as the objective function significantly accelerated the GA optimization. Finally, this study demonstrates the potential of ML-GA and high-performance computing (HPC) to reduce the number of CFD simulations to be performed for optimization problems without loss in accuracy, thereby providing significant cost savings compared to traditional approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Membrane assisted continuous production of solvents with integrated solvent removal using liquid-liquid extraction

The aim was to demonstrate an industrially relevant pilot scale integrated process to produce butanol and other solvents from lignocellulosic sugars produced with AVAP® biomass fractionation technology from southern pine wood. The concentrated sugars were concurrently fermented using genetically engineered Clostridium acetobutylicum to n-butanol, acetone, isopropanol and ethanol in continuous membrane assisted cell recycle fermentation with steady-state solvent productivity exceeding 10 g/L/h. The solvents from the cell free permeate were recovered with extractant of butyl butyrate in a continuous liquid-liquid extraction column and aqueous product (raffinate) along with unused sugars, nutrients, and metabolic intermediates were recycled back to the fermentors. The total solvent yield approached 0.30 g/g sugars.

09 BIOMASS FUELS↗
DOE OSTI2020

Independent Metadata Update VOL Connector for HDF5 (IMM VOL) v0.1

We have built a new framework that enables independent metadata updates within HDF5 files. Our framework includes the following components: an overlay network that's optimized for 1-to-All and All-to-All communication; root-less/server-less Bcast and AllReduce operations for reduced synchronization; a distributed consensus-making platform; an execution order keeping system, and a progress engine for generic threadless concurrence handling.

Li, Tonglin↗
DOE OSTI2018

Evaluation of Shot-to-Shot In-Nozzle Flow Variations in a Heavy-Duty Diesel Injector Using Real Nozzle Geometry

Cyclic variability in internal combustion engines (ICEs) arises from multiple concurrent sources, many of which remain to be fully understood and controlled. This variability can, in turn, affect the behavior of the engine resulting in undesirable deviations from the expected operating conditions and performance. Shot-to-shot variation during the fuel injection process is strongly suspected of being a source of cyclic variability. This study focuses on the shot-to-shot variability of injector needle motion and its influence on the internal nozzle flow behavior using diesel fuel. High-speed x-ray imaging techniques have been used to extract high-resolution injector geometry images of the sac, orifices, and needle tip that allowed the true dynamics of the needle motion to emerge. These measurements showed high repeatability in the needle lift profile across multiple injection events, while the needle radial displacement was characterized by a much higher degree of randomness. A robust and previously validated computational setup from the authors’ research group using a commercial computational fluid dynamics (CFD) code has been adapted for the eight-hole heavy-duty common-rail diesel injector used for the measurements. The simulation results obtained using the x-ray-scanned geometry have been validated against available experimental data of mass flow rate at the nozzle exit. The fuel mass flow rate has then been analyzed at three different injection pressures that cover the conditions at which the injector typically operates during normal engine operation. Finally, the average off-axis motion of the needle has been perturbed (based on the variability found in the experimental measurements) to generate three new cases that present different amplitude and phasing of the radial displacement with respect to the baseline average motion. Furthermore, this revealed the effects of off-axis motion on shot-to-shot and orifice-to-orifice variations.

42 ENGINEERING↗
DOE OSTI2017

Impact of detector simulation in particle physics collider experiments

Through the last three decades, precise simulation of the interactions of particles with matter and modeling of detector geometries has proven to be of critical importance to the success of the international high-energy physics experimental programs. For example, the detailed detector modeling and accurate physics of the Geant4-based simulation software of the CMS and ATLAS particle physics experiments at the European Center of Nuclear Research (CERN) Large Hadron Collider (LHC) was a determinant factor for these collaborations to deliver physics results of outstanding quality faster than any hadron collider experiment ever before. This review article highlights the impact of detector simulation on particle physics collider experiments. It presents numerous examples of the use of simulation, from detector design and optimization, through software and computing development and testing, to cases where the use of simulation samples made a difference in the accuracy of the physics results and publication turnaround, from data-taking to submission. It also presents the economic impact and cost of simulation in the CMS experiment. Future experiments will collect orders of magnitude more data, taxing heavily the performance of simulation and reconstruction software for increasingly complex detectors. Consequently, it becomes urgent to find solutions to speed up simulation software in order to cope with the increased demand in a time of flat budgets. The study ends with a short discussion on the potential solutions that are being explored, by leveraging core count growth in multicore machines, using new generation coprocessors, and re-engineering of HEP code for concurrency and parallel computing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Co-Optimization of Fuels & Engines: FY16 Year in Review

The U.S. Department of Energy's (DOE) Co-Optimization of Fuels & Engines (Co-Optima) initiative is accelerating the introduction of affordable, scalable, and sustainable fuels and high-efficiency, low-emission engines with a first-of-its-kind effort to simultaneously tackle fuel and engine research and development (R&D). This report summarizes accomplishments in the first year of the project. Co-Optima is conducting concurrent research to identify the fuel properties and engine design characteristics needed to maximize vehicle performance and affordability, while deeply cutting emissions. Nine national laboratories - the National Renewable Energy Laboratory and Argonne, Idaho, Lawrence Berkeley, Lawrence Livermore, Los Alamos, Oak Ridge, Pacific Northwest, and Sandia National Laboratories - are collaborating with industry and academia on this groundbreaking research.

09 BIOMASS FUELS↗
DOE OSTI2023

Enabling off-highway diesel engine downsizing and performance improvement using electrically assisted turbocharging

Internal combustion engine (ICE) downsizing through various turbocharging configurations is generally known by the powertrain design community as an effective means to reduce frictional losses, increase waste heat recovery, and improve fuel efficiency while increasing engine power density. However, often is the case that turbocharging strategies, including variable geometry turbochargers, and regulated two-stage turbochargers, incur the performance tradeoff between transient response and fuel economy (pumping losses) at high engine speeds. For off-highway vehicles having particularly transient and high-powered duty cycles, efforts to improve this tradeoff and increase operational flexibility have turned to evaluating various electrified air system architectures. In this study, a 4.5 L diesel-ICE configured with an electrically driven compressor (eBooster ® ) is placed in series with a conventional turbocharger and integrated into a 48 V mild-hybrid powertrain architecture. The objective of this powertrain configuration is to enable engine downsizing by 34%, replacing the current 6.8 L ICE platform with the hybridized 4.5 L ICE concept. The commercial 1-D simulation software GT-SUITE is used for powertrain system development and system optimization. Development and validation of the GT-SUITE model and air system controls is concurrently supported through experimental data collection. The simulation model development includes using machine learning methods for optimizing exhaust gas dilution, injection timing, and eBooster ® power to improve steady-state and transient brake-specific fuel consumption while minimizing criteria pollutant emissions. It was found that total specific fluid consumption over the standardized non-road transient engine duty cycle could be reduced by 18% over the current 6.8 L engine by using an optimized eBoosted 4.5 L engine. The hybridized 4.5 L engine concept concurrently showed sufficient transient response capability and nearly an order of magnitude reduction in duty cycle total soot production.

25 ENERGY STORAGE↗
DOE OSTI2026

Engineering Thermally Resilient and Kinetically Active Reversible Protonic Ceramic Cells via Interfacial Design

Achieving concurrent fast electrode kinetics and long-term thermo-mechanical durability remains a critical challenge for reversible protonic ceramic electrochemical cells (R-PCECs). Herein, we report an interfacial engineering strategy that integrates a perovs.kite-type PrBaRu0.1Co1.9O5+δ (PBRC) nanoparticle layer onto a PrBa0.5Sr0.5Co1.5Fe0.5O5+δ (PBSCF) substrate (PBRC-PBSCF), together with a modified pellet-assisted sintering approach to fabricate dense BaZr0.4Ce0.4Y0.1Yb0.1O3-δ (BZCYYb4411) electrolytes. The in situ reconstructed heterointerface enhances oxygen reduction/evolution reaction (ORR/OER) kinetics, promotes H2O adsorption/dissociation, and improves steam tolerance, as verified by electrochemical measurements and interfacial microstructural analyses. Density functional theory reveals that Ru-induced electronic modulation at the PBRC-PBSCF interface lowers the energy of oxygen vacancy formation and optimizes the position of the O 2p band center, thereby accelerating oxygen redox kinetics and stabilizing the interface. The resulting R-PCECs deliver an excellent peak power density of 1.112 W cm−2 and an electrolysis current density of −1.257 A cm−2 at 1.3 V in 3% H2O wet air at 600°C, with a reasonable faradaic efficiency. Furthermore, the cells demonstrate excellent stability, sustaining 100 h of thermal cycling (400–600°C, 200°C h−1) in both fuel cell and electrolysis modes, with 600 h of stability in electrolysis mode (600°C, −0.5 to −2 A cm−2).

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2013

Evaluation of American Indian Science and Engineering Society Intertribal Middle School Science and Math Bowl Project

The American Indian Science and Engineering Society (AISES) has been funded under a U.S. Department of Energy (DOE) grant (Grant Award No. DE-SC0004058) to host an Intertribal Middle-School Science and Math Bowl (IMSSMB) comprised of teams made up of a majority of American Indian students from Bureau of Indian Education-funded schools and public schools. The intent of the AISES middle school science and math bowl is to increase participation of American Indian students at the DOE-sponsored National Science Bowl. Although national in its recruitment scope, the AISES Intertribal Science and Math Bowl is considered a “regional” science bowl, equivalent to the other 50 regional science bowls which are geographically limited to states. Most regional bowls do not have American Indian student teams competing, hence the AISES bowl is meant to encourage American Indian student teams to increase their science knowledge in order to participate at the national level. The AISES competition brings together teams from various American Indian communities across the nation. Each team is provided with funds for travel to and from the event, as well as for lodging and meals. In 2011 and 2012, there were 10 teams participating; in 2013, the number of teams participating doubled to 20. Each Science and Math Bowl team is comprised of four middle school — grades 6 through 8 — students, one alternate, and a teacher who serves as advisor and coach — although in at least two cases, the coach was not a teacher, but was the Indian Education Coordinator. Each team member must have at least a 3.0 GPA. Furthermore, the majority of students in each team must be comprised of American Indian, Alaska Native or Native Hawaiian students. Under the current DOE grant, AISES sponsored three annual middle school science bowl competitions over the years 2011, 2012 and 2013. The science and math bowls have been held in late March concurrently with the National American Indian Science and Engineering Fair (NAISEF) and EXPO at the Albuquerque, NM Convention Center. Albuquerque is also the home of the AISES national office. The AISES staff also recruits volunteers to assist with implementation of the science and math bowl event. In 2011, there were 7 volunteers; in 2012, 15 volunteers, and in 2013, 19 volunteers. Volunteers are recruited from a variety of local sources, including Sandia Laboratories, Southwest Indian Polytechnic Institute students, Department of Defense, as well as family members of AISES staff. For AISES, the goals of the Intertribal Middle School Science and Math Bowl project are to have more Native students learn science, for them to gain confidence in competing, and to reward their effort in order to motivate them to pursue studies in the sciences and engineering. For DOE, the goals of the project are to get more Native students to compete at the National Science Bowl, held in Washington, DC.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2013

Application of High Performance Computing for Simulating the Unstable Dynamics of Dilute Spark-Ignited Combustion

In collaboration with a major automotive manufacturer, we are using computational simulations of in-cylinder combustion to understand the multi-scale nonlinear physics of the dilute stability limit. Because some key features of dilute combustion can take thousands of successive cycles to develop, the computation time involved in using complex models to simulate these effects has limited industrys ability to exploit simulations in optimizing advanced engines. We describe a novel approach for utilizing parallel computations to reveal long-timescale features of dilute combustion without the need to simulate many successive engine cycles in series. Our approach relies on carefully guided, concurrent, single-cycle simulations to create metamodels that preserve the long-timescale features of interest. We use a simplified combustion model to develop and demonstrate our strategy for adaptively guiding the concurrent simulations to generate metamodels. We next will implement this strategy with higher-fidelity, multi-scale combustion models on large computing facilities to generate more refined metamodels. The refined metamodels can then be used to accelerate engine development because of their efficiency. Similar approaches might also be used for rapidly exploring the dynamics of other complex multi-scale systems that evolve with serial dependency on time.

Finney, Charles↗
DOE OSTI2023

Bandwidth Control and Symmetry Breaking in a Mott‐Hubbard Correlated Metal

Abstract In Mott materials strong electron correlation yields a spectrum of complex electronic structures. Recent synthesis advancements open realistic opportunities for harnessing Mott physics to design transformative devices. However, a major bottleneck in realizing such devices remains the lack of control over the electron correlation strength. This stems from the complexity of the electronic structure, which often veils the basic mechanisms underlying the correlation strength. This study presents control of the correlation strength by tuning the degree of orbital overlap using picometer‐scale lattice engineering. This study illustrates how bandwidth control and concurrent symmetry breaking can govern the electronic structure of a correlated SrVO 3 model system. This study shows how tensile and compressive biaxial strain oppositely affect the SrVO 3 in‐plane and out‐of‐plane orbital occupancy, resulting in the partial alleviation of the orbital degeneracy. The spectral weight redistribution under strain is derived and explained, which illustrates how high tensile strain drives the system toward a Mott insulating state. Implementation of such concepts can push correlated electron phenomena closer toward new solid‐state devices and circuits. These findings therefore pave the way for understanding and controlling electron correlation in a broad range of functional materials, driving this powerful resource for novel electronics closer toward practical realization.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Exploiting ultra-large linear elasticity over a wide temperature range in nanocrystalline NiTi alloy

Many shape memory alloys can support large recoverable strains of a few percent by reversible stress-induced martensite transformation, yet they behave non-linear within a narrow operating temperature range. Developing the bulk metallic materials with ultra-large linear elasticity over a wide temperature range has proven to be difficult. In this work, a material design concept was proposed, that is true elastic deformation and reversible twinning-detwinning deformation run in parallel to overcome this challenge. By engineering the residual internal stress to realize the concurrency of true elastic deformation and twinning-detwinning deformation, a bulk nanocrystalline NiTi that possesses an ultra-large linear elastic strain up to 5.1 % and a high yield stress of 2.16 GPa over a wide temperature range of 270 °C was developed. This study offers a new avenue for developing the metallic materials with ultra-large linear elasticity over a wide temperature range of 270 °C (from 70 °C to -197 °C).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Production of carotenoids from aromatics and pretreated lignocellulosic biomass by Novosphingobium aromaticivorans

ABSTRACT Carotenoids are lipophilic compounds found in the membranes of various organisms. Individual carotenoids are also commodity chemicals, produced industrially for use as food additives, nutritional supplements, cosmetics, and pharmaceuticals. The alphaproteobacterium Novosphingobium aromaticivorans has previously been established as a potential platform microbe for converting aromatic compounds derived from lignocellulosic plant biomass into valuable extracellular products. Here, we show that N. aromaticivorans DSM 12444 cells naturally produce the carotenoid nostoxanthin, and we construct a set of gene deletion mutants that accumulate β-carotene, lycopene, or zeaxanthin, which are predicted intermediates in nostoxanthin biosynthesis as well as commodity chemicals. We also show that a mutant strain heterologously expressing a CrtW protein accumulates the carotenoid astaxanthin. When grown on vanillate as the carbon source, we find that the levels of carotenoids are not significantly affected by O 2 concentration in the tested range of 5% to 21% O 2 . We also show that these carotenoids are produced at comparable levels when strains are grown in liquor from alkaline pretreated sorghum biomass [sorghum alkaline pretreatment liquor (APL)], which contains a mixture of aromatics. Finally, we construct strains that produce zeaxanthin, β-carotene, or astaxanthin concurrently with 2-pyrone-4,6-dicarboxylic acid, a potential building block for biodegradable polymers, when grown in sorghum APL. Combined, our results show that N. aromaticivorans can simultaneously produce valuable intracellular and extracellular commodities when grown in the presence of either pure aromatics or pretreated lignocellulosic biomass. IMPORTANCE There is economic and environmental interest in generating commodity chemicals from renewable resources, such as lignocellulosic biomass, that can substitute for chemicals derived from fossil fuels. The bacterium Novosphingobium aromaticivorans is a promising microbial platform for producing commodity chemicals from lignocellulosic biomass because it can produce these from compounds in pretreated lignocellulosic biomass, which many industrial microbial catalysts cannot metabolize. Here, we show that N. aromaticivorans can be engineered to produce several valuable carotenoids. We also show that engineered N. aromaticivorans strains can produce these lipophilic chemicals concurrently with the extracellular commodity chemical 2-pyrone-4,6-dicarboxylic acid when grown in a complex liquor obtained from alkaline pretreated lignocellulosic biomass. Concurrent microbial production of valuable intra- and extracellular products can increase the economic value generated from the conversion of lignocellulosic biomass-derived compounds into commodity chemicals and facilitate the separation of water- and membrane-soluble products.

09 BIOMASS FUELS↗
DOE OSTI2022

Concurrency Supply Chain Optimization (Final Report)

Brunswick is experiencing unprecedented volume for engines and fully produced boats. Supply Chain (parts) has difficulty accurately forecasting demand needs which presents problems in the manufacturing process. Streamline ordering process enabling the Brunswick Boat Group (BBG) to more efficiently forecast part demand for the Mercury Engine Group by setting up an AI/ML model. Initial focus will be 15 SKUs with inaccurate demand forecasting based on current manual processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A pathway to synthesizing single-crystal Fe and FeCr films

Nuclear reactor environments provide a unique scientific and engineering challenge wherein materials must tolerate prolonged exposure to concurrent irradiation, elevated temperatures, and corrosive media. However, uncontrolled variability in material composition and structure often prohibits truly single-variable experiments that can reveal basic aspects of environmental damage. Here, Magnetron sputtering is used to provide a more controlled model system for these fundamental studies, yielding reproducible single-crystal Fe and FeCr thin films containing 8 and 18 at.% Cr. Electron microscopy is used to determine the systematic correlations between growth conditions and the resulting film microstructure and surface morphology. It is found that the substrate temperature and applied radio frequency (RF) bias can be tuned to obtain consistent homogeneous and single crystal films with a minimal amount of Ar impurities from the RF bias process. Epitaxial, single-crystal Fe films are obtained on MgO substrates at 500 °C with 10 Watt (W) RF bias deposition. However, when Cr is alloyed with Fe, higher substrate temperatures (600 °C) and applied RF biases (15 W) are required to achieve a similar epitaxial single-crystal FeCr film. Accelerated molecular dynamics simulations reveal that Cr impedes surface transport, explaining the need for higher temperature and bias during the growth of the Cr-bearing films.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Temperature-dependent thermal conductivity in silicon nanostructured materials studied by the Boltzmann transport equation

Nanostructured materials show low thermal conductivity because of the additional scattering due to phonon-boundary interactions. As these interactions are highly sensitive to the mean free path (MFP) of phonons, MFP distributions in nanostructures can be dramatically distorted relative to bulk. Here we calculate the MFP distribution in periodic nanoporous Si for different temperatures, using the recently developed MFP-dependent Boltzmann transport equation. After analyzing the relative contribution of each phonon branch to thermal transport in nanoporous Si, we find that at room temperature optical phonons contribute 17 % to heat transport, compared to 5 % in bulk Si. Interestingly, we observe a constant thermal conductivity over the range 200 K < T < 300 K . Furthermore, we attribute this behavior to the ballistic transport of acoustic phonons with long intrinsic MFP and the temperature dependence of the heat capacity. Our findings, which are in qualitative agreement with the temperature trend of thermal conductivities measured in nanoporous Si-based systems, shed light on the origin of the reduction of thermal conductivity in nanostructured materials and demonstrate the necessity of multiscale heat transport engineering, in which the bulk material and geometry are optimized concurrently.

36 MATERIALS SCIENCE↗
DOE OSTI2016

NREL Advancements in Methane Conversion Lead to Cleaner Air, Useful Products

Researchers at NREL leveraged the recent on-site development of gas fermentation capabilities and novel genetic tools to directly convert methane to lactic acid using an engineered methanotrophic bacterium. The results provide proof-of-concept data for a gas-to-liquids bioprocess that concurrently produces fuels and chemicals from methane. NREL researchers developed genetic tools to express heterologous genes in methanotrophic organisms, which have historically been difficult to genetically engineer. Using these tools, researchers demonstrated microbial conversion of methane to lactate, a high-volume biochemical precursor predominantly utilized for the production of bioplastics. Methane biocatalysis offers a means to concurrently liquefy and upgrade natural gas and renewable biogas, enabling their utilization in conventional transportation and industrial manufacturing infrastructure. Producing chemicals and fuels from methane expands the suite of products currently generated from biorefineries, municipalities, and agricultural operations, with the potential to increase revenue and significantly reduce greenhouse gas emissions.

09 BIOMASS FUELS↗