Search NASA⌕ Search

SEARCH · Search NASA

Results for “performance advantaged”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Comprehensive Comparative Analysis: VSI-based vs. CSI-based Motor Drive Systems with Sinusoidal Output Voltage

The emergence of wide-bandgap (WBG) power devices opens opportunities for current source inverters (CSIs) to provide a promising alternative drive configuration for motor drive applications. The sinusoidal output voltage of CSI offers significant advantages for numerous motor drive applications, including reducing the risks associated with motor terminal overvoltages and bearing discharge currents. This paper compares the performance of CSIs and voltage source inverters (VSIs) with the same performance capabilities. More specifically, sine filters are added to the VSI output terminals to match the CSI performance, and a front-end boost converter is added to the VSI to match the CSI’s voltage-boost capability. The results show that the CSI active components require 48% less volume than the VSI, combined with other performance advantages that make it an appealing candidate for some future motor drive applications.

current source inverter, motor drive system, passi↗
DOE OSTI2020

PARAFAC_T1

This is a method of performing trilinear analysis on large data sets using a modification of the PARAFAC-ALS algorithm. It iteratively decomposes the data matrix into a core matrix and three loading matrices based on the Tucker1 model. The algorithm is particularly useful for data sets that are too large to upload into a computer?s main memory. While the performance advantage in utilizing our algorithm is dependent on the number of data elements and dimensions of the data array, we have seen a significant performance improvement over operating PARAFAC-ALS on the full data set. SAND2020-12649 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Van Benthem, Mark↗
DOE OSTI2021

A Comparative Study of Model Predictive Control and Optimal Causal Control for Heaving Point Absorbers

Efforts by various researchers in recent years to design simple causal control laws that can be applied to WEC devices suggest that these controllers can yield similar levels of energy output as those of more complex non-causal controllers. However, most studies were established without adequately considering device and power conversion system constraints which are relevant design drivers from a cost and economic point of view. It is therefore imperative to understand the benefits of MPC compared to causal control from a performance and constraint handling perspective. In this paper, we compare linear MPC to a casual controller that incorporates constraint handling to benchmark its performance on a one DoF heaving point absorber in a range of wave conditions. Our analysis demonstrates that MPC provides significant performance advantages compared to an optimized causal controller, particularly if significant constraints on device motion and/or forces are imposed. We further demonstrate that distinct control performance regions can be established that correlate well with classical point absorber and volumetric limits of the wave energy conversion device.

42 ENGINEERING↗
DOE OSTI2021

BETO 2021 Peer Review - Inverse Bioproduct Design Through Machine Learning and Molecular Simulation

This work aims to identify performance advantaged bioproducts (PABPs) through property prediction, which will guide experimental synthesis. The impact of this work will be faster market adoption of bioproducts with greater performance relative to incumbent products. We have identified >106 bioproduct candidates, but only some will have superior performance to create a market pull. High-throughput property prediction, enabled by machine learning, and elucidation of structure-function relationships, enabled by molecular simulation, provide a hypothesis driven approach for down selection of candidate biomolecules to pursue experimentally. To enable machine learning and molecular simulation for bioproduct discovery, automated structure generation and embedding must capture relevant features for prediction, databases must cover domains applicable to biobased products, and best practices for simulation of polymer systems must be developed. To address these challenges, we have established bioproduct relevant datasets, developed high-throughput polymer structure generation, and built end-to-end neural networks that have predicted 8 properties for >1.4 x 106 biopolymers. A molecular simulation pipeline for building, running, and analyzing polymers and polymer additives is being used to predict performance and develop design principles of biobased products. In collaboration with the PABP synthesis project, these computational tools are guiding synthesis and informing design of PABPs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Solvent–Diluent Engineering of Normal-Concentration Organosulfur Electrolytes for High-Voltage Lithium-Ion Batteries

Enabling high‑voltage batteries depends critically on rational electrolyte design. Conventional carbonate electrolytes with LiPF 6 suffer from insufficient oxidative stability, motivating the use of lithium bis(fluorosulfonyl)imide (LiFSI) for its superior high‑temperature and high‑voltage performance. However, LiFSI is relatively costly, and its electrolytes often require high concentrations to suppress aluminum current collector corrosion in cathode architectures and to adequately passivate electrode interfaces. Here, a low‑concentration LiFSI electrolyte is realized by combining a high‑voltage organosulfur solvent, ethyl methyl sulfone (EMS), with a non‑solvating fluorinated diluent, fluorobenzene (FB). Extended cycling of LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) full cells to 4.5 V reveals substantial long‑term performance advantages over conventional carbonate electrolytes. Comparison with a popular cyclic sulfone, tetramethylene sulfone (TMS), shows that ring‑containing structures such as TMS strongly influence passivation behavior, ultimately limiting performance and narrowing the viable operating window. Overall, EMS is identified as a uniquely stable solvent for LiFSI‑based electrolytes operating at moderately high voltages.

Dato, Michael A. [Argonne National Laboratory (ANL↗
DOE OSTI2023

Basic Research Needs in Quantum Computing and Networking

Employing quantum mechanical resources in computing, information processing, and networking opens the door to potential exponential advantages over classical counterparts. However, quantifying and realizing such advantages poses extensive scientific and engineering challenges. Department of Energy (DOE) investments have driven steady progress in addressing such challenges. Recently developed quantum algorithms offer asymptotic exponential advantages in speed or accuracy for fundamental scientific problems. These problems include simulating physical systems, solving systems of linear equations, differential equations, and optimization problems. Empirical demonstrations on nascent quantum hardware suggest better performance on contrived computational tasks than classical analogs. However, the requirements for a quantum computer or network to demonstrate an end-to-end rigorously quantifiable performance improvement over classical analogs remains a grand challenge, especially for problems of practical value. In particular, what will be required for quantum technology to ultimately exhibit scalable, rigorous, and transformative performance advantages for practical applications? In July 2023, DOE’s Advanced Scientific Computing Research program in the Office of Science convened the Workshop on Basic Research Needs in Quantum Computing and Networking, where major opportunities and grand challenges were identified. The following five priority research directions (PRDs) were identified as a result of the workshop.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

In-Silico Design of Next Generation Cellulose-Derived Packaging Materials (CRADA Final Report)

Developing sustainable solutions for single-use packaging is an important objective to combat the environmental crisis of plastics pollution. Most embodiments of cellulose-based packaging materials, including CellophaneTM, are completely biodegradable in both terrestrial and marine environments. However, petroleum-derived alternatives offer some performance advantages for metrics such as moisture barriers and mechanical properties. This project leverages molecular dynamics simulation to investigate how molecular modifications to cellulose-based polymer assemblies impact their material properties. An important performance criterion for the modified materials was to retain biodegradability; thus, modifications by naturally occurring, biodegradable additives were the focus of this study. Specifically, we developed models with xylan and lignin of varying monomeric compositions into the cellulose matrix. The mechanical properties were investigated by performing stress-strain simulations, and the water barrier and hydrophobicity were investigated by simulating the water contact angle. Our findings indicate that the incorporation of xylan into the cellulose matrix tends to increase the mechanical properties with an optimal loading of ~27 wt%. We also predict that orienting the nanoscale directionality of the xylan chains such that they are perpendicular to the cellulose fibrils will dramatically increase mechanical strength. In contrast, the incorporation of lignin tends to weaken the composite at all loadings investigated. Simulations of water contact angle predicted that coating polymers on the surface of the cellulose assembly creates a more hydrophobic surface than incorporating them throughout the matrix. Of the coatings investigated, lignin resulted in the most hydrophobic surface, followed by pectin and keratin, which both imparted modest increases in hydrophobicity. Future experimental work done by Futamura will focus on designing material prototypes to capitalize on the predictions of performance enhancement obtained from molecular modeling. While substantial progress was made by the simulations performed in this project, there still exists a vast parameter space that we were unable to investigate, including branching, functional group decoration, and degree of polymerization of polymer additives. However, the methods developed in this initial investigation will facilitate more rapid evaluation of the impact of molecular characteristics on the performance of biopolymer composite materials and thereby accelerate future materials discovery efforts in this area.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Catalyst-Specific Accelerated Stress Tests in Proton Exchange Membrane Low-Temperature Electrolysis for Intermittent Operation

Device-level stress tests are developed that focus on anode catalyst layer degradation and future anticipated operating conditions, including intermittent load and reduced platinum group metal content. Square-wave cycles with an upper load limit of 2.5 A cm -2 are utilized to screen commercial iridium (Ir) materials. Performance losses are primarily due to decreasing kinetics and are accompanied by catalyst migration into the membrane, worsening catalyst/ionomer integration, and weakening of the catalyst/membrane interface. For ruthenium-containing catalysts, the in situ performances are higher but durabilities lower than Ir baselines, and any performance advantage is lost within the test. Increased loss is likely due to the higher dissolution rate; microscopy confirmed greater degrees of ruthenium migration. For Ir metal or mixed oxides, ex situ activity improvements generally did not translate to in situ performance. The durability, however, is significantly lower and the loss rate increased from 3 (oxide) to 9 (metal) μ V cycle -1 . These results are consistent with historical findings in literature, rationalize the continued use of iridium oxide as a baseline catalyst, and demonstrate that traditional catalyst development approaches may not improve device-level durability when focused on low-cost applications. A shift in focus may therefore be more effective at improving catalyst utilization and lessening load requirements.

08 HYDROGEN↗
DOE OSTI2022

Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical Systems

Koopman operator theory provides an alternative to study nonlinear networked dynamical systems by mapping the state space to an abstract higher dimensional space where the system evolution is linear. Recent works show the application of graph neural networks (GNNs) to learn state to object-centric embeddings and achieve centralized block-wise computation of Koopman operator (KO) under additional assumptions on the underlying agents properties and constraints on the KO structure. However, the computational complexity of learning the Koopman increases exponentially for networked systems where the number of possible system states grows in a combinatorial fashion with the number of nodes. The learning challenge is further amplified for sparse networks by two factors: 1) sample sparsity for learning the Koopman operator in the non-linear space, and 2) the divergence in the dynamics of individual nodes or from one subgraph to another. Our work aims to address these challenge by formulating the representation learning of networked dynamical systems into a multi-agent paradigm and learning the Koopman operator in a distributive manner. The computational as well as performance advantages of distributed Koopman is predominant for sparse networks whereas for fully connected networks, it is shown to coincide with the centralized one. The empirical study on rope system, network of oscillators and a synthetic power system show comparable and superior performance along with computational benefits with the state-of-the-art methods.

Mukherjee, Sayak↗
DOE OSTI2021

Ceramic cation exchange materials

Cation exchange membranes and materials including silica-based ceramics, and associated methods, are provided. In some aspects, cation exchange membranes that include a silica-based ceramic that forms a coating on and/or within a porous support membrane are described. The cation exchange membranes and materials may have certain structural or chemical attributes (e.g., pore size/distribution, chemical functionalization) that, alone or in combination, can result in advantageous performance characteristics in any of a variety of applications for which selective transport of positively charged ions through membranes/materials is desired. In some embodiments, the silica-based ceramic contains relatively small pores (e.g., substantially spherical nanopores) that may contribute to some such advantageous properties. In some embodiments, the cation exchange membrane or material includes sulfonate and/or sulfonic acid groups covalently bound to the silica-based ceramic.

Newbloom, Gregory Matthew↗
DOE OSTI2023

2.3.2.100 - Biological Lignin Valorization (BLV)

The Biological Lignin Valorization (BLV) project develops microbial strains and associated bioprocesses to convert lignin-derived aromatic compounds into value-added bioproducts. Our main objective in the BLV project is to achieve industrially relevant bioproduction metrics that can directly contribute to the economic viability and improved sustainability of the integrated lignocellulosic biorefinery, in collaboration with complementary BETO-funded lignin valorization projects. Specifically, the BLV project works closely with the BETO-funded Lignin Utilization project, which provides bio-available aromatic compounds from chemo-catalytic lignin depolymerization. We use the robust soil bacterium, Pseudomonas putida, as our primary microbial host for the conversion of lignin-derived compounds to bioproducts. To date, we have focused on atom-efficient bioproducts that can be used as either direct replacement chemicals or performance-advantaged bioproducts, including cis,cis-muconic acid, beta-ketoadipic acid, and 2-pyrone-4,6-dicarboxylic acid. From model aromatic substrates, we have achieved titers of each of these compounds approaching 40 g/L and productivity values ranging from 0.5 to over 1 g/L/hr, all at 90% molar yield or higher. From real lignin streams, we have thus far achieved 24 g/L, 0.66 g/L/hr, and theoretical yield of beta-ketoadipic acid. A major pursuit now is to reach industrially relevant performance metrics on an expanded slate of lignin-derived streams.

BIOMASS FUELS↗
DOE OSTI2021

Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs

Despite foreseeing tremendous speedups over conventional deep neural networks, the performance advantage of binarized neural networks (BNNs) has merely been showcased on general-purpose processors such as CPUs and GPUs. In fact, due to being unable to leverage bit-level-parallelism with a word-based architecture, GPUs have been criticized for extremely low utilization (1%) when executing BNNs. Consequently, the latest tensorcores in NVIDIA Turing GPUs start to experimentally support bit computation. In this work, we look into this brand new bit computation capability and characterize its unique features. We show that the stride of memory access can significantly affect performance delivery and a data-format co-design is highly desired to support the tensorcores for achieving superior performance than existing software solutions without tensorcores. We realize the tensorcore-accelerated BNN design, particularly the major functions for fully-connect and convolution layers — bit matrix multiplication and bit convolution. Evaluations on two NVIDIA Turing GPUs show that, with ResNet-18, our BTC-BNN design can process ImageNet at a rate of 5.6K images per second, 77% faster than state-of-the-art. Our BNN approach is released on https://github.com/pnnl/TCBNN.

Li, Ang↗
DOE OSTI2022

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Polyalkenamers as Drop-In Additives for Ring-Opening Metathesis Polymerization: A Promising Upcycling Paradigm

Here we report a distinct strategy to upcycle waste polyalkenamers such as polybutadiene into new, performance-advantaged materials by using them as drop-in additives for ring-opening metathesis polymerization (ROMP). The polyalkenamers serve as competent chain-transfer agents in ROMPs of common classes of cyclic olefin monomers, facilitating good molecular weight control, allowing low Ru catalyst loadings, and enabling efficient incorporation of the polyalkenamer into the synthesized polymeric material. We successfully demonstrate ROMP using model polyalkenamers and translate these learnings to leverage commercial polybutadiene and acrylonitrile butadiene styrene (ABS) as chain transfer agents for ROMP copolymerizations. Critically, our strategy is shown to be highly efficient and operationally simple, quantitatively incorporating the polyalkenamer and inheriting aspects of its thermomechanical performance. Our results highlight a promising pathway for the upcycling of polyalkenamers and provide an alternative to existing deconstruction and functional upcycling strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Conversion, Stability, and Selectivity Improvements Through Catalyst Development for the Reductive Etherification Reaction

Economical pathways from lignocellulosic non-food biomass feedstocks to biofuels with appropriate fuel properties are necessary for displacing fossil-fuel derived diesel use in medium-/heavy duty vehicles. Oxygenated compounds have recently shown promise due to low sooting tendencies and autoignition properties and molecules with sufficiently high carbon numbers can meet the requisite standards for water solubility and flash point. Coupling waste biomass derived small molecules into bioblendstock targets through the reductive etherification reaction shows potential for increasing molecular weight without losing the advantages of oxygenates. However, the transition to industrial viability for this process is limited by strong catalyst deactivation in typical flow reactor systems. Strong acid resins such as Amberlyst-15 (A-15) show excellent activity in batch reactions but are limited to low temperatures and are poisoned by the side-product, water. In this talk, I will describe our recent work in catalyst synthesis and testing to select superior options with improved aqueous and thermal stability in reductive etherification flow reactions. Water-tolerant solid acid catalysts have potential to improve the conversion and selectivity to the ether target at temperatures above the degradation point of A-15. Analysis techniques such as physisorption, chemisorption, x-ray diffraction and transmission electron microscopy are being used to characterize the active sites. These techniques are also being used to evaluate degradation of the catalyst after extensive flow reactions in addition to comparative efficiency testing with standardized cyclohexene hydrogenation reactions. Finally, I will discuss a demonstration of the full pathway from bio-derived butyric acid to the performance-advantaged ether product and the additional obstacles from side-products and purification limitations in the preliminary upgrading reactions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Improved Performance of Cu(InGa)(SeS) 2 PV Modules Using the Reaction of Metal Precursors. Final Report

This project “Improved Performance of Cu(InGa)(SeS) 2 PV Modules using the Reaction of Metal Precursors” was a partnership led by the Institute of Energy Conversion (IEC) at the University of Delaware with Columbia University and the Molecular Foundry at the Lawrence Berkeley National Laboratory. The aim was to develop pathways to improve Cu(InGa)(SeS) 2 (CIGSS) thin film photovoltaic modules using processes compatible with low manufacturing cost. The CIGSS approach investigated was a two-step process including deposition of metal precursor films following by reaction in hydride gases utilizing IEC’s novel reactor. The process was similar to that under commercial development by the project’s industry partner Stion. When Stion went out of business mid-project the focus changed to a rapid thermal process considered more commercially viable. Approaches to improve the performance of solar cells using the reacted films focused on two material innovations. First, the overall Ga content was increased to increase the operating voltage, which is desirable for scale-up to commercial modules. Second, the processing and performance advantages arising from Ag alloying were investigated. Advanced characterization guided process and material development including control of relative composition gradients. Research on the formation of Cu-Ga-In metal precursors utilized sputtering deposition which is normally used in commercial applications. The work resulted in processes for deposition of precursor stacks with increased relative Ga content and effects of deposition parameters on morphology and phase composition were established. It was shown that the metal precursor films have comparable phase composition and morphology so subsequent reaction follows from the same starting point. The addition of Ag to the metal precursors gave more uniform morphology and improved adhesion of reacted films which enable higher reaction temperature for faster processing. A novel outcome was the discovery of a previously undocumented material phase in sputter-deposited and evaporated Ag-Cu-In-Ga thin films. Hydride gas reaction processes including time-temperature-concentration profiles were developed for different precursor compositions. This enables control of composition profiles to engineer through-film gradients for solar cell optimization with characterization and simulations used to correlate measured film composition profiles to measurements of devices. In particular, the gradient of sulfur at the front of the CIGSS film was found to be critical. The simulations guided process development leading to improved reproducibility of devices improved performance with higher Ga content and higher voltage. With Ag-alloyed precursors, the reaction pathways leading were determined. A significant finding was that Ag-alloying increases the reaction rate to completely convert precursor films to the final chalcopyrite which could enable reduced reaction time to benefit manufacturability. To maintain potential commercial viability, the process under investigation was refocused to a rapid thermal process that could potentially be incorporated into an in-line process for manufacturing. Precursors with different composition were capped with an extra selenium layer and reacted in hydrogen sulfide 5-15 minutes, compared to typically 2 hours in the previous multi-step batch process. Critical RTP parameters were identified to control the reaction. Further optimization would be needed for high efficiency solar cells but pathways to high quality devices with further optimization and improved heating uniformity were developed. The project also developed new optoelectronic characterization approaches with a focus on development and application of spatial- and time-resolved photoluminescence and a custom mapping photoluminescence microscope built. It was shown how critical electronic transport properties strongly depend on the chemical composition of the material and that a wide range of samples show inhomogeneity on a length scale larger than the grains in the films. Additionally, two-photon excitation capability was developed to distinguish bulk vs surface losses. The project advances the state-of-the -art for precursor reaction processes in several ways that could impact manufacturing. This includes validation of approaches to increase voltage and establishment of model-guided control to form optimal composition profiles. The application of process control approaches with knowledge of phase formation and reaction pathways can be critically valuable in designing a large-scale process.

14 SOLAR ENERGY↗
DOE OSTI2026

Engineering B-ketoadipic Acid Production in Pseudomonas putida KT2440: Cooperative Research and Development (Final Report)

We propose a 2-year project to enable commercialization of an Agile Biofoundry (ABF) related, performance-advantaged bioproduct, B-ketoadipic acid (BKA), in an ABF-relevant, industrially-relevant host, Pseudomonas putida KT2440. Informed by techno-economic analysis (TEA), our BKA production goals are to enable a 40 g/L titer, 0.5 g/L/h productivity, and a 40% molar yield from hydrolysate sugars at the 1 kg scale. This material will be used to produce BKA-nylons.

09 BIOMASS FUELS↗
DOE OSTI2023

Structural and Chemical Evolution of Highly Fluorinated Li‐Rich Disordered Rocksalt Oxyfluorides as a Function of Temperature

Abstract Li‐rich disordered rocksalt (DRS) oxyfluorides have emerged as promising high‐energy cathode materials for lithium‐ion batteries. While a high level of fluorination in DRS materials offers performance advantages, it can only be achieved via mechanochemical synthesis, which poses challenges of reproducibility and scalability. The definition of relationships between fluorination and structural stability is required to devise alternative methods that overcome these challenges. In this study, the thermal evolution of three highly fluorinated phases, Li 2 TMO 2 F (TM = Mn, Co, and Ni), is investigated in an inert atmosphere. Diffraction and spectroscopic techniques are utilized to examine their electronic and chemical states up until conditions of decomposition. The analysis reveals that the materials phase‐separate above 400 °C, at most. It is also observed that heat‐treated DRS materials exhibit intricate changes in the local coordination of the metals, including their spin, and ordering compared to the pristine states. The changes upon annealing are accompanied by a modulation of the voltage profile, including reduced hysteresis, when used as electrodes. These results provide an in‐depth understanding of the fundamental crystal chemistry of DRS oxyfluorides in view of their promising role as the next generation of Li‐ion cathodes.

25 ENERGY STORAGE↗