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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Evolving Ensembles of Spiking Neural Networks for Neuromorphic Systems

Evolutionary algorithms have been proposed as a solution to overcome many of the challenges associated with training spiking neural networks. While evolutionary optimization for spiking neural networks is very flexible, its performance has difficulty scaling to complex tasks and correspondingly complex network structures. Here we propose a method for evolving ensembles of spiking neural networks. By using ensemble learning, the flexibility of evolutionary optimization is fully preserved while scaling to more challenging tasks. We test the performance of the proposed method using handwritten digit classification. We investigate multiple strategies for constructing ensembles of spiking neural networks, and demonstrate that evolving ensembles of SNNs offers significant performance advantages over evolutionary optimization.

Elbrecht, Daniel↗
DOE OSTI2022

Compressed basis GMRES on high-performance graphics processing units

Krylov methods provide a fast and highly parallel numerical tool for the iterative solution of many large-scale sparse linear systems. To a large extent, the performance of practical realizations of these methods is constrained by the communication bandwidth in current computer architectures, motivating the investigation of sophisticated techniques to avoid, reduce, and/or hide the message-passing costs (in distributed platforms) and the memory accesses (in all architectures). This article leverages Ginkgo’s memory accessor in order to integrate a communication-reduction strategy into the (Krylov) GMRES solver that decouples the storage format (i.e., the data representation in memory) of the orthogonal basis from the arithmetic precision that is employed during the operations with that basis. Given that the execution time of the GMRES solver is largely determined by the memory accesses, the cost of the datatype transforms can be mostly hidden, resulting in the acceleration of the iterative step via a decrease in the volume of bits being retrieved from memory. Together with the special properties of the orthonormal basis (whose elements are all bounded by 1), this paves the road toward the aggressive customization of the storage format, which includes some floating-point as well as fixed-point formats with mild impact on the convergence of the iterative process. We develop a high-performance implementation of the “compressed basis GMRES” solver in the Ginkgo sparse linear algebra library using a large set of test problems from the SuiteSparse Matrix Collection. We demonstrate robustness and performance advantages on a modern NVIDIA V100 graphics processing unit (GPU) of up to 50% over the standard GMRES solver that stores all data in IEEE double-precision.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Perspectives on multi-material additive manufacturing

The last two decades have seen enormous gains in industrial adoption of additive manufacturing (AM) technologies. Its layer-wise approach to fabrication offers designers the opportunity to create structures with unique performance advantages over their traditionally manufactured counterparts, and have created new manufacturing business models and supply chains. While today’s AM technologies have enabled the creation of new geometries, future AM systems that offer simultaneous processing of multiple materials in a single build open opportunities for new product functionality that cannot be achieved by traditional manufacturing methods. Advances in multi-material additive manufacturing, which integrate dissimilar material into a complex, three-dimensional object, is emerging but the advances have been sporadic. Moving beyond homogenous materials, adding multi-materials, gradient, functional and responsive materials, and materials with heterogeneous and graded properties means that a single additive process based on either energy delivery or material deposition alone may not be suitable. Finally, this perspective gives a brief overview of the current status, challenges, and future recommendations for multi-material additive manufacturing. The authors aim to expand the notion of multi-material additive manufacturing beyond combining materials with dissimilar properties, to combinations of materials at different length scales, material classes as well as multiple functionalities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Development of Engineered Metal-Organic Framework (MOF) materials for perfluorooctane sulfonate (PFOS) Remediation

In this project, an engineered form of a Metal-Organic Framework (MOF) based material is developed for the removal of perfluorooctane sulfonate (PFOS) for real-world applications. The powdered MOF material has been demonstrated at the Pacific Northwest National Laboratory (PNNL) to selectively capture PFOS from distilled (DI) water with a large performance advantage over granulated activated carbon (GAC). In this project, the powdered material was transformed into an engineered form (using a polymer) to demonstrate PFOS adsorption capacity in tap water. PNNL developed and processed the MOF material in the engineering form (granules). After thorough characterization and stability testing, these engineered MOF granules were provided to an industrial collaborator, AVANTech, LLC, for testing and demonstration of continuous, long-term PFOS removal from tap water. The preliminary results showed PFOS sorption capacities at parts per billion (ppb) concentrations in tap water under a flow system. Also provided insight into sorbent-based material utilization in a continuous flow system. Further studies are required to optimize and understand sorption in such industrial-scale applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Common Approach to Molten Salt Reactor Safety Evaluation [Slides]

The presentation describes the necessary elements of developing a common approach to molten salt reactor (MSR) safety adequacy evaluation. Developing a common approach to establishing adequate safety for MSRs could substantially decrease the developer risks. However, safety adequacy is intimately associated with reactor licensing which is an element with national sovereignty. No reactor class has a common, portable international license. The advantageous performance characteristics of MSRs can be leveraged to decrease the cost and time necessary to develop reasonable confidence of adequate safety. Adequate safety can be demonstrated though either accident prevention or mitigation. MSR have characteristics that may make accident mitigation a preferable safety adequacy evaluation approach. Adequate quality as well as a thorough understanding of accident progression validated through separate and integral effects testing remains central to any safety adequacy demonstration method.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Unrolled Video Super-Resolution Network with Autoregressive Prior for the Case of Known Motion

Real-time detection and classification of distant objects is necessary for many national security applications. However, when objects are far from the sensor, they occupy only a small number of pixels in the captured video, limiting the amount of visual detail available for recognition. State-of-the-art classification methods typically rely on high-resolution (HR) video streams to capture characteristic object features, but obtaining such detail is challenging for distant objects that occupy only a few pixels. This motivates the development of video super-resolution (VSR) methods that enhance object classification by recovering fine details from low-pixel representations. Current VSR methods rely either on model-based optimization, which is interpretable but computationally expensive, or on learning-based approaches, which are efficient and high-performing but often lack flexibility and interpretability. In this report, we propose an end-to-end trainable unrolled VSR network, UVSRNet, which super-resolves each frame in a video by exploiting sub-pixel motion between neighboring low-resolution (LR) frames as well as incorporating high-frequency detail from previously super-resolved frames. In particular, by unrolling a plug-and-play (PnP) half-quadratic splitting (HQS) algorithm, we leverage a model-based data-fitting module alongside a learning-based autoregressive prior module. This combination yields a method that maintains the flexibility and interpretability of model-based methods while achieving the performance advantages of learning-based methods.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

Polymers from Levoglucosenone and Other Anhydrosugars (CRADA Final Report)

The goal of this work is to identify promising drop-in or novel polymers that can be produced from levoglucosenone (LGO), CyreneTM, or other anhydrous sugars. LGO is a potentially important feedstock for polymers produced from renewable sources and can be produced in high yields by the company Circa from the pyrolysis of cellulose and biomass. NLR has a long history with anhydrosugars, pyrolysis and biomass-based polymers and has recently developed machine learning (ML) tools for predicting polymer properties. The DOE has recently supported efforts to identify coproducts from pyrolysis of biomass and their use in the production of performance advantaged polymers. DOE is also supporting the development of ML and molecular modeling for predicting and understanding polymer properties. NLR will leverage this expertise and experience to map out the pathways to polymers from LGO, use ML to identify the properties of novel polymers and estimate the market application space. From this effort NLR will deliver a summary report that outlines the most promising pathways based on quantitative and qualitative metrics for producing polymers from LGO.

09 BIOMASS FUELS↗
DOE OSTI2020

Exoproteomics Reveals Outer Membrane Vesicle Mediated Catabolism of Lignin-Derived Aromatics by P. putida KT2440

Lignin is an abundant component of plant cell walls which is heterogeneous and highly resistant to degradation. Biological funneling of lignin via microbes represents an efficient approach for reducing chemical heterogeneity and generating molecules which can be valorized into performance-advantaged materials. Pseudomonas putida KT2440 (P. putida) catabolizes lignin-derived aromatic monomers rapidly and efficiently via the b-ketoadipate (bKA) pathway and our data suggest P. putida may depolymerize HMW lignin as well [1]. The mechanism by which HMW lignin is depolymerized by bacteria remains unknown. Intriguingly, we found that P. putida secretes outer membrane vesicles (OMVs) of two distinct sizes in the presence of lignin. Here, we describe the temporal proteome of OMVs and the vesicle-free secretome (VFS) in the presence or absence of lignin. Enzymes involved in aromatic catabolism via the bKA pathway are temporally enriched into OMVs in the presence of lignin. Isolated OMVs enhance growth of P. putida mutants unable to grow on model lignin-derived aromatics, and turnover protocatechuic acid in vitro. Together, this work leads us to hypothesize mechanisms by which OMVs contribute to extracellular aromatic catabolism by P. putida. Ongoing and future work seeks to further understand this phenomena in the context of both fundamental an applied lignin conversion research areas.

09 BIOMASS FUELS↗
DOE OSTI2020

Encapsulated sulfide glass solid electrolytes and solid-state laminate electrode assemblies

Nanofilm-encapsulated sulfide glass solid electrolyte structures and methods for making the encapsulated glass structures involve a lithium ion conducting sulfide glass sheet encapsulated on its opposing major surfaces by a continuous and conformal nanofilm made by atomic layer deposition (ALD). During manufacture, the reactive surfaces of the sulfide glass sheet are protected from deleterious reaction with ambient moisture, and the nanofilm can be configured to provide additional performance advantages, including enhanced mechanical strength and improved chemical resistance.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Encapsulated sulfide glass solid electrolytes and solid-state laminate electrode assemblies

Nanofilm-encapsulated sulfide glass solid electrolyte structures and methods for making the encapsulated glass structures involve a lithium ion conducting sulfide glass sheet encapsulated on its opposing major surfaces by a continuous and conformal nanofilm made by atomic layer deposition (ALD). During manufacture, the reactive surfaces of the sulfide glass sheet are protected from deleterious reaction with ambient moisture, and the nanofilm can be configured to provide additional performance advantages, including enhanced mechanical strength and improved chemical resistance.

Visco, Steven J.↗
DOE OSTI2022

Encapsulated sulfide glass solid electrolytes and solid-state laminate electrode assemblies

Nanofilm-encapsulated sulfide glass solid electrolyte structures and methods for making the encapsulated glass structures involve a lithium ion conducting sulfide glass sheet encapsulated on its opposing major surfaces by a continuous and conformal nanofilm made by atomic layer deposition (ALD). During manufacture, the reactive surfaces of the sulfide glass sheet are protected from deleterious reaction with ambient moisture, and the nanofilm can be configured to provide additional performance advantages, including enhanced mechanical strength and improved chemical resistance.

Visco, Steven J.↗
DOE OSTI2022

Biological Lignin Valorization

Given lignin's heterogeneity, catalytic depolymerization results in aromatic compound mixtures, and conversion of this complex substrate to a single product is challenging. To that end, the Biological Lignin Valorization (BLV) project is pursuing biological funneling, wherein aromatic catabolic microbes are engineered to convert a mixture of lignin-derived compounds to a single product. Namely, we employ Pseudomonas putida and pursue atom-efficient products, such as muconic acid, which can be further converted to direct replacements or used in performance-advantaged bioproducts. Overall, biological lignin conversion can make major contributions to reduce the minimum fuel selling price of the integrated biorefinery. Early industrial efforts in this area are also leading to value-added products, including in collaboration with the BLV project. Primary challenges associated with BLV efforts include accessing bio-available monomers from lignin (with the Lignin Utilization project), enabling commercial titers, rates, and yields of bioproducts from lignin-derived compounds, and overcoming substrate and product toxicity. To date, we have 1) demonstrated 49 g/L of muconate from aromatic compounds and 4 g/L of muconate from lignin, 2) improved the toxicity tolerance of P. putida to key aromatic substrates, 3) debottlenecked biological funneling for higher rates, and 4) engineered P. putida to convert S, G, and H-type lignin-derived compounds to a single product.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2023

Simulating Quantum Systems with NWQ-Sim on HPC

NWQ-Sim is a cutting-edge quantum system simulation environment designed to run on classical multi-node, multi-CPU/GPU heterogeneous HPC systems. In this work, we provide a brief overview of NWQ-Sim and its implementation in simulating quantum circuit applications, such as the transverse field Ising model. We also demonstrate how NWQ-Sim can be used to examine the effects of errors that occur on real quantum devices, using a combined device noise model. Moreover, NWQ-Sim is particularly well-suited for implementing variational quantum algorithms where circuits are dynamically generated. Therefore, we also illustrate this with the variational quantum eigensolver (VQE) for the Ising model. In both cases, NWQ-Sim's performance is comparable to or better than alternative simulators. We conclude that NWQ-Sim is a useful and flexible tool for simulating quantum circuits and algorithms, with performance advantages and noise-aware simulation capabilities.

Suh, In-Saeng↗
DOE OSTI2024

Task 3.1: Research and Development Guiding Technoeconomic Analysis and Life-Cycle Assessment

Technologies under development aim to increase yields of desired end products, reduce overall raw material costs, and/or develop more energy-efficient strategies for product recovery. Techno-economic analysis (TEA) and life-cycle assessments (LCA) help assure that economic and sustainability predictions of the technologies are unbiased and compelling and provide guidance to experimentalists on areas that need focus. Building on previous work, the performance-advantaged bioproducts and bioprocessing separations project (SepCon) continues to use the integrated TEA and LCA to evaluate and guide technologies under development and target challenges relevant to the industry and the Bioenergy Technologies Office (BETO) priority pathways. The analysis team aims to provide credible, unbiased assessments for each technology under development, with ongoing assessments to guide experimental teams. Additionally, the team also supports journal publications highlighting key findings.

biofuels↗
DOE OSTI2024

Advanced Instrumentation for Metal Additive Manufacturing

Laser powder bed fusion (LPBF) is the most widely used process for metal additive manufacturing (AM), particularly where complex geometries provide performance advantages unattainable with traditional manufacturing techniques. However, LPBF is highly sensitive to innate variability in both the powder spreading and fusion steps, often leading to defects such as pores that are difficult to detect yet significantly impair component mechanical properties and fatigue life. This thesis presents a range of novel instruments enabling both precise assessment of powder layer characteristics and in-situ thermal metrology of metal AM to advance the quality control of LPBF. First, leveraging a custom X-ray microscope and a radiation-transport model developed through this work, transmission X-ray imaging is used to study spreading of thin metal powder layers. Effective layer depth is directly mapped at a process-relevant size scale, surpassing optical techniques that can only estimate local deposition from layer surface topography. Layer packing density and quality are shown to be influenced by powder flowability and particle size relative to nominal powder layer thickness. Layer quality is additionally connected to the geometry of the spreading implement and its velocity. This technique and its presented findings enable pairing feedstocks with spreading strategies that create layers with consistent packing density and uniformity. Second, a twofold approach is employed to optically interrogate the laser fusion step of LPBF for observing signatures of defect formation. Aperture division multiplexing is conceptualized, providing for simultaneous laser delivery and high-fidelity infrared (IR) process monitoring through a common optic. In-situ microscopy at 50 μm spatial resolution and at mid-wave IR wavelengths is proven readily achievable with the first purpose-built optic of this type. Next, a bespoke imaging spectrometer, along with a temperature-emissivity separation technique, is used to retrieve accurate process temperatures over a 1000 K range. Data from these instruments are correlated to porosity as fine as 4.3 μm in two LPBF test artifacts, as verified using computed tomography (CT), establishing the viability of robust optically-based component qualification.

Penny, Ryan↗
DOE OSTI2024

Exploring Object Detection and Image Classification Tasks for Niche Use Case in Naturalistic Driving Studies

Naturalistic driving studies consist of drivers using their personal vehicles and provide valuable real-world data, but privacy issues must be handled very carefully. Drivers sign a consent form when they elect to participate, but passengers do not for a variety of practical reasons. However, their privacy must still be protected. One large study includes a blurred image of the entire cabin which allows reviewers to find passengers in the vehicle; this protects the privacy but still allows a means of answering questions regarding the impact of passengers on driver behavior. A method for automatically counting the passengers would have scientific value for transportation researchers. We investigated different image analysis methods for automatically locating and counting the non-drivers including simple face detection and fine-tuned methods for image classification and a published object detection method. We also compared the image classification using convolutional neural network and vision transformer backbones. Our studies show the image classification method appears to work the best in terms of absolute performance, although we note the closed nature of our dataset and nature of the imagery makes the application somewhat niche and object detection methods also have advantages. We perform some analysis to support our conclusion.

Peruski, Ryan↗