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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Dry-processed electrodes enabled by polytetrafluoroethylene fibrillation for high-performance lithium-ion batteries

The dry processing technique of polytetrafluoroethylene (PTFE) fibrillation offers significant advancements in cost reduction, environmental impact, and electrochemical performance. Dry processing alone allows for ∼ 20–60% cost reduction and increases of up to 40 mg cm⁻2 of areal loading – it typically comes at a reduction of rate capability. By leveraging the network structure of fibers, this method enhances rate capability and cycling performance, providing a compelling alternative to the conventional and currently predominant wet processing. The review delves into PTFE fibrillation mechanisms and examines critical influencing factors including material properties and processing parameters. It also discusses challenges associated with the electrodes fabricated by PTFE fibrillation, including structural instability due to insufficient PTFE fibrillization, compromised electrical conductivity from an insufficient conductive network, inhomogeneous dispersion resulting from the absence of solvents, restricted ionic transport due to increased electrode thickness, inadequate adhesion to current collector because of low surface energy of PTFE, electrochemical degradation due to low lowest unoccupied molecular orbital (LUMO) level of PTFE, particle damage during processing and environmental concerns related PTFE being a perfluoroalkyl and polyfluoroalkyl substances (PFAS). Recent research innovations aimed at mitigating these issues, their application in beyond-lithium batteries, and future research directions are thoroughly discussed.

Park, Hyunji↗
DOE OSTI2018

Advancing graph-based algorithms for predicting flow and transport in fractured rock

Discrete fracture network (DFN) models are a powerful alternative to continuum models for subsurface flow and transport simulations because they explicitly include fracture geometry and network topology, thereby allowing for better characterization of the latter's influence on flow and transport through fractured media. Recent advances in high performance computing have opened the door for flow and transport simulations in large explicit three-dimensional DFN, but this increase in model fidelity and system size comes at a huge computational cost because of the large number of mesh elements required to represent thousands of fractures (with sizes that can range several orders of magnitude, from mm to km). In most subsurface applications, fracture characteristics are only known statistically and numerous realizations are needed to bound uncertainty in flow and transport in the system, thereby exacerbating the computational burden. blackGraphs provide a simple and elegant way to characterize, query, and interrogate fracture network connectivity. We propose a DFN model reduction framework where various graph-representations are used in conjunction with high-fidelity DFN simulations to increase computational efficiency while retaining accuracy of key quantities of interest. The appropriate choice of a graph-representation, namely, which attributes of the DFN are to be represented as nodes and which ones as edges connecting those nodes, depends on the relevant scientific questions. We demonstrate that the proposed DFN model reduction framework provides an efficient means for DFN modeling through both system reduction of the DFN using graph-based properties and combining DFN and graph-based flow and transport simulations.

58 GEOSCIENCES↗
DOE OSTI2023

Embedding security into ferroelectric FET array via in situ memory operation

Non-volatile memories (NVMs) have the potential to reshape next-generation memory systems because of their promising properties of near-zero leakage power consumption, high density and non-volatility. However, NVMs also face critical security threats that exploit the non-volatile property. Compared to volatile memory, the capability of retaining data even after power down makes NVM more vulnerable. Existing solutions to address the security issues of NVMs are mainly based on Advanced Encryption Standard (AES), which incurs significant performance and power overhead. In this paper, we propose a lightweight memory encryption/decryption scheme by exploiting in-situ memory operations with negligible overhead. To validate the feasibility of the encryption/decryption scheme, device-level and array-level experiments are performed using ferroelectric field effect transistor (FeFET) as an example NVM without loss of generality. Besides, a comprehensive evaluation is performed on a 128 × 128 FeFET AND-type memory array in terms of area, latency, power and throughput. Compared with the AES-based scheme, our scheme shows ~22.6×/~14.1× increase in encryption/decryption throughput with negligible power penalty. Furthermore, we evaluate the performance of our scheme over the AES-based scheme when deploying different neural network workloads. Our scheme yields significant latency reduction by 90% on average for encryption and decryption processes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries

Abstract Advances in the field of goal-directed molecular optimization offer the promise of finding feasible candidates for even the most challenging molecular design applications. One example of a fundamental design challenge is the search for novel stable radical scaffolds for an aqueous redox flow battery that simultaneously satisfy redox requirements at the anode and cathode, as relatively few stable organic radicals are known to exist. To meet this challenge, we develop a new open-source molecular optimization framework based on AlphaZero coupled with a fast, machine-learning-derived surrogate objective trained with nearly 100,000 quantum chemistry simulations. The objective function comprises two graph neural networks: one that predicts adiabatic oxidation and reduction potentials and a second that predicts electron density and local three-dimensional environment, previously shown to be correlated with radical persistence and stability. With no hard-coded knowledge of organic chemistry, the reinforcement learning agent finds molecule candidates that satisfy a precise combination of redox, stability and synthesizability requirements defined at the quantum chemistry level, many of which have reasonable predicted retrosynthetic pathways. The optimized molecules show that alternative stable radical scaffolds may offer a unique profile of stability and redox potentials to enable low-cost symmetric aqueous redox flow batteries.

25 ENERGY STORAGE↗
DOE OSTI2017

Integrated Nondestructive Assay Systems to Estimate Plutonium in Spent Fuel Assemblies

An integrated nondestructive assay (NDA) system combining active (neutron generator) and passive neutron detection and passive gamma (PG) detection is being analyzed in order to estimate the amount of plutonium, verify initial enrichment, burnup, and cooling time, and detect partial defects in a spent fuel assembly (SFA). Active signals are measured using the differential die-away (DDA), delayed neutron (DN), and delayed gamma (DG) techniques. Passive signals are measured using total neutron (TN) counts and both gross and spectral resolved gamma counts. To quantify how a system of several NDA techniques is expected to perform, all of the relevant NDA techniques listed above were simulated as a function of various reactor conditions such as initial enrichment, burnup, cooling time, assembly shuffling pattern, reactor operating conditions (including temperature, pressure, and the presence of burnable poisons) by simulating the NDA response for five sets of light water reactor assemblies. In this study, we compare the performance of several exploratory model-fitting options (including neural networks, adaptive regression with splines, iterative bias reduction smoothing, projection pursuit regression, and regression with quadratic terms and interaction terms) to relate data simulated with measurement and model error effects from various subsets of the NDA techniques to the total Pu mass. Isotope masses for SFAs and expected detector responses (DRs) for several NDA techniques are simulated using MCNP, and the DRs become inputs to the fitting process. Such responses include eight signals from DDA, one from DN, one from TN, and up to seven from PG; the DG signal will be examined separately. Results are summarized using the root-mean-squared estimation error for plutonium mass in held-out subsets of the data for a range of model and measurement error variances. Different simulation assumptions lead to different spent fuel libraries relating DRs to Pu mass. Some results for training with one library and testing with another library are also given.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2019

The Effect of Heavy Ion Irradiation on the Forward Dissolution Rate of Borosilicate Glasses Studied In Situ and Real Time by Fluid-Cell Raman Spectroscopy

Borosilicate glasses are the favored material for immobilization of high-level nuclear waste (HLW) from the reprocessing of spent fuel used in nuclear power plants. To assess the long-term stability of nuclear waste glasses, it is crucial to understand how self-irradiation affects the structural state of the glass and influences its dissolution behavior. In this study, we focus on the effect of heavy ion irradiation on the forward dissolution rate of a non-radioactive ternary borosilicate glass. To create extended radiation defects, the glass was subjected to heavy ion irradiation using 197 Au ions that penetrated ~50 µm deep into the glass. The structural damage was characterized by Raman spectroscopy, revealing a significant depolymerization of the silicate and borate network in the irradiated glass and a reduction of the average boron coordination number. Real time, in situ fluid-cell Raman spectroscopic corrosion experiments were performed with the irradiated glass in a silica-undersaturated, 0.5 M NaHCO 3 solution at temperatures between 80 and 85 °C (initial pH = 7.1). The time- and space-resolved in situ Raman data revealed a 3.7 ± 0.5 times increased forward dissolution rate for the irradiated glass compared to the non-irradiated glass, demonstrating a significant impact of irradiation-induced structural damage on the dissolution kinetics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs

Neural networks with sub-microsecond inference latency are required by many critical applications. Targeting such applications deployed on FPGAs, we present High Granularity Quantization (HGQ), a quantization-aware training framework that optimizes parameter bit-widths through gradient descent. Unlike conventional methods, HGQ determines the optimal bit-width for each parameter independently, making it suitable for hardware platforms supporting heterogeneous arbitrary precision arithmetic. In our experiments, HGQ shows superior performance compared to existing network compression methods, achieving orders of magnitude reduction in resource consumption and latency while maintaining the accuracy on several benchmark tasks. These improvements enable the deployment of complex models previously infeasible due to resource or latency constraints. HGQ is open-source and is used for developing next-generation trigger systems at the CERN ATLAS and CMS experiments for particle physics, enabling the use of advanced machine learning models for real-time data selection with sub-microsecond latency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2014

Climate Forcing Datasets for Agricultural Modeling: Merged Products for Gap-Filling and Historical Climate Series Estimation

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980-2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios.

Climate Forcing Data↗
NASA NTRS2011

Data Reduction and Control Software for Meteor Observing Stations Based on CCD Video Systems

The SPanish Meteor Network (SPMN) is performing a continuous monitoring of meteor activity over Spain and neighbouring countries. The huge amount of data obtained by the 25 video observing stations that this network is currently operating made it necessary to develop new software packages to accomplish some tasks, such as data reduction and remote operation of autonomous systems based on high-sensitivity CCD video devices. The main characteristics of this software are described here.

Madiedo, J. M.↗
DOE OSTI2025

Elucidating Pore Network Evolution in Laboratory‐ and Shaft‐Furnace Hydrogen‐Reduced Iron Pellets Using Nanotomographic Characterization

Direct reduced iron (DRI) is an increasingly important feedstock for modern steelmaking. Fundamental research into DRI properties is limited by the discrepancy between the behavior of industrially produced pellets and laboratory‐produced pellets, leading to nongeneralizable conclusions from laboratory work. Here, in this study, a detailed nano‐computed tomographic characterization of ore pellets, hydrogen DRI reduced in a pilot‐scale shaft furnace, and laboratory‐reduced DRI is presented to better understand the microstructurally influenced property differences between the two. The shaft‐furnace‐reduced pellets show lower overall porosity but larger average pore volume thickness and solid volume thickness than the laboratory‐reduced pellets. This effect is attributed to increased sintering behavior in the shaft furnace case. All pellet types show almost entirely connected pore volumes. The tortuosity of the pores is shown to increase with a degree of reduction, though the shaft furnace pellets show lower tortuosity than the laboratory‐reduced pellets. Again, this difference is attributed to the larger pore volume thickness for shaft‐furnace‐reduced pellets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mobility Energy Productivity Evaluation of Prediction-Based Vehicle Powertrain Control Combined with Optimal Traffic Management

Transportation vehicle and network system efficiency can be defined in two ways: 1) reduction of travel times across all the vehicles in the system, and 2) reduction in total energy consumed by all the vehicles in the system. The mechanisms to realize these efficiencies are treated as independent (i.e., vehicle and network domains) and, when combined, they have not been adequately studied to date. This research aims to integrate previously developed and published research on Predictive Optimal Energy Management Strategies (POEMS) and Intelligent Traffic Systems (ITS), to address the need for quantifying improvement in system efficiency resulting from simultaneous vehicle and network optimization. POEMS and ITS are partially independent methods which do not require each other to function but whose individual effectiveness may be affected by the presence of the other. In order to evaluate the system level efficiency improvements, the Mobility Energy Productivity (MEP) metric is used. MEP specifically measures the connectedness of a system while accounting for time and energy externalities of modes that provide mobility in a given location. A SUMO model is developed to reflect real traffic patterns in Fort Collins, Colorado and data is collected by a probe SUMO vehicle which is validated against data collected on a real vehicle driving the same routes through the city. Individual vehicle and system level efficiencies are calculated using SUMO outputs for scenarios which integrate POEMS and ITS independently as well as jointly. Results from application of POEMS and ITS show improvement in energy consumption and travel times respectively when compared to the respective baseline scenarios. Our conclusion is that there are promising synergistic benefits to travel time and energy efficiency when POEMS and ITS are combined.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Connecting the Dots: An Assessment of Cyber-risks in Networked Building and Municipal Infrastructure Systems

The buildings and city streets we walk down are changing. Driven by various data-driven use cases, there is increased interest in networking and integrating lighting and other building systems (e.g., heating, ventilation, and air conditioning (HVAC), security, scheduling) that were previously not internet-facing, and equipping them with sensors that collect information about their environment and the people that inhabit it. These data-enabled systems can potentially deliver improved occupant and resident experiences and help meet the U.S. Department of Energy (DOE) national energy and carbon reduction goals. Deploying connected devices new to being networked, however, is not without its challenges. This paper explores tools available to system designers and integrators that facilitate a cybersecurity landscape assessment – or more specifically the identification of threats, vulnerabilities, and adversarial behaviors that could be used against these networked systems. These assessments can help stakeholders shift security prioritization proactively toward the beginning of the development process.

cyber-risk assesment, adversarial behavior, MITRE ↗
DOE OSTI2022

Valuation of Novel Waste Heat Sources and a Path Towards Adoption: Preprint

It is recognized that beneficial electrification of most space heating will be necessary to avert the worst consequences of climate change. Fifth generation district heating and cooling (5GDHC) networks operate at near-ambient temperatures and thus facilitate heating electrification, as well as the use of waste heat sources at a wide range of temperatures. In this paper, we analyze the potential of waste heat to supply the required heat to these networks and describe the business models that could expand the use of these waste heat sources. Showcase single family and commercial districts in the US are discussed to demonstrate emerging technologies, district infrastructure approaches, and third-party thermal utility funding models for these leading 5GDHC networks. As part of this work, heat flow and temperature profiles from five different types of waste heat sources are analyzed. An example district is used to examine the integration potential and to quantify the potential reduction in energy consumption. The results indicate that 5GDHC networks are highly suitable systems for utilizing low to moderate temperature waste heat sources. In the studied district, waste heat can cover a share of up to 66% of the heat supplied to the ambient loop and reduce the overall electricity consumption for heating by up to 51%, depending on the characteristics of the specific power, temperature, and type of waste heat source. Through their moderate operating temperatures and use of waste heat sources combined with third party financed infrastructure, 5GDHC districts have significant potential to reduce carbon emissions associated with space conditioning.

district energy↗
DOE OSTI2014

Oxygen reduction reaction catalyzed by platinum nanonetwork prepared by template free one step synthesis for polymer electrolyte membrane fuel cells

Highlights: • Supportless Pt nanonetwork (Pt NN) synthesized by novel template free one step method as per our earlier reported procedure. • Electrocatalytic activity of Pt NN studied taking oxygen reduction reaction in acid medium. • Kinetic and thermodynamic parameters were deduced under hydrodynamic conditions. • ORR mechanistic pathway was proposed based on kinetic rate constants. • ADT analysis found enhanced stability (5000 cycles) for Pt NN than Pt NN/VC and reported Pt/C. - Abstract: The reduction reaction of molecular oxygen (ORR) was investigated using supportless Pt nanonetwork (Pt NN) electrocatalyst in sulfuric acid medium. Pt NN was prepared by template free borohydride reduction. The transmission electron microscope images revealed a network like nano-architecture having an average cluster size of 30 nm. The electrochemical characterization of supportless and Vulcan carbon supported Pt NN (Pt NN/VC) was carried out using rotating disc and ring disc electrodes at various temperatures. Kinetic and thermodynamic parameters were estimated under hydrodynamic conditions and compared with Pt NN/VC and reported Pt/C catalysts. The accelerated durability test revealed that supportless Pt NN is quite stable for 5000 potential cycles with 22% reduction in electrochemical surface area (ECSA). While the initial limiting current density has in fact increased by 11.6%, whereas Pt NN/VC suffered nearly 55% loss in ECSA and 13% loss in limiting current density confirming an enhanced stability of supportless Pt NN morphology for ORR compared to conventional Pt/C ORR catalysts in acid medium.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Neural computing for numeric-to-symbolic conversion in control systems

A type of neural network, the multilayer perceptron, is used to classify numeric data and assign appropriate symbols to various classes. This numeric-to-symbolic conversion results in a type of information extraction, which is similar to what is called data reduction in pattern recognition. The use of the neural network as a numeric-to-symbolic converter is introduced, its application in autonomous control is discussed, and several applications are studied. The perceptron is used as a numeric-to-symbolic converter for a discrete-event system controller supervising a continuous variable dynamic system. It is also shown how the perceptron can implement fault trees, which provide useful information (alarms) in a biological system and information for failure diagnosis and control purposes in an aircraft example.

Passino, Kevin M.↗
DOE OSTI2024

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
DOE OSTI2020

Tracking nickel oxide reduction in solid oxide cells via ex-situ ptychographic nano-tomography

In solid oxide cell (SOC) electrodes, nickel oxide reduction is a fundamental step occurring during the initial stages of cell operation. The reduction procedure is considered to be crucial in producing the final fuel electrode microstructure. This work studies the reduction reaction occurring in a SOC fuel electrode by means of X-ray ptychographic nanotomography. The same microstructure is analyzed in its pristine state and after 1 h of reduction at 850 °C, in dry hydrogen. The microstructure evolution is discussed in terms of changes in volume fractions, surface areas, and connectivity of the different constituent materials. The pore network in the unreduced cermet is revealed to be fully isolated, implying that reduction proceeds inwards from the edges of the sample towards the center. For the given experimental conditions we report that reduction is complete after 1 h of treatment but the resulting nickel network is not yet percolated and detached from the yttria-stabilized zirconia.

25 ENERGY STORAGE↗