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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Resilience-based Recovery Scheduling of Transportation Network in Mixed Traffic Environment: A Deep-Ensemble-Assisted Active Learning Approach

Devising effective post-hazard recovery strategies is critical in enhancing the resilience of transportation networks (TNs). However, existing work does not consider the multiclass users’ travel behavior in network functionality quantification and the metaheuristic solution procedures often suffer from extensive computational burden due to the exploration need in large solution space and the expensive functionality quantification. Here, this study develops a bilevel decision-making framework for the resilience-based recovery scheduling of the TN in a mixed traffic environment with connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). The lower level quantifies the TN's functionality over time considering different travel behavior of CAV and HDV users arisen from their different levels of information perception. The upper level presents a novel deep-ensemble-assisted active learning approach to balance optimization performance and computational cost. This framework can help decision makers better quantify the TN's functionality to support effective recovery scheduling of TN with different mixed traffic scenarios ranging from HDV-only to future CAV-dominant traffic. The optimization approach bears the potential to be extended to solving general large-scale network recovery scheduling problems effectively and efficiently. The proposed methodology is demonstrated using a real-world traffic network in Southern California under earthquake considering deterministic and stochastic repair durations.

42 ENGINEERING↗
DOE OSTI2020

Computing Bottleneck Structures at Scale for High-Precision Network Performance Analysis

The Theory of Bottleneck Structures is a recently-developed framework for studying the performance of data networks. It describes how local perturbations in one part of the network propagate and interact with others. This framework is a powerful analytical tool that allows network operators to make accurate predictions about network behavior and thereby optimize performance. Previous work implemented a software package for bottleneck structure analysis, but applied it only to toy examples. In this work, we introduce the first software package capable of scaling bottleneck structure analysis to production-size networks. Here, we benchmark our system using logs from ESnet, the Department of Energy's high-performance data network that connects research institutions in the U.S. Using the previously published tool as a baseline, we demonstrate that our system achieves vastly improved performance, constructing the bottleneck structure graphs in 0.21 s and calculating link derivatives in 0.09 s on average. We also study the asymptotic complexity of our core algorithms, demonstrating good scaling properties and strong agreement with theoretical bounds. These results indicate that our new software package can maintain its fast performance when applied to even larger networks. They also show that our software is efficient enough to analyze rapidly changing networks in real time. Overall, we demonstrate the feasibility of applying bottleneck structure analysis to solve practical problems in large, real-world data networks.

benchmark↗
DOE OSTI2021

Evaluation of the PhaseNet Model Applied to the IMS Seismic Network

Producing a complete and accurate set of signal detections is essential for automatically building and characterizing seismic events of interest for nuclear explosion monitoring. Signal detection algorithms have been an area of research for decades, but still produce large quantities of false detections and misidentify real signals that must be detected to produce a complete global catalog of events of interest. Deep learning methods have shown promising capabilities in effectively characterizing seismic signals for complex tasks such as identifying phase arrival times. We use the PhaseNet model, a UNet-based Neural Network, trained on local distance data from northern California to predict seismic arrivals on data from the International Monitoring System (IMS) global network. We use an analyst-curated bulletin generated from this data set to compare the performance of PhaseNet to that of the Short-Term Average/Long-Term Average (STA/LTA) algorithm. We find that PhaseNet has the potential of outperforming traditional processing methods and recommend the training of a new model with the IMS data to achieve optimal performance.

58 GEOSCIENCES↗
DOE OSTI2014

Pattern recognition with composite correlation filters designed with multi-object combinatorial optimization

Composite correlation filters are used for solving a wide variety of pattern recognition problems. These filters are given by a combination of several training templates chosen by a designer in an ad hoc manner. In this work, we present a new approach for the design of composite filters based on multi-objective combinatorial optimization. Given a vast search space of training templates, an iterative algorithm is used to synthesize a filter with an optimized performance in terms of several competing criteria. Furthermore, by employing a suggested binary-search procedure a filter bank with a minimum number of filters can be constructed, for a prespecified trade-off of performance metrics. Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.

42 ENGINEERING↗
DOE OSTI2016

The influence of interfaces on properties of thin-film inorganic structural isomers containing SnSe—NbSe₂ Subunits

Inorganic isomers ([SnSe] 1+δ ) m (NbSe₂) n ([SnSe] 1+δ ) p (NbSe₂) q ([SnSe] 1+δ ) r (NbSe₂) s where m , n , p , q , r , and s are integers and m + p + r = n + q + s = 4 were prepared using the modulated elemental reactant technique. This series of all six possible isomers provides an opportunity to study the influence of interface density on properties while maintaining the same unit cell size and composition. As expected, all six compounds were observed to have the same atomic compositions and an almost c-axis lattice parameter of ≈4.90 (5) nm, with a slight trend in the c-axis lattice parameter correlated with the different number of interfaces in the isomers: two, four and six. The structures of the constituents in the ab-plane were independent of one another, confirming the nonepitaxial relationship between them. The temperature dependent electrical resistivities revealed metallic behavior for all the six compounds. Surprisingly, the electrical resistivity at room temperature decreases with increasing number of interfaces. Hall measurements suggest this results from changes in carrier concentration, which increases with increasing thickness of the thickest SnSe block in the isomer. Carrier mobility scales with the thickness of the thickest NbSe₂ block due to increased interfacial scattering as the NbSe₂ blocks become thinner. The observed behavior suggests that the two constituents serve different purposes with respect to electrical transport. SnSe acts as a charge donor and NbSe₂ acts as the charge transport layer. This separation of function suggests that such heterostructures can be designed to optimize performance through choice of constituent, layer thickness, and layer sequence. A simplistic model, which predicts the properties of the complex isomers from a weighted sum of the properties of building blocks, was developed. A theoretical model is needed to predict the optimal compound for specific properties among the many potential compounds that can be prepared.

Alemayehu, Matti B.↗
DOE OSTI2019

Implosion performance of subscale beryllium capsules on the NIF

Many inertial fusion designs use capsules made of beryllium, as its high mass ablation rate is advantageous. here, we present the first systematic experimental study of indirectly driven beryllium capsules with a cryogenic deuterium-tritium fuel layer. “Subscale” capsules, 80% of the nominal National Ignition Facility point design radius, show optimal performance with the remaining mass of ~6–7%. A buoyancy-drag mix model explains the implosion performance, suggesting that fuel-ablator mix is the dominant degradation mechanism. Increasing the capsule scale is predicted to reduce the impact of fuel-ablator mix and achieve high performance.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Prototyping the BOPTEST Framework for Simulation-Based Testing of Advanced Control Strategies in Buildings

Advanced control strategies are becoming increasingly necessary in buildings in order to meet and balance requirements for energy efficiency, demand flexibility, and occupant comfort. Additional development and widespread adoption of emerging control strategies, however, ultimately require low implementation costs to reduce payback period and verified performance to gain control vendor, building owner, and operator trust. This is difficult in an already first-cost driven and risk-averse industry. Recent innovations in building simulation can significantly aid in meeting these requirements and spurring innovation at early stages of development by evaluating performance, comparing state-of-the-art to new strategies, providing installation experience, and testing controller implementations. This paper presents the development of a simulation framework consisting of test cases and software platform for the testing of advanced control strategies (BOPTEST - Building Optimization Performance Test). The objectives and requirements of the framework, components of a test case, and proposed software platform architecture are described, and the framework is demonstrated with a prototype implementation and example test case.

Blum, David↗
DOE OSTI2023

Statistical Approaches for Pebble Bed Reactor Operations and Safeguards

The design of pebble bed reactors (PBRs) and their method of operation align more closely with statistical approaches used in manufacturing and process control than traditional safeguards statistical approaches. The reason is PBRs will employ a nondestructive assay (NDA) measurement (burnup measurement system [BUMS]) that is part of the fuel handling system supporting discharge decisions in addition to the reactor code which monitor performance. The integration of these two approaches provides the opportunity to monitor reactor performance statistically for both operations and safeguards in ways not achievable using other reactor designs. For light water reactors (LWRs), knowledge about reactor code performance in predicting irradiated special nuclear material (SNM) content historically was only achieved from special measurement campaigns or from fuel reprocessing. Conversely, through statistical comparison of the BUMS with the reactor code-predicted values, PBRs can achieve this in real time. The resulting SNM distribution is an indicator of reactor performance because factors such as transit time and path of the pebble fuel through the reactor determine the plutonium production and uranium depletion. By analyzing the predicted and measured values, opportunities exist to adjust operating parameters, fuel design, and other characteristics to optimize performance and fuel utilization. From a safeguards perspective, this approach also provides the information necessary to validate declared values and evaluate whether the reactor is being operated as expected. This paper outlines statistical approaches for PBRs that can be used to support both operations and safeguards.

Ball, Cory↗
DOE OSTI2026

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer-Based Hydrogen Production Facility: Preprint

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at the National Renewable Energy Laboratory (NREL)'s Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗
DOE OSTI2022

Creating Functional Oxynitride–Silicon Interfaces and SrNbO 2 N Thin Films for Photoelectrochemical Applications

Photoelectrochemical performance dependence upon absorption length, carrier diffusion length, and surface area of an oxynitride photoabsorber is investigated. How best to fabricate optical-quality thin films of bandgap-tunable oxynitrides is also discussed. We targeted the stoichiometric compound SrNbO 2 N as an optimal wide-bandgap photoabsorber (1.9 eV) for use with silicon (1.1 eV) in a tandem structure photoelectrochemical cell. Preparation of perovskite oxynitrides at high-temperature as isolated powders is often straightforward, but it is difficult to integrate them as thin films in tandem junction devices with low-temperature materials. Here we develop the first method to prepare optical-quality SrNbO 2 N thin-films of tunable thickness and roughness on single-crystal silicon substrate. This achievement required an interfacial layer of ultra-thin TaN to be used as a barrier to reduce the inter-diffusion of silicon and oxygen during oxynitride syn-thesis. We produced a variety of SrNbO 2 N film thicknesses (20-440 nm) on n + -Si(100) surfaces. Roughness factor (0.14-21) scaled with thickness. The intrinsic photoelectrochemical activity of these devices was evaluated using a low-barrier sacrificial electron donor. Photocurrent density and photovoltage revealed a significant (and non-linear) dependence on film thickness and roughness. Furthermore, absorption length, carrier diffusion length, and surface area were each found to play key roles. Balancing these is required for optimally performing devices.

08 HYDROGEN↗
DOE OSTI2025

Using "AI Poincare" to analyze non-linear integrable optics

This study dives into the applicability of using automated discovery of conserved quantities in dynamical systems relevant to accelerator physics. Specifically, we explore the performance of AI Poincaré in analyzing numerical trajectory data obtained using the McMillan system of non-linear integrable optics. A comprehensive evaluation of the algorithm's performance is conducted through diverse methodologies. These include the analysis of the estimated number of conserved quantities embedded in a dataset and the deviation of interpolated points on the inferred manifold with respect to points in actually in the dataset. the investigation identifies an optimal range of perturbation distances where the underlying manifold extraction algorithm inside AI Poincaré exhibits optimal performance. Additionally, an improved neural network architecture is proposed based on the observed results. Finally, we apply the algorithm to preliminary experimental data from the Integrable Optics Test Accelerator at Fermilab to successfully infer the number of conserved quantities even in the presence of fast decoherence of the measured signal.

Osmanov, Lazare [Free U. Tbilisi]↗
DOE OSTI2015

Effects of Contact-Induced Doping on the Behaviors of Organic Photovoltaic Devices

Substrates can significantly affect the electronic properties of organic semiconductors. In this paper, we report the effects of contact-induced doping, arising from charge transfer between a high work function hole extraction layer (HEL) and the organic active layer, on organic photovoltaic device performance. Employing a high work function HEL is found to increase doping in the active layer and decrease photocurrent. Combined experimental and modeling investigations reveal that higher doping increases polaron–exciton quenching and carrier recombination within the field-free region. Consequently, there exists an optimal HEL work function that enables a large built-in field while keeping the active layer doping low. This value is found to be ~0.4 eV larger than the pinning level of the active layer material. As a result, these understandings establish a criterion for optimal design of the HEL when adapting a new active layer system and can shed light on optimizing performance in other organic electronic devices.

14 SOLAR ENERGY↗
DOE OSTI2025

Mitigating Transition Metal Dissolution from Mn-rich Cathodes: Influence of Processing and Testing Methods

Manganese-rich oxides continue to gain interest with respect to the development of Earth-abundant options for lithium-ion cathodes. Of the unique challenges that hinder the respective performance of various classes of such materials, manganese dissolution still stands as a common theme. The work herein explores Li 3 PO 4 as a robust surface protection layer on a prototypical, Co-free, manganese-rich cathode in the way of a lithium- and manganese-rich oxide. The study highlights the critical importance of synthesis and processing in realizing optimal performance of a given surface treatment by comparing sol-gel and atomic layer deposition methods. Furthermore, cycling protocols are emphasized as a critical factor in adequately gauging the efficacy of surface protection strategies to mitigate manganese dissolution and the subsequent electrochemical consequence. Optimized Li 3 PO 4 coatings coatings on lithium- and manganese-rich cathode particles are shown to greatly mitigate capacity fade, impedance rise, pore/void formation and mechanical damage during long-term cycling.

Mallick, Subhadip [Argonne National Laboratory (AN↗
DOE OSTI2021

Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data

The design space for inertial confinement fusion (ICF) experiments is vast, and experiments are extremely expensive. Researchers rely heavily on computer simulations to explore the design space in search of high-performing implosions. However, ICF multiphysics codes must make simplifying assumptions, and thus deviate from experimental measurements for complex implosions. For more effective design and investigation, simulations require input from past experimental data to better predict future performance. In this work, we describe a cognitive simulation method for combining simulation and experimental data into a common, predictive model. This method leverages a machine learning technique called “transfer learning,” the process of taking a model trained to solve one task, and partially retraining it on a sparse dataset to solve a different, but related task. In the context of ICF design, neural network models are trained on large simulation databases and partially retrained on experimental data, producing models that are far more accurate than simulations alone. Here, we demonstrate improved model performance for a range of ICF experiments at the National Ignition Facility and predict the outcome of recent experiments with less than 10% error for several key observables. We discuss how the methods might be used to carry out a data-driven experimental campaign to optimize performance, illustrating the key product—models that become increasingly accurate as data are acquired.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Advancements in Accelerator Physics and System Developments for the EIC

The development and enhancement of the Electron-Ion Collider (EIC) are founded on extensive research and development efforts aimed at achieving rigorous performance goals. This document outlines the crucial research and development (R&D) efforts that underpin the various systems and components of the EIC, including the polarized electron source and linear accelerator (linac) systems. Achieving the collider’s operational benchmarks relies on meticulously enhancing these components to optimize performance.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Achieving High‐Performance and 2000 h Stability in Anion Exchange Membrane Fuel Cells by Manipulating Ionomer Properties and Electrode Optimization

The primary function of the ionomers that are incorporated into fuel cell electrode catalyst layers is to provide pathways for ion transport between the catalyst active sites and the electrolyte. This is influenced by many variables, including the ion-exchange capacity, water uptake, and molecular weight. In anion exchange membrane fuel cells (AEMFCs), controlling ionomer water uptake is particularly important and tailoring this property in each electrode is an important consideration when looking to maximize cell performance. In this study, three poly(norbornene) tetrablock copolymer ionomers with a range of physical properties are synthesized and incorporated into AEMFC anode and cathode electrodes. Systematic electrode engineering with these ionomers allows the peak power density to be increased by 100% (1.6 W cm -2 → 3.2 W cm -2 ) and the current density at 0.2 V to be increased by 59% (5.9 A cm -2 → 9.4 A cm -2 ). Moreover, the top-performing electrode configuration is tested in an operating AEMFC at the US Department of Energy defined current density of 600 mA cm -2 for 2000 h, showing a record-low voltage decay rate of 15.36 µV h -1 – only 3.65% –a over 2000 h. Here, this work sets a new bar for AEMFCs, reporting the best combination of performance and durability of any AEMFC to date.

08 HYDROGEN↗
DOE OSTI2018

Role of structural hydroxyl groups in enhancing performance of electrochemically-synthesized bilayer V 2 O 5

Nanostructured electrode materials represent a promising path forward to dramatically improving the performance of both Li-ion and beyond Li-ion battery systems; however, difficulties in characterizing the structural and electrochemical changes that take place in nanoscale systems, which are often poorly crystalline or amorphous, make it difficult to develop design rules for synthesizing new materials with optimal performance. Bilayered vanadium oxide-based materials (BL-V 2 O 5 ) are an ideal platform for understanding the underlying physicochemical properties that determine capacity in nanomaterials, with electrochemically-synthesized V 2 O 5 (EC-V 2 O 5 ) exhibiting particularly high capacities. In this work we provide evidence that the source of high practical capacity in EC-V 2 O 5 is the presence of “structural hydroxyl groups” that are an intrinsic feature of the electrochemical synthesis method. Using both in situ and ex situ characterization methods, we demonstrate that structural OH species are highly stable and persist in the structure during reversible cycling. We hypothesize three important roles for structural OH groups: they maintain a sufficient interlayer spacing to allow the physical diffusion of cations over multiple cycles; they maintain a consistent solvating environment in the bilayer regardless of structural H 2 O content; and they reduce the symmetry of vanadium polyhedra to favor electron transfer over pseudocapacitive adsorption, making it possible to access close to theoretical capacity. Furthermore, these insights have broad implications for understanding the performance of a variety of hydrated oxide systems, and indicate that the formation of covalently-bound hydroxyoxide species can lead to further improvements in the performance of nanoscale materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Evaluation of a Bioabsorbable Self-Expandable Vein Stent-Base Made of Poly(l-lactide) In Vitro and In Vivo

PurposeThis study was designed to evaluate performance and tissue response to a self-expandable bioabsorbable vein stent-base cut from a tube with enhanced stiffness and strength in vitro and in vivo.MethodsA diamond-shaped stent-base was cut from a sequential biaxially strained poly(l-lactide) (PLLA) tube for optimized performance. The performance of the stent-base was evaluated in a finite element analysis model, and validation was attempted in vitro through a cyclic flat-plate compression and radial force measurement. The performance of the stent-base was tested in vivo using 3 sheep with 2 implants each for 2 and 3½ weeks, respectively.ResultsIn vitro the stent-base showed an elliptical deformation but no fractures. In vivo the stent-base showed adequate radial force and no migration. All implanted stent-bases showed multiple fractures not only at the predicted stress zones but at all connecting points. Fragments of the caudal stent-base stayed in the vein wall indicating sufficient tissue coverage to avoid embolization of the fractured stent pieces, whereas fragments from the cranial device remaining were few. Neointima formation was confirmed histologically at 2 and 3½ weeks.ConclusionA bioabsorbable self-expandable stent-base made from PLLA for large veins seems feasible, but over time, the PLLA used in this study appears too stiff and lacks the sufficient flexibility to move with the vena cava, causing multiple fractures.

62 RADIOLOGY AND NUCLEAR MEDICINE↗