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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

All Hands On Deck: Raising the Bar on Whole Building Performance-Based Code Compliance and Above-Code Programs

In 2019 a large-scale national effort was launched with support from U.S. Department of Energy, Pacific Northwest National Laboratory and Northwest Energy Efficiency Alliance (NEEA). The project aimed to develop a long-term vision and a roadmap for achieving a practical application of whole building performance-based code and above code program compliance in commercial buildings. The work was aided by engaging over 70 stakeholders across the country representing jurisdictions, above-code programs such as LEED and ENERGY STAR Multifamily Program, members of the ASHRAE Standard 90.1 and other Standard committees, IBPSA and software developers. The performance path requires whole building energy modeling and is gaining momentum. Many see it as the future of the commercial energy codes and the main pathway for achieving zero energy buildings. The paper describes the findings from a stakeholder survey funded by NEEA on the current state of performance-based path, its market penetration, trends, implementation practices and adoption challenges. It discusses the patterns revealed by experience of the states with decades-long history of performance-based compliance such as Florida and California and opportunities for taking advantage of the synergies between code and above-code programs. In addition, the paper provides an overview of the tools and resources developed as part of the effort including the compliance form for ASHRAE Standard 90.1 Section 11 and Appendix G, modeling submittal review manual, and modeler and reviewer qualification requirements. The tools and resources would help remove market barriers, deliver immediate benefits to jurisdictions and rating authorities, and achieve energy efficiency through whole-building performance path.

TSPR, Performance Based Complinace, Washington Sta↗
DOE OSTI2024

Performance of a second generation of the Fermilab Constant Fraction Discriminator ASIC for time-stamping long-strip AC-LGAD sensors.

We present the design and performance characterization results of the second generation of the novel Fermilab Constant Fraction Discriminator ASIC (FCFD) developed to readout AC-coupled low gain avalanche detector (AC-LGAD) sensors. This study presents the performance of the ASIC which was optimized specifically for reading out strip AC-LGAD sensors designed for ePIC experiment at EiC. Performance was measured using charge injection and particle beams with prototype AC-LGAD sensors wirebonded to the FCFD ASIC.

Apresyan, A.↗
DOE OSTI2021

Development of Passive HC/NOx Trap Catalysts for Low Temperature Gasoline Applications

This project aimed to develop fundamental understanding of the chemistry of NO adsorption and reaction in Pd/zeolites so as to facilitate the rational design of passive NOx adsorber catalysts. The approach adopted combined both experimental and computational methods, which together allow a deeper understanding of the governing chemistry than the use of either method alone. The workflow began with Pd/H-CHA and Pd/H-BEA catalyst synthesis and characterization, in which the Si/Al ratio and Al siting were systematically varied. This was followed by catalyst evaluation using temperature-programed adsorption/desorption methods, as well as in situ spectroscopic measurements to probe the chemistry of NO adsorption. In parallel, the adsorption of NO and other relevant species (H 2 O, CO, HCs) was studied by means of quantum chemical calculations in order to rationalize the experimental data and provide additional insights. Catalyst aging studies were also performed with the aim of elucidating the mechanism of catalyst degradation. Finally, the insights gained in this project were applied to the preparation of an optimized HC/NOx adsorber catalyst, the performance of which was studied using exhaust gas from an engine dynamometer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

The role of quantum computing in advancing scientific high-performance computing: A perspective from the ADAC institute

Quantum computing (QC) has gained significant attention over the past two decades due to its potential for speeding up classically demanding tasks. This transition from an academic focus to a thriving commercial sector is reflected in substantial global investments. While advancements in qubit counts and functionalities continue at a rapid pace, current quantum systems still lack the scalability for practical applications, facing challenges such as too high error rates and limited coherence times. Here, this perspective paper examines the relationship between QC and high-performance computing (HPC), highlighting their complementary roles in enhancing computational efficiency. It is widely acknowledged that even fully error-corrected QC will not be suited for all computational tasks. Rather, future compute infrastructures are anticipated to employ quantum acceleration within hybrid systems that integrate HPC and QC. While QC can enhance classical computing, traditional HPC remains essential for maximizing quantum acceleration. This integration is a priority for supercomputing centers and companies, sparking innovation to address the challenges of merging these technologies. The novelty of this work lies in its unique perspective, reflecting the collective insights of the Accelerated Data Analytics and Computing (ADAC) Institute, a global consortium of over 20 leading HPC centers. Recognizing the growing importance of QC, ADAC established a Quantum Computing Working Group in 2023 to foster collaboration and knowledge-sharing among its members. This paper synthesizes insights from the group’s collaborative efforts and incorporates findings from a member survey that captures shared experiences, ongoing projects, and strategic directions. By outlining the current landscape and challenges of QC integration into HPC ecosystems, this work offers HPC specialists practical and forward-looking guidance on the opportunities and implications of QC in computationally intensive endeavors.

Accelerated Data Analytics and↗
DOE OSTI2023

Predictive Engineering of Interfaces and Cathodes for High-Performance All Solid-State Lithium-Sulfur Batteries

The primary goal of this project is to leverage data-driven methods and machine learning strategies to develop accurate multi-physics models for all-solid-state Li-S battery (ASLSB) materials that can capture electrochemical and transport phenomena over atomic to mesoscopic length/timescales. These models will be rigorously validated by synthesis and advanced characterization experiments. The team will leverage the predictive power of these models, alongside synthesis/characterization experiments and battery fabrication to address longstanding issues at the electrode/electrolyte interfaces in ASLSBs. The project’s proposed technology involves the following: (1) halide-doped solid sulfide electrolytes that can concurrently provide high Li + ion conductivity and suppress dendrite growth; (2) novel mesoporous cathode composed of super-P and carbon nanotubes co-infiltrated with sulfur and sulfide electrolyte, which hold potential to allow high sulfur loading and optimal ion/electron pathways; and (3) functionalization of sulfide electrolyte with ionic liquids to improve physical contact and minimize impedance at the cathode/electrolyte interface. Successful development of proposed predictive models (at multiple scales) will bridge this knowledge gap and will advance fundamental understanding of reaction chemistry, kinetics, charge transfer, and dendrite growth at electrified SSIs. This will enable predictive design of effective strategies to mitigate interfacial problems in ASSLSBs, including poor interfacial contact, interfacial impedance to Li + ion transport, and poor electron/ion conduction within cathodes. Ultimately, the fundamental knowledge gained will lead to development of high-performance ASSLSBs.

25 ENERGY STORAGE↗
DOE OSTI2025

CFD Simulations of Lower Plenum Mixing

Review of model development and validation performed in the Advanced Reactor Technologies (ART) program for thermal mixing at the outlet of High Temperature Gas Reactors (HTGRs). Understanding the mixing that occurs in the lower plenum in an HTGR is necessary to facilitate design improvements and to perform reactor safety analysis. Numerical models are one possible approach to gain a better understanding of mixing in the lower plenum. Given the complexity of the geometry and the intense mixing present, it is important to perform validation of numerical models. Three models have been developed during FY2025: a porous media with Pronghorn, a Reynolds Averaged Navier Stokes (RANS) with STAR-CCM+, and a Large Eddy Simulation (LES) with NekRS. The reference facility is a scaled-down version of the lower plenum of the High Temperature Gas-Cooled Reactor - Pebble-bed Module (HTR-PM) demonstration reactor. Preliminary results of the porous media and the RANS shows general good agreement against experimental benchmark data. Future work will leverage high-fidelity results obtained through LES to guide model selection and improvements to the lower-fidelity models, with particular attention to the Pronghorn porous media.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Maximizing Strength: The Stimuli and Mediators of Strength Gains and Their Application to Training and Rehabilitation

Abstract Spiering, BA, Clark, BC, Schoenfeld, BJ, Foulis, SA, and Pasiakos, SM. Maximizing strength: the stimuli and mediators of strength gains and their application to training and rehabilitation. J Strength Cond Res 37(4): 919–929, 2023—Traditional heavy resistance exercise (RE) training increases maximal strength, a valuable adaptation in many situations. That stated, some populations seek new opportunities for pushing the upper limits of strength gains (e.g., athletes and military personnel). Alternatively, other populations strive to increase or maintain strength but cannot perform heavy RE (e.g., during at-home exercise, during deployment, or after injury or illness). Therefore, the purpose of this narrative review is to (a) identify the known stimuli that trigger gains in strength; (b) identify the known factors that mediate the long-term effectiveness of these stimuli; (c) discuss (and in some cases, speculate on) potential opportunities for maximizing strength gains beyond current limits; and (d) discuss practical applications for increasing or maintaining strength when traditional heavy RE cannot be performed. First, by conceptually deconstructing traditional heavy RE, we identify that strength gains are stimulated through a sequence of events, namely: giving maximal mental effort, leading to maximal neural activation of muscle to produce forceful contractions, involving lifting and lowering movements, training through a full range of motion, and (potentially) inducing muscular metabolic stress. Second, we identify factors that mediate the long-term effectiveness of these RE stimuli, namely: optimizing the dose of RE within a session, beginning each set of RE in a minimally fatigued state, optimizing recovery between training sessions, and (potentially) periodizing the training stimulus over time. Equipped with these insights, we identify potential opportunities for further maximizing strength gains. Finally, we identify opportunities for increasing or maintaining strength when traditional heavy RE cannot be performed.

Sport Sciences↗
DOE OSTI2026

Enhancement of mid-/high-Z impurity transport by continuous Li-granule dropping in a stellarator plasma

An enhancement of core impurity transport is observed in high-density plasmas of the stellarator large helical device, heated by neutral beam injection, when continuous lithium (Li) granule dropping is performed. In the reported experiments, in which the tracer-encapsulated solid pellet is employed to inject trace amounts of titanium (Ti) and molybdenum (Mo) into the plasma core, confinement times for these impurities are seen to reduce significantly when Li dropping is applied, this reduction being more notable for Mo. To gain some initial insight into these observations, simulations are performed using the drift-kinetic transport code SFINCS for the Mo case. These simulations indicate that, while neoclassical transport prevails for the main plasma components (electrons, majority ions, and low-Z impurities), the classical contribution appears dominant for transporting Mo impurities. In summary, this work reports the first experimental observation of the degradation of mid-Z and high-Z impurity confinement induced by the continuous dropping of Li granules into a high-density stellarator plasma. In the case of the Mo impurity, simulations suggest that classical transport is the key mechanism underlying the enhanced impurity transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

LGAD-Based Silicon Sensors for 4D Detectors

Low-Gain Avalanche Diodes (LGAD) are a class of silicon sensors developed for the fast detection of Minimum Ionizing Particles (MIPs). The development was motivated by the need of resolving piled-up tracks of charged particles emerging from several vertexes originating from the same bunch-crossing in High-Energy Physics (HEP) collider experiments, which, however, are separated not only in space but also in time by a few tens of picoseconds. Built on thin silicon substrates and featuring an internal moderate gain, they provide fast signals for excellent timing performance, which are therefore useful to distinguish the different tracks. Unfortunately, this comes at the price of poor spatial resolution. To overcome this limitation, other families of LGAD-based silicon sensors which can deliver in the same substrate both excellent timing and spatial information are under development. Such devices are, to name a few, capacitively coupled LGADs (AC-LGAD), deep-junction LGADs (DJ-LGAD) and trench-isolated LGADs (TI-LGADs). These devices can be fabricated by even small-scale research-focused clean rooms for faster development within the scientific community. However, to scale up production, efforts towards integrating these sensor concepts in CMOS substrates, with the obvious advantage of the possibility of integrating part of the read-out electronics in the same substrate, have begun.

spatial resolution↗
DOE OSTI2020

Optimizing Bifacial PV Systems (Q3 FY2020 Project Report)

This project has four main technical objectives. Develop and improve bifacial performance models by adding the capability to evaluate electrical behavior and performance of bifacial modules and arrays under realistic field conditions including irradiance variability caused by racking, module frame, and position in the array. Instrument and monitor performance of fielded bifacial systems to validate performance models and to measure, analyze and publish on bifacial energy gain. These should include both research and commercial bifacial systems and cover a variety of deployment applications. Evaluate optimal bifacial system designs using simulations leveraging high performance computing, and also using full sized and miniaturized experimental field deployments. Establish and contribute to international test standards for bifacial system performance, testing, and safety, and work with the community to establish installation and siting best practices.

14 SOLAR ENERGY↗
DOE OSTI2020

Recent improvements in beam orbit feedback at NSLS-II

This paper discusses improvements made to the fast orbit feedback (FOFB) system at NSLS-II in 2019. We first characterized the system by measuring the transfer function and the closed-loop gain of the system. We also constructed a model to predict the performance, which was based on physical behaviors. After finding and fixing the sources of the latency, we improved the closed-loop gain’s bandwidth from 250 Hz to 400 Hz in the horizontal plane and 250 Hz to 300 Hz in the vertical plane. As a result, the orbit beam instability improved by 30% and 10% in the horizontal and vertical planes, respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Economic and Environmental Analysis to Evaluate the Potential Value of Co-Optima Diesel Bioblendstocks to Petroleum Refiners

The U.S. petroleum refining sector is undergoing a period of historic transformation, catalyzed by the decarbonization of the U.S. economy. Diesel-boiling-range bioblendstocks have gained traction, owing to their superior fuel properties and environmental performance as compared to traditional petroleum fuels. This work couples refinery linear programming models with life cycle assessment to quantify the potential economic and environmental benefits, and trade-offs, of blending diesel-boiling-range bioblendstocks at petroleum refineries. Linear programming models were developed in Aspen Process Industry Modeling Systems (PIMS) for three representative petroleum refinery configurations of differing complexity. Seven diesel-boiling-range bioblendstocks: 4-butoxyheptane, 5-ethyl-4-propylnonane, soy biodiesel, sludge hydrothermal liquefaction diesel, polyoxymethylene ethers, renewable diesel, and hexyl hexanoate, were investigated to identify key fuel properties that influence refineries' economics and to track the effect of adding bioblendstocks on refinery-wide cradle-to-gate greenhouse gases (GHG) emissions. These analyses considered blending levels from 10 to 30 vol% and fuel demand projections over the period 2040 to 2050. This analysis determines that bioblendstock sulfur content and cetane number are the primary fuel attributes with the potential to provide value to refiners. Life cycle assessment results indicate that the use of diesel-boiling-range bioblendstocks can reduce cradle-to-gate refinery GHG emissions by up to ~ 40 % relative to conventional refinery operations when considering carbon uptake in the supply chain of the bioblendstock. Refinery-wide marginal GHG abatement costs range from 120 to 3,600 USD2016/metric tons carbon dioxide equivalent avoided across the scenarios evaluated. Reducing the price of bioblendstocks is identified as a key to their adoption.

ADVANCED PROPULSION SYSTEMS,BIOMASS FUELS,ENERGY P↗
DOE OSTI2024

Analyzing the effect of doping concentration in split-well resonant-phonon terahertz quantum cascade lasers

The effect of doping concentration on the temperature performance of the novel split-well resonant-phonon (SWRP) terahertz quantum-cascade laser (THz QCL) scheme supporting a clean 4-level system design was analyzed using non-equilibrium Green’s functions (NEGF) calculations. Experimental research showed that increasing the doping concentration in these designs led to better results compared to the split-well direct-phonon (SWDP) design, which has a larger overlap between its active laser states and the doping profile. However, further improvement in the temperature performance was expected, which led us to assume there was an increased gain and line broadening when increasing the doping concentration despite the reduced overlap between the doped region and the active laser states. Through simulations based on NEGF calculations we were able to study the contribution of the different scattering mechanisms on the performance of these devices. We concluded that the main mechanism affecting the lasers’ temperature performance is electron-electron (e-e) scattering, which largely contributes to gain and line broadening. Interestingly, this scattering mechanism is independent of the doping location, making efforts to reduce overlap between the doped region and the active laser states less effective. Optimization of the e-e scattering thus could be reached only by fine tuning of the doping density in the devices. By uncovering the subtle relationship between doping density and e-e scattering strength, our study not only provides a comprehensive understanding of the underlying physics but also offers a strategic pathway for overcoming current limitations. This work is significant not only for its implications on specific devices but also for its potential to drive advancements in the entire THz QCL field, demonstrating the crucial role of e-e scattering in limiting temperature performance and providing essential knowledge for pushing THz QCLs to new temperature heights.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Optimization of key energy and performance metrics for drug product manufacturing

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors, this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. Here, the results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Quantifying Thermal Performance of the Building Envelope - Beyond Common Practice

Performance metrics serve as useful tools and indicators for many applications. For the building industry, various metrics exist; typically, these metrics do not assess the whole building performance. Instead, these represent separate components or heat transfer mechanisms of a building like R-value, U-factor/value, and airtightness. The mechanisms defined under these metrics all contribute significantly to the overall thermal performance of a building but will rarely act independently. There are metrics that account for the overall energy performance of a building, such as Energy Use Intensity (EUI). However, these metrics tend to be highly influenced by how the building is operated and used. There are also metrics which provide assessment on a relative basis. These indices are based upon comparison to a baseline, which complicates meaningful metric-based energy analyses. This paper presents a performance metric developed to account for various thermal properties and behaviors of a building. The metric accounts for all the relevant mechanisms that influence heat losses and gains in buildings and is thus directly related to the energy performance. In addition, the metric is designed to comprise air infiltration and allows users to account for workmanship quality, and imperfections of the building thermal resistance due to penetrations and other installations. This paper will describe how the metric can be applied for various building types and in different climates.

Pallin, Simon B.↗
DOE OSTI2023

Biopolymer‐assisted Synthesis of P‐doped TiO 2 Nanoparticles for High‐performance Lithium‐ion Batteries: A Comprehensive Study

Abstract TiO 2 material has gained significant attention for large‐scale energy storage due to its abundant, low‐cost, and environmentally friendly properties, as well as the availability of various nanostructures. Phosphorus doping has been established as an effective technique for improving electronic conductivity and managing the slow ionic diffusion kinetics of TiO 2 . In this study, non‐doped and phosphorus doped TiO 2 materials were synthesized using sodium alginate biopolymer as chelating agent. The prepared materials were evaluated as anode materials for lithium‐ion batteries (LIBs). The electrodes exhibit remarkable electrochemical performance, including a high reversible capacity of 235 mAh g −1 at 0.1 C and excellent first coulombic efficiency of 99 %. An integrated approach, combining operando XRD and ex‐situ XAS, comprehensively investigates the relationship between phosphorus doping, material structure, and electrochemical performance, reinforced by analytical tools and first principles calculations. Furthermore, a full cell was designed using 2 %P‐doped TiO 2 anode and LiFePO 4 cathode. The output voltage was about 1.6 V with high initial specific capacity of 148 mAh g −1 , high rate‐capability of 120 mAh g −1 at 1 C, and high‐capacity retention of 96 % after 1000 cycles at 1 C.

El Halya, Nabil↗
DOE OSTI2024

Deep learning based event reconstruction for cyclotron radiation emission spectroscopy

The objective of the cyclotron radiation emission spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time–frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization—may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment—a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium β - -decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

RU-net for automatic characterization of TRISO fuel cross sections

During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗