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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Benefits and Weaknesses of Containerizing Software for HPC

Containerization technology has emerged as a transformative tool for software engineers, offering consistent development and deployment environments, simplifying dependency management, and enhancing scalability and portability across diverse systems. However, its application in High-Performance Computing (HPC) presents unique challenges, including the management of virtualization overhead, the need for efficient resource allocation, and the maintenance of optimal performance for compute-intensiv

Ho, Eric Victor [Sandia National Laboratories (SNL↗
DOE OSTI2020

Brilliant Execution of Smart Labs: Employing Best Practices to Improve Safety and Reduce Energy for Sustainable Labs: Preprint

Laboratory buildings often dominate the energy use on research campuses, consuming three to four times the energy of a typical office building. In most cases, these facilities are defined as “mission critical,” with excessive energy use as simply an unfortunate truth that cannot be avoided without compromising safety. However, case studies indicate cost savings opportunities of up to 40% in laboratories (compared to standard practices) by employing energy efficient design strategies that also improve the safety of lab spaces (Better Buildings 2018). To help stakeholders implement improvement measures, the Smart Labs Toolkit describes a proven approach to optimizing performance of laboratories and critical control environments in new or existing facilities. The Toolkit outlines distinct phases for building a successful Smart Labs program that include: Plan, Assess, Optimize, and Manage. Each phase has specific tasks, training videos, and resources to educate users, support the efforts of key stakeholders, and maximize benefits for the organization in implementing high-performance labs. The Toolkit also includes case studies from the 17 Better Buildings Smart Labs Accelerator partners, sharing lessons learned and supporting the Smart Labs approach to safe, efficient world class science. This paper reviews the technology choices for implementing Smart Labs ventilation strategies, controls, and other efficiency measures. Better Building Smart Lab Accelerator partners provide case study examples of safety and energy-savings results of using Smart Labs approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Optimizing immobilized enzyme performance in cell-free environments to produce liquid fuels

The overall goal of this project was to optimize enzyme performance for the production of bio-diesel fuel. Enzyme immobilization has attracted much attention as a means to increase productivity. Mesorporous silica materials have been known to be best suited for immobilizing enzymes. A major challenge is to ensure that the enzymatic activity is retained after immobilization. Two major factors which drive enzymatic deactivation are protein-surface and inter-protein interactions. Previously, we studied protein stability inside pores and how to optimize protein-surface interactions to minimize protein denaturation. In this work we studied eh effect of surface curvature and chemistry on inter-protein interactions. Our goal was to find suitable immobilization supports which minimize these inter-protein interactions. Our studies carried out in the frame work of Hydrophobic-Polar (HP) model showed that enzymes immobilized inside hydrophobic pores of optimal sizes are best suited to minimize these inter-protein interactions. Besides, this study is also of biological importance to understand the role of chaperonins in protein disaggregation. Both of these aspects profited immensely with collaborations with our experimental colleague, Prof. Georges Belfort (RPI), who performed the experimental analog of our theoretical works.

09 BIOMASS FUELS↗
DOE OSTI2019

Performance Improvements to the Prototype Design of the AGU

The existing design for the Magnet Structure of the prototype multi-segment AGU (Adaptive- Gap Undulator) has undergone analysis (see TN #298). To optimize performance, after review of said analysis, certain design modifications are proposed herein. A primary constraint of this proposed design is to use the existing fabricated parts from initial assembly SR-ID-SAGU-4300 with three pairs of feed-throughs spaced along the platen length of a single segment, but with foresight for the eventual multi-segment prototype. Measurements of the center segment are to be conducted on the frame and gap-drive system of the NSLS X9 MGU with its single driven axis. The eventual multi-segment prototype will also use the sister devices (X13, X27) for simultaneous measurement of both central and outboard segments at various segment gaps. Criteria effecting IVU performance like Gap Tolerance, Taper Error, and the critical Planarity of pole plane as in Figure 1 are used to evaluate the design. The error in planarity to be minimized is the deviation of vertical displacement (Y direction) with Platen length (beam Z direction).

36 MATERIALS SCIENCE↗
DOE OSTI2016

Developing Materials Processing to Performance Modeling Capabilities and the Need for Exascale Computing Architectures (and Beyond)

Additive Manufacturing techniques are presenting the Department of Energy and the NNSA Laboratories with new opportunities to consider novel component production and repair processes, and to manufacture materials with tailored response and optimized performance characteristics. Additive Manufacturing technologies already are being applied to primary NNSA mission areas, including Nuclear Weapons. These mission areas are adapting to these new manufacturing methods, because of potential advantages, such as smaller manufacturing footprints, reduced needs for specialized tooling, an ability to embed sensing, novel part repair options, an ability to accommodate complex geometries, and lighter weight materials. To realize the full potential of Additive Manufacturing as a game-changing technology for the NNSA’s national security missions; however, significant progress must be made in several key technical areas. In addition to advances in engineering design, process optimization and automation, and accelerated feedstock design and manufacture, significant progress must be made in modeling and simulation. First and foremost, a more mature understanding of the process-structure-property-performance relationships must be developed. Because Additive Manufacturing processes change the nature of a material’s structure below the engineering scale, new models are required to predict materials response across the spectrum of relevant length scales, from the atomistic to the continuum. New diagnostics will be required to characterize materials response across these scales. And not just models, but advanced algorithms, next-generation codes, and advanced computer architectures will be required to complement the associated modeling activities. Based on preliminary work in each of these areas, a strong argument for the need for Exascale computing architectures can be made, if a legitimate predictive capability is to be developed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

District heating utilizing waste heat of a data center: High-temperature heat pumps

Data centers are energy-intensive facilities with substantial low-grade waste heat. High-temperature heat pumps can be critical in boosting the data center’s waste heat for district heating, improving the system-level energy efficiency of data centers, and reducing CO 2 emissions in district heating. This study built thermodynamic models to assess high-temperature heat pumps with six configurations using low global warming potential refrigerants to supply heat up to 120 °C. The heat pump configurations include single-stage or two-stage cycles with advanced components, such as internal heat exchanger, economizer, flash tank, or parallel compressor. The refrigerants include R1234ze(Z), R1233ed(E), R1224yd(Z), R600, and R600a, and R245fa is used as a reference. A case study was carried out to recover the waste heat from the Frontier high-performance computing data center and provide hot water for district heating at the US Department of Energy’s Oak Ridge National Laboratory campus. The optimized performance of high-temperature heat pumps is characterized with various effectiveness of internal heat exchangers, and the operating parameters of economizer or flash tank, as well as their combination. The results show that the configurations of two-stage cycles with internal heat exchanger + flash tank and internal heat exchanger + economizer/parallel-compressor provide the highest coefficient of performance under scenarios of the maximum allowable value and a fixed value (0.3) of the internal heat exchangers’ effectiveness, respectively. R1234ze(Z) and R600a are the most promising refrigerants, considering trade-offs between the coefficient of performance and the volumetric heating capacity. The single-stage cycle with internal heat exchanger + economizer/parallel-compressor using R1234ze(Z) is recommended for utilizing Fronter’s waste heat in district heating. A one mega-watt high-temperature heat pump will reduce 33,100–33,200 metric tons of CO2 emission annually, corresponding to 85.4 %–85.6 % of equivalent CO2 emissions from natural gas boilers. Here, this study provides good guidelines for designing and deploying high-temperature heat pumps to support sustainable data centers and decarbonize district heating in the US.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Advancing the Performance of Anion Exchange Membrane Electrolysis by Employing a Powder-Based Ionomer during Anode Catalyst Layer Fabrication

The performance of anion exchange membrane water electrolysis (AEMWE) can be significantly improved by utilizing powdered ionomers during the fabrication of the anode catalyst layer (CL) to modify the CL properties. When comparing powdered ionomers to dispersed ionomers across various catalysts including cobalt oxide (Co 3 O 4 ), nickel−iron oxide (NiFe 2 O 4 ), and iridium oxide (IrO 2 ) the anode fabricated with powdered ionomers demonstrates improved performance in polarization curves, enhanced charge transfer kinetics, and reduced ohmic and transport losses, as evidenced by voltage breakdown and electrochemical impedance spectroscopy analyses. Optimal performance is achieved using a Co 3 O 4 catalyst with a 10 wt % powdered ionomer via the catalystcoated substrate method. Microscopy analyses reveal that electrodes formed with powdered ionomers during fabrication exhibit a more uniform catalyst and ionomer distribution, increased porosity with smaller pore areas, improved electronic conduction with less catalyst agglomeration isolated by a nonconductive ionomer, and enhanced interfacial contact with the membrane and transport layer. These findings highlight that ionomers in a powdered form can promote beneficial properties and are a promising approach to improving AEMWE efficiency.

08 HYDROGEN↗
DOE OSTI2024

Assessing the Feasibility of Bordered Block Diagonal Reordering in Power System Matrices using Fully Convolutional Network

In electromagnetic transient (EMT) simulations for power systems and inverter-based resources (IBRs), the arrangement of states within the system's linear equations, represented by matrix A in Ax=b, is critical. The state ordering in matrix A can highlight distinct characteristics of the system's graph, and identifying an optimal state ordering is crucial for efficient computation. The choice of state ordering, however, is dependent on the solver used, as each solver may perform optimally with different matrix patterns. With a wide array of matrix reordering algorithms available, selecting the most suitable one becomes challenging without insights into the matrix's ideal configuration. To address this, the paper proposes a fully convolutional network (FCN) to evaluate the reordering potential of the A matrix into a bordered block diagonal (BBD) pattern, which is commonly observed in power system and IBR modeling. The FCN's assessment aims to streamline the solver's operation, which in turn could substantially reduce the computational time required to find a solution.

Xia, Qianxue↗
DOE OSTI2018

Quantitative Analysis of Color Differences within High Contrast, Low Power Reversible Electrophoretic Displays

Here, quantitative color analysis is performed on videos of high contrast, low power reversible electrophoretic deposition (EPD)-based displays operated under different applied voltages. This analysis is coded in an open-source software, relies on a color differentiation metric, ΔE * 00 , derived from digital video, and provides an intuitive relationship between the operating conditions of the devices and their performance. Time-dependent ΔE * 00 color analysis reveals color relaxation behavior, recoverability for different voltage sequences, and operating conditions that can lead to optimal performance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fuel cells for single-aisle regional aircraft: System configuration, performance and cost

A hydrogen fuel cell propelled electric aircraft can compete with incumbent turbofan technologies for single-aisle regional aircraft by coupling design of stack, air handling, thermal management, propulsion, and airframe to optimize performance. The stack operates at 95°C to facilitate heat rejection during take-off and below 75°C during cruise to extend lifetime and is oversized to satisfy power requirements at end of life. A multi-stage turbocompressor with a compression ratio >10 is selected to reach high stack power density at 11,300-m cruise altitude. The propulsion system is configured to accommodate air handling within the core duct, an inclined heat exchanger in the outer duct to limit the nacelle size, and variable area nozzles to independently control mass flows through the core and bypass ducts. The airframe is modified for maximum lift coefficient and longer balanced field length for dramatically reduced thrust during take-off, and the fuselage is stretched by 20% to store liquid hydrogen (LH 2 ). Modularization of power systems promotes safety in one engine inoperative scenarios and allows reaching specific power metrics for stack, balance-of-plant and fuel cell system (FCS), necessary for acceptable take-off weight. In conclusion, cost parity requires increase in FCS lifetime, LH 2 cost reduction, and improved FCS specific power.

Catalyst durability↗
DOE OSTI2023

The Uniform Methods Project: Smart Thermostat Evaluation Protocol

A smart thermostat is an internet-connected device that controls home heating, ventilation, and air-conditioning (HVAC) equipment and can automatically adjust temperature set points to optimize performance and achieve energy savings. Smart thermostat features often include two way communication, occupancy detection (such as geofencing and occupancy sensors), schedule learning, and seasonal optimization algorithms. Smart thermostats can control most conventional HVAC systems, including central air conditioners, heat pumps, and forced air furnaces. Several types of residential utility programs offer smart thermostats as replacements measures. Working with smart thermostat vendors, utilities can offer separate optimization programs to produce energy savings beyond those achieved by installing a smart thermostat. From an evaluation perspective, smart thermostat programs have several noteworthy features. First, the energy savings from a smart thermostat may change over the life of the device. As a smart thermostat is connected to the internet, original equipment manufacturers can update the thermostat software to improve the thermostat's energy efficiency. Likewise, users can adjust the thermostat settings and schedules over time in response to changes in weather, thermal comfort, energy prices, or preferences for energy efficiency. Additionally, many thermostat manufacturers offer seasonal optimization programs that recommend changes or make minor, automated adjustments to the thermostat settings to improve energy efficiency. These opt-in programs are now standard offerings for many smart thermostat manufacturers and provided at no additional cost to users. The potential for software updates and continuous optimization and the evolving nature of user interactions mean future energy savings may differ from first-year savings and the energy savings of smart thermostats may need to be evaluated more than once. Second, smart thermostats often have small unit energy savings relative to a home's total energy consumption, especially in comparison to whole- home retrofit programs. This can make it difficult to detect the smart thermostat savings in billing or advanced metering infrastructure (AMI) meter consumption data. For example, as cooling loads in many regions average about 20% of annual electricity consumption, smart thermostat savings of 10% of cooling energy use would equate to a 2% reduction in home electricity consumption. Evaluators should use regression analysis of whole-home billing consumption or advanced metering infrastructure (AMI) meter consumption data to evaluate smart thermostat savings because, as explained at greater length below , these data are usually available to evaluators and regression can control for the impacts of weather and other potentially confounding factors on a home's energy consumption. Finally, as with other energy efficiency programs, participation in smart thermostat programs is self-selective. As discussed at greater length below , smart thermostat participants tend to be, among other things, younger, higher-income, and more likely to adopt electric vehicles (EVs) and internet connected devices than nonparticipants. These differences are often unobservable to the evaluator and correlated with a home's energy consumption, creating the potential for bias in estimating savings. Due to the small unit savings of thermostats, errors and biases from self-selection that may not be very consequential when evaluating a whole- home retrofits (e.g., ±2% of home electricity consumption) can have a major impact when evaluating the savings and cost-effectiveness of smart thermostat programs. A percentage point change in the estimated savings could affect the cost-effectiveness of a program. This means it is important for evaluators to assess and to minimize the potential for error from selection bias in estimating smart thermostat program savings. The Uniform Methods Project provides model protocols for determining energy savings and demand reductions that result from specific energy efficiency measures implemented through state and utility programs. In most cases, the measure protocols are based on a particular option identified by the International Performance Verification and Measurement Protocol ; however, this work provides a more detailed approach to implementing that option. Each chapter is written by technical experts in collaboration with their peers, reviewed by industry experts, and subject to public review and comment. The UMP protocols can be used by utilities, program administrators, public utility commissions, evaluators, and other stakeholders for both program planning and evaluation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Bridging interfacial properties and cell performance: A multiscale model for proton-exchange-membrane fuel cells

Here, to elucidate the impact of local interfaces on mass-transport resistance and overall cell performance of low-loaded proton-exchange-membrane fuel cells (PEMFCs), we present a multiscale modeling framework incorporating a novel modified agglomerate model. The model considers three distinct Pt-electrolyte interfaces: Pt on the carbon surface covered by either ionomer or water film and Pt inside carbon nanopores. Detailed mass-transport voltage-loss breakdowns reveal that coupled agglomerate-interface-scale mass transport dominates the mass-transport loss. The ionomer poisons the exterior-Pt surface through suppressing O 2 adsorption and intrinsic ORR activity, leading to low current-density performance. Conversely, interior-Pt interface enhances the kinetic performance but limits high current-density performance due to its low interfacial permeability. The exterior-Pt/water interface demonstrates superior kinetic performance and mass transport, though its practical implementation requires ensuring proton transport. By coupling the multiscale CL properties with ink parameters, the model identifies an optimal I to C ratio of approximately 0.5, a moderate value where the ionomer content is sufficient to guarantee proton transport without fully covering the Pt surface and forming large agglomeration, thus allowing the utilization of the Pt-water interface and avoiding high mass-transport loss. Overall, the model helps unravel limiting phenomena across different operating regimes and provides routes for optimizing performance.

Cell diagnostic↗
DOE OSTI2024

Hybrid power plant design for low-carbon hydrogen in the United States

In this study, we provide a nationwide techno-economic analysis of clean hydrogen production powered by a hybrid renewable energy plant for over 50,000 locations in the United States. We leverage the open-source Hybrid Optimization Performance Platform (HOPP) tool to simulate the hourly performance of an off-grid wind-solar plant integrated with a 1-GW polymer exchange membrane electrolyzer system. The levelized cost of hydrogen is calculated for varying technology costs, and tax credits to explore cost sensitivities independent of plant design, performance, and site selection. Our findings suggest that strategies for cost reduction include selecting sites with abundant wind resources, complementary wind and solar resources, and optimizing the sizing of wind and solar assets to maximize the hybrid plant capacity factor. These strategies are linked to increased hydrogen production and reduced electrolyzer stack replacements, thereby lowering the overall cost of hydrogen.

08 HYDROGEN↗
DOE OSTI2018

Prometheus: Coherent Exploration of Hardware and Software Optimizations using Aspen

With the dramatic increase in scale expected for Exascale computing, there is a dire need for tuning of hardware configurations and software optimizations such that they are in unison. However, the expected increase in tunable hardware parameters makes searching through the design space for optimal hardware-and-software configurations much more challenging. Towards this end, we propose a composable hardware-software optimization framework called Prometheus. Prometheus uses a combination of analytical and machine-learning techniques to capture application characteristics and subsequently determine the hardware-software configuration for near-optimal performance. We evaluate Prometheus for its efficacy using four widely used proxy applications: LULESH, CoMD, CG and CoEVP. We demonstrate that Prometheus identifies near-optimal hardwaresoftware configurations and verify the results via brute-force search of the design space.

Umar, Mariam↗
DOE OSTI2018

Improved Performance of Asymptotically Optimal Rapidly Exploring Random Trees

Three algorithms that improve the performance of the asymptotically optimal Rapidly exploring Random Tree (RRT*) are presented here. First, we introduce the Goal Tree (GT) algorithm for motion planning in dynamic environments where unexpected obstacles appear sporadically. The GT reuses the previous RRT* by pruning the affected area and then extending the tree by drawing samples from a shadow set. The shadow is the subset of the free configuration space containing all configurations that have geodesics ending at the goal and are in conflict with the new obstacle. Smaller, well defined, sampling regions are considered for Euclidean metric spaces and Dubins' vehicles. Next, the Focused-Refinement (FR) algorithm, which samples with some probability around the first path found by an RRT*, is defined. The third improvement is the Grandparent-Connection (GP) algorithm, which attempts to connect an added vertex directly to its grandparent vertex instead of parent. The GT and GP algorithms are both proven to be asymptotically optimal. Finally, the three algorithms are simulated and compared for a Euclidean metric robot, a Dubins' vehicle, and a seven degrees-of-freedom manipulator.

42 ENGINEERING↗
DOE OSTI2023

Optimizing Communication in 2D Grid-Based MPI Applications at Exascale

The new reality of exascale computing faces many challenges in achieving optimal performance on large numbers of nodes. A key challenge is the efficient utilization of the message-passing interface (MPI), a critical component for process communication. This paper explores communication optimization strategies to harness the GPU-accelerated architectures of these supercomputers. We focus on MPI applications where processors form a two-dimensional process grid, a common arrangement in applications involving dense matrix operations. This configuration offers a unique opportunity to implement innovative strategies to improve performance and maintain effective load distribution. We study two applications— Dist-FW (Apsp:all-pair-shortest-path) and HPL-MxP (LU factorization with Mixed precision)—on two accelerated systems: Summit (IBM Power and NVIDIA V100) and Frontier (AMD EPYC and MI250X). These supercomputers are operated by the Oak Ridge Leadership Computing Facility (OLCF) and are currently ranked #1 and #5 on the Top500 list. We show how to scale up both applications to exascale levels and tackle the MPI challenges related to implementation, synchronization, and performance. We also compare the performance of several communication strategies at an unprecedented scale. Accurately predicting application performance becomes crucial for cost reduction as the computational scale grows. To address this, we suggest a hyperbolic model as a better alternative to the traditional one-sided asymptotic model for predicting future application performance at such large scales.

Lu, Hao↗
DOE OSTI2024

Impact of cycling conditions on lithium-ion battery performance for electric vertical takeoff and landing applications

The development of better electrochemical energy storage systems has sparked significant interest in using Li-ion batteries for electric vertical takeoff and landing (eVTOL) applications. To ensure the optimal performance and safety of onboard batteries, their behavior under different charging/discharging protocols and environmental conditions must be understood. Here, this paper presents a comprehensive evaluation of commercial Li-ion batteries for eVTOL applications, focusing on their responses to varying charging/discharging strategies and mechanical vibrations experienced during flight. Through controlled experiments, the effects of rapid cycling on battery performance were investigated, including effects on lifespan, capacity, and internal resistance. Additionally, the impact of mechanical vibrations on battery behavior was assessed to identify potential challenges for onboard batteries. The results of this study revealed intriguing insights into the interplay between temperature, vibration, and battery performance. This work contributes to the broader adoption of electric aerial transportation, promising a greener and safer future for urban mobility.

18650 cells↗
DOE OSTI2016

Dissecting jets and missing energy searches using $n$-body extended simplified models

Simplified Models are a useful way to characterize new physics scenarios for the LHC. Particle decays are often represented using non-renormalizable operators that involve the minimal number of fields required by symmetries. Generalizing to a wider class of decay operators allows one to model a variety of final states. This approach, which we dub the $n$-body extension of Simplified Models, provides a unifying treatment of the signal phase space resulting from a variety of signals. In this paper, we present the first application of this framework in the context of multijet plus missing energy searches. The main result of this work is a global performance study with the goal of identifying which set of observables yields the best discriminating power against the largest Standard Model backgrounds for a wide range of signal jet multiplicities. Our analysis compares combinations of one, two and three variables, placing emphasis on the enhanced sensitivity gain resulting from non-trivial correlations. Utilizing boosted decision trees, we compare and classify the performance of missing energy, energy scale and energy structure observables. We demonstrate that including an observable from each of these three classes is required to achieve optimal performance. In conclusion, this work additionally serves to establish the utility of $n$-body extended Simplified Models as a diagnostic for unpacking the relative merits of different search strategies, thereby motivating their application to new physics signatures beyond jets and missing energy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗