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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The circular bioeconomy: a driver for system integration

Background: Human and earth system modeling, traditionally centered on the interplay between the energy system and the atmosphere, are facing a paradigm shift. The Intergovernmental Panel on Climate Change’s mandate for comprehensive, cross-sectoral climate action emphasizes avoiding the vulnerabilities of narrow sectoral approaches. Our study explores the circular bioeconomy, highlighting the intricate interconnections among agriculture, forestry, aquaculture, technological advancements, and ecological recycling. Collectively, these sectors play a pivotal role in supplying essential resources to meet the food, material, and energy needs of a growing global population. We pose the pertinent question of what it takes to integrate these multifaceted sectors into a new era of holistic systems thinking and planning. Results: The foundation for discussion is provided by a novel graphical representation encompassing statistical data on food, materials, energy flows, and circularity. This representation aids in constructing an inventory of technological advancements and climate actions that have the potential to significantly reshape the structure and scale of the economic metabolism in the coming decades. In this context, the three dominant mega-trends—population dynamics, economic developments, and the climate crisis—compel us to address the potential consequences of the identified actions, all of which fall under the four categories of substitution, efficiency, sufficiency, and reliability measures. Substitution and efficiency measures currently dominate systems modeling. Including novel bio-based processes and circularity aspects might require only expanded system boundaries. Conversely, paradigm shifts in systems engineering are expected to center on sufficiency and reliability actions. Effectively assessing the impact of sufficiency measures will necessitate substantial progress in inter- and transdisciplinary collaboration, primarily due to their non-technological nature. In addition, placing emphasis on modeling the reliability and resilience of transformation pathways represents a distinct and emerging frontier that highlights the significance of an integrated network of networks. Conclusions: Existing and emerging circular bioeconomy practices can serve as prime examples of system integration. These practices facilitate the interconnection of complex biomass supply chain networks with other networks encompassing feedstock-independent renewable power, hydrogen, CO 2 , water, and other biotic, abiotic, and intangible resources. Elevating the prominence of these connectors will empower policymakers to steer the amplification of synergies and mitigation of tradeoffs among systems, sectors, and goals.

09 BIOMASS FUELS↗
DOE OSTI2026

An upgraded frequency-selectable laser source (FLS) calibrator for CMB bandpass characterization

One of the biggest challenges for Cosmic Microwave Background (CMB) experiments comes from the uncertainty in instrument bandpass calibration. Uncertainties in bandpass can limit foreground removal and spectral fitting, which are critical for inflationary and galaxy cluster measurements. CMB experiments currently use Fourier Transform Spectrometers (FTSes) to measure instrument bandpasses. However, FTS systems are currently systematics-limited, so significant improvements in bandpass measurements require novel calibrators. To this end, we developed a Frequency-selectable Laser Source (FLS) calibrator, which uses a laser with adjustable frequency coupled to a system that allows for laser power attenuation. Following initial testing with the first FLS prototype, we developed an upgraded version of the calibrator with improved performance. We present the upgrades to the FLS calibrator, the characterization of the upgraded calibrator and new laser source, and plans for testing with microwave instruments in the field.

Saunders, Lauren J. [Fermilab] (ORCID:000000016367↗
DOE OSTI2021

Advanced PGM-free Cathode Engineering for High Power Density and Durability

Polymer electrolyte fuel cells (PEFCs) are among the most promising technologies for future electric vehicles by using clean H2 with much-improved energy conversion efficiency, longer range, and rapid refueling. However, due to a large amount of platinum group metal (PGM) catalyst used in their electrodes, their prohibitively high cost hinders broad commercialization of PEFCs for transportation. Therefore, there is a critical need to develop low-cost, high-performance PGM-free cathode catalysts that have the potential to dramatically transform the economics of PEFC commercialization by reducing catalyst costs by one to two orders of magnitude. However, before PGM-free cathodes become viable, several technical challenges associated with PGM-free cathodes must be addressed, including insufficient activity and stability of the catalysts, as well as severe water flooding and large transport losses in the electrodes. Overcoming those barriers and ultimately meeting the challenging automotive PEFC performance targets was the focus of this comprehensive research and development effort on new PGM-free cathodes. To this end, we assembled a team including leading researchers from universities and industry with different but complementary expertise and capabilities. The project combined three novel and promising approaches: Advanced metal-organic framework (MOF)-derived M-N-C catalysts with a high activity and impressive durability, Novel PGM-free specific cathode architectures and fabrication strategies capable of addressing the substantial flooding and transport resistances in thicker cathodes by introducing engineered hydrophobicity through additives and support layers, and Advanced electrode ionomers with high proton conductivity for low ohmic losses across the electrode and more uniform catalyst utilization. The implementation of these new materials and electrode designs was supported by a suite of advanced experimental and simulation tools that allows us to identify performance and durability bottlenecks, devise solutions, and establish rational material design and synthesis targets. These methods include advanced electrochemical characterization, high-resolution imaging, and multi-scale modeling. In addition, the project team leveraged a broad cross-section of the ElectroCat consortium’s national laboratory facilities and expertise in advancing these materials and design strategies. Finally, the industry partners on the project facilitated the evaluation of scaled-up synthesis and manufacturing in the United States. Over its four-year period, the project made significant year-over-year advances in PGM-free cathode performance and viability. A combination of high activity and highly durable catalysts were developed through novel catalyst synthesis strategies, which met several performance and durability targets. More specifically, a catalyst prepared from MOFs and Fe2O3 nanoparticles with ammonium chloride and chemical vapor deposition treatments yielded a significant advancement in PGM-free cathode durability. Several novel strategies for fabricating cathodes were demonstrated, including those designed to reduce flooding and thickness of the cells for significantly increased volumetric power density. An optimized cathode with high conductivity ionomer and tuned ink processing for hydrophobicity yielded high fuel cell performance with new levels power density and maximum current. The scientific studies and modeling assessment also provided an outlook for future efforts, including a focus on catalysts with an increased density of the highly stable active sites developed in this project.

08 HYDROGEN↗
DOE OSTI2022

Resolving extreme jet substructure

We study the effectiveness of theoretically-motivated high-level jet observables in the extreme context of jets with a large number of hard sub-jets (up to N = 8). Previous studies indicate that high-level observables are powerful, interpretable tools to probe jet substructure for N ≤ 3 hard sub-jets, but that deep neural networks trained on low-level jet constituents match or slightly exceed their performance. We extend this work for up to N = 8 hard sub-jets, using deep particle-flow networks (PFNs) and Transformer based networks to estimate a loose upper bound on the classification performance. A fully-connected neural network operating on a standard set of high-level jet observables, 135 N-subjetiness observables and jet mass, reach classification accuracy of 86.90%, but fall short of the PFN and Transformer models, which reach classification accuracies of 89.19% and 91.27% respectively, suggesting that the constituent networks utilize information not captured by the set of high-level observables. We then identify additional high-level observables which are able to narrow this gap, and utilize LASSO regularization for feature selection to identify and rank the most relevant observables and provide further insights into the learning strategies used by the constituent-based neural networks. The final model contains only 31 high-level observables and is able to match the performance of the PFN and approximate the performance of the Transformer model to within 2%.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Long-term, non-invasive FTIR detection of low-dose ionizing radiation exposure

Non-invasive methods of detecting radiation exposure show promise to improve upon current approaches to biological dosimetry in ease, speed, and accuracy. Here we developed a pipeline that employs Fourier transform infrared (FTIR) spectroscopy in the mid-infrared spectrum to identify a signature of low dose ionizing radiation exposure in mouse ear pinnae over time. Mice exposed to 0.1 to 2 Gy total body irradiation were repeatedly measured by FTIR at the stratum corneum of the ear pinnae. We found significant discriminative power for all doses and time-points out to 90 days after exposure. Classification accuracy was maximized when testing 14 days after exposure (specificity > 0.9 with a sensitivity threshold of 0.9) and dropped by roughly 30% sensitivity at 90 days. Infrared frequencies point towards biological changes in DNA conformation, lipid oxidation and accumulation and shifts in protein secondary structure. Since only hundreds of samples were used to learn the highly discriminative signature, developing human-relevant diagnostic capabilities is likely feasible and this non-invasive procedure points toward rapid, non-invasive, and reagent-free biodosimetry applications at population scales.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2026

Simulating fermions with a digital quantum computer

Quantum computers are expected to become a powerful tool for studying physical quantum systems. Consequently, a number of quantum algorithms to determine the physical properties of such systems have been developed. Although qubit-based quantum computers are naturally suited to the study of spin-1/2 systems, systems containing other degrees of freedom must first be encoded into qubits. Transformations to and from fermionic degrees of freedom have long been an important tool in physics and chemistry, which is now finding another application in the simulation of fermionic systems on quantum computers based on qubits. In this work, we discuss methods for encoding fermionic degrees of freedom into qubits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

A Voltage Injection-Based Current Harmonics Suppression Strategy for Six-Phase PMSM With Nonsinusoidal Back EMF

This article presents a voltage injection-based current harmonic minimization method for wide bandgap device-based six-phase permanent magnet synchronous machine (PMSM) drives. The presence of low-order harmonics is common in the back EMF of PMSMs with low slot and high pole numbers. The back EMF harmonics generate low-order harmonic currents that increase torque ripples. Furthermore, zero-sequence current (ZSC) is also observed if all six phases are connected to a common neutral point. ZSC does not affect the torque ripple but reduces the system efficiency. A special technique is required to reduce ZSC for variable speed drives as direct ac component to synchronous reference frame (SRF) dc transformation is not possible for ZSC. In this article, a voltage injection-based current harmonics reduction method is proposed. The injected harmonic voltage coefficients are calculated from the harmonic estimations of back EMF and updated using closed-loop harmonic compensators to improve steady-state performance. For ZSC reduction, third harmonics power controller, and for 6n ± 1 (n = 1, 2,...) order harmonic current reduction, SRF current controllers are presented in this article. Furthermore, the current harmonics minimization method is experimentally verified using a GaN-based three-level active neutral point clamped inverter-driven six-phase PMSM.

42 ENGINEERING↗
DOE OSTI2024

Preliminary Study of Iodine Gas Removal in Sodium Pools

Potential iodine gas release from failed fuel pins is a critical factor in the source term analysis of oxide fuel-loaded sodium fast reactors (SFRs). The accumulated iodine-containing gas mixtures inside pin plenums are expected to be ejected during pin failures and rise through sodium pool, with potential release of gaseous iodine to the cover gas region. Due to its potential radiological impacts, a proper assessment of iodine behavior is necessary for an accurate source term assessment. Throughout the bubble rise trajectory in the sodium pool, iodine gas is continuously removed or transformed at the bubble interface by diffusion, as the combining reaction between the iodine and sodium to form sodium iodide (NaI) is a chemically preferred process. As the final amount of iodine released from the facility is strongly influenced by the removal phenomenon inside the sodium pool, experiments were previously performed by PNC (Power Reactor and Nuclear Fuel Development Corporation) to provide insight into this phenomenon. To assess the accuracy of present methods for predicting iodine gas removal within sodium pools, several candidate approaches, available in source term analysis codes, have been summarized and evaluated in this study. Spherical cap bubbles and spherical bubbles are considered in accordance with the methods adopted in each approach, and different forms of correlations for major parameters have been implemented in accordance with the original adoptions. Based on the summarized results, important aspects to be considered have been derived.

Decontamination↗
DOE OSTI2023

Ultrabroadband flying-focus using an axiparabola-echelon pair

Flying-focus pulses promise to revolutionize laser-driven secondary sources by decoupling the trajectory of the peak intensity from the native group velocity of the medium over distances much longer than a Rayleigh range. Previous demonstrations of the flying focus have either produced an uncontrolled trajectory or a trajectory that is engineered using chromatic methods that limit the duration of the peak intensity to picosecond scales. Here we demonstrate a controllable ultrabroadband flying focus using a nearly achromatic axiparabola-echelon pair. Spectral interferometry using an ultrabroadband superluminescent diode was used to measure designed super- and subluminal flying-focus trajectories and the effective temporal pulse duration as inferred from the measured spectral phase. The measurements demonstrate that a nearly transform- and diffraction-limited moving focus can be created over a centimeter-scale—an extended focal region more than 50 Rayleigh ranges in length. This ultrabroadband flying-focus and the novel axiparabola-echelon configuration used to produce it are ideally suited for applications and scalable to >100 TW peak powers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem

The widespread adoption of electric vehicles (EVs) in recent years has necessitated the development of effective charging infrastructures. However, charging infrastructure expansion is a multifaceted problem that requires careful consideration of the existing infrastructure, spatiotemporal distribution of charging demands, power-grid capacity, and budget constraints. Here, to approach this complex problem, we present CHARGE-MAP, a data-driven simulation-optimization framework, focused on ensuring meaningful charging experience for individual EV owners. CHARGE-MAP integrates three modules: an agent-based simulation module that estimates spatiotemporal distribution of charging demands by modeling EV adopter mobility and charging behavior; an optimization module that determines optimal new charging station/charger locations and capacities, while minimizing expected detour distances and wait-times with a limited number of new stations; and a power module that determines how to connect the stations to the power grid while maintaining its stability. Using the state of Virginia (consisting of 95 counties and 38 independent cities) as a case study, our results show that CHARGE-MAP can meet the demand of ~198,600 predicted EVs with 1,305 new public charging stations and 2,164 new chargers. It reduces average detour distances for charging by 66% and wait-times at stations by 72% compared to the existing infrastructure. Furthermore, transformer capacity requirement analysis reveals that only 1.8% of residential transformers require upgrades, while over 80% of commercial charging locations can be supported with modest transformer infrastructure (25 to 50 kVA). This indicates that targeted investments can facilitate cost-effective EV integration. Consequently, CHARGE-MAP provides policymakers and urban planners with crucial data-driven insights for effective EV charging infrastructure expansion. Sign up for PNAS alerts.

charging infrastructure↗
DOE OSTI2021

PMU-data-driven Event Classification in Power Transmission Grids

This paper presents an event classification in transmission grids. The convolutional neural network (CNN)-based classifier is proposed to capture the temporal similarity of time-synchronized data stream from phasor measurement units (PMUs). The proposed CNN is trained using Bayesian optimization to search for the best hyperparameters. The effectiveness of the proposed event classification is validated through the real-world dataset from the U.S. transmission grids. This dataset includes line outage, transformer outage, frequency, and oscillation events. The validation process also includes different PMU outputs, such as voltage magnitude, phase angle, current magnitude, frequency, and rate of change of frequency (ROCOF). The results show that ROCOF gives the best classification performance compared to other PMU outputs. In addition, it is shown that the classifier trained with a larger dataset has higher accuracy. Moreover, the superiority of the proposed method is validated through comparison with other state-of-the-art classification methods.

Niazazari, Iman↗
DOE OSTI2026

Spatiotemporal Learning in Power Modules: Wavelet-Enhanced Forecasting of Thermomechanical Degradation

Detecting internal defects in power electronics packages is critical for their performance and reliability, especially under extreme operating conditions, as these defects can lead to catastrophic failure if not properly addressed. Confocal scanning acoustic microscopy (C-SAM) plays a key role in the nondestructive evaluation of bond layer degradation within a power electronics package by detecting defects such as delamination, voids, and cracks. However, accurately quantifying and predicting these defects from C-SAM images remains a significant challenge due to the low noise-to-signal ratio, which typically arises from both imaging process and bond patterns itself. In this paper, we explore machine learning strategies for processing C-SAM images and providing predictive models of defect growth. We use C-SAM images of sintered copper and sintered silver samples, which are obtained under accelerated thermal experiments, as the representative dataset for our study. We investigate the effect of Fourier transforms and wavelet transforms on these datasets to remove high-frequency noise and address noise across multiple scales with histogram equalization to enhance the contrast and improve the visibility of defects. As a result, defect boundaries can be clearly distinguished, enabling more accurate tracking of their growth over time. We then employ different time-series forecasting algorithms on the denoised images to formulate an image-based lifetime prediction model. Statistical models and deep-learning techniques are trained on images obtained in the early stages of thermal shock, and defect growth in the later stages is predicted. Our work serves as a preliminary attempt to improve the accuracy of lifetime prediction models of power electronics packages, which is critical under extreme operating environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Transformational Solid Oxide Fuel Cell (SOFC) Technology

This project was conducted under the Co-operative Agreement No. DE-FE0027584 with the US Department Energy to developed advanced Solid Oxide Fuel Cell (SOFC) Technologies. The overall objective of this project was to advance SOFC technology at the cell and stack level to enhance cell robustness and durability, increase performance, and reduce balance-of-plant (BOP) requirements. By reducing system complexity combined with the increases in power density and efficiency, the ultimate goal of the project was to increased reliability and to reduce capital and operating costs of installed systems. The project was focused on pathways that will reduce the cost of the SOFC cell and stack, including the following areas: Robust, redox tolerant cell technology Lower cost cell manufacturing through advances in cell design, which will reduce the amount of material, energy and time used in the fabrication of SOFCs High performance, low temperature electrolyte based on improvement of established materials Innovative SOFC stack architecture which truly integrates Balance of Plant functionality into the stack level design Thermal management of the fuel cell stack for increased durability and expanded window of operation Novel stack design amenable for use in sub-MW to multi-MW-scale power plants and having low replacement cost The incorporation of balance-of-plant (BOP) equipment into the stack platform increased the economic viability of smaller scale systems. The project objectives were met by a multi-prong approach, including new cell design complemented with modifications to existing cell technology, as well as a new stack design incorporating components typically included in the BOP, such as heat exchangers, oxidizer, fuel reformers, and recycle systems. The project culminated with demonstration of a stack test validating the viability of the cell and stack improvements. A cost model was also developed to estimate costs for the advanced stack technology at high volume manufacturing levels. The net outcome of the project is SOFC cell and stack technology with costs significantly below current DOE targets without compromising and, in some cases, improving on the performance and degradation rate demonstrated with the current state-of-the-art stack design. The results of this project advanced the reliability, robustness, and endurance of low-cost SOFC technology that ultimately are ready to be deployed in coal power systems with greater than 60 percent efficiency (based on higher heating value of fuel) and the capability for ≥97% CO 2 capture at a cost-of-electricity that is approximately 40 percent below presently available Integrated Gasification Combined Cycle systems.

03 NATURAL GAS↗
DOE OSTI2021

Promoting Atomically Dispersed MnN 4 Sites via Sulfur Doping for Oxygen Reduction: Unveiling Intrinsic Activity and Degradation in Fuel Cells

Carbon supported and nitrogen coordinated single Mn site catalysts (Mn-N-C) catalysts are the most desirable platinum group metal (PGM)-free cathode catalysts for proton exchange membrane fuel cells (PEMFCs) due to their insignificant Fenton reactions (vs. Fe), earth abundances (vs. Co) and encouraging activity and stability. However, current Mn-N-C catalysts suffer from high over-potential due to low intrinsic activity and less dense MnN 4 sites. Herein, we present sulfur-doped Mn-N-C catalyst (Mn-N-C-S) synthesized through an effective adsorption-pyrolysis process. Using electron microscopy and X-ray absorption spectroscopy (XAS) techniques, we verify the uniform dispersion of MnN 4 sites and confirm the effect of S doping on the Mn-N coordination. The Mn-N-C-S catalyst exhibits a favorable oxygen reduction reaction (ORR) activity in acidic media relative to the S-free Mn-N-C catalyst. The corresponding membrane electrode assembly (MEA) generated a remarkable performance with a peak power density of 500 mW cm -2 under a realistic H 2 /air environment. The constant voltage tests of fuel cells confirm the much-enhanced stability of the Mn-N-C-S catalyst compared to the Fe-N-C and Fe-N-C-S catalysts. The electron microscopy and Fourier transform XAS analysis provide insights into catalyst degradation associated with Mn oxidation and agglomeration. The theoretical calculation elucidates that the promoted ORR activity is mainly attributed to the spatial effect stemmed from the repulsive interaction between the ORR intermediates and adjacent S do-pants.

25 ENERGY STORAGE↗
DOE OSTI2026

Scalable, low-cost ink-based processing of high-performance silver selenide thermoelectrics

The growing global energy demand and its accelerating contribution to climate change emphasize the urgent need for sustainable energy conversion/harvesting technologies. Thermoelectric (TE) devices offer a compelling route to directly convert waste heat into electricity and enable solid-state cooling without moving parts or harmful refrigerants. Achieving their full potential requires not only higher TE performance (zT) but also scalable, low-cost manufacturing processes. Here, we introduce a transformative ink-based processing approach for scalable manufacturing of high-performance silver selenide-based TE materials and devices. Using a simple, high-throughput ink-mixing and blade coating strategy, our Ag 2 Se-based materials under the optimized composition and processing conditions yield an ultrahigh room-temperature power factor of 2.8 mW m −1 K −2 , over 100% higher than baseline samples and a reproducible figure of merit zT of 1 at room temperature. A thermoelectric generator (TEG) achieves a very competitive power density of 112 mW cm −2 at a 90 °C temperature difference between the hot and cold sides of the device, which is among the highest reported for silver selenide-based TE devices to date. This facile, scalable ink-based processing establishes a practical pathway toward industrial-scale manufacturing and widespread adoption of thermoelectric devices, advancing sustainable energy technologies.

Bappy, Md. Omarsany [University of Notre Dame, IN ↗
DOE OSTI2023

KG-Hub—building and exchanging biological knowledge graphs

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract–transform–load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial–environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Infrared Light-Emitting Devices from Antenna-Coupled Luttinger Liquid Plasmons In Carbon Nanotubes

Electrically driven light-emitting devices provide highly energy-efficient lighting at visible wavelengths, and they have transformed photonic and electronic lighting applications. Efficient infrared light-emitting devices, however, have been challenging because band gap emission from semiconductors becomes inefficient in the mid-infrared to far-infrared spectral range. Here, in this paper, we investigate infrared light-emitting devices (IRLEDs) based on Luttinger liquid (LL) plasmons in one-dimensional (1D) metallic carbon nanotubes. Elementary excitations in LL are characterized by collective charge and spin excitations, i.e., plasmons and spinons. Consequently, electrons injected into the nanotubes transform efficiently into LL plasmons, a hybrid excitation of electromagnetic fields and electrons. We design nanoantennas coupled to the carbon nanotube to radiate LL plasmons into the far field. LL-based IRLEDs can be designed to selectively emit at wavelengths across the far- and mid-infrared spectra. An electrical-to-optical power conversion efficiency up to 3.2% may be achieved. Such efficient and narrowband LL-based IRLEDs can enable novel infrared nanophotonic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Characterization and Optimization of the Fitting of Quantum Correlation Functions

This case study presents a characterization and optimization of an application code for extracting parton distribution functions from high energy electron-proton scattering data. Profiling this application code reveals that the phase-space density computation accounts for 93% of the overall execution time for a single iteration on a single core. When executing multiple iterations in parallel on a multicore system, the application spends 78% of its overall execution time idling due to load imbalance. We address these issues by first transforming the application code from Python to C++ and then tackling the application load imbalance via a hybrid scheduling strategy that combines dynamic and static scheduling. These techniques result in a 62% reduction in CPU idle time and a 2.46x speedup in overall execution time per node. In addition, the typically enabled power-management mechanisms in supercomputers (e.g., AMD Turbo Core, Intel Turbo Boost, and RAPL) can significantly impact intra-node scalability when more than 50% of the CPU cores are used. This finding underscores the importance of understanding system interactions with power management, as they can adversely impact application performance, and highlights the necessity of intra-node scaling tests to identify performance degradation that inter-node scaling tests might otherwise overlook.

Chuang, Pi-Yueh [Virginia Tech,Dept. of Computer S↗