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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

The ocean model for E3SM global applications: Omega version 0.1.0 – a new high-performance computing code for exascale architectures

This paper introduces Omega, the Ocean Model for E3SM Global Applications. Omega is a new ocean model designed to run efficiently on high performance computing (HPC) platforms, including exascale heterogeneous architectures with accelerators, such as Graphics Processing Units (GPUs). Omega is written in C and uses the Kokkos performance portability library. These were chosen because they are well-supported and will help future-proof Omega for upcoming HPC architectures. Omega will eventually replace the Model for Prediction Across Scales-Ocean (MPAS-Ocean) in the US Department of Energy's (DOE's) Energy Exascale Earth System Model (E3SM). Omega runs on unstructured horizontal meshes with variable-resolution capability and implements the same horizontal discretization as MPAS-Ocean. This work documents the design and performance of Omega Version 0.1.0 (Omega-V0), which solves the shallow water equations with passive tracers and is the first step towards the full primitive equation ocean model. On Central Processing Units (CPUs), Omega-V0 is 1.4 times faster than MPAS-Ocean with the same configuration. Omega-V0 is more efficient on GPUs than CPUs on a per-watt basis – by a factor of 5.3 on Frontier and 3.6 on Aurora, two of the world's fastest exascale computers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Transforming Energy Through Computational Excellence: High-Performance Computing for Energy Innovation

The challenges associated with energy efficiency of manufacturing and advanced materials often cannot be addressed through experimentation alone, whether because of scale, complexity, or practicality. High-performance computing (HPC) enables fast tackling of these challenges in the manufacturing sector - vital to achieving net-zero carbon emissions by 2050. The National Renewable Energy Laboratory (NREL) and industry partners leverage HPC to apply advanced modeling, simulation, and data analysis to improve manufacturing efficiency, explore new materials for energy applications, and develop technologies to manage carbon across the life cycle. From improving additive manufacturing processes to increasing the energy efficiency of jet-engine components, advanced computing can help manage emissions produced by manufacturing in a wide variety of ways.

advanced materials↗
DOE OSTI2020

High performance Nb/TiNi nanocomposites produced by packaged accumulative roll bonding

Taking full advantage of intrinsic high strength of nano-reinforcements has been proven difficult in a composite until the concept of strain matching was experimental verified in a nanowire Nb/TiNi in-situ composite, which was obtained via solidifying eutectic Nb/TiNi and subsequent severe wire drawing. However, the volume fraction of the nano-reinforcement was dictated by eutectic reaction, which severely limited the development of high-performance composites. Here, the martensitic transformation nanocomposites were successfully acquired with increased volume fraction and changeable morphology of the nano-reinforcement through hot packaged ARB (PARB) and subsequent wire drawing. The nano-reinforcement with lamellar configuration in the composite could exhibit a large lattice strain of similar to 3.8%, which demonstrated the applicability and effectiveness of the strain matching principle in nano-lamellar composite. The obtained composites show a yield strength over 2.0 GPa and a quasi-linear elastic strain as larger as similar to 6.5% with adjustable apparent elastic modulus from 30 to 50 GPa.

42 ENGINEERING↗
DOE OSTI2023

Eco-Friendly High-Performance Carbon Building Material Development from Coal

The development of eco-friendly high-performance building materials based on coal-derived materials is greatly beneficial for promoting environmental sustainability and the coal industry. This project focused on advancing technology to create innovative and scalable construction materials from two domestic sources: (1) coal-derived pyrolyzed char (PC), and (2) solvent-extracted coal deposit, extracts, and residue (CDER). It aimed to simultaneously develop and produce two types of coal-derived building products: (1) char-based concrete bricks (CCB) for wall construction in buildings, and (2) carbon-based structural units (CSU) intended as alternatives to traditional wood, concrete, or steel framing structures. The project team has successfully developed methods for manufacturing CCB samples. Extensive experiments were conducted to assess various manufacturing techniques for these samples. This included measuring their physical properties such as thermal conductivity (ranging from 0.26 to 0.38 W/mK) and bulk density (ranging from 0.85 to 1.15 g/cm 3 ), along with their mechanical properties (e.g., compressive strength ranging from 14 to 16 MPa at 28 days) for samples with a PC composition of 70%. Quality assurance and quality control tests, focusing on density and compressive strength, were also performed on samples selected randomly from pilot-scale production, and cured for a long term (over three months). The average long-term compressive strength of two randomly selected CCB samples was measured as 15.1 MPa, which was higher than the required target strength of 14 MPa. This indicates that the manufactured CCB samples from the preliminary pilot scale production run have good quality control and long-term strength performance.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Experimental Evaluation of a High-Performance Cold-Climate Heat Pump Using Tandem Vapor-Injection Compressors

A high-performance cold-climate heat pump (CCHP) was developed and experimentally validated through comprehensive laboratory and field testing. The system utilized two equal-size tandem vapor-injection (VI) compressors with an inter-stage flash tank to improve efficiency and capacity retention under subfreezing conditions. Laboratory testing was conducted on a 3-ton prototype in a controlled environmental chamber equipped with calibrated thermocouples, refrigerant-side mass flow and pressure transducers, and precision airflow measurement for energy balance verification. The prototype achieved heating coefficients of performance (COPs) of 4.4 at 47 °F, 3.1 at 17 °F, and 2.0 at –13 °F, while maintaining 88% of its rated heating capacity at –13°F. These results confirm strong low-temperature performance and indicate the potential for significant reductions in electric resistance backup use. A field prototype was installed and monitored in a residential building in Fairbanks, Alaska, during a heating season. Instrumentation included real-time power, temperature, and refrigerant state measurements to evaluate performance under dynamic outdoor conditions. The heat pump operated reliably down to –30 °F, delivering 75% of its rated heating capacity with a COP of 1.8, while maintaining stable operation, effective defrost control, and indoor comfort without auxiliary heating. The combined laboratory and field results demonstrate that the tandem VI compressor configuration provides a practical and energy-efficient approach for residential heat pumps designed for cold and very cold climate regions.

Hu, Yifeng [ORNL] (ORCID:0000000242875185)↗
DOE OSTI2022

ExaTN: Scalable GPU-Accelerated High-Performance Processing of General Tensor Networks at Exascale

We present ExaTN (Exascale Tensor Networks), a scalable GPU-accelerated C++ library which can express and process tensor networks on shared- as well as distributed-memory high-performance computing platforms, including those equipped with GPU accelerators. Specifically, ExaTN provides the ability to build, transform, and numerically evaluate tensor networks with arbitrary graph structures and complexity. It also provides algorithmic primitives for the optimization of tensor factors inside a given tensor network in order to find an extremum of a chosen tensor network functional, which is one of the key numerical procedures in quantum many-body theory and quantum-inspired machine learning. Numerical primitives exposed by ExaTN provide the foundation for composing rather complex tensor network algorithms. We enumerate multiple application domains which can benefit from the capabilities of our library, including condensed matter physics, quantum chemistry, quantum circuit simulations, as well as quantum and classical machine learning, for some of which we provide preliminary demonstrations and performance benchmarks just to emphasize a broad utility of our library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High Performance Ionene Architectures for Membrane Separations (Final Technical Report)

Our DOE BES research grant (DE-SC0018181, 8/15/17-2/28/22) focused on the synthesis, characterization, and performance of a new class of polymers known as high-performance (HP)-ionenes as gas separation membranes, including associated computational studies. This research resulted in 11 peer-reviewed journal publications with an additional ~7 manuscripts currently in review or under preparation for submission. This DOE BES funding has also resulted in 3 U.S. patent applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High Performance Pd/4H-SiC Epitaxial Schottky Barrier Radiation Detectors for Harsh Environment Applications

Although many refractory metals have been investigated as the choice of contact metal in 4H-SiC devices, palladium (Pd) as a Schottky barrier contact for 4H-SiC radiation detectors for harsh environment applications has not been investigated adequately. Pd is a refractory metal with high material weight-to-thickness ratio and a work function as high as nickel, one of the conventional metal contacts for high performing 4H-SiC Schottky barrier detectors (SBDs). In this article, Pd/4H-SiC epitaxial SBDs have been demonstrated for the first time as a superior self-biased (0 V applied bias) radiation detector when compared to benchmark Ni/4H-SiC SBDs. The Pd/4H-SiC SBD radiation detectors showed a very high energy resolution of 1.9% and 0.49% under self- and optimized bias, respectively, for 5486 keV alpha particles. The SBDs demonstrated a built-in voltage (V bi ) of 2.03 V and a hole diffusion length (L d ) of 30.8 µm. Such high V bi and L d led to an excellent charge collection efficiency of 76% in the self-biased mode. Capacitance mode deep level transient spectroscopy (DLTS) results revealed that the “lifetime-killer” Z 1/2 trap centers were present in the 4H-SiC epilayer. Another deep level trap was located at 1.09 eV below the conduction band minimum and resembles the EH5 trap with a concentration of 1.98 × 10 11 cm –3 and capture cross-section 1.7 × 10 –17 cm –2 ; however, the detector performance was found to be limited by charge trapping in the Z 1/2 center. The results presented in this article revealed the unexplored potential of a wide bandgap semiconductor, SiC, as high-efficiency self-biased radiation detectors. Such high performance self-biased radiation detectors are poised to address the longstanding problem of designing self-powered sensor devices for harsh environment applications e.g., advanced nuclear reactors and deep space missions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Controlling Surface Phase Transition and Chemical Reactivity of O3-Layered Metal Oxide Cathodes for High-Performance Na-Ion Batteries

O3-layered metal oxides are promising cathode materials for high-energy Na-ion batteries (SIBs); however, they suffer from fast capacity fade. In this work, we develop a high-performance O3-NaNi 0.68 Mn 0.22 Co 0.10 O 2 cathode for SIBs toward practical applications by suppressing the formation of a rock salt layer at the cathode surface with an advanced electrolyte. The cathode can deliver a high specific capacity of ~196 mAh g –1 and demonstrates >80% capacity retention over 1000 cycles. NaNi 0.68 Mn 0.22 Co 0.10 O 2 –hard carbon full-cells with practical loading (>2.5 mAh cm –2 ) and lean electrolyte (~40 μL) demonstrate ~82% capacity retention after 450 cycles. A 60 mAh single-layer pouch cell has also been fabricated and demonstrated stable performance. This work represents a significant leap in SIB development and brings new insights to the development of advanced layered metal oxide cathodes for alkaline-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Software stewardship and advancement of a high-performance computing scientific application: QMCPACK

Here, we provide an overview of the software engineering efforts and their impact in QMCPACK, a production-level ab-initio Quantum Monte Carlo open-source code targeting high-performance computing (HPC) systems. Aspects included are: (i) strategic expansion of continuous integration (CI) targeting CPUs, using GitHub Actions own runners, and NVIDIA and AMD GPUs used in pre-exascale systems, (ii) incremental reduction of memory leaks using sanitizers, (iii) incorporation of Docker containers for CI and reproducibility, and (iv) refactoring efforts to improve maintainability, testing coverage, and memory lifetime management. We quantify the value of these improvements by providing metrics to illustrate the shift towards a predictive, rather than reactive, maintenance approach. Our goal, in documenting the impact of these efforts on QMCPACK, is to contribute to the body of knowledge on the importance of research software engineering (RSE) for the stewardship and advancement of community HPC codes to enable scientific discovery at scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

High-performance finite elements with MFEM

The MFEM (Modular Finite Element Methods) library is a high-performance C++ library for finite element discretizations. MFEM supports numerous types of finite element methods and is the discretization engine powering many computational physics and engineering applications across a number of domains. Furthermore, this paper describes some of the recent research and development in MFEM, focusing on performance portability across leadership-class supercomputing facilities, including exascale supercomputers, as well as new capabilities and functionality, enabling a wider range of applications. Much of this work was undertaken as part of the Department of Energy’s Exascale Computing Project (ECP) in collaboration with the Center for Efficient Exascale Discretizations (CEED).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

High Performance Computing Peak Shaving for Microreactor Operation

There are multiple nuclear microreactors currently under development that are designed to provide autonomous power for as many as ten or more years without refueling and are designed to power high performance computing (HPC) datacenters. But the load-follow speeds for a nuclear microreactor will be much slower than grid power and slower than the power variance typical of a HPC system. HPC datacenters experience peak power load variance driven by several factors ranging from the operation of cooling systems to remove heat from the servers to supporting a wide range of user application workflows and architectures each with different power signatures. One mechanism to support the limited load-follow of a microreactor is peak shaving where an energy storage mechanism is used to shed peak load and reduce significant power variance. This work explores peak electrical load shaving using uninterruptible power supply (UPS) systems designed for HPC support in the context of peak shaving when operating using a nuclear microreactor with a load-follow limited to 10% of load per minute. Using a self contained HPC datacenter complete with stand-alone cooling system and provisioned with an x86 cluster, an ARM cluster, and a graphics processing unit (GPU) cluster, peak shaving for microreactor operation using the UPS battery backup is explored while running two classes of typical HPC user applications. HPC architecture suitability for microreactor operation under this type of peak shaving is examined.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Fully Exploited Oxygen Redox Reaction by the Inter-Diffused Cations in Co-Free Li-Rich Materials for High Performance Li-Ion Batteries

To meet the growing demand for global electrical energy storage, high-energy-density electrode materials are required for Li-ion batteries. To overcome the limit of the theoretical energy density in conventional electrode materials based solely on the transition metal redox reaction, the oxygen redox reaction in electrode materials has become an essential component because it can further increase the energy density by providing additional available electrons. However, the increase in the contribution of the oxygen redox reaction in a material is still limited due to the lack of understanding its controlled parameters. Here, it is first proposed that Li-transition metals (TMs) inter-diffusion between the phases in Li-rich materials can be a key parameter for controlling the oxygen redox reaction in Li-rich materials. The resulting Li-rich materials can achieve fully exploited oxygen redox reaction and thereby can deliver the highest reversible capacity leading to the highest energy density, ≈1100 Wh kg -1 among Co-free Li-rich materials. The strategy of controlling Li/transition metals (TMs) inter-diffusion between the phases in Li-rich materials will provide feasible way for further achieving high-energy-density electrode materials via enhancing the oxygen redox reaction for high-performance Li-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2022

High Performance SOFCs with a Superior Stability for Reliable and Durable Power Systems

Next generation of fuel cells, electrolyzers, and batteries requires higher power, faster kinetics, and larger energy density, which necessitate the use of compositionally complex oxides to achieve multifunctionalities and activity. These compositionally complex oxides may change their phases and structures during an electrochemical process – a so-called “electrochemically driven phase transformation”. The origin for such a phase change has remained obscure. More importantly, there is a need to develop high performance solid oxide fuel cells with an enhanced stability. In this work, the La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3 (LSCF) cathode surface is modified by infiltration of Pr 6 O 11 and the power density at 0.8V and 750 °C is improved by 21%. In addition, by replacing the traditional barrier layer Gd 0.2 Ce 0.8 O 1.9 with mixed conducting Pr 0.1 Gd 0.1 Ce 0.8 O 1.9 , the power density increases by 38%. The different mechanism of promotions was investigated by electrochemical impedance spectroscopy. The ohmic resistance is dramatically reduced by applying the PGCO interlayer, and the distribution of relaxation time was used to analyze the mechanism for which the polarization resistance was decreased attributing to the mixed conduction nature in PrO x . An increase of power density at 0.8 V of 0.358 W/cm 2 (71%) is achieved with the implementation of both surface modification and buffer layer engineering. An experimental study and a theoretical analysis were then carried out on phase evolution in praseodymium nickelates. Nickelate-based electrodes show up to 60× greater phase transformation during operation when compared to thermally annealed ones. Theoretical analysis suggests that the presence of a reduced oxygen partial pressure at the interface between the oxygen electrode and the electrolyte is the origin for the phase change in an oxygen electrode. Guided by the theory, an addition of the electronic conduction in the interface layer leads to the significant suppression of phase change, while improving cell performance and performance stability. When an oxygen electrode is under polarization, the oxygen partial pressure at the interface between the oxygen electrode and the electrolyte is lower than that of incoming oxidant. Under a high polarization, the environment at the aforementioned interface may lead to phase transformation of the oxygen electrode. The local oxygen partial pressure is determined by the transport properties at the interfaces. An addition of the electronic conduction in the interface layer, for instance using (Pr,Gd)-doped ceria to replace Gd-doped ceria, results in improved cell performance and performance stability, while the phase transformation is significantly suppressed. This work provides a fundamental understanding of the origin for phase transformation in oxygen electrodes during operation and use this knowledge to develop a high-performance electrode that exhibits improved performance stability.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

In-depth analysis on parallel processing patterns for high-performance Dataframes

The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more complexities to data engineering applications, which are now integrated into data processing pipelines to process terabytes of data. Typically, a significant amount of time is spent on data preprocessing in these pipelines, and hence improving its efficiency directly impacts the overall pipeline performance. The community has recently embraced the concept of Dataframes as the de-facto data structure for data representation and manipulation. However, the most widely used serial Dataframes today (R, pandas) experience performance limitations while working on even moderately large data sets. We believe that there is plenty of room for improvement by taking a look at this problem from a high-performance computing point of view. In a prior publication, we presented a set of parallel processing patterns for distributed dataframe operators and the reference runtime implementation, Cylon. In this paper, we are expanding on the initial concept by introducing a cost model for evaluating the said patterns. Furthermore, we evaluate the performance of Cylon on the ORNL Summit supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Stabilizing and understanding the interface between nickel-rich cathode and PEO-based electrolyte by lithium niobium oxide coating for high-performance all-solid-state batteries

The pursuit of high energy density and safe lithium ion batteries (LIBs) is the urgent goal for the development of next-generation electric vehicles (EVs). All-solid-state batteries (ASSBs) with the combination of poly(ethylene oxide) (PEO)-based solid polymer electrolyte (SPE) and Ni-rich lithium nickel manganese cobalt oxide LiNi0.8Mn0.1Co0.1O2 (NMC811) cathode are promising candidates for EVs due to their improved energy density and safety. However, the low electrochemical oxidation window of PEO-based SPE and the instability of NMC811 at the charge/discharge process seriously restrict the battery performance. Herein, a high voltage stable solid-state electrolyte layer lithium niobium oxide (LNO) is coated on the NMC811 electrode surface by atomic layer deposition for stabilizing NMC811-PEO solid polymer batteries. Electrochemical tests show that LNO coating can stabilize the NMC811 active materials and mitigate the decomposition of SPE upon the cycling process, rendering a good performance of NMC811-PEO solid polymer battery. Mechanism studies by SEM, STEM, XAS, and XPS disclose that the uncoated NMC811 suffers from chemomechanical degradations along with oxygen release triggering the decomposition of SPE, which results in unstable cathodic electrolyte interphase. With LNO coating, chemomechanical degradations and oxygen release are inhibited and the decomposition of SPE is mitigated. This paper renders a stable and high-performance high-energy-density SSB for high voltage application, which paves the way toward next-generation solid-state LIBs.

25 ENERGY STORAGE↗
DOE OSTI2024

A Framework for Integrating Quantum Simulation and High Performance Computing

Scientific applications are starting to explore the viability of quantum computing. This exploration typically begins with quantum simulations that can run on existing classical platforms, albeit without the performance advantages of real quantum resources. In the context of high-performance computing (HPC), the incorporation of simulation software can often take advantage of the powerful resources to help scale-up the simulation size. The configuration, installation and operation of these quantum simulation packages on HPC resources can often be rather daunting and increases friction for experimentation by scientific application developers. We describe a framework to help streamline access to quantum simulation software running on HPC resources. This includes an interface for circuit-based quantum computing tasks, as well as the necessary resource management infrastructure to make effective use of the underlying HPC resources. The primary contributions of this work include a classification of different usage models for quantum simulation in an HPC context, a review of the software architecture for our approach and a detailed description of the prototype implementation to experiment with these ideas using two different simulators (TNQVM & NWQ-Sim). We include initial experimental results running on the Frontier supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) using a synthetic workload generated via the SupermarQ quantum benchmarking framework.

Shehata, Amir [ORNL] (ORCID:0000000224531426)↗
DOE OSTI2022

A High-Performance Design for Hierarchical Parallelism in the QMCPACK Monte Carlo code

We introduce a new high-performance design for parallelism within the Quantum Monte Carlo code QMCPACK. We demonstrate that the new design is better able to exploit the hierarchical parallelism of heterogeneous architectures compared to the previous GPU implementation. The new version is able to achieve higher GPU occupancy via the new concept of crowds of Monte Carlo walkers, and by enabling more host CPU threads to effectively offload to the GPU. The higher performance is expected to be achieved independent of the underlying hardware, significantly improving developer productivity and reducing code maintenance costs. Scientific productivity is also improved with full support for fallback to CPU execution when GPU implementations are not available or CPU execution is more optimal.

Luo, Ye↗