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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Evaluation of Portable Acceleration Solutions for LArTPC Simulation Using Wire-Cell Toolkit

The Liquid Argon Time Projection Chamber (LArTPC) technology plays an essential role in many current and future neutrino experiments. Accurate and fast simulation is critical to developing efficient analysis algorithms and precise physics model projections. The speed of simulation becomes more important as Deep Learning algorithms are getting more widely used in LArTPC analysis and their training requires a large simulated dataset. Heterogeneous computing is an efficient way to delegate computationally intensive tasks to specialized hardware. However, as the landscape of compute accelerators quickly evolves, it becomes increasingly difficult to manually adapt the code to the latest hardware or software environments. A solution which is portable to multiple hardware architectures without substantially compromising performance would thus be very beneficial, especially for long-term projects such as the LArTPC simulations. In search of a portable, scalable and maintainable software solution for LArTPC simulations, we have started to explore high-level portable programming frameworks that support several hardware backends. In this paper, we present our experience porting the LArTPC simulation code in the Wire-Cell Toolkit to NVIDIA GPUs, first with the CUDA programming model and then with a portable library called Kokkos. Preliminary performance results on NVIDIA V100 GPUs and multi-core CPUs are presented, followed by a discussion of the factors affiecting the performance and plans for future improvements.

Yu, Haiwang↗
DOE OSTI2023

Performance Analysis and Optimal Node-aware Communication for Enlarged Conjugate Gradient Methods

Krylov methods are a key way of solving large sparse linear systems of equations but suffer from poor strong scalability on distributed memory machines. Furthermore, this is due to high synchronization costs from large numbers of collective communication calls alongside a low computational workload. Enlarged Krylov methods address this issue by decreasing the total iterations to convergence, an artifact of splitting the initial residual and resulting in operations on block vectors. In this article, we present a performance study of an enlarged Krylov method, Enlarged Conjugate Gradients (ECG), noting the impact of block vectors on parallel performance at scale. Most notably, we observe the increased overhead of point-to-point communication as a result of denser messages in the sparse matrix-block vector multiplication kernel. Additionally, we present models to analyze expected performance of ECG, as well as motivate design decisions. Most importantly, we introduce a new point-to-point communication approach based on node-aware communication techniques that increases efficiency of the method at scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Toward a Seamless Integration of Computing, Experimental, and Observational Science Facilities: A Blueprint to Accelerate Discovery

The Department of Energy, Office of Science operates world-leading facilities for experimental, observational, and computational science. DOE supercomputing facilities will reach performance at the scale of ExaFLOPs in the coming years, enabling new vistas of scale and precision for large scale simulations and data analysis. Experimental scientific facilities are undergoing similar upgrades that will lead to higher data rates and correspondingly larger computational demands, and will increase the need for near-real-time processing and resilient support for more complex workflows. A transformation of science is underway, with workloads at supercomputing facilities increasingly driven by this explosion of data from instruments and experimental facilities, as well as the accelerating use of Artificial Intelligence (AI) as a tool for scientific discovery. A seamless integration of computing, networking, instruments, and experimental facilities is required to support these emerging workloads and open up a new frontier of U.S. leadership in scientific discovery. We propose to accomplish this by providing frictionless access to the ASCR supercomputing facilities. We describe our vision of combining the power of ASCR supercomputers and networking infrastructure into an integrated scalable fabric, available to end user scientists via interfaces that aim to automate and simplify access to high performance computing systems. This will enable unprecedented computational science capabilities for experimental and observational facilities, and will create new opportunities to combine large simulations and modeling with experimental facility data analysis. This blueprint for creating an integrated network of computational and experimental facilities will provide an enriched discovery environment and open doors for new scientific communities to access the DOE’s world-leading computing and networking capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Characterizing the Performance of Executing Many-tasks on Summit

Many scientific workloads are comprised of many tasks, where each task is an independent simulation or analysis of data. The execution of millions of tasks on heterogeneous HPC platforms requires scalable dynamic resource management and multi-level scheduling. RADICAL-Pilot (RP) -- an implementation of the Pilot abstraction, addresses these challenges and serves as an effective runtime system to execute workloads comprised of many tasks. In this paper, we characterize the performance of executing many tasks using RP when interfaced with JSM and PRRTE on Summit: RP is responsible for resource management and task scheduling on acquired resource; JSM or PRRTE enact the placement of launching of scheduled tasks. Our experiments provide lower bounds on the performance of RP when integrated with JSM and PRRTE. Specifically, for workloads comprised of homogeneous single-core, 15 minutes-long tasks we find that: PRRTE scales better than JSM for > O(1000) tasks; PRRTE overheads are negligible; and PRRTE supports optimizations that lower the impact of overheads and enable resource utilization of 63% when executing O(16K), 1-core tasks over 404 compute nodes.

Matteo, Turilli↗
DOE OSTI2021

Manufacturing scalability implications of materials choice in inorganic solid-state batteries

The pursuit of scalable and manufacturable all-solid-state batteries continues to intensify, motivated by the rapidly increasing demand for safe, dense electrical energy storage. Here, we describe the numerous, often conflicting implications of materials choices that have been made in the search for effective mitigations to the interfacial instabilities plaguing solid-state batteries. Specifically, we show that the manufacturing scalability of solid-state batteries can be governed by at least three principal consequences of materials selection: (1) the availability, scaling capacity, and price volatility of the chosen materials’ constituents, (2) the manufacturing processes needed to integrate the chosen materials into full cells, and (3) the cell performance that may be practically achieved with the chosen materials and processes. While each of these factors is, in isolation, a pivotal determinant of manufacturing scalability, we show that consideration and optimization of their collective effects and trade-offs is necessary to more completely chart a scalable pathway to manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2025

Electroplated Polymer‐Modified Carbon Fiber for Performance‐Enhancing Composite Interfaces

Carbon fiber composite performance relies on the fiber-matrix interface for effective load transfer. To enhance interfacial properties between the fiber and matrix, often carbon fiber surfaces are oxidatively and covalently modified to incorporate chemical functional groups. By contrast, here, noncovalent electrodeposition of functional polyelectrolyte is applied onto conducting carbon fibers from aqueous solutions. A natural polymer, chitosan (CS), is electro-deposited onto the fiber surface, which undergoes multi-scale physical interactions. The bound CS layer with abundant amine functionalities reacts with epoxy moieties within the matrix to improve the interfacial properties. The scalable and energy-efficient electrodeposition eliminates traditional functionalization and sizing requirements of carbon fiber while delivering significantly higher mechanical performance with enhanced consistency. For continuous fiber reinforced composites, compared to conventional fibers, apparent interlaminar shear strength increases by 27%, reaching ≈86 MPa. The short fiber composites with only 2–11 wt.% fibers exhibit ≈20% increase in tensile strength with a peak performance of 120 MPa. Unlike traditionally treated and sized carbon fiber, this approach delivers coated fibers with long shelf-life and allows recovery of both CS in electrolyte form and carbon fiber by continuous electrochemical processing of the modified fibers with inverse polarity; thus, it promotes overall fiber recyclability.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Approaching the Radiative Efficiency Limit in Perovskite Solar Cells with Scalable Defect Passivation and Selective Contacts

This award aimed to enable perovskite solar cells to approach the radiative efficiency limit in scalable manufacturing environments by controlling recombination losses, especially surface recombination losses at electrodes and interfaces. The project combined organic molecular synthesis, perovskite film processing and characterization, and spectroscopic tool development for probing recombination centers. The project ultimately achieved record-low surface recombination velocity (SRV) in mixed cation methylammonium-free perovskite thin films, demonstrated photoluminescence as an effective process metrology tool to optimizing processing of device stacks, and showed that the aminopropyltrimethoxysilane (APTMS) is suitable for passivating the exposed perovskite interface in p-i-n stack devices. The project used combinations of phosphonic acids to modify the transparent conducting oxide and APTMS to passivate the perovskite/electron transport layer interface, thereby demonstrating reduction of SRVs in both partial and full device stacks. The project showed concomitant improvements in device performance, and demonstrated that APTMS passivation was compatible with large area coating of external stakeholder perovskite films using both scalable solution and vapor methods. Notably, the project also supplied surface passivating materials to a number of other US based and SETO-funded teams.

14 SOLAR ENERGY↗
DOE OSTI2021

Concentrated dual-cation electrolyte strategy for aqueous zinc-ion batteries

Rechargeable Zn-ion batteries are highly promising for stationary energy storage because of their low cost and intrinsic safety. However, due to the poor reversibility of Zn anodes and dissolution of oxide cathodes, aqueous Zn-ion batteries encounter rapid performance degradation when operating in conventional low-concentration electrolytes. Herein, we demonstrate that an aqueous Zn2+ electrolyte using a supporting Na salt at a high concentration is efficient to address these issues without sacrificing the power densities, cycling stability, and safety of zinc-ion batteries. We show that the high-concentration solute minimizes the number of free water molecules and the changes in the electronic state of the electrolyte. A combination of experimental and theoretical investigations reveals that a unique interphase, formed on the Zn anode, enables reversible and uniform Zn plating. Utilizing a cathode of sodium vanadate synthesized through a scalable strategy, the Zn–sodium vanadate battery with the concentrated bi-cation electrolyte shows improved cycling stability, decent rate performance, and low self-discharge. This work provides new insights on electrolyte engineering to achieve high-performance aqueous batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles

Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DNN) based algorithmic approaches. However, most DNN models do not provide uncertainty estimates, which are crucial for establishing the trustworthiness of these techniques in downstream decision making tasks and scenarios. In recent years, ensemble-based methods have achieved significant success for the uncertainty quantification in DNNs on a number of benchmark problems. However, their performance on real-world applications remains under-explored. In this work, we present an automated approach to DNN discovery and demonstrate how this may also be utilized for ensemble-based uncertainty quantification. Specifically, we propose the use of a scalable neural and hyperparameter architecture search for discovering an ensemble of DNN models for complex dynamical systems. We highlight how the proposed method not only discovers high-performing neural network ensembles for our tasks, but also quantifies uncertainty seamlessly. This is achieved by using genetic algorithms and Bayesian optimization for sampling the search space of neural network architectures and hyperparameters. Subsequently, a model selection approach is used to identify candidate models for an ensemble set construction. Afterwards, a variance decomposition approach is used to estimate the uncertainty of the predictions from the ensemble. We demonstrate the feasibility of this framework for two tasks — forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature. In conclusion, we demonstrate superior performance from the ensemble in contrast with individual high-performing models and other benchmarks.

Deep ensembles↗
DOE OSTI2022

An Engineered Multifunctional Composite for Passive Sensing, Power Harvesting, and In Situ Damage Identification with Enhanced Mechanical Performance

A multifunctional fiber-reinforced composite with passive self-sensing, energy-harvesting, and damage detection capabilities is presented. In this work, barium titanate piezoelectric microparticles are deposited on basalt fibers by a scalable, low-cost, environmentally friendly continuous feed-through process. The resulting composite derives a superior interlaminar shear strength from the microparticle-modified fiber–matrix interfaces. The composite also demonstrates passive self-sensing capabilities that produce electrical signals proportional to various dynamic loading events. Vibration and strain-controlled experiments are performed on composite beams to quantify the sensitivity and power output as a function of input acceleration and strain. Furthermore, these composite-generated electrical signals are used to identify in situ damage initiation for structural health monitoring to inform composite damage prior to structural failure. In brief, this truly multifunctional composite simultaneously displays a sensitivity of 0.5–2.6 mV g -1 at a resolution of 0.045–0.20g (g = gravitational acceleration), energy harvesting in the range of nW cc -1 , and prediction of early damage by exhibiting 0.017–1.17 mV peaks in voltage–time history profiles while assuring ≈20% improved interlaminar shear strength.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Refactoring the elastic–viscous–plastic solver from the sea ice model CICE v6.5.1 for improved performance

This study focuses on the performance of the elastic–viscous–plastic (EVP) dynamical solver within the sea ice model, CICE v6.5.1. The study has been conducted in two steps. First, the standard EVP solver was extracted from CICE for experiments with refactored versions, which are used for performance testing. Second, one refactored version was integrated and tested in the full CICE model to demonstrate that the new algorithms do not significantly impact the physical results. The study reveals two dominant bottlenecks, namely (1) the number of Message Parsing Interface (MPI) and Open Multi-Processing (OpenMP) synchronization points required for halo exchanges during each time step combined with the irregular domain of active sea ice points and (2) the lack of single-instruction, multiple-data (SIMD) code generation. The standard EVP solver has been refactored based on two generic patterns. The first pattern exposes how general finite differences on masked multi-dimensional arrays can be expressed in order to produce significantly better code generation by changing the memory access pattern from random access to direct access. The second pattern takes an alternative approach to handle static grid properties. The measured single-core performance improvement is more than a factor of 5 compared to the standard implementation. The refactored implementation of strong scales on the Intel® Xeon® Scalable Processors series node until the available bandwidth of the node is used. For the Intel® Xeon® CPU Max series, there is sufficient bandwidth to allow the strong scaling to continue for all the cores on the node, resulting in a single-node improvement factor of 35 over the standard implementation. This study also demonstrates improved performance on GPU processors.

58 GEOSCIENCES↗
DOE OSTI2026

Carbon-Based Quantum Information Science with Symmetry Protected Topological States (Final Report, DOE-BES award DE-SC0023105)

This research program established the scientific foundation for the rational, bottom-up design, synthesis, isolation, and investigation of symmetry-protected topological (SPT) electron spin qubits embedded in graphene nanoribbons (GNRs). The work focused on integrating atomically precise low-dimensional carbon nanostructures with emerging quantum logic architectures, providing a pathway toward scalable quantum materials for next-generation computing and sensing technologies. A central component of the program was the elucidation of fundamental relationships between real-space molecular architecture, local spin density distributions, electronic band dispersion, and energy level alignment in atomically precise GNR systems. These correlations define key operational parameters of SPT qubits and were systematically investigated to establish quantitative benchmarks against established molecular and solid-state spin qubit platforms. Attention was given to properties critical for quantum device performance, e.g. decoherence times, spectral sharpness of energy transitions, and tunable exchange interactions between spin states. The research demonstrated that these parameters can be engineered with atomic precision through scalable bottom-up synthetic strategies. Theory-guided design played a central role in identifying candidate structures hosting topologically protected spin states. Experimental validation was performed using both ensemble measurements and single-molecule characterization. In addition to advances in quantum materials synthesis, the program developed and applied spin-sensitive scanning probe microscopy techniques capable of directly probing quantum states and dynamic processes with atomic-scale spatial resolution. These capabilities enabled direct observation and characterization of quantum structures at the single-atom level. While the research activities were primarily hypothesis-driven fundamental investigations, the program adopted a comprehensive materials-by-design framework aimed at translating scientific discoveries into technological concepts compatible with scalable and intelligent manufacturing approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Enhancing scalability of a matrix-free eigensolver for studying many-body localization

We propose several techniques to enhance the parallel scalability of a matrix-free eigensolver designed for studying many-body localization (MBL) of quantum spin chain models with nearest-neighbor interactions and on-site disorder. This type of problem is computationally challenging because the dimension of the associated Hamiltonian matrix grows exponentially with respect to the number of spins L, and we need to average over different realizations of the random disorder to obtain relevant statistical behavior. For each disorder realization, we need to compute eigenvalues from different regions of the spectrum and their corresponding eigenvectors. In previous work, the interior eigenstates for a single eigenvalue problem are computed via the shift-and-invert Lanczos algorithm. Due to the extremely high memory footprint of the LU factorizations, this technique is not well suited for large L’s. For example, we need thousands of compute nodes on modern high performance computing infrastructures to go beyond L = 24. The matrix-free approach does not suffer from this memory bottleneck, however, its scalability is limited by a computation and communication load imbalance. To reduce this imbalance and to significantly enhance the scalability of the matrix-free eigensolver, we reorder the matrix and leverage the consistent space runtime, CSPACER. We also show its efficiency in managing irregular communication patterns at scale compared to optimized MPI non-blocking two-sided and one-sided RMA implementation variants. This effort enables us to study MBL for spin chains with a larger number of spins. The efficiency and effectiveness of the proposed algorithm is demonstrated by computing eigenstates on a massively parallel many-core high performance computer.

METIS↗
DOE OSTI2026

Novel thermal energy storage component: Development, performance, and phase transition diagnosis

Thermal energy storage (TES) using phase change materials (PCMs) is a promising technology for capturing and storing excess thermal energy for later use. However, challenges such as poor heat transfer efficiency and a lack of modular, scalable designs have limited widespread adoption of TES in real-world applications. This study developed and evaluated modular brick-type and blade-type TES prototypes featuring an aluminum housing, an embedded serpentine coil for active or passive thermal exchange, and a cost-effective metal mesh to enhance PCM thermal conductivity. The blade-type TES achieved notable geometric efficiency, with a thickness-to-length ratio of 0.03 and a thickness-to-width ratio of 0.08, enabling highly compact and modular thermal storage suitable for space-constrained applications. The paper presents a detailed evaluation of the TES prototypes’ performance. The comparative analysis indicated that the TES prototypes provide a highly cost-effective, thermally optimized alternative for compact energy storage and load shifting. A novel diagnostic technique was also introduced: using a portable endoscope to capture real-time visualizations of PCM phase transitions inside the TES. This method provides critical insights into internal heat transfer mechanisms, identifies potential issues, and offers valuable support for optimizing the TES design and developing the control algorithm. Overall, the modular brick-type and blade-type TES designs demonstrated in this work provide a scalable, efficient, and economically viable solution for advancing TES across residential, commercial, and industrial sectors. The designs’ compact structure, enhanced thermal performance, and integrated diagnostic capabilities make them strong candidates for future deployment in energy-efficient systems.

Gao, Zhiming [ORNL] (ORCID:0000000271397995)↗
DOE OSTI2022

Scalable Direct Recycling of Cathode Black Mass from Spent Lithium‐Ion Batteries

Abstract End of life (EoL) lithium‐ion batteries (LIBs) are piling up at an intimidating rate, which is alarming for environmental health. With further expected rapid growth of LIB use, the magnitude of spent battery accumulation is also expected to grow. LiNi x Co y Mn z O 2 (NCM) cathode materials are a dominant chemistry in high energy LIBs, and make up a huge portion of this waste accumulation. Direct recycling is one of the most promising ways to turn this waste to wealth, but has been limited to lab‐scale, due to lack of robustness, namely the tedious pretreatment required that involves toxic organic solvents. Herein, a process that integrates the pretreatment and relithiation of the cathode black mass is demonstrated. Cathode material from EoL electric vehicle (EV) batteries is treated in a 100 g per batch operation and the regenerated cathode active material demonstrates 100% electrochemical performance recovery, with 91% yield rate, and shows promise for further scale up. This process has the advantages of integration, scalability, and universality, which clears the barricade for direct recycling to move from lab to industry scale with considerable profitability.

Chemistry↗
DOE OSTI2024

Chalcogen effect on the photovoltaic performance of nonfused-ring small molecular electron acceptors for efficient organic solar cells

Fused-ring electron acceptors (FREAs) are the current working horse for the top performing organic solar cells (OSCs). Nevertheless, these FREAs surfer from high synthetic complexity, production costs and poor scalability, hindering their industrialization. Developing nonfused-ring electron acceptors (NFREAs) is a more feasible alternative solution towards future photovoltaic applications. Here, in this work, a series of NFREAs have been designed and synthesized by introducing chalcogen atoms on the side chains for OSCs. The introduced chalcogen atoms (O, S, and Se) not only modulated the energy levels but also finely tuned the intermolecular interactions. Especially, the formed S···S and Se···Se intermolecular interactions in TTS-4F and TTSe-4F resulted in higher molecular crystallinity than TTO-4F. Unexpectedly, the strong Se···Se interactions also led the aggregation of TTSe-4F and formation of large domains in the PM6:TTSe-4F blend. The moderate S···S interactions in TTS-4F enabled an optimal phase separation with more ideal nano fibrils distributed in the PM6:TTS-4F blend, facilitating the charge separation and transport. As a result, TTS-4F based devices achieved a champion power conversion efficiency (PCE) of 14.74%, higher than the TTO-4F (8.76%) and TTSe-4F (11.56%) based devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting

Strongly interacting artificial spin systems are moving beyond mimicking naturally occurring materials to emerge as versatile functional platforms, from reconfigurable magnonics to neuromorphic computing. Typically, artificial spin systems comprise nanomagnets with a single magnetization texture: collinear macrospins or chiral vortices. Here, by tuning nanoarray dimensions we have achieved macrospin–vortex bistability and demonstrated a four-state metamaterial spin system, the ‘artificial spin-vortex ice’ (ASVI). ASVI can host Ising-like macrospins with strong ice-like vertex interactions and weakly coupled vortices with low stray dipolar field. Vortices and macrospins exhibit starkly differing spin-wave spectra with analogue mode amplitude control and mode frequency shifts of Δf = 3.8 GHz. The enhanced bitextural microstate space gives rise to emergent physical memory phenomena, with ratchet-like vortex injection and history-dependent non-linear fading memory when driven through global magnetic field cycles. We employed spin-wave microstate fingerprinting for rapid, scalable readout of vortex and macrospin populations, and leveraged this for spin-wave reservoir computation. ASVI performs non-linear mapping transformations of diverse input and target signals in addition to chaotic time-series forecasting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

AI-assisted detector design for the EIC (AID(2)E)

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using G EANT 4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.

97 MATHEMATICS AND COMPUTING↗