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DOE OSTI2022

MoS 2 -Decorated Graphene@porous Carbon Nanofiber Anodes via Centrifugal Spinning

Sodium-ion batteries (SIBs) are promising alternatives to lithium-ion batteries as green energy storage devices because of their similar working principles and the abundance of low-cost sodium resources. Nanostructured carbon materials are attracting great interest as high-performance anodes for SIBs. Herein, a simple and fast technique to prepare carbon nanofibers (CNFs) is presented, and the effects of carbonization conditions on the morphology and electrochemical properties of CNF anodes in Li- and Na-ion batteries are investigated. Porous CNFs containing graphene were fabricated via centrifugal spinning, and MoS 2 were decorated on graphene-included porous CNFs via hydrothermal synthesis. The effect of MoS 2 on the morphology and the electrode performance was examined in detail. The results showed that the combination of centrifugal spinning, hydrothermal synthesis, and heat treatment is an efficient way to fabricate high-performance electrodes for rechargeable batteries. Furthermore, CNFs fabricated at a carbonization temperature of 800 °C delivered the highest capacity, and the addition of MoS 2 improved the reversible capacity up to 860 mAh/g and 455 mAh/g for Li- and Na-ion batteries, respectively. A specific capacity of over 380 mAh/g was observed even at a high current density of 1 A/g. Centrifugal spinning and hydrothermal synthesis allowed for the fabrication of high-performance electrodes for sodium ion batteries.

25 ENERGY STORAGE↗

DOE OSTI2020

Impact of electrode thick spot irregularities on polymer electrolyte membrane fuel cell initial performance

Polymer electrolyte membrane fuel cells have great potential for power generation in many applications, but to implement them on a larger scale, understanding of quality requirements, tolerances, and controls during manufacturing must improve. As part of this broad challenge, irregularities resulting from the membrane electrode assembly production process must be studied to determine if they impact the performance of the cell. One potential irregularity is a locally increased thickness of the coated or deposited electrode. In order to understand and provide insight into the impact of such irregularities, we intentionally created thick spots in the center of cathode electrodes. The thick spots fabricated by two different approaches were carefully characterized by optical microscopy, X-ray fluorescence spectroscopy and scanning electron microscopy (SEM). We found that the impact of the thick spots on the electrochemically active catalyst area and mass activity was minimal. However, specific electrode thick spot irregularities significantly impacted the mass transport properties of the cells. Based on SEM analysis, the likely cause of the performance drop is morphological changes associated with the creation of the thick spot irregularity. Spatial performance results obtained with a segmented cell system further indicated that the area of the performance impact exceeded that of the intentionally created thick spot.

25 ENERGY STORAGE↗

DOE OSTI2023

Elevating the Practical Application of Sodium-Ion Batteries through Advanced Characterization Studies on Cathodes

Sodium-ion batteries (SIBs) have emerged as promising alternatives to their lithium-ion counterparts due to the abundance of sodium resources and their potential for cost-effective energy storage solutions. The chemistry for SIBs has been investigated since the 1980s, but it went through a slow research and development process. Recently, there has been an acceleration in technology maturation due to a supply chain crisis originating from unequal resource distribution and sustainability and safety concerns regarding lithium-ion batteries. However, the practical application of SIBs has been hindered primarily by challenges related to cathode materials, specifically, surface and structural stabilities in different conditions. Through the integration of advanced techniques such as in situ spectroscopy, operando diffraction, and high-resolution microscopy, a comprehensive understanding of the cathode’s dynamic behavior and degradation mechanisms can be achieved. The identified structural modifications, phase transitions, and degradation pathways offer critical insights into the design of robust cathode materials with prolonged cycling stability, fast charging capability, high energy density, great low-temperature performance, and safety. This review underscores the pivotal role of cutting-edge characterization techniques in guiding the development of high-performance sodium-ion batteries, thereby fostering the realization of sustainable and efficient energy storage solutions for diverse technological applications.

25 ENERGY STORAGE↗

DOE OSTI2023

Scalable Incremental Checkpointing using GPU-Accelerated De-Duplication

Writing large amounts of data concurrently to stable storage is a typical I/O pattern of many HPC workflows. This pattern introduces high I/O overheads and results in increased storage space utilization especially for workflows that need to capture the evolution of data structures with high frequency as checkpoints. In this context, many applications, such as graph pattern matching, perform sparse updates to large data structures between checkpoints. For these applications, incremental checkpointing techniques that save only the differences from one checkpoint to another can dramatically reduce the checkpoint sizes, I/O bottlenecks, and storage space utilization. However, such techniques are not without challenges: it is non-trivial to transparently determine what data has changed since a previous checkpoint and assemble the differences in a compact fashion that does not result in excessive metadata. State-of-art data reduction techniques (e.g., compression and de-duplication) have significant limitations when applied to modern HPC applications that leverage GPUs: slow at detecting the differences, generate a large amount of metadata to keep track of the differences, and ignore crucial spatiotemporal checkpoint data redundancy. This paper addresses these challenges by proposing a Merkle tree-based incremental checkpointing method to exploit GPUs' high memory bandwidth and massive parallelism. Experimental results at scale show a significant reduction of the I/O overhead and space utilization of checkpointing compared with state-of-the-art incremental checkpointing and compression techniques.

Tan, Nigel↗

DOE OSTI2020

Analysis of a Fluidized-Bed Particle/Supercritical-CO2 Heat Exchanger in a Concentrating Solar Power System

Concentrating solar power (CSP) development has focused on increasing the energy conversion efficiency and lowering the capital cost. To improve performance, CSP research is moving to high-temperature and high-efficiency designs. One technology approach is to use inexpensive, high-temperature heat transfer fluids and storage, integrated with a high-efficiency power cycle such as the supercritical carbon dioxide (sCO 2 ) Brayton power cycle. The sCO 2 Brayton power cycle has strong potential to achieve performance targets of 50% thermal-to-electric efficiency and dry cooling at an ambient temperature of up to 40 °C and to reduce the cost of power generation. Solid particles have been proposed as a possible high-temperature heat transfer or storage medium that is inexpensive and stable at high temperatures above 1000 °C. The particle/sCO 2 heat exchanger (HX) provides a connection between the particles and sCO 2 fluid in emerging sCO 2 power cycles. This article presents heat transfer modeling to analyze the particle/sCO 2 HX design and assess design tradeoffs including the HX cost. The heat transfer process was modeled based on a particle/sCO 2 counterflow configuration, and empirical heat transfer correlations for the fluidized bed and sCO 2 were used to calculate heat transfer area and estimate the HX cost. A computational fluid dynamics simulation was applied to characterize particle distribution and fluidization. This article shows a path to achieve the cost and performance objectives for a particle/sCO 2 HX design by using fluidized-bed technology.

14 SOLAR ENERGY↗

DOE OSTI2022

Modular, Crushed-Rock Thermal Energy Storage Pilot Design (Final Report)

The goal of this project was to design a next-step pilot to advance near-term energy storage integrated with a fossil plant to provide a facility capable of being viable and effective in a market with growing penetration of variable renewable energy (VRE). Thermal energy storage (TES) represents an ideal technology for this purpose. The completed effort included a feasibility study to prepare for the Phase II pre-front end engineering design (pre-FEED) for implementing a crushed-rock TES system integrated with a natural gas combined cycle (NGCC) plant. The crushed rock storage technology, which is being developed by Brenmiller Energy, is a modular TES system termed bGen™, which can accommodate both thermal and electrical inputs and output steam, hot water, or hot air. For this application, the estimated efficiency is 80% thermal to thermal. For the feasibility study, the Brenmiller technology was designed to operate on a slipstream from NYPA’s Eugene W. Zeltmann Power Project (Zeltmann) NGCC plant in Astoria, New York. The projected size of the system will be up to 4 MWe with at least 4 hours of storage duration, or 16 MWh-e total. The study also included a techno-economic evaluation of a 200 MWh commercial-scale demonstration. Prior to this project, EPRI had reviewed Brenmiller’s technology, which is being built to demonstrate bGen™ at 1.7 MWe on a solar plant (Rotem) and has been designed for an NGCC facility in Italy, assessing it at technology readiness level (TRL) 5. Brenmiller is also conducting a separate 1-MWth pilot with NYPA that pairs a bGen™ module with a microturbine for a combined-heat-and-power (CHP) application to improve efficiency and provide flexibility. The next-step pilot being designed as part of this project would represent a 5-fold increase in scale, versus Rotem, and would show the technology’s ability to provide effective and economical energy storage, bringing the technology to TRL 6. This pilot would represent the next-to-last demonstration scale before the technology could be commercial ready at GWh-e scales in the 2030 timeframe. The main objective of the work completed by the Electric Power Research Institute, Inc. (EPRI), Brenmiller Energy (Brenmiller), New York Power Authority (NYPA), and United E&C (formerly AECOM) was to perform a Phase I feasibility study on the integration of a crushed-rock thermal energy storage (TES) with a fossil plant. Under this project, the EPRI-led team successfully completed a feasibility study to prepare for the potential future Phase II pre-front end engineering design (pre-FEED) to implement a crushed-rock TES system integrated with a natural gas combined cycle (NGCC) plant. Specific deliverables under this project included the Technology Maturation Plan, Conceptual Study, Techno-Economic Assessment, Technology Gap Assessment, Project Plan for Phase II (submitted as Phase II Renewal Application), Commercialization Plan, and the Final Report (this document). This Final Report includes a compilation of the various summary reports that were prepared during the 12-month schedule of the Phase I project execution under award DE-FE0032017.

03 NATURAL GAS↗

DOE OSTI2025

Toward a persistent event-streaming system for high-performance computing applications

High-performance computing (HPC) applications have traditionally relied on parallel file systems and file transfer services to manage data movement and storage. Alternative approaches have been proposed that use direct communications between application components, trading persistence and fault tolerance for speed. Event-driven architectures, as popularized in enterprise contexts, present a compelling middle ground, avoiding the performance cost and API constraints of parallel file systems while retaining persistence and offering impedance matching between application components. However, adapting streaming frameworks to HPC workloads requires addressing challenges unique to HPC systems. This paper investigates the potential for a streaming framework designed for HPC infrastructures and use cases. We introduce Mofka, a persistent event-streaming framework designed specifically for HPC environments. Mofka combines the capabilities of a traditional streaming service with optimizations tailored to the HPC context, such as support for massively multicore nodes, efficient scaling for large producer-consumer workflows, RDMA-enabled high-performance network communications, specialized network fabrics with multiple links per node, and efficient handling of large scientific data payloads. Built using the Mochi suite of HPC data service components, Mofka provides a lightweight, modular, and high-performance solution for persistent streaming in HPC systems. We present the architecture of Mofka and evaluate its performance against Kafka and Redpanda using benchmarks on diverse platforms, including Argonne's Polaris and Oak Ridge's Frontier supercomputers, showing up to 8× improvement in throughput in some scenarios. We then demonstrate its utility in several real-world applications: a tomographic reconstruction pipeline, a workflow for the discovery of metal-organic frameworks for carbon capture, and the instrumentation of Dask workflows for provenance tracking and performance analysis.

HPC↗

DOE OSTI2021

Deployment of Energy Storage to Improve Environmental Outcomes of Hydropower

This paper describes how the installation of energy storage systems, co-sited with hydroelectric projects, can offer both economic and environmental benefits by enabling a broader range of electricity performance, capitalizing on its flexibility and grid reliability, while mitigating critical environmental impacts or improving environmental outcomes across U.S. rivers and streams. The paper attempts to link environmental outcomes to energy storage utilization. It offers a comprehensive inventory of research-grade work, site-specific studies, policies, and pilot projects regarding energy storage and hydropower that show significant environmental implications. It provides an outline of methodologies given the known costs and attributes of storage technologies, with case study illustrations. It outlines the key components of a methodology that could be applied within the context of specific projects to reveal the environmental benefits of energy storage paired with hydropower production to properly size the storage systems to seize benefits. The actual methodology would be developed in future work. Finally, we provide a set of gaps to be addressed in future work as this important idea gains traction.

13 HYDRO ENERGY↗

DOE OSTI2022

Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data

The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Report for the ASCR Workshop on the Management and Storage of Scientific Data

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Sparse matrix‐vector and matrix‐multivector products for the truncated SVD on graphics processors

Summary Many practical algorithms for numerical rank computations implement an iterative procedure that involves repeated multiplications of a vector, or a collection of vectors, with both a sparse matrix and its transpose. Unfortunately, the realization of these sparse products on current high performance libraries often deliver much lower arithmetic throughput when the matrix involved in the product is transposed. In this work, we propose a hybrid sparse matrix layout, named CSRC, that combines the flexibility of some well‐known sparse formats to offer a number of appealing properties: (1) CSRC can be obtained at low cost from the popular CSR (compressed sparse row) format; (2) CSRC has similar storage requirements as CSR; and especially, (3) the implementation of the sparse product kernels delivers high performance for both the direct product and its transposed variant on modern graphics accelerators thanks to a significant reduction of atomic operations compared to a conventional implementation based on CSR. This solution thus renders considerably higher performance when integrated into an iterative algorithm for the truncated singular value decomposition (SVD), such as the randomized SVD or, as demonstrated in the experimental results, the block Golub–Kahan–Lanczos algorithm.

Aliaga, José I.↗

DOE OSTI2022

Ion Coulomb Crystals in Storage Rings for Quantum Information Science

Quantum information science is a growing field that promises to take computing into a new age of higher performance and larger scale computing as well as being capable of solving problems classical computers are incapable of solving. The outstanding issue in practical quantum computing today is scaling up the system while maintaining interconnectivity of the qubits and low error rates in qubit operations to be able to implement error correction and fault-tolerant operations. Trapped ion qubits offer long coherence times that allow error correction. However, error correction algorithms require large numbers of qubits to work properly. We can potentially create many thousands (or more) of qubits with long coherence states in a storage ring. For example, a circular radio-frequency quadrupole, which acts as a large circular ion trap and could enable larger scale quantum computing. Such a Storage Ring Quantum Computer (SRQC) would be a scalable and fault tolerant quantum information system, composed of qubits with very long coherence lifetimes. With computing demands potentially outpacing the supply of high-performance systems, quantum computing could bring innovation and scientific advances to particle physics and other DOE supported programs. Increased support of R$\&$D in large scale ion trap quantum computers would allow the timely exploration of this exciting new scalable quantum computer. The R$\&$D program could start immediately at existing facilities and would include the design and construction of a prototype SRQC. We invite feedback from and collaboration with the particle physics and quantum information science communities.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Machine-learning-assisted high-temperature reservoir thermal energy storage optimization

High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving the goal of the net-zero carbon economy, given its capability to balance the intermittent nature of renewable energy generation. In this study, a machine-learning-assisted computational framework is presented to co-optimize the performance metrics of HT-RTES by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Further, neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources: Preprint

Integration of a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problem requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (controllable DERs) including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperformed other benchmark controllers regarding computation time without compromising operational performance.

DER↗

DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources

Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (controllable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.

DER↗

DOE OSTI2020

Development of a High-Rate Front-End ASIC for X-Ray Spectroscopy and Diffraction Applications

We developed a new front-end application-specific integrated circuit (ASIC) to upgrade the Maia X-ray microprobe. The ASIC instruments 32 configurable channels that perform either positive or negative charge amplification, pulse shaping, peak amplitude, and time extraction along with buffered analog storage. At a gain of 3.6 V/fC, 1-μs peaking time, and a temperature of 248 K, an electronic resolution of 13 and 10 e- rms was measured with and without a silicon drift detector (SDD) sensor, respectively. A spectral resolution of 170-eV full-width at half-maximum (FWHM) at 5.9 keV was obtained with an 55Fe source. The channel linearity was better than ± 1% with rate capabilities up to 40 kcps. The ASIC was fabricated in a commercial 250-nm process with a footprint of 6.3 mm × 3.9 mm and dissipates 167 mW of static power.

Vernon, Emerson↗

DOE OSTI2025

Transforming CO 2 to Porous Carbon as a High-Performing Sodium-Ion Battery Anode via Electrochemical Reduction in Molten Carbonates

The prevalence of sodium over lithium prompts exploration of sodium-ion batteries (SIBs) as a viable alternative to lithium-ion batteries (LIBs). Hard carbon has emerged as a promising anode material for SIBs, yet its synthesis poses sustainability challenges and emits pollutants. Here, in this study, we introduce CO 2 -derived porous carbon (graphitic and amorphous) as an anode for SIBs via electrochemical reduction of CO 2 in a molten eutectic carbonate salt at a lower temperature that yields materials with controlled microstructure, morphology, and porosity conducive to energy storage. Our CO 2 -derived carbon demonstrates remarkable specific capacity, superior rate capability, and stable cycling performance as a SIB anode. This innovative strategy harnesses waste CO 2 toward advancing SIB energy technology.

CO2-derived carbon↗

DOE OSTI2025

Deploying and Operating CephFS for Scientific Applications at Fermilab

Fermilab has been running a Ceph cluster in production for several years to support high-throughput scientific computing. Our primary use case is CephFS, which serves interactive data analysis workloads, with growing interest in using RGW for scalable object storage of scientific datasets. In this talk, we'll share lessons learned from successfully deploying and maintaining our Ceph cluster with cephadm, including challenges faced, performance tuning, and operational practices. We'll also present custom tools we've developed to streamline monitoring and management and discuss how Ceph fits into our broader storage architecture for large-scale scientific research.

Peisker, Alison [Fermilab]↗