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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Microstructure‐Dependent Sodium Storage Mechanisms in Hard Carbon Anodes

Sustainable energy storage is essential to support the transition to renewables and meet the increasing demand for energy. Sodium‐ion batteries (NIBs) are attractive for grid‐scale energy storage due to the abundance and low cost of sodium, sustainability of other battery components, and electrochemical performance. Hard carbon (HC) is a leading anode material for NIBs, but its complex microstructure complicates the understanding of sodium storage mechanisms. Using X‐ray total scattering and density functional theory calculations, this study clarifies how HC's microstructural variations influence sodium storage across the slope (high potential) and plateau (low potential) regions of the potential capacity curve. In the slope region, sodium initially adsorbs at high‐binding energy defect sites and subsequently intercalates between graphene layers, adsorbing at low‐binding energy defect sites, correlating with different slopes observed during initial sodiation. Initial irreversibility arises from sodium trapping at surface defects and solid electrolyte interface formation. In the plateau region, sodium simultaneously intercalates and fills pores, influenced by pore size, interlayer spacing, and defect concentration. HCs with larger pore sizes form larger sodium clusters. In conclusion, the proposed mechanism underscores the role of microstructure engineering in enhancing HC performance and advancing NIBs for grid‐scale energy storage.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Opportunities for low-carbon generation and storage technologies to decarbonise the future power system

Alternatives to cope with the challenges of high shares of renewable electricity in power systems have been addressed from different approaches, such as energy storage and low-carbon technologies. However, no model has previously considered integrating these technologies under stability requirements and different climate conditions. In this study, we include this approach to analyse the role of new technologies to decarbonise the power system. The Spanish power system is modelled to provide insights for future applications in other regions. After including storage and low-carbon technologies (currently available and under development), batteries and hydrogen fuel cells have low penetration, and the derived emission reduction is negligible in all scenarios. Compressed air storage would have a limited role in the short term, but its performance improves in the long term. Flexible generation technologies based on hydrogen turbines and long-duration storage would allow the greatest decarbonisation, providing stability and covering up to 11-14 % of demand in the short and long term. The hydrogen storage requirement is equivalent to 18 days of average demand (well below the theoretical storage potential in the region). When these solutions are considered, decarbonising the electricity system (achieving Paris targets) is possible without a significant increase in system costs (< 114 euro/MWh).

08 HYDROGEN↗
DOE OSTI2021

Evolution of thermo-mechanical properties of concrete with calcium aluminate cement and special aggregates for energy storage

Highlights: • CAC Concrete with stable aggregates is suitable for high temperatures with cycles. • Concrete suffered performance changes after the first thermal cycle (dehydration). • Subsequent thermal cycles showed stabilization and slight performance decreases. With the ever-expanding presence of solar energy, the design of cost-efficient heat energy storage systems is becoming increasingly relevant. Concrete is a potential solid material for these systems but raises questions of its response at high temperatures in operating conditions. This work studies concrete designed with calcium aluminate cement and three types of aggregates that are stable at high temperatures. The designed concrete mixes were exposed to thermal cycles (290–550 °C), and their thermal fatigue response was evaluated with mechanical, cracking, thermal, and microstructural analyses at room temperature after heat cycles. The obtained results show that the main changes are mostly produced after the first thermal cycle and are stabilized during successive thermal cycles, with only a slight decrease in the thermo-mechanical properties. The results showed stabilization of cracking, crack widths between 10 and 80 μm, reduction of compressive strength, around 50–65%, and reduction of the thermal conductivity by 30%.

36 MATERIALS SCIENCE↗
DOE OSTI2024

h5bench: A unified benchmark suite for evaluating HDF5 I/O performance on pre‐exascale platforms

Summary Parallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputers. With massive amounts of data produced or consumed by compute nodes, high‐performant parallel I/O is essential. I/O benchmarks play an important role in this process; however, there is a scarcity of I/O benchmarks representative of current workloads on HPC systems. Toward creating representative I/O kernels from real‐world applications, we have created h5bench , a set of I/O kernels that exercise hierarchical data format version 5 (HDF5) I/O on parallel file systems in numerous dimensions. Our focus on HDF5 is due to the parallel I/O library's heavy usage in various scientific applications running on supercomputing systems. The various tests benchmarked in the h5bench suite include I/O operations (read and write), data locality (arrays of basic data types and arrays of structures), array dimensionality (one‐dimensional arrays, two‐dimensional meshes, three‐dimensional cubes), I/O modes (synchronous and asynchronous). In this paper, we present the observed performance of h5bench executed along several of these dimensions on existing supercomputers (Cori and Summit) and pre‐exascale platforms (Perlmutter, Theta, and Polaris). h5bench measurements can be used to identify performance bottlenecks and their root causes and evaluate I/O optimizations. As the I/O patterns of h5bench are diverse and capture the I/O behaviors of various HPC applications, this study will be helpful to the broader supercomputing and I/O community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Single-node Partitioned-Memory for Huge Graph Analytics: Cost and Performance Trade-offs

Nonvolatile memory NVDIMMs, available as Intel Optane, are less expensive than DRAM and bring large byte-addressable storage within reach to many applications. Evaluations on graph analytics have shown promising performance only when DRAM is used as a hardware cache (Memory mode). An open question is whether graph applications can exploit Optane and DRAM directly (AppDirect mode) and achieving better-than-DRAM average bandwidth and run times. We evaluate Optane as a volatile pool on two large-scale graph applications with very different computational patterns, Grappolo and Ripples. We show that AppDirect mode can deliver better-than-DRAM performance, by allocating data structures to Optane and DRAM according to their access characteristics, resulting in higher average memory bandwidth and lower average latency. Memory mode provides DRAM-competitive performance with capacity equal to persistent memory. We demonstrate occasional 4x improvement using the latest AppDirect option and frequently observe competitive performance between Optane AppDirect Memory modes and DRAM.

Ghosh, Sayan↗
DOE OSTI2021

TRANSIENT THERMAL MODELING OF THE HIGH BURNUP DEMONSTRATION RESEARCH PROJECT CASK USING STAR-CCM+ AND COBRA-SFS

The Department of Energy in collaboration with the Electric Power Research Institute is in the process of conducting the High Burnup Demonstration Project. Where the objective is to characterize the performance of high-burnup fuel in long term storage. As part of this demonstration, a TN-32B dry storage cask was instrumented and loaded with spent nuclear fuel at North Anna Nuclear Generating Station in November 2017. The project cask provides a unique opportunity to gain information on spent fuel and cask performance from an in-service operational system. The cask was instrumented with thermocouples inside of the fuel assemblies, then loaded and dried using normal procedures. After the drying process a thermal soak period where the cask was left indoors was used to obtain steady temperatures for model comparison along with surface temperatures. This paper details thermal modeling validation work that was done to model both the steady state and transient cases. Two modeling tools were used to predict temperatures in the cask. The general purpose CFD and heat transfer code STAR-CCM+ was used with both a detailed pin-by-pin model was used along with a more efficient k-effective simplification. The other code is COBRA-SFS, a purpose-built detailed thermal modeling tool developed and maintained at PNNL. Results from all the modeling tools were compared blind to the test data. Each model utilized general design information and compared reasonably well to the blind steady state data using convection and surface temperature boundary conditions. Post-test adjustments were later made to better reflect the “as built” conditions of the cask. Based on lessons learned from the steady state analysis the vacuum drying process was modeled in both codes. Utilizing the measured loading conditions the transient models were able to compare very well with the measured data. Overall the project showed an ability to model spent fuel storage conditions very well and future work is planned to generalize the methodology used for vacuum drying.

Thermal Analysis, Spent Fuel, Dry Storage↗
DOE OSTI2022

DenKv: Addressing Design Trade-offs of Key-value Stores for Scientific Applications

High-performance computing (HPC) facilities have employed flash-based storage tier near to compute nodes to absorb high I/O demand by HPC applications during periodic system-level checkpoints. To accelerate these checkpoints, proxy-based distributed key-value stores (PD-KVS) gained particular attention for their flexibility to support multiple backends and different network configurations. PD-KVS rely internally on monolithic KVS, such as LevelDB or RocksDB, to exploit the KV interface and query support. However, PD-KVS are unaware of the high redundancy factor in checkpoint data, which can be up to GBs to TBs, and therefore, tend to generate high write and space amplification on these storage layers. In this paper, we propose DenKv which is deduplication-extended node-local LSM-tree-based KVS. DenKv employs asynchronous partially inline dedup (APID) and aims to maintain the performance characteristics of LSM-tree-based KVS while reducing the write and space amplification problems. We implemented DenKv atop BlobDB and showed that our proposed solution maintains performance while reducing write amplification up to 2× and space amplification by 4× on average.

Khan, Awais↗
DOE OSTI2025

Broad range material-to-system screening of metal–organic frameworks for hydrogen storage using machine learning

Hydrogen is pivotal in the transition to sustainable energy systems, playing major roles in power generation and industrial applications. Metal–organic frameworks (MOFs) have emerged as promising mediums for efficient hydrogen storage. However, identifying potential candidates for deployment is challenging due to the vast number of currently available synthesized MOFs. This study integrates molecular simulations, machine learning, and techno-economic analysis to evaluate the performance of MOFs across broad operation conditions for hydrogen storage applications. While previous screenings of MOF databases have predominantly emphasized high hydrogen capacities under cryogenic conditions, this study reveals that optimal temperatures and pressures for cost minimization depend on the raw price of the MOF. Specifically, when MOFs are priced at $15/kg, among the 9720 MOFs tested, 9692 MOFs achieve the lowest cost at temperatures between 170 K and 250 K and a pressure of 150 bar. Under these optimal conditions, 362 MOFs deliver a lower levelized cost of storage than 350 bar compressed gas hydrogen storage. Furthermore, this study reveals key material properties that result in low system cost, such as high surface areas (>3000 m2/g), large void fractions (>0.78), and large pore volumes (>1.1 cm3/g).

Hydrogen storage↗
DOE OSTI2021

NRAP-Open-IAM: Open Wellbore Component (V.2.0)

The Open Wellbore component of NRAP-Open-IAM is applicable to the calculation of Area of Review at a carbon storage site and fast estimation of dense gas dispersion from multiple continuous CO 2 surface leakage sources for risk assessment. The Open Wellbore Component of NRAP-Open-IAM is a lookup table model based on the drift-flux approach. The National Research Assessment Partnership’s second-generation integrated assessment model, NRAP-Open-IAM, is open-source software written in Python for use in performance and quantitative risk assessment of geologic carbon storage (GCS) systems. The look-up table for open well leakage is an updated version the previous model which provides finer resolution of reservoir depth and more accurate fluid properties than the previous open well look-up table, especially in calculating the gas (CO 2 -rich) phase properties. The component model input parameters include the reservoir transmissivity, aquifer transmissivity, brine salinity, well radius, and well top and bottom depths. The reservoir transmissivity and brine salinity were set to values consistent with the reservoir component, and the well top and bottom depths varied with location. The possible outputs from the Open Wellbore component are leakage rates of CO 2 and brine to either an aquifer or atmosphere, depending on the depth of the well top. Several python test scripts are presented that demonstrate that the Open Wellbore component works correctly. Use of the open wellbore model in NRAP-Open-IAM may result in a large CO 2 leakage rates, comparable to the leakage rates of CO 2 blow out.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Cost Projections for Utility-Scale Battery Storage (2020 Update)

In this work we describe the development of cost and performance projections for utility-scale lithium-ion battery systems, with a focus on 4-hour duration systems. The projections are developed from an analysis of 19 publications that consider utility-scale storage costs. The suite of publications demonstrates varied cost reductions for battery storage over time. Figure ES-1 shows the low, mid, and high cost projections developed in this work (on a normalized basis) relative to the published values. Figure ES-2 shows the overall capital cost for a 4-hour battery system based on those projections, with storage costs of $\$$144/kWh, $\$$208/kWh, and $\$$293/kWh in 2030 and $\$$88/kWh, $\$$156/kWh, and $\$$219/kWh in 2050. Battery variable operations and maintenance costs, lifetimes, and efficiencies are also discussed, with recommended values selected based on the publications surveyed.

14 SOLAR ENERGY↗
DOE OSTI2021

Cost Projections for Utility-Scale Battery Storage: 2021 Update

In this work we describe the development of cost and performance projections for utility-scale lithium-ion battery systems, with a focus on 4-hour duration systems. The projections are developed from an analysis of recent publications that consider utility-scale storage costs. The suite of publications demonstrates varied cost reductions for battery storage over time. Figure ES-1 shows the low, mid, and high cost projections developed in this work (on a normalized basis) relative to the published values. Figure ES-2 shows the overall capital cost for a 4-hour battery system based on those projections, with storage costs of $\$$143/kWh, $\$$198/kWh, and $\$$248/kWh in 2030 and $\$$87/kWh, $\$$149/kWh, and $\$$248/kWh in 2050. Battery variable operations and maintenance costs, lifetimes, and efficiencies are also discussed, with recommended values selected based on the publications surveyed.

25 ENERGY STORAGE↗
DOE OSTI2023

Cost Projections for Utility-Scale Battery Storage: 2023 Update

In this work we describe the development of cost and performance projections for utility-scale lithium-ion battery systems, with a focus on 4-hour duration systems. The projections are developed from an analysis of recent publications that include utility-scale storage costs. The suite of publications demonstrates wide variation in projected cost reductions for battery storage over time. We use the recent publications to create low, mid, and high cost projections. Projected storage costs are $245/kWh, $326/kWh, and $403/kWh in 2030 and $159/kWh, $226/kWh, and $348/kWh in 2050. Battery variable operations and maintenance costs, lifetimes, and efficiencies are also discussed, with recommended values selected based on the publications surveyed.

25 ENERGY STORAGE↗
DOE OSTI2025

Cost Projections for Utility-Scale Battery Storage: 2025 Update

In this work we describe the development of cost and performance projections for utility-scale lithium-ion battery systems, with a focus on 4-hour duration systems. The projections are developed from an analysis of recent publications that include utility-scale storage costs. The suite of publications demonstrates wide variation in projected cost reductions for battery storage over time. We present the suite of projected cost reductions collected from the literature as well as the low, mid, and high cost projections developed in this work. Storage cost projections are $\$$152/kWh, $\$$247/kWh, and $\$$349/kWh in 2035 and $\$$111/kWh, $\$$184/kWh, and $\$$333/kWh in 2050 for the low, mid, and high cases respectively. Battery variable operations and maintenance costs, lifetimes, and efficiencies are also discussed, with recommended values selected based on the publications surveyed.

25 ENERGY STORAGE↗
DOE OSTI2021

Low-Cost Transformation of Biomass-Derived Carbon to High-Performing Nano-graphite via Low-Temperature Electrochemical Graphitization

Graphite, an essential component of energy storage devices, is traditionally synthesized via an energy-intensive thermal process (Acheson process) at ~3300 K. However, the battery performance of such graphite is abysmal under fast-charging conditions, which is deemed essential for the propulsion of electric vehicles to the next level. Herein, a low-temperature electrochemical transformation approach has been demonstrated to afford a highly crystalline nano-graphite with the capability of tuning interlayer spacing to enhance the lithium diffusion kinetics in molten salts at 850 °C. The essence of our strategy lies in the effective electrocatalytic transformation of carbon to graphite at a lower temperature that could significantly increase the energy savings, reduce the cost, shorten the synthesis time, and replace the traditional graphite synthesis. The resulting graphite exhibits high purity, crystallinity, a high degree of graphitization, and a nanoflake architecture that all ensure fast lithium diffusion kinetics (~2.0 × 10 –8 cm2 s –1 ) through its nanosheet. Such unique features enable outstanding electrochemical performance (~200 mA h g –1 at 5C for 1000 cycles, 1C = 372 mA g –1 ) as a fast-charging anode for lithium-ion batteries. Overall, this finding paves the way to make high energy-density fast-charging batteries that could boost electromobility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

On Performance Prediction of Big Data Transfer in High-performance Networks

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

Liu, Wuji↗
DOE OSTI2025

Optimizing Management of Persistent Data Structures in High-Performance Analytics

Large-scale data analytics workflows ingest massive input data into various data structures, including graphs and key-value datastores. These data structures undergo multiple transformations and computations and are typically reused in incremental and iterative analytics workflows. Persisting in-memory views of these data structures enables reusing them beyond the scope of a single program run while avoiding repetitive raw data ingestion overheads. Memory-mapped I/O enables persisting in-memory data structures without data serialization and deserialization overheads. However, memory-mapped I/O lacks the key feature of persisting consistent snapshots of these data structures for incremental ingestion and processing. The obstacles to efficient virtual memory snapshots using memory-mapped I/O include background writebacks outside the application’s control, and the significantly high storage footprint of such snapshots. To address these limitations, we present Privateer, a memory and storage management tool that enables storage-efficient virtual memory snapshotting while also optimizing snapshot I/O performance. Here, we integrated Privateer into Metall, a state-of-the-art persistent memory allocator for C++, and the Lightning Memory-Mapped Database (LMDB), a widely-used key-value datastore in data analytics and machine learning. Privateer optimized application performance by 1.22× when storing data structure snapshots to node-local storage, and up to 16.7× when storing snapshots to a parallel file system. Privateer also optimizes storage efficiency of incremental data structure snapshots by up to 11× using data deduplication and compression.

Computer science↗
DOE OSTI2024

Simulation-Based Assessment of Energy Consumption of Alternative Powertrains in Agricultural Tractors

The objectives of this research were to develop simulation models for agricultural tractors with different powertrain technologies and evaluate the energy consumption in typical agricultural operations. Simulation models were developed for conventional, parallel hybrid electric, series hybrid electric, fuel cell hybrid, and battery electric powertrains. Autonomie vehicle simulation software (version 2022) was used for the simulations and the tractor models were simulated in two tilling cycles and in a road transport cycle with a trailer. The alternative powertrains were configured to have at least the same tractive performance as the conventional, diesel engine-powered tractor model. The simulation results showed that the potential of the parallel and series hybrid powertrains to improve energy efficiency depends heavily on the tractor size and the operating cycle conditions. The fuel cell hybrid and battery electric powertrains have a higher potential to reduce energy consumption and emissions but still have inherent technical challenges for practical operation. The battery-powered electric tractor would require improvements in the storage energy density to have a comparable operational performance in comparison to other powertrains. The fuel cell hybrid tractor already provided an adequate operating performance but the availability of hydrogen and refueling infrastructure could be challenging to resolve in the farming context.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A hierarchical framework for aggregating grid-interactive buildings with thermal and battery energy storage

The behind-the-meter (BTM) thermal and battery energy storage can help improve energy efficiency, reduce energy costs, and enhance energy resilience, particularly in rural areas and for disadvantaged communities. Aggregating numerous BTM energy storage systems can act as a price influencer with a significant source of load shifting and peak demand reduction. An integrated and scalable control mechanism is required to effectively utilize energy storage systems and flexible building loads to maximize the economic benefits, considering various distribution system constraints. Here, this paper presents an innovative hierarchical coordination framework for energy storage and flexible load in buildings, considering various factors such as electricity prices, thermal comfort, and distribution system modeling and constraints. At the upper level, a distribution system operator optimizes the power flow to minimize its power procurement costs from the electricity wholesale market, while at the lower level, aggregators determine the optimal dispatch of battery and thermal energy storage systems in multiple buildings on behalf of end-users to minimize operating costs according to the power prices. These problems are solved using a game-theoretic approach through negotiations between the distribution system operator and aggregators as a bi-level decision model. Simulation case studies have been performed for a test distribution network with a number of building end-users using energy storage systems to quantify the performance of aggregators. The results demonstrate that the proposed strategy can reduce peak load for a reliable electricity distribution network while saving electricity bills for customers.

25 ENERGY STORAGE↗