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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The power reliability event simulator tool (PRESTO): A novel approach to distribution system reliability analysis and applications

The growing interest in onsite solar photovoltaic and energy storage systems is partially motivated by customer concerns regarding grid reliability. However, accurately assessing the effectiveness of PVESS in mitigating these interruptions requires a comprehensive understanding of location-specific outage patterns and the ability to simulate realistic scenarios. To address the gap, we introduce the Power Reliability Event Simulation TOol (PRESTO), the first publicly available tool that simulates location-specific power interruptions at the county level. PRESTO allows for a more realistic assessment of system reliability by considering the unpredictability and location-specific patterns of power interruptions. We applied PRESTO in a case study of a single-family home across three U.S. counties, examining the performance of a solar photovoltaic system with 10kWh of battery storage during short-duration power interruptions. Our findings show that this system reliably met 93% of energy demand for essential non-heating and cooling loads, fully serving these loads in 84% of events, despite the constraints of daily time-of-use bill management which limits the battery's state-of-charge reserve. However, when heating and cooling loads were included, system performance decreased significantly, with only 70% of demand met and full service in 43% of events. These results highlight the challenges of using solar photovoltaic and energy storage systems for short-duration outages, emphasizing the need to consider factors like battery size and grid charging strategies to improve reliability. Our study demonstrates the practical applications of PRESTO, providing valuable insights into potential mitigation strategies including grid charging and optimizing battery size.

14 SOLAR ENERGY↗
DOE OSTI2026

Level-2 Milestone 9009: Flux and Rabbit Capabilities on El Capitan

This document is the milestone delivery report for the ASC 2025 L2 milestone (See Table 1) for advanced I/O capabilities for El Capitan via Flux Workload Manager support and the new I/O hardware designed for El Capitan, the Rabbit Storage System. In this document we describe the design of the Rabbit Storage System and how it is managed by Flux. We evaluate the performance and usability of Rabbit using ARES, IOR, and an AI inference workload. Overall, we find that Rabbit shows good scalability, especially in node-local storage configurations, and is more scalable than the global Lustre parallel file system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Measurement of radiolytic hydrogen generation and impact of drying treatments on reactor exposed and surrogate aluminum materials

Technical challenges associated with dry storage of Aluminum-clad Spent Nuclear Fuel (ASNF) include a need to better understand the potential extent of gaseous molecular hydrogen (H 2 ) production through radiolytic degradation of the aluminum (oxy)hydroxide films present on ASNF. The characterization of radiolytic gas generation from ASNF (oxy)hydroxide layers has been identified as a key knowledge gap which poses a technical challenge to the long-term storage of ASNF. Task 2 of the action plan for the extended (>50 years) dry storage of ASNF addresses this gap. Previous radiolysis studies performed under Task 2 established baseline estimates of H 2 generation rates from the attendant hydrated oxides. The results confirmed that net radiolytic H 2 production has a dependency on absorbed gamma dose, as well as relative humidity and cover gas composition (air, nitrogen, and argon). Further experiments revealed that the physisorbed water on the samples may significantly impact the radiolytic H 2 yield. This phenomenon complicates the determination of H 2 generation rates for hydrated oxides, which may lead to inaccurate modeling predictions of the long-term H 2 yields in sealed storage systems containing ASNF, particularly when compared to spent fuel casks which have undergone some drying process intended to remove physically- and chemically-bound water. This report describes the testing methods utilized and the hydrogen generation results obtained in an investigation of the effects of gamma irradiation on aluminum materials for a variety of (oxy)hydroxide surface compositions and drying conditions. The testing methods included small-area aluminum material testing in ampules, and large-area aluminum material testing in steel vessels. Test material preparation, irradiation, and radiolytic H 2 measurement methods are summarized. The measured H 2 concentrations, which reflect the variable initial hydrated inventory and drying treatments, are compared to one another as well as to previously published data, to identify the primary factors affecting radiolytic H 2 generation rates and potential equilibrium H 2 concentrations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Integration of RNTuple in ATLAS Athena

After using ROOT’s TTree I/O subsystem for over two decades and storing more than an exabyte of compressed High Energy Physics (HEP) data, advances in technology have motivated a complete redesign, RNTuple, which breaks backward-compatibility to take better advantage of these storage options. The RNTuple I/O subsystem has been designed to address performance bottlenecks and other shortcomings of TTree. Specifically, RNTuple comes with an updated, more compact binary data format that can be stored both in ROOT files and natively in object stores. It is designed for modern storage hardware (e.g. high-throughput low-latency NVMe SSDs), and provides robust and easy to use interfaces. The binary format of RNTuple is scheduled to become production grade in 2024, and recently has become mature enough to start exploring the integration into software used by HEP experiments. In this contribution, we discuss the developments to support the features as required by the ATLAS analysis Event Data Model (EDM) in RNTuple, which will enable its integration into the Athena software framework. With these developments in place, we evaluate the performance of the current most recent versions of RNTuple-based ATLAS data sets and compare this to that of TTree.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL controllers are still limited since they require a considerable amount of time before converging to a near-optimal solution. This issue is currently addressed in literature through the offline pre-training of the DRL agent. However this solution results in two main critical issues: (1) the need to develop a building surrogate model to perform the training task, and (2) the need to perform a fine-tuning process over several training episodes to obtain a near-optimal control policy. In this context, this paper introduces an Online Transfer Learning (OTL) strategy that exploits two knowledge-sharing techniques, weight-initialization and imitation learning, to transfer a DRL control policy from a source office building to various target buildings in a simulation environment coupling EnergyPlus and Python. A DRL controller based on discrete Soft Actor–Critic (SAC) is trained on the source building to manage the operation of a cooling system consisting of a chiller and a thermal storage. Several target buildings are defined to benchmark the performance of the OTL strategy with that of a Rule-Based Controller (RBC) and two DRL-based control strategies, deployed in offline and online fashion. The strategy adopted for OTL emulates the real world implementation with a simulation process by implementing the transferred DRL agent for a single episode in the target buildings. Target buildings have the same geometrical features and are served by the same energy system as the source building, but differ in terms of weather conditions, electricity price schedules, occupancy patterns, and building envelope efficiency levels. The results show that the OTL strategy can reduce the cumulated sum of temperature violations on average by 50% and 80% respectively when compared to RBC and online DRL while enhancing the energy system operation with electricity cost savings ranging between 20% and 40%. Furthermore, the OTL agent performs slightly worse than the offline DRL controller but it does not require any modeling effort and can be implemented directly on target buildings emulating a real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Synergistic Heat Pumped Thermal Storage and Flexibly Carbon Capture System

As the U.S. grid evolves toward a lower-carbon system, fossil generation assets need to operate in energy markets with high variable renewable energy (VRE) penetration while also decreasing carbon emissions. Current carbon capture and storage (CCS) technologies suffer from high capital cost and an inability to operate flexibly during periods of oscillating demand. The ARPA-E FLECCS Program Phase 1 was created to fund designing and optimizing innovative CCS processes that enable flexibility on a high-VRE grid. To address this need, the Colorado State University (CSU) Team won funding to design a synergized system of thermal energy storage, power generation, and flexible carbon capture to enable breakthrough system performance that achieves an LCOE <$75/MWh with >99% capture rate. This approach will target new or existing natural gas combined cycle power plants. The proposed design utilizes novel hot and cold thermal energy storage (TES) technologies that store low-cost, off-peak electricity as thermal energy to power CCS solvent regeneration and boost plant output during periods of peak demand. The design provides an overall optimized net present value (NPV) by maximizing low carbon power to grid while prices are highest using Storworks Power’s concrete TES technology. The team also capitalizes on decades of ION Clean Energy’s (ION) development in low cost and flexible pioneering solvent technology, which has proven reductions in energy consumption and overall cost of 28% and 38%, respectively, compared with state-of-the-art CCS.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Micro-Environmental Control System (Final Report)

This project developed an innovative micro-environmental control system (µX) that enables office buildings to reduce energy used for heating and cooling by 15% or more. The µX is a compact, quiet, and ergonomic device that is designed to be installed under an office workstation; it is designed to maintain occupant comfort when room thermostat setpoints are incremented by 4°F or more (warmer in the summer and cooler in the winter) to save energy. When ambient room temperatures are outside of the usual comfort range, the µX maintains occupant comfort by delivering personalized cooling or heating locally to each office worker. The µX provides personalized cooling using a micro vapor compression system that includes a new high-performance micro-scroll compressor and a novel thermal storage unit. The vapor compression system operates at night to freeze a phase-change material (PCM). During the workday, the cooling stored in the PCM is released as a cool breeze of air to make occupants more comfortable. The micro-scroll compressor was developed specifically for this application; it is smaller than any of its type, minimizing the amount of power needed. In heating mode, the µX maintains occupant comfort using a foot heating mat with an infrared reflective box. The µX R&D project was conducted by Syracuse University in collaboration with United Technologies Research Center, Air Innovations, Bush Technical, and Cornell University. Over the course of the initial three-year project, the team developed and evaluated four versions of the unit, advancing the concept to Technology Readiness Level 6. The capabilities of individual proof-of-concept prototypes were verified in tests that were conducted with: 1) units in psychrometric chambers, 2) an instrumented manikin in a laboratory, and 3) human subjects in laboratories that simulate office environments. The tests verified that the µX prototypes met or exceeded all performance targets required to enable office buildings to reduce energy used for heating and cooling by 15% or more by maintaining occupant comfort when thermostat setpoints are incremented by 4°F or more.

25 ENERGY STORAGE↗
DOE OSTI2020

AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations

Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.

Rao, Nageswara↗
DOE OSTI2022

Design and implementation of dynamic I/O control scheme for large scale distributed file systems

In this paper, we have analyzed the input/output (I/O) activities of Cori, which is a high-performance computing system at the National Energy Research Scientific Computing Center at Lawrence Berkeley National Laboratory. Our analysis results indicate that most users do not adjust storage configurations but rather use the default settings. In addition, owing to the interference from many applications running simultaneously, the performance varies based on the system status. To configure file systems autonomously in complex environments, we developed DCA-IO, a dynamic distributed file system configuration adjustment algorithm that utilizes the system log information to adjust storage configurations automatically. Our scheme aims to improve the application performance and avoid interference from other applications without user intervention. Moreover, DCA-IO uses the existing system logs and does not require code modifications, an additional library, or user intervention. To demonstrate the effectiveness of DCA-IO, we performed experiments using I/O kernels of real applications in both an isolated small-sized Lustre environment and Cori. Our experimental results shows that our scheme can improve the performance of HPC applications by up to 263% with the default Lustre configuration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Enhancing Even Gas Distribution in Porous Media with Radial Flow

Packed beds, such as those used in thermal energy storage (TES) systems, typically use flow from one end to the other. This axial flow configuration leads to inefficiencies due to dispersion effects and high pressure drop. Instead of axial flow, this work proposes radial flow in packed beds, where a central tube provides flow that transports fluid from the center to the bed’s wall. Radial flow could be a promising solution to increase the efficiency of charging/discharging processes in TES systems. For instance, studies have been conducted on the thermal behavior during the charging process using radial flow and axial flow, and it was found that radial flow is better than axial flow in terms of thermal performance, where more energy can be stored in the storage tank during the charging process. However, the design of the radial pipe should be optimized to improve even flow distribution. In this work, numerical investigations on different designs were analyzed to enhance even flow gas distribution in the porous media when using this radial technique. This work shows the impact of different parameters on the even flow distribution into the packed bed for two different designs: 1) one radial tube at the center to provide the radial flow along with an annular tube at the wall to receive the flow, and 2) one radial tube at the center and four radial tubes at the wall. Air was used as fluid and 6 mm alumina beads as packing materials. Computational fluid dynamics (CFD) models in COMSOL Multiphysics were used to simulate the behavior of air flow through the piping and packed bed. It was found that the flow into the bed from the radial tube can be affected by different parameters: space between the holes, size/diameter of the holes, number of segments/zones, number of the holes in each zone and the length of each zone.

Beck, David M.↗
DOE OSTI2023

Orchestration of materials science workflows for heterogeneous resources at large scale

In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Evaluation of Radiolysis Data for Hydrogen Gas Generation During Gamma Irradiation of Pre-Corroded and Pristine Aluminum Samples - An Aluminum SNF Dry Storage Study Interim Report

Information and data from radiolysis testing to measure hydrogen (H₂) generated from hydrated oxides on aluminum exposed to ⁶⁰Co radiation were reviewed to evaluate hydrogen generation with radiation dose. Hydrogen generation rate is a primary input to the coupled thermal-chemical system model of the performance of aluminum-clad spent nuclear fuel in a dry storage canister (ASNF-in-canister) over its storage life. Hydrogen generation data and hydrogen generation rate (hydrogen generated per absorbed radiation dose, $\Delta$(H₂)/$\Delta$(dose)) were reported in two reports for radiolysis testing [1, 2]. The testing involved first-time data¹ for radiolytic yield of hydrogen from hydrated-oxides-on-aluminum substrates, and from pristine (non-corroded) aluminum substrates. The laboratory-grown hydrated-oxides-on-aluminum substrates contained a ~ 5 μm film consisting of the trihydroxide bayerite (Al(OH)₃) with amounts of the oxyhydroxide boehmite (AlOOH). These specimens were assumed to also contain physisorbed water (unquantified). The pristine substrates were flat coupons of aluminum that were ground to a 600-grit finish but were not immersed in water to grow a hydrated oxide. The pristine substrates were tested as companion specimens to provide information on hydrogen generation from material without chemisorbed water and were assumed to contain physisorbed water (unquantified).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Model Predictive Control for a Grid-interactive Efficient Thermal Storage-integrated Heat Pump System

Building heating and cooling systems can be used to overcome the mismatch between the intermittent supply of renewable power and the fluctuating demand for electricity. A novel underground thermal energy storage integrated with a dual-source heat pump has been proposed to mitigate the mismatch while meeting the thermal demand of buildings efficiently. Conventional thermostat control with heuristic rules cannot provide intelligent decisions to maximize the thermal efficiency and flexibility of the proposed system. Advanced control strategies like model predictive control (MPC) have provided a new paradigm for grid-interactive efficient building operation with the advancement of computation and sensing. This study developed an MPC for the proposed system to provide grid service for Demand Side Management and minimize the operating cost of building owners. A control-oriented dynamic model of the proposed system has been developed. Given an objective function and proper constraints, an optimization problem is formulated to determine the optimal control strategy of the system. Dynamic Programming is adopted to solve the optimization problem. A rule-based control (RBC) is also developed to achieve similar goals. Short-term simulations are conducted to compare the system performance resulting from the two controls. The simulation results indicate that the MPC performs more intelligently than the RBC in charging thermal energy storage and selecting heat pump sources by taking advantage of the predicted cooling demands of the building and the performance of the integrated system. As a result, the MPC could save energy and reduce operating costs compared with the RBC. A case study shows that, for a 3-day operation, the MPC saves 36.9% energy and reduces 38.5% operating cost compared with the RBC.

Shi, Liang↗
DOE OSTI2024

Cellulose regulated lignin/cellulose-based carbon materials with hierarchical porous structure for energy storage

Lignin has gained extensive attention as an ideal carbon precursor due to its abundance and high carbon content. However, the agglomeration of lignin and additional corrosive and unrecyclable reagents in direct pyrolysis still limit the development of lignin-based porous carbons. Herein, a facile and eco-friendly strategy was proposed to fabricate hierarchical porous lignin/cellulose-based carbon materials (LCs). In the process, cellulose nanofibrils acted as the skeleton of the bio-aerogels, which supported lignin and benefit the preparation of the LCs. Moreover, the specific surface area and the graphitization degree of LCs can be regulated by varying the cellulose content. Without activation, the bio-based carbon material (LC30) had a high specific surface area of 1770 m 2 g –1 , which displayed high specific capacitance of 216.2 F g –1 at the current density of 0.5 A g –1 . The supercapacitor based on LC30 also showed outstanding energy density of 12.3 Wh kg –1 at the power density of 50 W kg –1 . The sustainable raw material, simple and harmless preparation process, and remarkable electrochemical performance enable LC30, a promising supercapacitor electrode for energy storage.

25 ENERGY STORAGE↗
DOE OSTI2020

Novel platinum group metal-free catalyst ink deposition system for combinatorial polymer electrolyte fuel cell performance evaluation

Here, a new system for rapid and precise fabrication of polymer electrolyte fuel cell (PEFC) membrane-electrode assemblies (MEAs) is introduced to facilitate rapid testing and characterization of platinum group metal-free (PGM-free) catalysts, with minimal waste of materials, using commercial combinatorial hardware and multi-channel potentiostat. The commercial combinatorial system, designed for high-throughput catalyst activity evaluation (i.e., low overpotential and current densities) using gas-diffusion electrodes (GDEs), has been further developed to enable simultaneous evaluation of the high current density performance on air of twenty-five cathodes prepared by coating catalyst-ionomer inks directly onto the membrane using a newly-developed deposition system. A nanopipette or micropipette coupled to a precise peristaltic pump is being used for precise and even catalyst ink deposition onto the membrane in each of twenty-five circular electrodes with areas of <1 cm 2 and in the precise locations required by the commercial combinatorial MEA hardware. The automated ink deposition system is coupled with a custom-designed heated vacuum table mounted on a computer-controlled XY stage to prevent deformation of the membrane and electrode layers during ink deposition onto the membrane. This system was utilized to evaluate the impact of ionomer to catalyst ratio on the hydrogen-air fuel cell performance of the PGM-free catalyst.

25 ENERGY STORAGE↗
DOE OSTI2024

Optimizing Metadata Exchange: Leveraging DAOS for ADIOS Metadata I/O

In HPC I/O middleware like the Adaptable I/O System (ADIOS) often mediates data transfers between applications. The metadata I/O generated by such systems often presents significant scaling and performance limitations. This work seeks improvement opportunities for metadata I/O by leveraging the DAOS storage systems, a recent storage system solution deployed on high-end systems such as the Aurora supercomputer. We investigate the tradeoffs and the design space for integrating I/O engines for the ADIOS middleware based on the different storage mechanisms supported by DAOS. We present a new DAOS-Array-ChunkSize-aligned engine which provides up to 2.3× improved performance than when using the existing DAOS-POSIX interface, without requiring any application modifications.

Venkatesh, Ranjan Sarpangala↗
DOE OSTI2023

Impact of sanitizer application on Salmonella mitigation and microbiome shift on diced tomato during washing and storage

Here, this study examined the shift in Salmonella and indigenous microbiota on diced tomato after washing with three different sanitizers and during post-wash cold storage. Roma tomatoes were inoculated with a Salmonella cocktail (initial level ~ 5.6 CFU g –1 ), diced and washed along with uninoculated diced tomato in simulated flume wash water with sanitizers, including 10 mg L –1 free chlorine (FC10), 90 mg L –1 peracetic acid (PA90), PA90 in combination with a proprietary acidified surfactant blend (PS90), and unsanitized control (CK). Salmonella, total mesophilic aerobic bacteria (MAB), and yeast and mold (YM) populations on both inoculated and uninoculated samples were measured before and after washing, and during storage at 4 °C. 16S rRNA amplicon sequencing was also performed to determine the shift in bacterial microbiome. Washing with all tested sanitizers, especially PS90 (> 4 log Salmonella reduction), reduced Salmonella and MAB populations on inoculated diced tomatoes. Additionally, application of sanitizers mitigated Salmonella cross-contamination onto uninoculated samples. PS90 treatment inhibited the proliferation of most dominant bacteria on diced tomatoes during storage, including Erwiniaceae, Curtobacterium, Pantoea, Erwinia and Enterobacterales, which may benefit product quality and safety.

16S rRNA amplicon sequencing↗