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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Storage events: distributed users, federation and beyond

For federated storage to work well, some knowledge from each storage system must exist outside that system, regardless of the use case. This is needed to allow coordinated activity; e.g., executing analysis jobs on worker nodes with good accessibility to the data.Currently, this is achieved by clients notifying central services of activity; e.g., a client notifies a replica catalogue after an upload. Unfortunately, this forces end users to use bespoke clients. It also forces clients to wait for asynchronous activities to finish.dCache provides an alternative approach: storage events. In this approach the storage systems (rather than the clients) become the coordinating service, notifying interested parties of key events.At DESY, we are investigating storage events along with Apache OpenWhisk and Kubernetes to build a "serverless" cloud, similar to AWS Lambda or Google Cloud Functions, for photon science use cases.Storage events are more generally useful: catalogues are notified whenever data is uploaded or delete, tape becomes more efficient because analysis can start immediately after the data is on disk, caches can be "smart" fetching new datasets preemptively.In this paper we will present work within dCache to support a new event-based interface, with which these and other use cases become more efficient.

Millar, A.Paul↗
DOE OSTI2021

Improving Performance of Tape Restore Request Scheduling in the Storage System dCache

Given the anticipated increase in the amount of scientific data, it is widely accepted that primarily disk based storage will become prohibitively expensive. Tape based storage, on the other hand, provides a viable and affordable solution for the ever increasing demand for storage space. Coupled with a disk caching layer that temporarily holds a small fraction of the total data volume to allow for low latency access, it turns tape based systems into active archival storage (write once, read many) that imposes additional demands on data flow optimization compared to traditional backup setups (write once, read never). In order to preserve the lifetime of tapes and minimize the inherently higher access latency, different tape usage strategies are being evaluated. As an important disk storage system for scientific data that transparently handles tape access, dCache is making efforts to evaluate its recall optimization potential and is introducing a proof-of-concept, high-level stage request scheduling component within its SRM implementation.

Morschel, Lea↗
DOE OSTI2024

Extending Rucio with modern cloud storage support

Rucio is a software framework designed to facilitate scientific collaborations in efficiently organising, managing, and accessing extensive volumes of data through customizable policies. The framework enables data distribution across globally distributed locations and heterogeneous data centres, integrating various storage and network technologies into a unified federated entity. Rucio offers advanced features like distributed data recovery and adaptive replication, and it exhibits high scalability, modularity, and extensibility. Originally developed to meet the requirements of the high-energy physics experiment ATLAS, Rucio has been continuously expanded to support LHC experiments and diverse scientific communities. Recent R&D projects within these communities have evaluated the integration of both private and commercially-provided cloud storage systems, leading to the development of additional functionalities for seamless integration within Rucio. Furthermore, the underlying systems, FTS and GFAL/Davix, have been extended to cater to specific use cases. This contribution focuses on the technical aspects of this work, particularly the challenges encountered in building a generic interface for self-hosted cloud storage, such as MinIO or CEPH S3 Gateway, and established providers like Google Cloud Storage and Amazon Simple Storage Service. Additionally, the integration of decentralised clouds like SEAL is explored. Key aspects, including authentication and authorisation, direct and remote access, throughput and cost estimation, are highlighted, along with shared experiences in daily operations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Data Placement Optimization for ATLAS in a Multi-Tiered Storage System within a Data Center

Scientific experiments and computations, especially in High Energy Physics, are generating and accumulating data at an unprecedented rate. Effectively managing this vast volume of data while ensuring efficient data analysis poses a significant challenge for data centers, which must integrate various storage technologies. This paper proposes addressing this challenge by designing and developing a precise data popularity prediction model utilizing state-of-theart AI/ML techniques. This model is crafted from the analysis of ATLAS data and access patterns. It enables us to migrate infrequently accessed data to more economical storage media, such as tape drives, while storing frequently accessed data on faster yet costlier storage media like HDD or SSD. This strategic approach ensures data is placed optimally into the appropriate storage classes, thereby maximizing storage capacity while minimizing data access latency for end-users. Furthermore, the paper includes a performance evaluation of the prediction model using various key metrics such as F1 score, accuracy, precision and recall. Finally, we present a prototype use case, leveraging real-world file access data to assess the model’s impact on performance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Total cost of carbon capture and storage implemented at a regional scale: northeastern and midwestern United States

We model the costs of carbon capture and storage (CCS) in subsurface geological formations for emissions from 138 northeastern and midwestern electricity-generating power plants. The analysis suggests coal-sourced CO 2 emissions can be stored in this region at a cost of $52–$60 ton -1 , whereas the cost to store emission from natural-gas-fired plants ranges from approximately $80 to $90. Storing emissions offshore increases the lowest total costs of CCS to over $60 per ton of CO 2 for coal. Because there apparently is sufficient onshore storage in the northeastern and midwestern United States, offshore storage is not necessary or economical unless there are additional costs or suitability issues associated with the onshore reservoirs. For example, if formation pressures are prohibitive in a large-scale deployment of onshore CCS, or if there is opposition to onshore storage, offshore storage space could probably store emissions at an additional cost of less than $10 ton -1 . Finally, it is likely that more than 8 Gt of total CO 2 emissions from this region can be stored for less $60 ton -1 , slightly more than the $50 ton -1 Section 45Q tax credits incentivizing CCS.

Life Sciences & Biomedicine - Other Topics↗
DOE OSTI2022

Autoresonant excitation of nonlinear beam motion in storage rings

Understanding and controlling of nonlinear beam dynamics are essential to the performance of storage ring light sources. In order to measure and correct limitations due to nonlinear beam dynamics, an effective method is needed to excite the beam oscillation to large amplitudes. Autoresonance is a method that enables the control of the amplitude of a driven nonlinear oscillator by sweeping the frequency of the driver. In this study, autoresonant excitation of beam transverse oscillations in storage rings is studied. The threshold of the drive amplitude for autoresonance is theoretically obtained, for cases with or without damping. The results are in good agreement with simulations for a simple storage ring model, as well as for models of actual storage rings. Application of the theory to experimental data on the SPEAR3 storage ring also validates the results

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Algorithmic optimization of quantum optical storage in solids

Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low storage efficiency. We use passive optimization and algorithmic optimization techniques to demonstrate nearly a sixfold enhancement in quantum memory efficiency. In this regime, we demonstrate coherent and single-photon-level storage with a high signal-to-noise ratio. The optimization technique presented here can be applied to most solid-state quantum memories to significantly improve the storage efficiency without compromising the memory bandwidth. Published by the American Physical Society 2024

Lei, Yisheng↗
DOE OSTI2022

Equitable Design of Behind-the-Meter Energy Storage Programs: A Customer Perspective

Utility deployment of energy storage is done as a utility-scale asset connected directly to the grid (front of meter) or in partnership with a customer on the customer’s premises (behind the meter). By offering incentives for customers to install behind-the-meter storage, utilities can gain operational control of the assets and operate them in a manner that benefits all customers. Some states have also begun providing increased incentive levels to low-income customers to ensure that they can equitably share in the benefits of behind-the-meter energy storage programs. This paper evaluates different approaches to energy storage procurement from the customer’s perspective and evaluates how behind-the-meter programs can be equitably structured while keeping customers financially indifferent between front-of-meter and behind-the-meter energy storage procurements.

Twitchell, Jeremy B.↗
DOE OSTI2024

Energy Storage Market Transformation Through Stored Energy Targets

Aggressive energy decarbonization targets will require the rapid deployment of renewable energy in the U.S. in coming years. As the amount of variable generation on the grid grows, there will be an increasing need for long-duration energy storage (LDES) technologies to maintain grid reliability. Meeting that need will require a suite of technology alternatives with different capabilities. Many of these alternatives, however, are in a nascent state and face significant commercialization challenges. In the last decade, 10 U.S. states have enacted energy storage mandates as a vehicle for market transformation to accommodate energy storage. Those mandates, however, fail to send appropriate investment signals for LDES technologies because they are based on energy storage capacity and treat all technologies the same, regardless of duration. This paper explores the idea of replacing capacity-based storage procurement targets with stored energy targets, which would be technology-neutral policies designed to enable the market transformation necessary to support a competitive LDES industry.

Twitchell, Jeremy B.↗
DOE OSTI2020

Optimal Sizing of PV and Energy Storage in an Electric Vehicle Extreme Fast Charging Station

This paper proposes an optimization model for the optimal sizing of photovoltaic (PV) and energy storage in an electric vehicle extreme fast charging station considering the coordinated charging strategy of the electric vehicles. The proposed model minimizes the annualized cost of the extreme fast charging station, including investment and maintenance cost of PV and energy storage, cost of purchasing energy from utility and demand charge. The decision variables are capacity of invested PV and the power and energy ratings of invested energy storage. To further reduce the annualized cost of the extreme fast charging station, the charging strategy of electric vehicles are integrated into the optimization model and coordinated with the power output of PV and charging/discharging of energy storage. Results of numerical simulations indicate that investment of PV and energy storage could help reduce the annualized cost of the extreme fast charging station significantly. Meanwhile, the impacts of various parameters on the optimal solution are investigated by sensitivity analysis.

Liu, Guodong↗
DOE OSTI2016

Impact of Data Placement on Resilience in Large-Scale Object Storage Systems

Distributed object storage architectures have become the de facto standard for high-performance storage in big data, cloud, and HPC computing. Object storage deployments using commodity hardware to reduce costs often employ object replication as a method to achieve data resilience. Repairing object replicas after failure is a daunting task for systems with thousands of servers and billions of objects, however, and it is increasingly difficult to evaluate such scenarios at scale on realworld systems. Resilience and availability are both compromised if objects are not repaired in a timely manner. In this work we leverage a high-fidelity discrete-event simulation model to investigate replica reconstruction on large-scale object storage systems with thousands of servers, billions of objects, and petabytes of data. We evaluate the behavior of CRUSH, a well-known object placement algorithm, and identify configuration scenarios in which aggregate rebuild performance is constrained by object placement policies. After determining the root cause of this bottleneck, we then propose enhancements to CRUSH and the usage policies atop it to enable scalable replica reconstruction. We use these methods to demonstrate a simulated aggregate rebuild rate of 410 GiB/s (within 5% of projected ideal linear scaling) on a 1,024-node commodity storage system. We also uncover an unexpected phenomenon in rebuild performance based on the characteristics of the data stored on the system.

Carns, Philip↗
DOE OSTI2018

Storage for Advanced Scientific Use-Cases and Beyond

The dCache project provides open-source storage software deployed internationally to satisfy ever more demanding scientific storage requirements. Its multifaceted approach provides an integrated way of supporting different use-cases with the same storage, from high throughput data ingest, through wide access and easy integration with existing systems. In this paper, we describe some of the recent features that facilitate the use of storage to maximise the gain from stored data, including quality-of-service management, heterogeneous systems - both through integrated tertiary storage support and geographical locality - the parallel NFS (pNFS) extension, and innovative delegated authorisation schemes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

Sharing Storage in a Smart Grid: A Coalitional Game Approach

Sharing economy is a transformative socio-economic phenomenon built around the idea of sharing underused resources and services, e.g., transportation and housing, thereby reducing costs and extracting value. Anticipating continued reduction in the cost of electricity storage, we look into the potential opportunity in electrical power system where consumers share storage with each other. We consider two different scenarios. In the first scenario, consumers are assumed to already have individual storage devices and they explore cooperation to minimize the realized electricity consumption cost. In the second scenario, a group of consumers is interested to invest in joint storage capacity and operate it cooperatively. The resulting system problems are modeled using cooperative game theory. Finally, in both cases, the cooperative games are shown to have non-empty cores and we develop efficient cost allocations in the core with analytical expressions. Thus, sharing of storage in cooperative manner is shown to be very effective for the electric power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Real-time deep-learning inversion of seismic full waveform data for CO 2 saturation and uncertainty in geological carbon storage monitoring

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

58 GEOSCIENCES↗
DOE OSTI2013

Preservation of H 2 production activity in nanoporous latex coatings of Rhodopseudomonas palustris CGA009 during dry storage at ambient temperatures: Preservation of R.palustris latex coatings

To assess the applicability of latex cell coatings as an ‘off-the-shelf’ biocatalyst, the effect of osmoprotectants, temperature, humidity and O 2 on preservation of H 2 production in Rhodopseudomonas palustris coatings was evaluated. Immediately following latex coating coalescence (24 h) and for up to 2 weeks of dry storage, rehydrated coatings containing different osmoprotectants displayed similar rates of H 2 production. Beyond 2 weeks of storage, sorbitol-treated coatings lost all H 2 production activity, whereas considerable H 2 production was still detected in sucrose- and trehalose-stabilized coatings. We stored the coatings at a relative humidity level which significantly impacts the recovery and subsequent rates of H 2 production. After 4 weeks storage under air at 60% humidity, coatings produced only trace amounts of H 2 (0–0.1% headspace accumulation), whereas those stored at < 5% humidity retained 27–53% of their H 2 production activity after 8 weeks of storage. Furthermore, when stored in argon at < 5% humidity and room temperature, R. palustris coatings retained full H 2 production activity for 3 months, implicating oxidative damage as a key factor limiting coating storage. Ultimately, the results demonstrate that biocatalytic latex coatings are an attractive cell immobilization platform for preservation of bioactivity in the dry state.

09 BIOMASS FUELS↗
DOE OSTI2018

On‐line discrimination of storage shelf‐life and prediction of post‐harvest quality for strawberry fruit by visible and near infrared spectroscopy

Abstract Quick assessment of storage time is important for the guarantee of fruit shelf‐life. On‐line discrimination of refrigerated storage time and prediction of post‐harvest quality for strawberry fruit was carried out by visible and near‐infrared (Vis/NIR) spectroscopy. For the classification of samples with storage time of 1, 3, 5, and 7 days, partial least square discriminant analysis (PLS‐DA) achieved correct classified rates of 93.3–97.4% in prediction based on selected wavelengths obtained by competitive adaptive reweighted sampling (CARS) at speeds of 0.05, 0.10, and 0.15 m/s. Moreover, for prediction of soluble solids content (SSC) in strawberry, the determination coefficient ( r p 2 ), root mean square error of prediction (RMSEP), and RPD value obtained by PLS coped with CARS at fruit moving speed of 0.10 m/s was 0.733, 0.699 °Brix and 1.96, respectively. Overall, this study indicates that Vis–NIR spectroscopy offers the potential as an on‐line tool for quality assurance of strawberry fruit. Practical applications Visible and near‐infrared (Vis/NIR) spectroscopy is a very attractive technique for nondestructive evaluation of quality of food products. Strawberry has an extremely short shelf‐life which is estimated to be less than 5 days. Rapid identification its postharvest storage time is essential for the evaluation of its shelf‐life. Based on the results reported here, Vis–NIR has the potential for on‐line monitoring of postharvest storage time and post‐harvest quality for strawberry fruit in real situation.

Shen, Fei↗
DOE OSTI2018

Catalyst Ammonia Storage Measurements Using Radio Frequency Sensing1

Motivated by increasingly strict nitrogen oxides (NOx) limits, engine manufacturers have adopted selective catalytic reduction (SCR) technology to reduce engine-out NOx. In the SCR process, NOx react with ammonia (NH3) to form nitrogen and water vapor. The reaction is influenced by several variables, including stored ammonia on the catalyst, exhaust gas composition, and catalyst temperature. Currently, measurements from NOx and/or NH3 sensors upstream and downstream of the SCR are used with predictive models to estimate ammonia storage levels on the catalyst and control urea dosing. This study investigated a radio frequency (RF)-based method to directly monitor the ammonia storage state of the SCR. This approach utilizes the catalyst as a cavity resonator, in which an RF antenna excites electromagnetic waves within the cavity to monitor changes in the catalyst state. Ammonia storage causes changes in the dielectric properties of the catalyst, which directly impacts the RF signal. Changes in the RF signal relative to stored ammonia (NH3) were evaluated over a wide range of frequencies, temperatures, and exhaust conditions. The RF response to NH3 storage, desorption, and oxidation on the SCR was well correlated with changes in the catalyst state. Calibrated RF measurements demonstrate the ability to monitor the adsorption state of the SCR to within 10% of the sensor full scale. The results indicate direct measurement of SCR ammonia storage levels, and resulting catalyst feedback control, via RF sensing to have significant potential for optimizing the SCR system to improve NOx conversion and decrease urea consumption.

Engineering↗
DOE OSTI2021

Experimental Study of Thermal Management and Enhancement of Adsorption-Based Onboard Hydrogen Storage System

Abstract Although hydrogen has one of the highest specific energies, its energy density in terms of volume is very poor compared to liquid fuels. Thus, to achieve attractive energy density for hydrogen, either high-pressure compression or a storage method is needed. For onboard (vehicles) hydrogen storage, up to 700 bars are needed for commercial fuel cell vehicles. This creates extreme requirements for material strength and thus safety concerns. A new metal-organic framework 5 (MOF-5) was selected as the adsorbent for H2 storage, as it provides promising storage capacity and is commercially available. Under the same H2 storage capacity and tank volume, the adsorption system is expected several folds reduction in pressure. Under the current study, a unique thermal management design using Modular Adsorbing Tank Insert (MATI) is paired with conduction-enhanced compressed MOF-5 beds. Compared to bare beds without conduction enhancement, all beds with conduction enhancement using either aluminum pins or expanded natural graphite (ENG) have shown various levels of improvement on bed thermal response, which can potentially help expedite system charge and discharge cycle times for real applications.

Electrochemistry↗