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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Operando Neutron Imaging of Reaction Extent and Particle Swelling Informs Limiting Factors for Salt Hydrate Thermochemical Energy Storage

Salt hydrates are a promising thermochemical energy storage medium that stores heat through the reversible uptake (hydration) and release (dehydration) of water vapor. Our study deploys operando neutron imaging to investigate salt hydrate performance with high spatial resolution (42 μm pixels). For flow over a packed bed with diffusion-driven transport, measurements reveal the formation of a solid diffusion layer due to particle swelling for the pure SrBr2 salt. In contrast, the SrBr2–vermiculite composite exhibits significantly less swelling and more than a 2-fold increase in the apparent water vapor diffusivity. For axial flow through a packed bed, neutron imaging confirms theoretically predicted transitions from a moving reaction front to a homogeneous profile with an increase in humid air flow rate. Our study establishes neutron imaging as a powerful technique to advance fundamental understanding of thermochemical systems and help guide composite material design.

Kinzer, Bryan [ORNL] (ORCID:0000000337804910)↗
DOE OSTI2021

Optimization models for integrated biorefinery operations

Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US’s cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system’s reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. Furthermore, the sample average approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory’s biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution, increases utilization of the reactor and reduces operational costs.

09 BIOMASS FUELS↗
DOE OSTI2020

Compositions including nano-particles and a nano-structured support matrix and methods of preparation as reversible high capacity anodes in energy storage systems

The present invention relates to compositions including nano-particles and a nano-structured support matrix, methods of their preparation and applications thereof. The compositions of the present invention are particularly suitable for use as anode material for lithium-ion rechargeable batteries. The nano-structured support matrix can include nanotubes, nanowires, nanorods, and mixtures thereof. The composition can further include a substrate on which the nano-structured support matrix is formed. The substrate can include a current collector material.

Kumta, Prashant Nagesh↗
DOE OSTI2021

On the possibility of footprint compression with one lens in nonlinear accelerator lattice

Electromagnetic interaction of colliding beams along with other nonlinear fields often limits the beams' lifetimes and luminosities. Nonlinearities result in the spread of betatron frequencies (footprint) and, thus, may enhance dynamic diffusion of particles due to high order resonances. One of the possible ways to eliminate nonlinearities and overcome the corresponding difficulties is compensation of nonlinear forces, but, in practice, it is hardly possible to obtain exact linearity of the system. The compensation with a single nonlinear lens cannot cope with distributed nonlinearities, nonlinearities due to parasitic crossings, etc. Here, we present a method to compute parameters of nonlinear element (lens) that eliminates both the footprint and resonance strength without achieving full compensation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Numerical modeling of a proton spin-flipping system in the spin transparency mode at an integer spin resonance in JINR's Nuclotron

In this paper we propose a lattice insertion for the Nuclotron ring called a “spin navigator” that can adjust any direction of the proton polarization in the orbital plane using weak solenoids. The polarization control is realized in the spin transparency mode at the energy of 108 MeV, which corresponds to the integer spin resonance γ G = 2. The requirements on the navigator solenoid fields are specified considering the criteria for stability of the spin motion during any manipulation of the polarization direction in an experiment. Additionally, this paper presents the results of numerical modeling of the proton spin dynamics in the Nuclotron ring operated in the spin transparency mode. The verified spin navigator is aimed at an experimental study of a spin-flipping system using the Nuclotron ring. The results are relevant to the NICA (JINR), EIC (BNL) and COSY (FZJ) facilities where the spin transparency mode can be applied for polarization control.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

guppy i : a code for reducing the storage requirements of cosmological simulations

ABSTRACT As cosmological simulations have grown in size, the permanent storage requirements of their particle data have also grown. Even modest simulations present a major logistical challenge for the groups which run these boxes and researchers without access to high performance computing facilities often need to restrict their analysis to lower quality data. In this paper, we present guppy, a compression algorithm and code base tailored to reduce the sizes of dark matter-only cosmological simulations by approximately an order of magnitude. guppy is a ‘lossy’ algorithm, meaning that it injects a small amount of controlled and uncorrelated noise into particle properties. We perform extensive tests on the impact that this noise has on the internal structure of dark matter haloes, and identify conservative accuracy limits which ensure that compression has no practical impact on single-snapshot halo properties, profiles, and abundances. We also release functional prototype libraries in C, Python, and Go for reading and creating guppy data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Cold ion beam in a storage ring as a platform for large-scale quantum computers and simulators: Challenges and directions for research and development

The purpose of this paper is to evaluate the possibility of constructing a large-scale storage-ring-type ion-trap system capable of storing, cooling, and controlling a large number of ions as a platform for scalable quantum computing (QC) and quantum simulations. In such a trap, the ions form a crystalline beam moving along a circular path with a constant velocity determined by the frequency and intensity of the cooling lasers. In this paper, we consider a large leap forward in terms of the number of ions that serve as qubits in QC, from fewer than 100 available in state of the art linear ion-trap devices today to an order of 10 5 crystallized ions in the storage-ring setup. This new trap design unifies two different concepts: the storage rings of charged particles and the linear ion traps used for QC and mass spectrometry. In this paper, we use the language of particle accelerators to discuss the ion state and dynamics. We outline the differences between the above concepts, analyze challenges of the large ring with a revolving chain of ions, and propose goals for the research and development required to enable future quantum computers with 1000 times more qubits than available today. The challenge of creating such a large-scale quantum system while maintaining the necessary coherence of the qubits and the high fidelity of quantum logic operations is significant. Performing analog quantum simulations may be an achievable initial goal for such a device. Quantum calculations and simulations of complex quantum systems will move forward both the fundamental science and the applied research. Nuclear and particle physics, many-body quantum systems, lattice gauge theories, and nuclear structure calculations are just a few examples in which a large-scale quantum simulation system will become a very powerful tool to move forward our understanding of nature.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Techno-Economic Analysis of a 50MWth Light-Trapping Cavity-Planar Solar Receiver Tower Capital Expenditures and its Cost Mitigation Strategies

To maximize thermal efficiency, the National Renewable Energy Laboratory (NREL) has proposed a light-trapping cavity-planar receiver design intended to capture energy from reradiating surfaces. This system implements a macroscale light trapping mechanism induced by panels with triangular channels, this mechanism allows for elevated temperatures on the receiver panels and in turn the temperature of the HTF; using this design coupled with the implementation of a fluidized particle flow as the HTF, it can be expected for some components to reach a peak working temperature of nearly 1000 degrees C cyclically throughout each day-night cycle. While these temperatures correlate to higher efficiency of the CSP tower they also demand intense thermomechanical properties from the materials used to make the receiver panels. In this analysis, we will list our assumptions to provide clarity on the significance of our calculation. This cost analysis will be conducted using a combination of both case study data and surveying industry to determine costs that are relevant to the current market trends. This analysis represents an early attempt to establish the Capital Expenditures required for a CSP tower of such design to determine the feasibility of implementing such a system in the industry.

concentrated solar power (CSP)↗
DOE OSTI2025

Thermomechanical Modeling and Analysis of a High-Temperature Light Trapping Planar Cavity Receiver

Solar energy harnessed through concentrating solar power (CSP) systems offers a promising path to sustainable energy production, with the efficiency and longevity of these systems relying on key components like solar receivers. This study analyzes the thermomechanical behavior of an innovative enclosed light-trapping solar receiver optimized for particle heating applications. The receiver utilizes sheet metal alloys to form enclosed cavities that reflect and trap incoming solar flux, as well as enclosed channels that contain fluidized particle beds absorbing solar heat. Finite element analysis (FEA) is applied to predict the receiver's thermomechanical performance under extreme solar flux conditions. Temperature distributions from a thermal model simulating a multi-panel assembly at steady state are input into the FEA thermomechanical model for stress analysis. A key aspect of the analysis focuses on evaluating creep-fatigue damage, with a design target of achieving a 30- year service life. Various stress relief techniques are also proposed to extend the receiver's service life. The results highlight the significant impact of the particle-to-wall heat transfer coefficients (HTCs), ranging from 800 W/m2*K to 1400 W/m2*K. The 800 W/m2*K case shows a maximum von Mises stress of 164 MPa, while the 1400 W/m2*K case reduces it to 150 MPa. The creep life increases from 4,000 hrs in the 800 W/m2*K case to over 100,000 hrs in the 1400 W/m2*K case with Inconel 740H used, indicating that higher HTCs reduce stress and extend lifespan. This research advances the design of high-efficiency, low-stress solar receivers for particle-based thermal energy storage in CSP and industrial heating applications.

concentrating solar power↗
DOE OSTI2023

2022 LDRD Annual Report

This summary report provides an overview of all LDRD projects at Argonne that concluded in Fiscal Year 2022. Many projects are funded for multiple years, the initial fiscal year for each project is indicated by the first four digits of the LDRD project number.

25 ENERGY STORAGE↗
DOE OSTI2023

Neural Network-Enhanced Reproducing Kernel Particle Method for Image-Based Multiphysics Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant stresses during charge/discharge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), image-based modeling techniques are used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electrochemical-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in capturing crack propagation due to mesh dependency. Additionally, commonly used damage models, such as the continuous damage model and the cohesive zone model, often have steep tradeoffs between discontinuous field accuracy and computational expense. In this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022.

damage modeling↗
DOE OSTI2023

Enhancing the Chemical Energy Flux in a High-Temperature Tubular Counterflow Solid Fuel Synthesis Reactor Using a Bypass

Redox reactions of metal oxides offer a path towards using intermittent renewable resources for high-density thermochemical energy storage. Thermochemical energy storage often involves the flow of a particulate media. We describe a novel method to increase the throughput in a gravity-driven high-temperature thermochemical storage reactor flowing pelletized MgMnO. The moving bed reactor operates under counter-flow conditions and encounters particle flowability problems at temperatures of 1500 °C leading to sintering of the bed. Inertial forces of a counter-flowing gas can overcome the gravitational forces on the particles and limit the chemical energy storage rate of the reactor. We found that the insertion of a gas bypass (a slotted tube) into the reactor results in a 100% increase of the flow rates and achieved a 50% higher chemical energy storage flux compared to the operation without a bypass tube while mitigating the effects of sintering on the particles. As a result, the higher solid flow rates require a longer heated zone to reach a comparable residence time and extent of reduction compared to the lower flow rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Technoeconomic Analysis of Steel Production with Electric Thermal Energy Storage

The iron and steel industry is an important manufacturing sector and one of the largest energy consumers in the United States and globally. Hydrogen direct reduction of iron ore (H2DRI) is considered a promising process that could enhance domestic steel production. This process requires hydrogen inlet temperatures up to 950 degrees C to drive the endothermic reduction of the iron ore pellets. In this work we investigate the technoeconomic performance of an H2DRI plant using electric thermal energy storage (ETES) technologies for the hydrogen heating, compared to conventional natural gas fired heaters, hydrogen fired heaters, and electric hydrogen heaters. A technoeconomic analysis framework for the plant is developed and used in multiple case studies, covering different hydrogen prices, grid electricity profiles, and financing scenarios. The levelized cost of steel production is found out to be in the range of $775-950/mt, which is mostly inside the benchmarked steel price of $941/mt. ETES-based hydrogen heating is found to be in par with conventional natural gas fired heaters, and cheaper than hydrogen fired heaters and electric hydrogen heaters. The major cost drivers are the iron ore and hydrogen feedstock, followed by the hydrogen compression and heating capital. Several insights and suggested future directions are identified.

08 HYDROGEN↗
DOE OSTI2024

Numerical modeling of a proof-of-principle experiment on optical stochastic cooling at an electron storage ring

Cooling of beams circulating in storage rings is critical for many applications including particle colliders and synchrotron light sources. A method enabling unprecedented beam-cooling rates, optical stochastic cooling (OSC), was recently demonstrated in the Integrable Optics Test Accelerator (IOTA) electron storage ring at Fermilab [J. Jarvis , ]. This paper describes the numerical implementation of the OSC process in the particle-tracking program and discusses the validation of the developed model with available experimental data. The model is also employed to highlight some features associated with different modes of operation of OSC. The developed simulation tool should be valuable in guiding future configurations of optical stochastic cooling and, more broadly, modeling self-field-based beam manipulations. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Measurements of scattering and absorption properties of submillimeter bauxite and silica particles

Submillimeter solid particles have been considered as thermal storage media for concentrated solar power applications. Knowledge of the scattering and absorption properties of individual particles is crucial for modeling the radiative heat transfer of the particle bed. In this work, a laser scatterometer is used to measure the single-particle scattering properties at a wavelength of 635 nm by using two configurations: (1) a falling particle curtain and (2) a taped particle layer. Because a one-particle nominal thickness is formed with area fractions of 5-55% depending on the configuration, multiple scattering is minimized and hence the single scattering phase function, averaged over all illuminated particles, is directly measured. Bauxite-based ceramic particles that are strongly absorbing in the solar spectrum and silica particles that are nonabsorbing in the visible and near-infrared are investigated. The directional-hemispherical reflectance and transmittance of the taped particles are also measured to deduce the forward and backward scattering efficiency factors and the absorption efficiency factors. Only weak wavelength dependence is observed in the measured region from 380 nm to 1020 nm. Furthermore, the scattering phase functions of all bauxite-based particles with varying sizes and compositions are very similar and can be fitted to a Henyey-Greenstein phase function with an asymmetry factor g = -0.20. For the silica particles, forward scattering dominates and g = 0.45 yields the best fit. A Monte Carlo method is developed to model the particle scattering characteristics, and reasonable agreements between the modeling and experimental results are observed by introducing a specularity parameter.

42 ENGINEERING↗
DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part I: Implementation and multi-fidelity hierarchies for the single-particle model

To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapidly. Here, this study seeks to reduce the computational resources needed to determine a battery's internal states by replacing physics-based Li-ion battery models - such as the single-particle model (SPM) and the pseudo-2D (P2D) model - with a physics-informed neural network (PINN) surrogate. The surrogate model makes high-throughput techniques, such as Bayesian calibration, tractable to determine battery internal parameters from voltage responses. This manuscript is the first of a two-part series that introduces PINN surrogates of Li-ion battery models for parameter inference (i.e., state-of-health diagnostics). In this first part, a method is presented for constructing a PINN surrogate of the SPM. A multi-fidelity hierarchical training, where several neural nets are trained with multiple physics-loss fidelities is shown to significantly improve the surrogate accuracy when only training on the governing equation residuals. The implementation is made available in a companion repository (https://github.com/NREL/PINNSTRIPES). The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.

25 ENERGY STORAGE↗