Search NASA⌕ Search

SEARCH · Search NASA

Results for “CAE”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Technical Feasibility of Compressed Air Energy Storage (CAES) Utilizing a Porous Rock Reservoir

Pacific Gas & Electric Company (PG&E) conducted a project to explore the viability of underground compressed air energy storage (CAES) technology. CAES uses low-cost, off-peak electricity to compress air into a storage system in an underground space such as a rock formation or salt cavern. When electricity is needed, the air is withdrawn and used to drive a generator for electricity production.

25 ENERGY STORAGE↗
DOE OSTI2024

WAVES-CAE: Release 0.1.0

Demo problem for running WAVES workflows with CAE files. May eventually become part of the WAVES “supplemental” lessons.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Hybrid simulations of sub-cyclotron compressional and global Alfvén eigenmode stability in spherical tokamaks

A comprehensive numerical study has been conducted in order to investigate the stability of beam-driven, sub-cyclotron-frequency compressional Alfvén eigenmodes (CAEs) and global Alfvén eigenmodes (GAEs) in low-aspect-ratio plasmas for a wide range of beam parameters. The presence of CAEs and GAEs has previously been linked to anomalous electron temperature profile flattening at high beam powers in NSTX experiments, prompting a further examination of the conditions necessary for their excitation. Linear simulations have been performed with the hybrid MHD–kinetic initial value code HYM in order to capture the general Doppler-shifted cyclotron resonance that drives the modes. Additionally, three distinct types of modes were found in the simulations—co-CAEs, cntr-GAEs, and co-GAEs—with differing spectral and stability properties. The simulations revealed that unstable GAEs are more ubiquitous than unstable CAEs, which is consistent with experimental observations, as they are excited at lower beam energies and generally have larger growth rates. Local analytic theory is used to explain key features of the simulation results, including the preferential excitation of different modes based on beam injection geometry and the growth rate dependence on the beam injection velocity, critical velocity, and degree of velocity space anisotropy. The background damping rate is inferred from the simulations and analytically estimated for relevant sources absent from the simulation model, indicating that co-CAEs are closer to marginal stability than modes driven by the cyclotron resonances.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Hybrid simulations of sub-cyclotron compressional and global Alfven Eigenmode stability in spherical tokamaks

A comprehensive numerical study has been conducted in order to investigate the stability of beam-driven, sub-cyclotron frequency compressional (CAE) and global (GAE) Alfven Eigenmodes in low aspect ratio plasmas for a wide range of beam parameters. The presence of CAEs and GAEs has previously been linked to anomalous electron temperature profile flattening at high beam power in NSTX experiments, prompting further examination of the conditions for their excitation. Linear simulations are performed with the hybrid MHD-kinetic initial value code HYM in order to capture the general Doppler-shifted cyclotron resonance that drives the modes. Three distinct types of modes are found in simulations -- co-CAEs, cntr-GAEs, and co-GAEs -- with differing spectral and stability properties. The simulations reveal that unstable GAEs are more ubiquitous than unstable CAEs, consistent with experimental observations, as they are excited at lower beam energies and generally have larger growth rates. Local analytic theory is used to explain key features of the simulation results, including the preferential excitation of different modes based on beam injection geometry and the growth rate dependence on the beam injection velocity, critical velocity, and degree of velocity space anisotropy. The background damping rate is inferred from simulations and estimated analytically for relevant sources not present in the simulation model, indicating that co-CAEs are closer to marginal stability than modes driven by the cyclotron resonances.

Compressional Alfven Eigenmodes↗
DOE OSTI2025

Techno-Economic Analysis of Compressed Air Energy Storage and Hydrogen Production from Variable Renewable Energy

This study presents the techno-economic analysis (TEA) results of integrating electrolysis hydrogen (H 2 ) production, compressed air energy storage (CAES), and H 2 -fired combustion turbines in a high variable renewable energy (VRE) market environment. The inconsistent nature of VRE creates challenges for power producers in maintaining a stable electrical grid as it is increasingly utilized. The mission of the National Energy Technology Laboratory (NETL) is driving innovation and delivering energy solutions, and a multiangle approach involving H 2 production, energy storage, and next-level H 2 -fired combustion turbine generator (CTG) technologies could provide one solution for the nation’s growing electrical grid issues with increasing VRE sources. The H 2 production and CAES concept were investigated due to its ability to provide utility-scale H 2 -fueled power generation with large-scale energy storage capabilities. Two facilities with CAES and natural gas-fired CTGs (in McIntosh, Alabama, and Huntorf, Germany) have been operating for decades. In contrast, this study investigates the potential to replace the natural gas fuel with H 2 fuel. This concept has been publicly presented by both Siemens Energy and Bechtel Global Engineering, Construction & Project Management (Bechtel) at two different power generating levels. The CAES and air expansion/combustion turbine power generation in this study are primarily based on the Siemens Energy system (Bailie, Aug. 10-11, 2021) (Scheller, Feb. 21, 2023). The inclusion of a H 2 turboexpander generator is from Bechtel (Gülen, Sep. 6, 2022). A block flow diagram of the proposed process is illustrated in Exhibit ES-1.

08 HYDROGEN↗
DOE OSTI2021

De-noising drift chambers in CLAS12 using convolutional auto encoders

Modern Nuclear Physics experimental setups run experiments with higher beam intensity resulting in increased noise in detector components used for particle track reconstruction. Increased uncorrelated signals (noise) result in decreased particle reconstruction efficiency. In this paper, we investigate the usage of Machine Learning, specifically Convolutional Neural Network Auto-Encoders (CAE), for de-noising raw hits from drift chambers in the CLAS12 detector. To the best of our knowledge, this is the first time CAE is employed to perform such an operation in this field. During the de-noising phase, it is important to remove as much noise as possible while retaining the valid hits to avoid losing crucial information about the experiment. Here, we show that using CAE, it is possible to remove noise hits while retaining up to 94% of valid tracks for a beam current of 110nA while for lower beam currents (45-55nA), we get up to 98% efficiency. Studies on experimental conditions with increasing noise show that CAE performs better than conventional tracking algorithms in isolating hits belonging to tracks. Specifically, the de-noising algorithm results in tracking efficiency improvements greater than 15%, in real data production procedures with nominal conditions, and up to two times better efficiency in synthetically generated data with high luminosity conditions (90-110nA), indicating that machine learning can lead to significantly shorter times for conducting physics experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Illinois Compressed Air Energy Storage

Compressed Air Storage Energy (CAES) is one of the few mid- technology readiness level (TRL) energy storage technologies that can address the long-duration infrastructure needed for dealing with variable electric output from renewable energy sources and be reliable backup source for replacing natural gas during supply interruptions. In CAES the goal is to capture and store compressed air in subsurface sedimentary strata when off-peak power is available, or there is a need for grid balancing. The stored high-pressure air is returned to the surface and used to power turbines during reductions in either renewable energy or supply issues with fossil fuels. The Illinois CAES project evaluates the feasibility of capturing surplus electrical energy from renewable sources and off-peak energy at a fossil fuel power plant at the University of Illinois Urbana - Champaign (UIUC) campus. The UIUC Abbott Power Plant uses natural gas and coal to generate electricity (capacity: 35 MWe by coal and 49 MWe by NG). UIUC receives additional electricity from on campus solar farm, and off-campus wind farm. Also, UIUC offsets electricity usage by integrating geothermal energy systems into building heating Also, UIUC offsets steam, hot and chilled water usage by integrating geothermal energy systems into building heating and cooling systems. Furthermore, the two UIUC solar farms (Solar Farm 1 is 21 acres and Solar Farm 2 is 54 acres) to generate 4.68 megawatts (MW) and 12.1 MW, respectively. Campus receives 8.6% of the wind-generated electricity from the Rail Splitter Wind Farm. The project objectives were to design an integrated system to 1) capture surplus electrical energy from renewable sources and the Abbott Power Plant using a CAES system, 2) store both the compressed air and the thermal heat generated by compression in the subsurface as part of an adiabatic system, 3) simulate the movement of the air and heat in the subsurface, 4) recover the compressed air and stored thermal heat to rotate turbine generators during sustained interruption due to weather events or fossil fuel disruptions.

03 NATURAL GAS↗
DOE OSTI2022

Benchmark Exercise for the Control Rod Swelling Evaluation

The VTR core has six reactivity control assemblies and three safety assemblies. The control assemblies or primary control rods are adjusted during the normal operation to balance the core reactivity and to control the reactor power. A typical control assembly radial layout is presented in Figure 1. The figure shows the swelled absorber (B 4 C) rod. Initially, helium gas fills the gap between the pin and the cladding before irradiation swelling takes place. For VTR, HT9 steel was selected as the cladding and duct material. The main neutron absorbing material used in the VTR is B 4 C. When residing in the core, the neutronics, thermophysical, and mechanical properties of the materials used in a control assembly will degrade due to accumulated neutron damage. Material degradation limits how long a control assembly can reside in the core. Many phenomena affect the control assembly lifetime, such as the loss of reactivity worth due to B 4 C depletion, the mechanical interaction of the absorber rod and the cladding due to B 4 C swelling, the helium gas buildup in the pin due to B-10 capture, etc. B 4 C swelling, which causes closure of the gap between the absorber rod and the cladding, is usually considered as the main limiting factor from past experience. An initial study was conducted at PNNL to evaluate the irradiation behavior of a VTR control assembly. The evaluation was performed using the CNRD2 code that was initially developed for the FFTF. The study also included an assessment of the VTR control assembly and focused on a 61-pin control assembly design, which is different from that used (37-pin design) in the core design study. The study conducted by PNNL was reviewed independently by ANL. A Python script referred to as the Control Assembly Evaluation Script (CAES) was developed for the independent review and additional assessment of 37-pin control assembly design. The script has focused on the assessment of the absorber rod swelling for its importance in determining the control assembly lifetime. CAES uses geometry, neutronics, materials data as input to predict the swelling of the absorber rod during its residence in the reactor core. The results from CAES showed some non-negligible differences against the PNNL results. Some of the differences can be attributed to the different interpretation of the control rod assembly dimensions. To resolve this issue, a benchmark exercise was proposed. The benchmark specification was developed by PNNL. The benchmark exercise was performed independently at PNNL and ANL using different codes/scripts (CRND2 and CAES). This memo documents the results calculated using the different codes. However, this report is limited to presenting the results obtained. Further investigation of the cause of the observed difference will be performed as part of future activities, pending continuation of the VTR program.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Techno-Economic Analysis of a Thermally Integrated Solid Oxide Fuel Cell and Compressed Air Energy Storage Hybrid System

Natural-gas-fueled solid oxide fuel cell (SOFC) systems have the potential for high-efficiency conversion of carbon to power due to the underlying electrochemical conversion process while readily facilitating carbon capture through the separation of the fuel and oxidant sources. Compressed air energy storage (CAES) technology can potentially store significant quantities of energy for later use with a high round-trip efficiency and lower cost when compared with state-of-the-art battery technology. The base load generation capability of SOFC can be coupled with CAES technology to provide a potentially flexible, low-carbon solution to meet the fluctuating electricity demands imposed by the increasing share of intermittent variable renewable energy (VRE) production. SOFC and CAES can be hybridized through thermal integration to maximize power output during periods of high electrical demand and then store power when either demand is low or renewable generation reduces power prices. The techno-economics of a low-carbon hybrid SOFC and CAES system was developed and investigated in the present study. The proposed hybrid system was found to be cost-competitive with other power-generating base-load facilities when power availability was considered. The hybrid system shows increased resilience to changes in a high VRE grid market scenario.

25 ENERGY STORAGE↗
DOE OSTI2022

A comparison of neural network architectures for data-driven reduced-order modeling

The popularity of deep convolutional autoencoders (CAEs) has engendered new and effective reduced-order models (ROMs) for the simulation of large-scale dynamical systems. Despite this, it is still unknown whether deep CAEs provide superior performance over established linear techniques or other network-based methods in all modeling scenarios. To elucidate this, the effect of autoencoder architecture on its associated ROM is studied through the comparison of deep CAEs against two alternatives: a simple fully connected autoencoder, and a novel graph convolutional autoencoder. Through benchmark experiments, it is shown that the superior autoencoder architecture for a given ROM application is highly dependent on the size of the latent space and the structure of the snapshot data, with the proposed architecture demonstrating benefits on data with irregular connectivity when the latent space is sufficiently large.

42 ENGINEERING↗
DOE OSTI2021

Evaluation of Energy Storage Potential of Unconventional Shale Reservoirs Using Numerical Simulation of Cyclic Gas Injection

Compressed air energy storage (CAES) stores energy as compressed air in underground formations, typically salt dome caverns. When electricity demand grows, the compressed air is released through a turbine to produce electricity. CAES in the US is limited to one plant built in 1991, due in part to the inherent risk and uncertainty of developing subsurface storage reservoirs. As an alternative to CAES, we propose using some of the hundreds of thousands of hydraulically fractured horizontal wells to store energy as compressed natural gas in unconventional shale reservoirs. To store energy, produced or “sales” natural gas is injected back into the formation using excess electricity and is later produced through an expander to generate electricity. To evaluate this concept, we performed numerical simulations of cyclic natural gas injection into unconventional shale reservoirs using cmg-gem commercial reservoir modeling software. We tested short-term (diurnal) and long-term (seasonal) energy storage potential by modeling well injection and production gas flowrates as a function of bottom-hole pressure. First, we developed a conceptual model of a single fracture stage in an unconventional shale reservoir to characterize reservoir behavior during cyclic injection and production. Next, we modeled cyclic injection in the Marcellus shale gas play using published data. Results indicate that Marcellus unconventional shale reservoirs could support both short- and long-term energy storage at capacities of 100–1000 kWe per well. The results indicate that energy storage in unconventional shale gas wells may be feasible and warrants further investigation.

25 ENERGY STORAGE↗
DOE OSTI2023

Techno-Economic Analysis and Optimization of a Compressed-Air Energy Storage System Integrated with a Natural Gas Combined-Cycle Plant

To address the rising electricity demand and greenhouse gas concentration in the environment, considerable effort is being carried out across the globe on installing and operating renewable energy sources. However, the renewable energy production is affected by diurnal and seasonal variability. To ensure that the electric grid remains reliable and resilient even for the high penetration of renewables into the grid, various types of energy storage systems are being investigated. In this paper, a compressed-air energy storage (CAES) system integrated with a natural gas combined-cycle (NGCC) power plant is investigated where air is extracted from the gas turbine compressor or injected back into the gas turbine combustor when it is optimal to do so. First-principles dynamic models of the NGCC plant and CAES are developed along with the development of an economic model. The dynamic optimization of the integrated system is undertaken in the Python/Pyomo platform for maximizing the net present value (NPV). NPV optimization is undertaken for 14 regions/cases considering year-long locational marginal price (LMP) data with a 1 h interval. Design variables such as the storage capacity and storage pressure, as well as the operating variables such as the power plant load, air injection rate, and air extraction rate, are optimized. Results show that the integrated CAES system has a higher NPV than the NGCC-only system for all 14 regions, thus indicating the potential deployment of the integrated system under the assumption of the availability of caverns in close proximity to the NGCC plant. The levelized cost of storage is found to be in the range of 136–145 $/MWh. Roundtrip efficiency is found to be between 74.6–82.5%. A sensitivity study with respect to LMP shows that the LMP profile has a significant impact on the extent of air injection/extraction while capital expenditure reduction has a negligible effect.

25 ENERGY STORAGE↗
DOE OSTI2026

Covalent adaptable networks for electrolyte–binder integration in recyclable lithium metal batteries

Lithium-metal batteries (LMBs) are considered a promising next-generation energy storage technology due to their exceptionally high energy density. However, the development of solid polymer electrolytes and cathode binders for LMBs faces critical challenges, including interfacial instability, poor recyclability, and growing environmental concerns. In particular, current systems often rely on non-recyclable components featuring permanently crosslinked networks and polyfluoroalkyl substances (PFAS), such as poly(vinylidene fluoride) (PVDF), which cause battery waste and environmental harm. Herein, we introduce a multifunctional covalent adaptable network (CAN) platform based on thermally reversible Diels–Alder (DA) chemistry, designed for dual functionality as a CAN-based electrolyte (CAE) and a CAN-based cathode binder. The CAE achieves high ionic conductivity and strong storage modulus (1.4 mS cm −1 and ∼ 10 5 Pa at room temperature, respectively) and enables stable long-term cycling in symmetric Li||Li cells for over 2000 h with low overpotential. When it is applied as a cathode binder in LiFePO 4 (LFP) composite electrodes (C-LFP), the CAN matrix significantly reduces interfacial resistance and enhances discharge capacity compared to conventional PVDF-based systems. Thermal treatment induces self-healing at the cathode–electrolyte interface, further improving contact and yielding a discharge capacity of 150 mAh g −1 at 0.5 C. Moreover, the dynamic CAN architecture allows efficient recovery and reuse of lithium salts from spent electrolytes through retro-DA reactions under mild conditions (∼80 °C), establishing a low-energy, cost-effective recycling pathway. In conclusion, this work presents a scalable and sustainable strategy for high-performance LMBs by integrating recyclability, interfacial healing, and PFAS-free design, offering a holistic solution aligned with circular economy principles and next-generation battery demands.

Diels–Alder↗
DOE OSTI2021

Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders

A common strategy for the dimensionality reduction of nonlinear partial differential equations (PDEs) relies on the use of the proper orthogonal decomposition (POD) to identify a reduced subspace and the Galerkin projection for evolving dynamics in this reduced space. However, advection-dominated PDEs are represented poorly by this methodology since the process of truncation discards important interactions between higher-order modes during time evolution. In this study, we demonstrate that encoding using convolutional autoencoders (CAEs) followed by a reduced-space time evolution by recurrent neural networks overcomes this limitation effectively. We demonstrate that a truncated system of only two latent space dimensions can reproduce a sharp advecting shock profile for the viscous Burgers equation with very low viscosities, and a six-dimensional latent space can recreate the evolution of the inviscid shallow water equations. Additionally, the proposed framework is extended to a parametric reduced-order model by directly embedding parametric information into the latent space to detect trends in system evolution. Furthermore, our results show that these advection-dominated systems are more amenable to low-dimensional encoding and time evolution by a CAE and recurrent neural network combination than the POD-Galerkin technique.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Compressional Alfvén eigenmodes excited by runaway electrons

Compressional Alfvén eigenmodes (CAEs) driven by energetic ions have been observed in magnetic fusion experiments. In this paper, we show that the modes can also be driven by runaway electrons formed in post-disruption plasma, which may explain kinetic instabilities observed in DIII-D disruption experiments with massive gas injection. The spatial structure is calculated, as are the frequencies which are in agreement with experimental observations. Using a runaway electron distribution function obtained from a kinetic simulation, the mode growth rates are calculated and found to exceed the collisional damping rate when the runaway electron density exceeds a threshold value. The excitation of CAEs poses a new possible approach to mitigate seed runaway electrons during the current quench and surpassing the avalanche.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗