Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Storage Complex”

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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

ComStock™ 2024 Release 1 [SWR-19-33 and SWR-20-32]

ComStock™ is an NREL model of the U.S. commercial building stock. The model takes some building characteristics from the U.S. Department of Energy's (DOE's) Commercial Prototype Building Models and Commercial Reference Building. However, unlike many other building stock models, ComStock also combines these with a variety of additional public- and private-sector data sets. Collectively, this information provides high-fidelity building stock representation with a realistic diversity of building characteristics. This repository contains the source code used to build and execute ComStock models, including upgrade scenarios. In addition, the sampling of buildings characteristics used for the initial ComStock (V1.0) release is provided. The ComStock model is under active calibration and development, which is publicly visible on this repository. Execution of the ComStock workflow is managed through the buildstockbatch repository, a shared asset of ResStock™ and ComStock™ , specifically developed to scale to execution of tens of millions of simulations through multiple infrastructure providers. The dataset output from the initial ComStock (V1.0) release can be found at the accompanying ComStock data viewer website and additional information about ComStock found on the NREL Buildings Website. For more details about ongoing model development please consult the End Use Load Profiles website. ComStock is a direct result of the NREL residential stock modeling tool ResStock™ (recipient of a R&D100 award) and was inspired by the high-fidelity solar & storage adoption model dGen™. Additionally, this tool would not be possible without the decades of work undertaken by the OpenStudio® and EnergyPlus® visionaries and contributors, significant funding, feedback and support from the Los Angeles Department of Water and Power, and the Department of Energy's Building Technology Office ongoing support of and investment in building energy modeling software. is an analytic methodology for modeling the energy usage of the commercial building stock within the United States of America. The commercial building stock is represented through a sampling of complex probabilistic distributions of various features of interest for modeling energy usage within commercial buildings. Each sample from these distributions is converted into a building energy model based on the features of that specific sample. Each building energy model can be simulated as is, but additional changes can be made to the model through addition of energy conservation measures, component faults, or other desired alterations. The results of the simulations are then processed to provide insights for various stakeholders, including but not limited to policy makers, engineers, and marketers.

Horsey, Henry↗
DOE OSTI2021

Integrated System and Application Continuous Performance Monitoring and Analysis Capability

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia's Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia's ElectroMagnetic Plasma In Realistic Environments ( EMPIRE ) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia's HPC users.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Integrated System and Application Continuous Performance Monitoring and Analysis Capability (Final)

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia’s Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia’s ElectroMagnetic Plasma In Realistic Environments (EMPIRE) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia’s HPC users.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Salt Stress Induces Paramylon Accumulation and Fine-Tuning of the Macro-Organization of Thylakoid Membranes in Euglena gracilis Cells

The effects of salt stress condition on the growth, morphology, photosynthetic performance, and paramylon content were examined in the mixotrophic, unicellular, flagellate Euglena gracilis. We found that salt stress negatively influenced cell growth, accompanied by a decrease in chlorophyll (Chl) content. Circular dichroism (CD) spectroscopy revealed the changes in the macro-organization of pigment-protein complexes due to salt treatment, while the small-angle neutron scattering (SANS) investigations suggested a reduction in the thylakoid stacking, an effect confirmed by the transmission electron microscopy (TEM). At the same time, the analysis of the thylakoid membrane complexes using native-polyacrylamide gel electrophoresis (PAGE) revealed no significant change in the composition of supercomplexes of the photosynthetic apparatus. Salt stress did not substantially affect the photosynthetic activity, as reflected by the fact that Chl fluorescence yield, electron transport rate (ETR), and energy transfer between the photosystems did not change considerably in the salt-grown cells. We have observed notable increases in the carotenoid-to-Chl ratio and the accumulation of paramylon in the salt-treated cells. We propose that the accumulation of storage polysaccharides and changes in the pigment composition and thylakoid membrane organization help the adaptation of E. gracilis cells to salt stress and contribute to the maintenance of cellular processes under stress conditions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Functionalized Magnetic Nanoparticles for Technetium Sequestration from Groundwater

Technetium 99 (Tc) is among the most common environmental contaminants at DOE sites and one of the most common risk drivers in low- and high-level waste disposal sites. The majority of Tc is generated from anthropogenic sources, such as nuclear power plants, global weapons, nuclear storage facilities and medical applications. Through these sources, Tc contamination has been unintentionally introduced in to the environment. The most common chemical form of Tc is Tc(VII)O{sub 4}{sup -}. Due to its high solubility and mobility, Tc can enter the food chain and cause adverse health effects to humans. Currently, ion exchange resins and reduction processes are the most common approaches for Tc immobilization. Although these techniques have shown to be effective, they also possess major drawbacks, such as high cost, low adsorption capacity, and complex creation and maintenance. Therefore, development of more efficient and simple technologies for the remediation of Tc-contaminated systems are needed. Functionalized magnetic nanoparticles have been used to remove organic and inorganic contaminants from water resources. These nanoparticles have attracted extensive attention as an adsorbent material due to their large surface area, high efficiency, low-cost, easy functionalization and separation with a magnet. This study seek to develop functionalized magnetic iron oxide nanoparticles for the efficient removal of Tc and other heavy metal contaminants from water resources under ambient conditions. Objectives: Synthesize magnetic iron oxide nanoparticles and functionalize their surface with Cetyltrimethylammonium Bromide (CTAB) and tetraethyl-orthosilicate (TEOS). Characterize the synthesized nanoparticles using scanning electron microscopy (SEM) coupled with energy dispersive X-ray spectroscopy (EDS), Dynamic Light Scattering (DLS) and Zeta PALS. Perform adsorption studies to evaluate their adsorption behavior and capacity for (a) Technetium using Rhenium (ReO{sub 4}{sup -}) as a surrogate and (b) heavy metals, e.g. Cu{sup 2+}. Conclusions: Magnetic iron oxide nanoparticles were successfully functionalized with CTAB and TEOS. The functionalization of the iron oxide nanoparticles affects their surface charge and their hydrodynamic diameter. The addition of CTAB or TEOS decreased the hydrodynamic diameter of the nanoparticles due to repulsive and steric forces. The SEM micrographs show spherical nanoparticles of different sizes. The EDX analysis shows the presence of iron and oxygen from the iron oxide crystalline structure, and the different constituents of the CTAB and TEOS molecules. Proof-of-concept shows the successful adsorption of rhenium (ReO{sub 4}{sup -}) and copper Cu{sup 2+}) onto CTAB-Fe{sub 2}O{sub 3} and TEOS-Fe{sub 2}O{sub 3} respectively.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Rapid determination of supercritical CO 2 and brine relative permeability using an unsteady-state flow method

In this study, relative permeability of supercritical CO 2 (scCO 2 ) and brine was determined in reactive and non-reactive rock cores using a combination of unsteady-state methodology and computed tomography. Experiments were conducted using a medical grade CT scanner to determine saturation using a custom Python script. The saturation and differential pressure across the core were then used to derive four empirical constants to calculate relative permeability. This methodology increases temporal efficiency while reducing experimental complexity. Additionally, we show that the method can be used to determine scCO 2 relative permeability in a wide range of lithologies and flow rates, and with the ability to account for matrix dissolution during scCO 2 flooding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Enhancing durability of polymer electrolyte membrane using cation size selective agents

Radical species generated during proton exchange membrane fuel cell operation considerably limit the achievable durability, particularly for heavy-duty vehicle applications. A promising solution to the problem is the incorporation of radical scavenger additives such as cerium which mitigates chemical attacks on the membrane. However, these additives migrate during fuel cell operation causing a loss in durability and performance due to detrimental interaction with various components of the fuel cell. Here, we study cation size selective agents as a means to immobilize cerium within perfluorosulfonic acid (PFSA) membranes. We synthesized an organometallic complex of cerium with 15-Crown-5 and investigated the effectiveness of this complex to immobilize cerium. Over 300% increase in cerium retention and an 80% increase in chemical durability were observed owing to the stabilization effect of crown ethers on cerium. Migration under a potential gradient can be eliminated while the complex also contributes to the enhancement in cerium radical scavenging activity. In conclusion, current challenges with the proposed solution are highlighted and future work is discussed.

25 ENERGY STORAGE↗
DOE OSTI2022

Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization

Thermal energy storage (TES) for a cooling plant is a crucial resource for load flexibility. Traditionally, simple, heuristic control approaches, such as the storage priority control which charges TES during the nighttime and discharges during the daytime, have been widely used in practice, and shown reasonable performance in the past benefiting both the grid and the end-users such as buildings and district energy systems. However, the increasing penetration of renewables changes the situation, exposing the grid to a growing duck curve, which encourages the consumption of more energy in the daytime, and volatile renewable generation which requires dynamic planning. The growing pressure of diminishing greenhouse gas emissions also increases the complexity of cooling TES plant operations as different control strategies may apply to optimize operations for energy cost or carbon emissions. This paper presents a model predictive control (MPC), site demonstration and evaluation results of optimal operation of a chiller plant, TES and behind-meter photovoltaics for a campus-level district cooling system. The MPC was formulated as a mixed-integer linear program for better numerical and control properties. Compared with baseline rule-based controls, the MPC results show reductions of the excess PV power by around 25%, of the greenhouse gas emission by 10%, and of peak electricity demand by 10%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Variable rate neural compression for sparse detector data

Particle colliders produce data at extraordinary rates, posing major challenges for transmission and storage. High-throughput compression algorithms are therefore essential. In the sPHENIX experiment taking data at the Relativistic Heavy Ion Collider, a time projection chamber records three-dimensional (3D) particle trajectories that are highly sparse, making conventional learning-free lossy compression ineffective. Convolutional neural networks have surpassed traditional methods in compression ratio and accuracy. However, they fail to exploit sparsity for efficiency. To address these gaps, we present BCAE-VS, a bicephalous convolutional autoencoder with variable compression ratio for sparse data, which adapts compression to input complexity through key-point identification and sparse convolution. BCAE-VS achieves higher accuracy and compression ratios than prior neural approaches while being orders of magnitude smaller. Moreover, its throughput increases with sparsity—a property not observed in other methods. Although it was developed for collider experiments, BCAE-VS readily extends to other sparse data domains, such as light detection and ranging (LiDAR) sensing and 3D microscopy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Development of a Testbed for Pipeline Flushing - 20425

Pipelines which carry high-level waste within the DOE complex should be properly flushed in the cases where stationary or moving beds of solid sediment occur, or lines are prone to hydrogen gas buildup. Present guidelines establish a minimum for the amount of water (flush volume) and flush velocity values used for post-transfer flushing operations to achieve satisfactory cleaning of pipelines. However, further studies are needed to find optimal operating velocity modes/values in order to minimizing the flush volume and consequent downstream waste. These efforts are significantly helpful to DOE waste remediation sites by preserving tank storage, preventing additional waste processing, and minimizing dilution and changes in waste chemistry. An experimental test loop was recently developed for study of non-Newtonian slurry flushing at the Florida International University (FIU). This loop was designed to create sediment beds of various materials and bed heights and investigate parameters that effect the efficiency of flushing operations. The objective is to find flush velocity values/modes which lead to satisfactory cleaning of transport lines with a minimum amount of water usage. A 165 ft experimental test loop made of 3-inch carbon steel pipes was constructed and used in initial testing campaigns. This pipe loop was equipped with real-time monitoring instruments to record variation of pressure, density, mass flow rate, and temperature during testing. Post-flush in-situ no-flow evaluations were performed using ultrasonic, endoscopy, and visual inspection. Additional post-flush evaluations were conducted using filtration and density monitoring of residuals within a designated circulations loop. These provisions enable comparison of pipeline cleanness before the start and after termination of flushing operations for efficiency evaluation. This paper presents efforts associated with flushing of kaolin-water mixtures at different concentrations and with various flush volume and flush velocity values/modes. In initial testing, efforts focused on creating repeatable sediment beds inside the pipeline in both fully-flooded (no pre-flush drainage) and gravity-drained (pre-flush drained) systems. Bed characterization (i.e., measurement of sediment height and solids concentration) was conducted to ensure about consistency of initial conditions between tests. Preliminary results for flushing study of 10% vol. kaolin-water mixtures starting from a fully-flooded initial condition showed that flushing with 1-line volume (65 gallons) could remove up to 72 percent of solids from the pipeline. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects

This is the conference paper accompanying an oral presentation “Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects” at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. Carbon capture and storage (CCS) technology is critical for mitigating climate change but requires effective subsurface reservoir management to ensure safe containment of injected CO2. Accurate predictions of reservoir pressure and saturation are essential for assessing long-term CCS performance. Traditional numerical simulations, while effective, are computationally intensive, time-consuming, and constrained by data discretization. Previous work has shown the effectiveness of MeshGraphNets (MGN), a graph-based machine learning framework, as an innovative alternative for predicting reservoir behavior. MGN leverages graph neural networks (GNNs) and mesh representations to model complex geological formations, offering superior adaptability across different discretizations and reservoir configurations. Classic MGN implementations utilize an autoregressive technique to predict future behavior based on current predictions, but this technique is hampered by error accumulation over time. To enhance the model accuracy in time-series predictions, this study implemented a multi-step rollout strategy that integrates autoregressive predictions during training to stabilize prediction of saturation over time. Using the Illinois Basin – Decatur Project (IBDP) dataset, comprising 100 simulations of CO2 injection, pressure, and saturation changes, the framework demonstrated its ability to learn spatial dependencies and temporal dynamics. With inputs including permeabilities, porosities, and injection rates, MGN accurately predicted CO2 plume evolution over time, even with limited training data. Moreover, the addition of a multi-step rollout procedure during training improved the ability of MGN to predict stably over time by ~15%. This research positions MGN, enhanced with multi-step rollout capabilities, as a robust and efficient tool for CCS applications. It advances the field by enabling precise, computationally efficient predictions of reservoir behavior, providing a foundation for the broader adoption of machine learning frameworks in CCS and other geoscience domains.

Holcomb, Paul↗
DOE OSTI2021

A Co-design Framework for Online Data Analysis and Reduction

Science applications preparing for the exascale era are increasingly exploring in situ computations comprising of simulation-analysis-reduction pipelines coupled in-memory. Efficient composition and execution of such complex pipelines for a target platform is a codesign process that evaluates the impact and tradeoffs of various application- and system-specific parameters. In this article, we describe a toolset for automating performance studies of composed HPC applications that perform online data reduction and analysis. We describe Cheetah, a new framework for composing parametric studies on coupled applications, and Savanna, a runtime engine for orchestrating and executing campaigns of codesign experiments. Furthermore, this toolset facilitates understanding the impact of various factors such as process placement, synchronicity of algorithms, and storage versus compute requirements for online analysis of large data. Ultimately, we aim to create a catalog of performance results that can help scientists understand tradeoffs when designing next-generation simulations that make use of online processing techniques. We illustrate the design of Cheetah and Savanna, and present application examples that use this framework to conduct codesign studies on small clusters as well as leadership class supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Understanding Cation Selectivity in Carbon Nanopores with Hybrid First-Principles/Continuum Simulations: Implications for Water Desalination and Separation Technologies

Understanding ion adsorption in porous carbons is crucial for a range of technologies, including water desalination and energy storage. In this work, we combined density functional theory with a continuum solvation model to investigate thermodynamics and kinetics of the adsorption process of alkali metal ions from aqueous solutions into carbon nanopores with different sizes and geometries. We found that cations with a larger ionic radius are more favorable to enter the nanopores because of a lower energy penalty of dehydration. In addition, the pore size and geometry were found to have a significant impact on the ion–pore interaction under confinement and cation selectivity. Our study highlights a complex interplay among nanopore geometry, ion size, and hydration on the cation adsorption selectivity, suggesting that tuning the porosity could represent a general strategy for improving ion separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A Workflow for Characterizing Legacy Wells as Potential Leakage Pathways for Integration to NRAP-Open-IAM

Carbon capture and storage is a crucial component of climate change mitigation strategies, involving the capture of carbon dioxide (CO2) from point sources and its injection into permeable subsurface formation. Many suitable CO2 storage sites coincide with legacy wells since the conditions that kept hydrocarbons in-situ for thousands of years are also ideal for storage of carbon dioxide. To protect underground sources of drinking water (USDW) during greenhouse gas injection, the Environmental Protection Agency (EPA) mandates area of review evaluations. These evaluations ensure that drinking water sources would not be contaminated by injected fluids. They include identification of legacy wellbores, integrity assessments, and implementing any necessary corrective action. Previous assessment approaches of legacy wells include high-level scoring of regional data and well construction and abandonment evaluation. This work describes a novel methodology that evaluates well construction and abandonment, ranks them based on complexity, and performs a risk assessment with NRAP-Open-IAM. A workflow of the methodology is presented, highlighting its capabilities and limitations.

Wise, Jarrett↗
DOE OSTI2020

Battery cell engineering and design to reach high energy

Improved high energy capacity designs for lithium ion batteries are described that take advantage of the properties of high specific capacity anode active compositions and high specific capacity cathode active compositions. In particular, specific electrode designs provide for achieving very high energy densities. Furthermore, the complex behavior of the active materials is used advantageously in a radical electrode balancing design that significantly reduced wasted electrode capacity in either electrode when cycling under realistic conditions of moderate to high discharge rates and/or over a reduced depth of discharge.

25 ENERGY STORAGE↗
DOE OSTI2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

PDEs↗
DOE OSTI2024

Overcoming obstacles to IPv6 on WLCG

The transition of the Worldwide Large Hadron Collider Computing Grid (WLCG) storage services to dual-stack IPv6/IPv4 is almost complete; all Tier-1 and 94% of Tier-2 storage are IPv6 enabled. While most data transfers now use IPv6, a significant number of IPv4 transfers still occur even when both endpoints support IPv6. This paper presents the ongoing efforts of the HEPiX IPv6 working group to steer WLCG toward IPv6-only services by investigating and fixing the obstacles to the use of IPv6 and identifying cases where IPv4 is used when IPv6 is available. Removing IPv4 use is essential for the long-term agreed goal of IPv6-only access to resources within WLCG, thus eliminating the complexity and security concerns associated with dual-stack services. We present our achievements and ongoing challenges as we navigate the final stages of the transition from IPv4 to IPv6 within WLCG.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Multiphysics Degradation Modeling of Energy Storage Materials via RKPM with a Neural Network-Enhancement

In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation is developed, and a patch-test is formulated to certify optimal convergence of the proposed RKPM method for the coupled physics system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is then used to represent the complex material microstructures for modeling the coupled physics of these systems. Further, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. 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. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

electro-chemo-mechanical coupling↗