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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Thermal Modeling and Simulation of the Packed-bed Thermal Energy Storage Combined with INL Thermal Energy Distribution System

Dynamic Energy Transport and Integration Lab (DETAIL) at Idaho National Laboratory is to support experimental demonstration and validation research on Nuclear-Renewable Hybrid Energy System [1]. The Thermal Energy Distribution System (TEDS) is a thermal-hydraulic flow loop in DETAIL with its own dedicated control system to support the integration of co-located multiple experimental systems, where a packed-bed thermal energy storage (TES) is installed as a thermal buffer and storage unit. Among various TES options, the packed-bed TES is adopted in TEDS because it offers a low-cost single-tank thermal storage option compared to the traditional two-tank TES. However, since the TES tank is filled with granular fillers having different thermophysical properties from those of TES tank wall, there is a potential thermo-mechanical issue to be carefully addressed like thermal ratcheting which may pose a significant design concern for the packed-bed TES tank. Thermal ratcheting is a thermomechanical process caused by the repeated rearrangement of granular filler inside a TES tank during continuous thermal cycling operation of the packed-bed TES system. If the thermally induced stress exceeds the yield strength of a TES tank wall, catastrophic consequences may happen like rupture of the TES tank. Thus, it is important to understand the phenomenon to ensure the robust operation of the packed-bed TES tank. Given that thermal ratcheting is caused by complex interaction of thermal transport in the porous bed and solid mechanics, the accurate prediction of transient thermal behavior of the packed-bed TES tank, which is the focus of this paper, is critical to the reliable thermal ratcheting analysis. This paper discusses the numerical modeling, simulation, and validation studies that are ongoing at INL to investigate the transient thermal behavior of the packed-bed TES. Of particular concern is the transient thermal process occurring in the packed-bed TES unit that is operated in conjunction with the INL TEDS. The main goal of this research is three-fold: (i) provide preliminary insights into the transient thermal behavior of the TEDS TES tank, (ii) support the thermal measurement and validation plan for TEDS experiment, and (iii) provide transient thermal boundary conditions to support the reliable thermal ratcheting analysis of the TEDS TES tank. For the transient thermal modeling and analysis, a CFD model was developed, and the validity of the modeling approach was examined via comparing the numerical simulation results with the experimental data obtained from various design characteristics of packed-bed TES tanks. Then, the present modeling method was applied for the transient thermal analysis of the TEDS TES tank, and the results are discussed along with the potential improvement of data acquisition strategy for the future TEDS experiments for more precise validation study.

25 ENERGY STORAGE↗
DOE OSTI2024

A General Model for the Ductility of Intermetallics Applied to Fe-Co Alloys

The mechanical properties, specifically ductility, of high performing soft magnets such as Fe-Co alloys are a limiting factor to their broader use in a number of systems. The understanding of the mechanical robustness in these materials is currently insufficient to be able to support the growing interest in applications such as magnetic shielding or electric motors. Fe-Co alloys provide the highest commercially available magnetic saturation and high magnetic permeability but have very poor ductility. The addition of vanadium to these alloys has allowed for significant commercialization and some ductility improvements, but the fundamental reasons for the observed improvements are not well understood. In most published work on mechanical properties in these alloys, the precise chemistry of the alloys investigated, often a critical aspect of intermetallics, is not reported or controlled and thermal history is unclear. This work creates a ductility model that is sensitive to changes in chemistry and can predict relative strain to failure as well as brittle fracture mode for intermetallics and is applied to Fe-Co alloys. Through the application of density functional theory (DFT), this model identifies defect stabilities, anti-phase boundary (APB) energies, the energy necessary to cross-slip, and cleavage energies and combines them through energetic competition to determine a relative failure strain. The model correctly predicts ductility improvements with the addition of vanadium as well as the transition from intergranular to transgranular cleavage, though more precise experiments are necessary to appropriately validate the various improvements observed.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Deriving effective electrode–ion interactions from free-energy profiles at electrochemical interfaces

Understanding ion adsorption at electrified metal–electrolyte interfaces is essential for accurate modeling of electrochemical systems. Here, in this study, we systematically investigate the free energy profiles of Na + , Cl − , and F − ions at the Au(111)–water interface using enhanced sampling molecular dynamics with both classical force fields and machine-learned interatomic potentials (MLIPs). Our classical metadynamics results reveal a strong dependence of predicted ion adsorption on the Lennard-Jones parameters, highlighting that—without due care—standard mixing rules can lead to qualitatively incorrect descriptions of ion–metal interactions. We present a systematic methodology for tuning the cross term LJ parameters to control adsorption energetics in agreement with more accurate models. As a surrogate for an ab initio model, we employed the recently released Universal Models for Atoms MLIP, which validates classical trends and displays strong specific adsorption for chloride, weak adsorption for fluoride, and no specific adsorption for sodium, in agreement with experimental and theoretical expectations. By integrating molecular-level adsorption free energies into continuum models of the electric double layer, we show that specific ion adsorption substantially alters the interfacial ion population, the potential of zero charge, and the differential capacitance of the system. Our results underscore the critical importance of force field parameterization and advanced interatomic potentials for the predictive modeling of ion-specific effects at electrified interfaces and provide a robust framework for bridging molecular simulations and continuum electrochemical models.

Roncoroni, Fabrice [Lawrence Berkeley National Lab↗
DOE OSTI2026

Power and particle exhaust for the ARC fusion power plant

To successfully show that fusion is an attractive energy source, the ARCTM fusion power plant will need to operate with a robust, integrated power and particle exhaust solution. To maximise ARC’s fusion power output while avoiding excessive erosion of the plasma-facing components, we will need to radiatively dissipate most of the power crossing the last-closed flux surface, injecting radiating impurities such as argon or neon to access divertor detachment. Divertor detachment will need to be integrated with a high-performance core plasma, and with efficient impurity pumping to prevent the accumulation of helium ash in the core. To access and control detachment in high-performance plasmas, we have designed ARC with up–down-symmetric divertors, with secondary X-points in long, tightly baffled outer legs. Using a core-edge modelling workflow, we predict that with this divertor design, ARC will access detachment with modest argon seeding in the divertor (c Ar,div ∼0.9%), which should have minimal impact on the core ( Z eff,core <0.5) for reasonable argon enrichment (c Ar,div /c Ar,core =6). Due to the high predicted divertor neutral pressure (p div ∼20 Pa), we predict that ARC will sufficiently pump helium to limit ash accumulation in the core (c He,core <2%) for a helium enrichment of c He,div /c He,core =0.4. ARC’s divertor design is expected to increase the stability of a detachment front in the outer divertor leg, helping to prevent divertor reattachment during smaller heat-flux transients such as scrape-off-layer filaments associated with the quasi-continuous exhaust regime. However, this buffering will not be sufficient to prevent divertor reattachment during large type-I edge-localised modes (ELMs), and as such these will need to be avoided on ARC. Experiments on SPARC will be used to select an integrated scenario which avoids or mitigates type-I-ELMs while maintaining access to detachment, good core fusion performance and sufficient impurity exhaust. SPARC experiments will also be used to finalise ARC’s divertor design, by studying the impact of magnetic and first-wall geometry on detachment stability, impurity enrichment and neutral baffling under conditions similar to those expected for ARC. In conclusion, our analysis finds that ARC will have a viable power and particle exhaust solution which is compatible with high-power operations, and this solution will be validated in experiments on SPARC.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

What regulates decomposition in agroecosystems? Insights from reading the tea leaves

Litter decomposition is a critical Earth process, recycling nutrients and setting a portion of plant tissue on a path toward soil organic matter. Despite this importance, we still lack a good understanding of local factors that regulate decomposition, especially in agroecosystems where management plays an outsized role. Using a narrow range of climate and soils, we buried 1,308 pre-manufactured “litter bags” of differing residue quality (i.e., green and rooibos tea leaves) in 109 plots across several management practices to (1) explore the local controls on decomposition in agroecosystems and (2) test the robustness of the Tea Bag Index (TBI). We found that management practices intended to increase soil ecosystem services, that is, soil health, altered the decomposition of both teas. For example, adding nitrogen fertilizer and implementing perennial cropping decreased the extent of green tea decomposition (carbon-to-nitrogen ratio, or C:N = 12.8). No-tillage increased, but perennial cropping decreased, the rate of rooibos tea decomposition (C:N = 50.1). Cropped prairie accelerated green tea decomposition and increased the extent of red tea decomposition. A random forest regression model showed that soil temperature was the strongest predictor of green tea decomposition, but a soil health score also played a significant role in predicting the mass remaining. Soil texture and nutrient availability best predicted rooibos tea decomposition. Finer textured soils seemed to decelerate rooibos decomposition but increased the extent of decomposition. Furthermore, we demonstrated that the TBI metrics correlated somewhat well with empirically derived decomposition constants and were similarly sensitive to the effects of management. Still, the green tea stabilization factor had a substantial prediction bias. Our study increased our basic understanding of what regulates decomposition in agroecosystems. It also showed that the TBI can be a scientifically rigorous citizen science approach to monitoring changes in soil health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

42 ENGINEERING↗
DOE OSTI2025

Latent space mapping: Revolutionizing predictive models for divertor plasma detachment control

The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., ~ 0.2 ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over 10 8 compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.

Artificial neural networks↗
DOE OSTI2021

BETO 2021 Peer Review - FCIC Task 6: High Temperature Conversion

The impacts of feedstock variability on pyrolysis processes are significant but poorly defined. Current engineering designs are based on empirical guidelines, useful only over a narrow range of feedstock properties. The objectives of this project are to (1) Develop science-based knowledge of how feedstock attributes and operational parameters impact pyrolysis process reliability and product quality; and (2) Build an experimental and computational toolset that predicts these outcomes, enabling processes to optimize reliability and product quality. Biomass is a complex feedstock. Controlling for and testing the effects of individual attributes is very challenging. This project couples multiscale experimentation and modeling to accurately capture the fundamental physics and chemistry of biomass flow and conversion behavior in feeding and pyrolysis reactor operations. Our focus is on pine residue attributes – anatomical fraction (bark, needles, wood), particle morphology (size/shape distribution, density, porosity), and chemical composition (extractives, biopolymers, alkali metals) – that impact product quality for downstream catalytic upgrading. Because detailed pyrolysis product characterization is limited, cutting-edge analytical techniques are being developed to reveal impactful product attributes. The tools and knowledge developed here will enable integrated pyrolysis-based processes that are more robust, flexible, and market-responsive with respect to feedstock variability.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Synthesis of wafer-scale SWCNT forests with remarkably invariant structural properties in a bulk-diffusion-controlled kinetic regime

Robust synthesis of vertically aligned carbon nanotubes (VACNT) at large scale is required to accelerate deployment of numerous cutting-edge devices to emerging commercial applications. To address this need, this work demonstrates that the structural characteristics of single-walled CNTs (SWCNTs) produced in a growth regime dominated by bulk diffusion of the gaseous carbon precursor are remarkably invariant over a broad range of process conditions (precursor concentration increased up to 30-fold, catalyst substrate area from 1 cm 2 to 180 cm 2 , growth pressure from 20 to 790 mbar, and gas flowrates up to 8-fold). A simple growth kinetics model is derived that accounts for both reactant bulk diffusion and competing byproduct formation in the presence of excess hydrogen, which quantitatively reproduces the experimental data in the entire isothermal parameter space. The model enables critical predictions for process scale-up optimization including a potential 6-fold increase in CNT production rate, a ~90% carbon conversion efficiency in select conditions, and elimination of reaction rate decay observed at high pressures by using a hydrogen-free growth environment. This model-guided opportunity to substantially improve synthesis throughput and efficiency, in conjunction with the remarkably invariant CNT properties, is appealing for reliable VACNT device fabrication at both small and industrial scales.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Impact of various DIII-D diagnostics on the accuracy of neural network surrogates for kinetic EFIT reconstructions

Abstract Kinetic equilibrium reconstructions make use of profile information such as particle density and temperature measurements in addition to magnetics data to compute a self-consistent equilibrium. They are used in a multitude of physics-based modeling. This work develops a multi-layer perceptron (MLP) neural network (NN) model as a surrogate for kinetic Equilibrium Fitting (EFITs) and trains on the 2019 DIII-D discharge campaign database of kinetic equilibrium reconstructions. We investigate the impact of including various diagnostic data and machine actuator controls as input into the NN. When giving various categories of data as input into NN models that have been trained using those same categories of data, the predictions on multiple equilibrium reconstruction solutions (poloidal magnetic flux, global scalars, pressure profile, current profile) are highly accurate. When comparing different models with different diagnostics as input, the magnetics-only model outputs accurate kinetic profiles and the inclusion of additional data does not significantly impact the accuracy. When the NN is tasked with inferring only a single target such as the EFIT pressure profile or EFIT current profile, we see a large increase in the accuracy of the prediction of the kinetic profiles as more data is included. These results indicate that certain MLP NN configurations can be reasonably robust to different burning-plasma-relevant diagnostics depending on the accuracy requirements for equilibrium reconstruction tasks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Transforming ESM Physical Parameterization Development Using Machine Learning Trained on Global Cloud-Resolving Models and Process Observations

ESMs robustly predict that 21st century greenhouse warming will slowly increase global mean precipitation, rapidly increase extreme precipitation, and increase subtropical drought. ESMs agree less about precipitation trends and extremes over particular land regions critical to human societies, e. g. in semi-arid regions such as California or the Sahel, or in wetter climates prone to monsoonal rainfall (e. g. southeast Asia) or to tropical cyclones and flooding from mesoscale convective systems (e. g. the southeastern U.S.) Deep convective parameterizations and poor representation of orography and complex vegetated land surfaces contribute to this inter-model spread; clouds, aerosols and sea-surface temperature biases are also key. Reducing regional precipitation projection uncertainty has enormous planning value for water supplies, land use, wildfire, hydropower, flood control, etc. IPCC-class ESMs are making painfully slow progress on this.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

Presentation 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) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗
DOE OSTI2024

Enhancing Fluid Flow Pressure and Saturation Prediction Accuracy and Reducing Uncertainty with Committee Machine – Illinois Basin Decatur Project (IBDP) as a Case Study

This is the conference paper accompanying an oral presentation 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) is a way to play a critical role in the global transition to a low-emission economy. Current progress is hampered by a number of factors, among which the lack of risk-informed design tools and decision support frameworks is seen as a major roadblock. Significant interest exists in using artificial intelligence to accelerate CCS site feasibility studies, as well as to facilitate the permit application process. Existing works commonly train a single deep learning model. This work investigates the feasibility of using a conventional ensemble learning (committee machine) technique to further improve prediction accuracy. Ensemble-based algorithms generally improve over individual base learners in terms of robustness and accuracy. Deep ensembles, however, are time-consuming to create and train. A pragmatic question is whether small-sized ensembles may lead to prediction improvement. Here we evaluated the efficacy of an ensemble learning technique using the latent spectral model (LSM), an efficient deep neural operator algorithm, as base learners. Preliminary results, obtained using the Illinois Basin-Decatur Project (IBDP) carbon sequestration data/model, show that small-sized ensembles can improve prediction over the base learners, achieving prediction accuracy of ~1.6 psi root mean square error (RMSE) on pressure (relative the average reservoir pressure of 3150 psi), and less than 1.3% for saturation.

Sun, Alexander↗
DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗
DOE OSTI2023

Thermo-Hydro-Mechanical Modeling of Brine Migration in a Heated Borehole Test in Bedded Salt

Abstract This research paper focuses on the thermo-hydro-mechanical (THM) modeling of brine migration in a heated borehole test conducted as part of the ongoing Brine Availability Test in Salt (BATS) at the Waste Isolation Pilot Plant (WIPP) in New Mexico. It is a component of the international collaboration project DECOVALEX-2023 (DEvelopment of COupled models and their VALidation against EXperiments), which aims to understand the THM processes governing brine flow in heated rock salt repositories through collaborative analysis by multiple research teams. Using the TOUGH–FLAC simulator, THM simulations were performed and compared with data from the BATS phase 1a. This experiment involved two identical horizontal-borehole arrays, one heated and one serving as a control, both equipped with sensor arrays. Analysis of measurements revealed water flow rate surges during heater power transitions, with the highest jump observed during cooling. Acoustic emission activity exhibited distinct patterns in response to heater power changes, suggesting that damage to rock salt is particularly pronounced during the cooling phase. The THM simulations successfully captured these phenomena, highlighting the significance of thermal effects, brine migration, and mechanical behavior in predicting brine availability in heated and damaged rock salt. Our modeling also revealed the critical interplay between heating and cooling-induced damage and its influence on flow properties, particularly affecting brine inflow estimation. Notably, we found that cooling-induced brine inflow spikes result from increased permeability due to tensile dilatancy. These findings have important implications for the development of robust containment strategies and enhance our understanding of the complex processes involved in repository performance.

58 GEOSCIENCES↗
DOE OSTI2025

High-throughput and data-driven search for stable optoelectronic AMSe 3 materials

The rapid advancement in emerging optoelectronic technologies demands highly efficient, affordable, and ecofriendly materials. In this context, ternary chalcogenides, especially ternary selenides, show early promise as a material class due to their stability and remarkable electronic, optical, and transport properties. In this work, we integrate first-principles-based high-throughput computations with machine learning (ML) techniques to predict the thermodynamic stability and optoelectronic properties of 920 valency-satisfied selenide compounds. Through investigating polymorphism, our study reveals the edge-sharing orthorhombic Pnma phase (NH 4 CdCl 3 -type) as the most stable structure for most ternary selenides. High-fidelity supervised ML models are trained and tested to accelerate stability and band gap predictions. These data-driven models pin down the most influential features that dominantly control key material characteristics. The multistep high-throughput computations identify the ternary selenides with optimal direct band gaps, light carrier masses, and strong optical absorption edges. The extensive materials screening considering phase stability, toxicity, and defect tolerance, finally identifies the seven most suitable candidates for photovoltaic applications. Two of these final compounds, SrZrSe 3 and SrHfSe 3 , have already been synthesized in a single-phase form, with the latter showing an optically suitable band gap, aligning well with our findings. The non-adiabatic molecular dynamics reveal sufficiently long photoexcited charge carrier lifetimes (on the order of nanoseconds) in some of these selected selenide materials, indicating their exciting characteristics. Overall, our study suggests a robust in silico framework that can be extended to screen large datasets of various material classes for identifying promising photoactive candidates.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Three‐Dimensional Permeability Inversion Using Convolutional Neural Networks and Positron Emission Tomography

Abstract Quantification of heterogeneous multiscale permeability in geologic porous media is key for understanding and predicting flow and transport processes in the subsurface. Recent utilization of in situ imaging, specifically positron emission tomography (PET), enables the measurement of three‐dimensional (3‐D) time‐lapse radiotracer solute transport in geologic media. However, accurate and computationally efficient characterization of the permeability distribution that controls the solute transport process remains challenging. Leveraging the relationship between local permeability variation and solute advection behavior, an encoder‐decoder based convolutional neural network (CNN) is implemented as a permeability inversion scheme using a single PET scan of a radiotracer pulse injection experiment as input. The CNN can accurately capture the 3‐D spatial correlation between the permeability and the radiotracer solute arrival time difference maps in geologic cores. We first test the inversion accuracy using synthetic test datasets and then test the accuracy on a suite of experimental PET imaging datasets acquired on four different geologic cores. The network‐predicted permeability maps from the geologic cores are used to parameterize forward numerical models that are directly compared with the experimental PET imaging data. The results indicate that a single trained network can generate robust 3‐D permeability inversion maps in seconds. Numerical models parameterized with these permeability maps closely capture the experimentally observed solute arrival time behavior. This work provides an unprecedented approach for efficiently characterizing multiscale permeability heterogeneity in complex geologic samples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Cybersecurity Enhancement in Digital Substations: Hidden Markov Model-Based Smart Cyber Switching and Threat Response

The rising incidence of cyber-attacks on critical infrastructure and power grids poses significant threats to the stability and reliability of electrical substations, with potentially devastating consequences such as extended blackouts. This paper introduces an advanced cybersecurity framework aimed at safeguarding IEC 61850-based substations through the integration of software-defined networking (SDN) and digital twin (DT) technologies. The proposed DT-based framework employs smart cyber switching (SCS) for proactive threat mitigation and concurrent intelligent electronic device (CIED) for swift system restoration, thereby maintaining continuous operational integrity and robust cybersecurity defenses. Central to this framework is the adaptive port controller (APC), which enables dynamic port management to adapt to evolving threats, and an intrusion detection system (IDS) designed to detect and neutralize malicious attacks on IEC 61850-based sampled value (SV) and generic object-oriented substation event (GOOSE) messages within the substation’s communication network. Further, novel predictive intrusion detection and response (PIDR) algorithm is implemented on a digital substation (DS) to predict the best route to be taken by the attacker. The efficacy of these comprehensive cybersecurity frameworks is validated through rigorous simulations and a hardware-in-the-loop (HIL) testbed, showcasing the system’s ability to sustain substation operations amidst cyber-attacks.

Digital substation↗