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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 145 records · Page 8

Composition-Nanoarchitecture-Performance Analysis of High Energy Density Electrodeposited Silicon for Lithium-Ion Battery Anodes

Electrodeposition of silicon (Si) was previously demonstrated as a promising method for fabricating 3D-structured lithium-ion battery anodes. However, the relationship between the electrochemical performance and chemical composition of the relatively impure electrodeposited silicon is not well understood. Here, we report the electrodeposition of a Si-dominant active material (EDEP-Si) onto 3D-structured nickel (Ni) scaffolds and systematically compare the electrochemical properties, elemental composition, atomistic Si coordination, and molecular structure of EDEP-Si with high-purity amorphous Si grown via static chemical vapor deposition. Despite the considerable amount of carbon (9–11 at %) and oxygen (42–44 at %) present in EDEP-Si, the cycling stability and high reversible specific capacity are remarkably similar to those of CVD-Si on a silicon basis (~2400 mA h/g-Si after 100 cycles). The primary difference is that EDEP-Si exhibits reduced cycling efficiency over the first 10–20 cycles. Reactions between carbon and, more importantly, oxygen in EDEP-Si with lithium are likely responsible for the reduced early cycle performance and lower capacity of the total deposit. Finally, our observations suggest ultrapure Si is not necessary for high electrochemical access to reversible charge storage, although limiting the presence of incorporated impurity species would improve energy density and first cycle efficiency.

25 ENERGY STORAGE↗

Direct observation of ultrafast cluster dynamics in supercritical carbon dioxide using X-ray Photon Correlation Spectroscopy

Supercritical fluids exhibit distinct thermodynamic and transport properties, making them of particular interest for a wide range of scientific and engineering applications. These anomalous properties emerge from structural heterogeneities due to the formation of molecular clusters at conditions above the critical point. While the static behavior of these clusters and their effects on the thermodynamic response functions have been recognized, the relation between the ultrafast cluster dynamics and transport properties remains elusive. By measuring the intermediate scattering function in carbon dioxide at conditions near the critical point with X-ray photon correlation spectroscopy, we directly capture the cross-over dynamics between 4 and 13 picoseconds, revealing the transition between ballistic and diffusive motion. Complementary analysis using large-scale molecular dynamics simulations reveals that this behavior arises from collisions between unbound molecules and clusters. This study provides direct evidence of the ultrafast momentum exchange between clusters, which has significant impact on transport properties, solvation processes, and reaction kinetics in supercritical fluids.

carbon capture and storage↗

Dynamic STEM-EELS for single-atom and defect measurement during electron beam transformations

This study introduces the integration of dynamic computer vision–enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)–based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS 2 , uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli.

47 OTHER INSTRUMENTATION↗

Precipitation hardening of laser powder bed fusion Ti-6Al-4V

Here, the laser powder bed fusion (PBF-L) additive manufacturing (AM) community has dedicated significant efforts into process optimization and control for defect-free Ti-6Al-4V. As defects become less of an issue for PBF-L Ti-6Al-4V, the processing-structure-properties (PSP) relationships between AM microstructures can now be explored to optimize mechanical properties. Lower temperature aging treatments around 550 °C in wrought Ti-6A-4V have been historically understood to precipitation harden the α phase with an ordered, hexagonal close packed (HCP) α 2 phase. The α 2 phase existed as nanoscale Ti 3 Al precipitates coherent with the parent α phase. The goal of the present investigation was to implement a vacuum heat treatment of 545 °C for 100 hours on PBF-L Ti-6Al-4V. This vacuum heat treatment took place after an initial hot isostatic pressure (HIP) treatment that decomposed the as-built, martensitic microstructure. The vacuum aging successfully produced nanoscale precipitates of α 2 phase within α-laths, confirmed via atom probe tomography (APT). Microstructural-length scale and quasi-static mechanical properties were investigated by nanoindentation and uniaxial tensile tests of miniaturized test specimens. Given the sensitivity of Ti-6Al-4V mechanical properties to small changes in chemistry, all test specimens originated from the same build. The α 2 precipitation resulted in significantly harder α-laths (≈ 2 GPa) as measured via nanoindentation, as well as a relative yield and ultimate tensile strength increase of 60 MPa and 38 MPa, respectively. Analysis of Variance (ANOVA) of the datasets revealed no statistical differences in total elongation between the HIPed and HIPed + aged specimens, indicative of the aging treatment producing a net benefit of strength with no loss in ductility.

36 MATERIALS SCIENCE↗

Uncertainty Quantification and Sensitivity Analysis of Non-Nuclear Advanced Controls Testbed Reactor Mockup

The research presented in this report describes our progress in applying stochastic methods and uncertainty quantification, parametric study, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated with Griffin, with the goal of developing a reduced order (surrogate) model which can be rapidly sampled while perturbing multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums, which are controlled by a hybrid proportional, integral and derivative (PID) controller, actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time and $\beta$ from a transient Griffin simulation executed with the improved quasi-static method to provide the kinetic parameters) as inputs to functions which control the CD rotation angle. Using a number of stochastic method approaches, we developed a dual purpose training-surrogate model of the NTP system using polynomial regression. The trained model can be rapidly sampled while simultaneously perturbing various input parameters of the model, such as coefficients on the PID control, or temperature (directly affect the neutron cross section). The surrogate model delivers accurate results orders-of-magnitude faster (minutes, not days) than the base model. Once the base model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs and their effect on the output. For example, coefficients used in the PID control system may vary due to some physical interference, or there may be uncertainty in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters and operational boundaries can be determined. The goal of this work is to support development of an advanced control system to operate CDs in a functioning NTP system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

A Flexible Quasi-Static Mooring Design Optimization Method for Floating Structures

This paper presents a flexible and efficient design method for optimizing the mooring systems of floating structures. Mooring system optimization is challenging because of the strong nonlinearity of mooring system behavior and the many technical constraints that must be satisfied. Furthermore, different mooring configurations can have very different design spaces. While some successful examples of mooring design optimization exist in the literature, developing an optimization approach that can work across various mooring design problems is a larger challenge. We present such a method based on a flexible parameterization that allows a wide variety of mooring designs to be described by a list of variables, a quasi-static mooring model that provides efficient evaluation of a mooring design without directly considering mooring system dynamics, and an optimization framework that generates, evaluates, and adjusts the mooring design while considering user-specified constraints such as offset limits, strength safety factors, and seabed contact limits. We demonstrate the design optimization framework on four mooring design problems, each for a different type of mooring system. We compare the use of different design modes to simplify the optimization problem, showing that they can reduce the computation time by up to 75%. We also compare different optimization algorithms and find that the resulting computational speed can vary by up to 51 times. We perform a sensitivity study on one design and find that the local sensitivity of anchoring radius to water depth has a positive correlation of 0.29, but the global sensitivity shows large nonlinearities. Lastly, we perform a coupled dynamic analysis on one of the optimized designs and find that the predicted mean platform motions and mooring line tensions are within 1% of dynamic results and the extreme motions and tensions are within 14%. Lastly, we show that a DEA-Chain-Polyester mooring configuration is cost-optimal for the given design problem of the demonstrations, which aligns with general industry practice.

16 TIDAL AND WAVE POWER↗

Dynamic response of a freely rotating butterfly valve in the advanced test reactor − dynamic coefficients modeling

Here, in evaluating the water hammer issue pertaining to the primary-coolant-regulating butterfly valve in the Advanced Test Reactor, the dynamic fluid body interaction (DFBI) approach was implemented in the analysis covered in Part I. Although DFBI modeling accurately and simultaneously solved the dynamic motion of the valve’s disk along with the flow field of the surrounding fluid, it shed little light on the reason behind such motion. For Part II, the reacting torque of the fluid on the disk was decomposed into representations of the dynamic coefficients in terms of stiffness, damping, and added mass. These were evaluated via simulations with steady-state static (stiffness), constant angular speed (damping), and variable angular speed (added mass) disks. Substituting the dynamic coefficients into Newton’s second law enabled the response trajectories to be obtained. Stable (by average) and unstable equilibrium positions and thrust tendencies of the valve were determined based on the stiffness coefficient (or static torque), the response amplitude was dampened or enlarged by the damping coefficient (minorly affected by added mass), and the response frequency was altered by the damping and added mass coefficients. Although the dynamic coefficient approach renders slightly different trajectories, due to the averaging effect of the torque in comparison to the DFBI method, the overall trend of the response aligns with the DFBI simulation, thus confirming the conclusion in Part I that a fix to the current butterfly valve is necessary.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

MRCI Subtask 2.4/2.5: Regional/Subregional Analysis and Risk Assessment Final Technical Summary Report

The objective of the Midwest Regional Carbon Initiative (MRCI) project is to implement a collaborative Regional Initiative (RI) to accelerate the deployment of carbon capture, and storage (CCS) in the Midwest-Northeastern quadrant of the United States. This report is a Technical Summary report describing work performed on Tasks 2.4 (Conducting Regional/Subregional Analysis) and 2.5 (Assessing and Managing Risk) during the MRCI project. In Task 2.4, detailed numerical reservoir simulation models were developed for selected carbon storage (CS) systems identified under Task 2.1 (Battelle, 2021a) in the MRCI study area. The objective of Task 2.4 is to demonstrate a dynamic modeling methodology for evaluating the suitability of the (selected) CS systems in the MRCI region for hosting a commercial-scale storage project. In this study, CO2 injectivity was evaluated for different CS systems with an annual injection rate of 1 million metric tonnes (MMT) of CO2 considered as the minimum requirement for a commercial scale project. The objective of Task 2.5 is to assess key risks associated with storing CO2 in the different CS systems across the MRCI region and to demonstrate a method(s) for assessing these risks that may be used by developers of future CO2 storage projects in the region. The risk analysis was limited to evaluating two types of leakage risks (i.e., wellbore leakage, flow across unfractured caprock) at three modeled sites considered in Task 2.4.

MRCI,Report,Summary,Technical Challenges,dynamic m↗

Embedded symmetric positive semi-definite machine-learned elements for reduced-order modeling in finite-element simulations with application to threaded fasteners

Here, we present a machine-learning strategy for finite element analysis of solid mechanics wherein we replace complex portions of a computational domain with a data-driven surrogate. In the proposed strategy, we decompose a computational domain into an “outer” coarse-scale domain that we resolve using a finite element method (FEM) and an “inner” fine-scale domain. We then develop a machine-learned (ML) model for the impact of the inner domain on the outer domain. In essence, for solid mechanics, our machine-learned surrogate performs static condensation of the inner domain degrees of freedom. This is achieved by learning the map from displacements on the inner-outer domain interface boundary to forces contributed by the inner domain to the outer domain on the same interface boundary. We consider two such mappings, one that directly maps from displacements to forces without constraints, and one that maps from displacements to forces by virtue of learning a symmetric positive semi-definite (SPSD) stiffness matrix. We demonstrate, in a simplified setting, that learning an SPSD stiffness matrix results in a coarse-scale problem that is well-posed with a unique solution. We present numerical experiments on several exemplars, ranging from finite deformations of a cube to finite deformations with contact of a fastener-bushing geometry. We demonstrate that enforcing an SPSD stiffness matrix drastically improves the robustness and accuracy of FEM–ML coupled simulations, and that the resulting methods can accurately characterize out-of-sample loading configurations with significant speedups over the standard FEM simulations.

97 MATHEMATICS AND COMPUTING↗

CAMFeND: Credibility-Aware Multimodal Fake News Detection with Rotational Attention

In the evolving digital landscape, fake news is a significant challenge, influencing public perception and decision-making. Traditional detection approaches focus on single-modal data or simple multimodal fusion, often overlooking deeper interactions and news credibility. We propose a novel model addressing these limitations by introducing rotational attention and news domain information as a feature. Unlike static attention mechanisms, our rotational attention dynamically shifts query, key, and value roles across text and image inputs, enabling richer cross-modal interaction. Incorporating news domain information further enhances the model’s reliability by associating news posts with top domains extracted from Google search results, reducing false detections. This approach assesses both the content and the broader web context in which the news is discussed. Our model outperforms existing state-of-the-art methods by providing deeper, layered multimodal integration and domain information analysis, resulting in a more robust and adaptive fake news detection system.

Gupta, Nidhi↗

Uncertainty quantification and sensitivity analysis of a nuclear thermal propulsion reactor startup sequence

The research presented in this article describes progress in applying stochastic methods, uncertainty quantification, parametric studies, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated via the radiation transport code Griffin to simulate neutronics. Our goal is to develop a reduced-order (surrogate) model that can be rapidly sampled with perturbations to multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums (CDs), which is itself controlled by a hybrid proportional-integral-derivative (PID) controller actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time [Λ] and effective delayed neutron fraction [ β eff ] from a transient Griffin simulation executed via Griffin’s improved quasi-static solver to provide the kinetic parameters) as inputs to functions that control the CD rotation angle. By investigating numerous stochastic approaches, we developed a dual-purpose surrogate model of the NTP system, using polynomial regression in the Multiphysics Object-Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM). The trained model can be rapidly sampled while simultaneously perturbing various input parameters, such as coefficients on the PID control or temperature (directly affecting the neutron cross section). The surrogate model delivers accurate (within 5%) results at speeds orders of magnitude faster (minutes, not days of computational time) than the base model. Once the surrogate model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs as well as the effects of these inputs on the model output. For example, coefficients used in the PID control system may vary due to some type of physical interference, or uncertainty may exist in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters, and operational boundaries can be determined. The goal of this work is to support development of an advanced control system for operating CDs in a functioning NTP system. This work is a scoping study of the MOOSE STM.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

Experimental Analysis of Advanced Turbine for Supercritical CO2 Power Cycle

An advanced first stage high pressure turbine blade for a supercritical CO2 power cycle is tested in the Big Rig for Aerothermal Stationary Turbine Analysis (BRASTA) annular cascade at the Purdue Experimental Turbine Aerothermal Laboratory (PETAL) high pressure blow down facility alongside a baseline blade for comparison of aerodynamic performance. Both geometries are tested simultaneously using a novel off-axis rig design to allow for the blade geometries to be scaled up to achieve higher Reynolds numbers. The off-axis design necessitates the use of discrete sectors of airfoils, which are designed and additively manufactured in house using an mSLA printer. Printing the blades allows for unique routing of passage for blade surface static pressure taps that contour to the blade shape instead of requiring a straight line view from surface tap to egress, allowing all instrumentation for 15%, 50%, and 85% span to exist in the same passage. A flow conditioning gauze [1] is placed upstream of the blade passages to impart pressure, Mach, and swirl profiles to mimic the rotor relative frame inlet conditions to the blade. Downstream, a sonic valve is used to alter the backpressure to achieve different pressure ratios for different blowdown setpoints. The design of the rig, sonic wheel, and instrumentation are discussed along with the manufacturing and GD&T of the blades with respect to the build-up of this novel experimental apparatus. Initial commissioning data is obtained from inlet total pressure and temperature rakes, blade static pressure taps, and exit total pressure rakes. Oil visualization is performed using a silicone oil mixture containing titanium dioxide and pigmented for contrast of the blade suction side surface with the hub and shroud endwalls. The viscosity of the oil is tuned so that the oil does not fully thin and blow away during the time of the test, and endoscopic cameras are placed in the rig for live monitoring and recording. The preliminary oil visualization and rake profiles are presented, showcasing the operability of this additively manufactured off-axis flow path design.

Tuite, Logan [Purdue University]↗

Fundamental Studies of the Vibrational, Electronic, and Photophysical Properties of Tetrapyrrolic Architectures

The ability to capture and utilize light in the near-ultraviolet (NUV), visible and near-infrared (NIR-I and NIR-II) spectral regions (i.e., 320–400, 400–700, 700–1000, 1000–1700 nm) is essential for any solar-energy conversion scheme. Nature employs chlorophylls and bacteriochlorophylls in light-harvesting architectures to absorb light in the blue and red/NIR regions. Accessory pigments (carotenoids, bilins) augment absorption of the (bacterio)chlorophylls in the green region. The harvested energy is funneled to a reaction center protein, where charge separation occurs. Subsequent migration of the electron and the hole stabilizes and stores the energy from light via redox chemistry. The long-term objective of the Bocian/Holten&Kirmaier/Lindsey research program under this DOE grant has been to develop tetrapyrrole-based molecular architectures that absorb sunlight, funnel energy and separate charge with high efficiency. Integral to the program has been iterative cycles of design, synthesis and characterization that provided deep insights into the relationships between chemical composition, electronic structure, and key static and dynamic properties (vibrational, redox, photophysical, energy/charge transfer) of tetrapyrrolic systems. Such architectures included monomers, dyads, larger arrays, and complexes with accessory components. The objective was to develop molecular designs and guiding principles to enhance current and future energy-conversion schemes. Molecular arrays targeted to address one or more fundamental questions concerning light harvesting and energy/charge transfer were constructed from analogues of the naturally occurring hemes, chlorophylls and bacteriochlorophylls. Diverse, tunable synthetic building blocks were prepared that spanned the three respective tetrapyrrole families, which are the porphyrins, chlorins and bacteriochlorins. Thus, the research focused on porphyrins as well as synthetic surrogates for chlorophylls (chlorins, 13 1 -oxophorbines and chlorin-imides) and bacteriochlorophylls (bacteriochlorins, bacterio-13 1 -oxophorbines and bacteriochlorin-imides), generically termed hydroporphyrins. Although the three tetrapyrrole classes (porphyrins, chlorins and bacteriochlorins) absorb light strongly in the violet-blue spectral region, the long-wavelength absorption band typically lies in the green-orange, red, and NIR regions, respectively, with increasing intensity. Understanding the spectra, electronic structure, and energy/charge-transfer properties of such tetrapyrrolic macrocycles is of central importance for the rational design of molecular architectures for solar-energy conversion. Our integrated program of molecular design and synthesis coupled with a variety of spectroscopic, electrochemical, and computational studies have probed from first principles how structural and electronic properties of tetrapyrrolic macrocycles dictate spectral properties as well as the rates of ground-state hole/electron transfer and excited-state energy flow in multicomponent architectures. Individual molecules and multicomponent architectures were designed to test ideas of fundamental importance, often requiring the development of new synthetic methodology. The members of the collaborative team had almost daily discussions by phone and/or e-mail concerning design of molecules, flow of compounds between the labs, planning of physical characterization studies, discussing results and analysis and integrating into design of next generation architectures, and the preparation of manuscripts. Furthermore, students and postdocs in the different labs routinely communicated with one another to facilitate the advancement of the research activities. In short, a highly integrated and collaborative research program was well established among the groups. The research effort involved molecular design and synthesis of synthetic molecular architectures by the Lindsey group integrated with physicochemical and photophysical characterization by the Bocian group and the Holten&Kirmaier group (Figure 2). The Bocian group carried out electrochemical, electron paramagnetic resonance (EPR), resonance Raman (RR), and Fourier-transform infrared (FT-IR) studies, as well as density functional theory (DFT) calculations and the time-dependent extension (TDDFT) to gain insight into excited-state properties. The Holten&Kirmaier group carried out static and time-resolved absorption and fluorescence spectroscopy studies and simulated absorption spectra using molecular orbital (MO) energies from DFT as input to the four-orbital model to complement the TDDFT calculations. The combined measurements provided understanding of the vibrational/electronic properties of the individual molecules and the changes that occur upon incorporation into multicomponent architectures. This information underpinned elucidating the mechanisms and timescales of ground-state hole/electron transfer and excited-state energy and charge transfer.

14 SOLAR ENERGY↗

Ensemble Effects on Hydroxide Bond Dissociation Free Energies in Polyoxovanadate Clusters

Understanding structure-property relationships is foundational to numerous modern chemistries, such as proton-coupled electron transfer (PCET). However, an experimentally measured property is the result of the behavior from an ensemble of molecules. Neglecting ensemble effects, especially under complex chemical environments, may obfuscate these relationships and lead to discrepancies between theory and experiment. In this work, we demonstrate the impact of configurational entropy and local chemical environments on hydroxide bond dissociation free energies [BDFE- (O−H)] for a set of polyoxovanadate nanoclusters, at ambient conditions. The O−H bond strengths are investigated via density functional theory (DFT) coupled with statistical thermodynamic analysis and bilinear modeling, and compared with previous experimental results on the same systems, namely electrochemical solutions of: [V 6 O 13−x (OH) x (TRIOL R ) 2 ] −2 (x = 2, 4, 6; R = NO 2 , Me) and [V 6 O 11−x (OMe) 2 (OH) x (TRIOL NO 2 ) 2 ] −2 (x = 2, 4). Interestingly, we find that ensemble effects, even at room temperature, can account for a significant portion of the BDFE(O−H) trend with the degree of reduction via H atom binding, which cannot be fully captured by single-structure, static DFT calculations. Moreover, we find that the ensemble effects may be replicated statistically, requiring only enumeration of energetically accessible H-binding sites. With the ensemble effects resolved, we present a simple bilinear model to reconcile remaining biases between experiment and ensemble-informed theory, which corelate with clusterspecific electronic environment differences. The bilinear model achieves outstanding accuracy vs experiments with a root-mean squared error of 0.4 kcal/mol. Finally, based on the physicochemical characteristics of hydrogen interaction with polyoxometalates, we present a simple methodology that captures the BDFE(O−H) trend while dramatically reducing required DFT calculations by 98% and achieving accuracy within 1 kcal/mol. Overall, this work elucidates the roles and structural origins of configurational entropy and chemical effects on polyoxometalate hydroxide bond energies, with potential applicability to various atomically precise metal oxide systems. Importantly, it introduces models for rapid and highly accurate property calculations in connection with experiments.

Cluster chemistry↗

Minimization of Measurement Uncertainty in Optical Frequency Domain Reflectometry

Optical frequency domain reflectometry (OFDR) is a technique for interrogating optical fiber sensors to generate relative, quasi-distributed measurements. Although Optical frequency domain reflectometry (OFDR) is increasingly being adopted for aerospace, energy production, and structural monitoring applications, the quantification of uncertainty for OFDR measurements has not been developed beyond sparse empirical relationships. To address this knowledge gap, an uncertainty metric for OFDR measurements was developed. This uncertainty metric was applied to weight the edges between OFDR measurements on directed correlation graphs and analyzed to minimize the cumulative uncertainty. In conclusion, this work is the first to propose an uncertainty metric for OFDR and provides a generalized mathematical framework for optimizing OFDR hardware selection, optical fiber sensor selection, and postprocessing strategy.

42 ENGINEERING↗

Next-Generation Materials Design: Quantum Mechanics and Data-Driven Modeling

The future of materials design is rapidly advancing through the combination of quantum mechanics and data-driven modeling. These approaches integrate quantum principles with advanced data analysis, enabling precise insights into material behavior. This talk will highlight recent progress in using these methods for computational design, particularly in high-entropy alloy catalysts, emphasizing the role of hierarchical machine-learning architectures for accurate predictions. Additionally, I will discuss our work on developing machine learning interatomic potentials (MLPs) for single-element metals, metal oxides, and alloys under extreme conditions, focusing on melting behavior and phase properties at high temperatures and pressures. We have also refined our MLP models to capture dynamic surface interactions, such as CO2 and CO adsorption on MgO, using both static and molecular dynamics simulations. These models maintain high accuracy while significantly reducing computational costs compared to first-principles calculations. By enabling efficient and accurate simulations, this work supports broader community adoption, optimizes datasets for materials discovery, and extends the accessible time, size, and environmental conditions beyond the limits of experiments and traditional simulations.

machine learning↗

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on the Banshee Distribution Network: Preprint

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗