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At least 163 records · Page 9

Artificial Intelligence and Digital Engineering as Enablers for Systems Engineering in the Energy Sector

Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world?s energy demands and build energy security.

42 - ENGINEERING↗

A preliminary investigation of the use of throttles for emergency flight control

A preliminary investigation was conducted regarding the use of throttles for emergency flight control of a multiengine aircraft. Several airplanes including a light twin-engine piston-powered airplane, jet transports, and a high performance fighter were studied during flight and piloted simulations. Simulation studies used the B-720, B-727, MD-11, and F-15 aircraft. Flight studies used the Lear 24, Piper PA-30, and F-15 airplanes. Based on simulator and flight results, all the airplanes exhibited some control capability with throttles. With piloted simulators, landings using manual throttles-only control were extremely difficult. An augmented control system was developed that converts conventional pilot stick inputs into appropriate throttle commands. With the augmented system, the B-720 and F-15 simulations were evaluated and could be landed successfully. Flight and simulation data were compared for the F-15 airplane.

Burcham, F. W., Jr.↗

A preliminary investigation of the use of throttles for emergency flight control

A preliminary investigation was conducted regarding the use of throttles for emergency flight control of a multiengine aircraft. Several airplanes including a light twin-engine piston-powered airplane, jet transports, and a high performance fighter were studied during flight and piloted simulations. Simulation studies used the B-720, B-727, MD-11, and F-15 aircraft. Flight studies used the Lear 24, Piper PA-30, and F-15 airplanes. Based on simulator and flight results, all the airplanes exhibited some control capability with throttles. With piloted simulators, landings using manual throttles-only control were extremely difficult. An augmented control system was developed that converts conventional pilot stick inputs into appropriate throttle commands. With the augmented system, the B-720 and F-15 simulations were evaluated and could be landed successfully. Flight and simulation data were compared for the F-15 airplane.

Burcham, F. W., Jr.↗

Fielding An Amphibious UAV: Development, Results, and Lessons Learned

This report summarizes the work completed on the design and flight-testing of a small, unmanned, amphibious demonstrator aircraft that flies autonomously. The aircraft named ACAT (Autonomous Cargo Amphibious Transport) is intended to be a large cargo carrying unmanned aircraft that operates from water to avoid airspace and airfield conflict issues between manned and unmanned aircraft. To demonstrate the feasibility of this concept, a demonstrator ACAT was designed, built, and flown that has a six-foot wingspan and can fly autonomously from land or water airfield. The demonstrator was designed for a 1-hour duration and 1-mile telemetry range. A sizing code was used to design the smallest demonstrator UAV to achieve these goals. The final design was a six-foot wingspan, twin hull configuration that distributes the cargo weight across the span, reducing the wing structural weight. The demonstrator airframe was constructed from balsa wood, fiberglass, and plywood. A 4-stroke model airplane engine powered by methanol fuel was mounted in a pylon above the wing and powers the ACAT UAV. Initial flight tests from land and water were conducted under manual radio control and confirmed the amphibious capability of the design. Flight avionics that were developed by MLB for production UAVs were installed in the ACAT demonstrator. The flight software was also enhanced to permit autonomous takeoff and landing from water. A complete autonomous flight from ahard runway was successfully completed on July 5, 2001 and consisted of a take-off, rectangular flight pattern, and landing under complete computer control. A completely autonomous flight that featured a water takeoff and landing was completed on October 4, 2001. This report describes these activities in detail and highlights the challenges encountered and solved during the development of the ACAT demonstrator. hard runway was successfully completed on July 5, 2001 and consisted of a take-off, rectangular flight pattern, and landing under complete computer control. A completely autonomous flight that featured a water takeoff and landing was completed on October 4, 2001. This report describes these activities in detail and highlights the challenges encountered and solved during the development of the ACAT demonstrator.

Pisanich, Greg↗

Ultrasensitive Superconducting Transition Edge Sensors Based On Electron-Phonon Decoupling

We have successfully fabricated the superconducting transition edge sensor (TES), bolometer technology that centers on the use of electron-phonon decoupling (EPD) to thermally isolate the bolometer. Along with material characterization for large format antenna coupled bolometer arrays, we present the initial test results of bolometer based on EPD designed for THz detection. We have selected a design approach that separates the two functions of photon absorption and temperature measurement, allowing separate optimization of the performance of each element. We have integrated Molybdenum/Gold (Mo/Au) bilayer TES and ion assisted thermally evaporated (IAE) Bismuth (Bi) films as radiation absorber coupled to a low-loss microstripline from Niobium (Nb) ground plane to a twin-slot antenna structure. The thermal conductance and the time constant of these devices have been measured, and are consistent with our calculations. The device exhibits a single time constant at 0.1 K of approx.160 IlS, which is compatible with readout by a high-bandwidth single SQUID or a time domain SQUID multiplexer. The effects of thermal conductance and electrothermal feedback are major determinants of the time constant, but the electronic heat capacity also plays a major role. The NEP achieved in the device described above is 2.5x10(exp -17)W(gamma)Hz. Our plan is to demonstrate a reduction of the volume in the superconducting element to 5 microns x 5 microns in films of half the thickness at Tc = 60mK. By calculation, this new geometry corresponds to an NEP reduction of two orders of magnitude to 2.5x10(exp -19)W/(gamma)Hz, with a time constant of ~130/ls.

Jethava, Nikhil↗

The 1.2 micron CMOS technology

A set of test structures was designed using the Jet Propulsion Laboratory (JPL) test chip assembler and was used to evaluate the first CMOS-bulk foundry runs with feature sizes of 1.2 microns. In addition to the problems associated with the physical scaling of the structures, this geometry provided an additional set of problems, since the design files had to be generated in such a way as to be capable of being processed through p-well, n-well, and twin-well processing lines. This requirement meant that the files containing the geometric design rules as well as the structure design files had to produce process-insensitive designs, a requirement that does not apply to the more mature 3.0-micron CMOS feature size technology. Because of the photolithographic steps required with this feature size, the maximum allowable chip size was 10 x 10 mm, and this chip was divided into 24 project areas, with each area being 1.6 x 1.6 mm in size. The JPL-designed structures occupied 13 out of the 21 allowable project sizes and provided the only test information obtained from these three preliminary runs. The structures were used to successfully evaluate three different manufacturing runs through two separate foundries.

Pina, C. A.↗

Digital Twin Model for Advanced Manufacture of a Rotating Detonation Engine Injector

A digital twin material model (DTMM) of an additive manufacturing (AM) process was created to advance the state of the art in rotating detonation engine (RDE) injector design. Current RDE injectors are designed with large pressure drops, enabling a stable and repeatable combustion process. However, this comes at the cost of system efficiency. For the technology to transition to commercial fossil-based power generation, it is important to develop injectors with reduced flow losses. Low-loss injectors are difficult to design and manufacture with conventional manufacturing techniques. AM enables new design options, but the AM manufacturing process must be thoroughly understood to result in a robust design. A DTMM provides the necessary insight by defining the cause-effect relationships between process parameters, microstructure features, and properties. Therefore, a DTMM to support the design and manufacturing process was developed and applied to the design of a new additively manufactured low-loss injector. The injector combustion behavior was characterized through hot-fire tests, and mechanical performance was compared to the DTMM predictions. The two project goals were the successful development of the DTMM and the demonstration of an improved RDE injector design. The RDE injector design and DTMM developments occurred on parallel but dependent paths. The injector was designed to reduce pressure drop by increasing the cross-sectional flow area ratio between the injector air passages and the combustor annulus. This resulted in less structural material, raising the concern that thin members would be susceptible to high-cycle fatigue (HCF) under the periodic loading inherent to an RDE. It was most important for the DTMM to predict behavior in these features; therefore, the injector design concept guided the material thicknesses used in fatigue tests. The DTMM development started by manufacturing a series of coupons over the range of possible AM process variations. A design-of-experiment approach was used to select which process variable combinations gave the most efficient coverage relevant to the injector design space. The microstructure in each of these coupons was characterized, and then computational methods were used to create a numerical model of the correlation between process variables and microstructure. Next, a set of HCF samples were tested to calibrate existing models that map microstructure to HCF performance. Together, these two links formed the DTMM that calculates HCF behavior from AM process variables. Two injector prototypes were additively manufactured. The first injector design strategy aggressively pursued low-loss performance by substantially increasing the oxidizer flow area. The combination of manufacturing lead times and the fatigue testing schedule meant that the DTMM was not available when building this first prototype. Therefore, its process parameters were chosen based on a manual review of the available coupon data. This prototype was built successfully and evaluated in 58 combustion tests. Sustained detonation was achieved with remarkably reduced pressure loss, and some tests even displayed pressure loss characteristics similar to conventional gas turbine combustors. This achieved the project goal of improving RDE injector design. The second injector was manufactured according to the optimized parameters predicted by the DTMM. The flow area modifications of this injector were less aggressive than the first injector since demonstrating low pressure loss was not an objective of the second hot-fire test series. Rather, the test objective was to cause high cycle fatigue failure in the part due to periodic loading from the rotating detonation wave. The observed number of cycles to failure was to be compared to the number predicted by the DTMM and thereby assess the utility of the DTMM in component design. However, the required level of vibration was not obtained during combustion. Therefore, high cycle fatigue was not experienced in the hot-fire tests of the second injector. Fatigue data was obtained by further testing the second injector in a conventional HCF test apparatus. The injector demonstrated HCF strength above the DTMM prediction. In fact, it did not fail and testing was only discontinued due to reaching the end of the period of performance. This points to some success in the project’s primary goal of successfully developing and applying the DTMM to a component design. Implementing the DTMM recommendations for optimal processing parameters led to a part with acceptable properties. The DTMM was also shown to be an efficient correlator of data and to provide insight into the relationship between process settings, microstructure, and property performance. However, the failure of the DTMM prediction to match the experimental result of the injector fatigue test also points to the need to include significantly more data in the model development. In this project, coupons made with identical processing parameters exhibited drastically different properties from each other and from the injector part, which clearly influences the accuracy of a model that predicts performance based on parameters. Uncertainties in the build process must be quantified to develop more robust models. A denser and broader matrix of coupon process and geometry variations, several repeated builds of every point, more in-situ build process measurements, and direct observation of tensile and HCF sample microstructure (as opposed to separate microstructure specimens) are recommendations to improve future AM modeling efforts.

20 FOSSIL-FUELED POWER PLANTS↗

Development of Slat-gap and Slat-cove Filler Treatments for Noise Reduction Assessment on the High Lift Common Research Model in the NASA LaRC 14x22

Airframe noise is an important issue during the approach and landing phases of flight because engines are throttled back, and airframe sources become an equal or greater contributor to environmental noise. The leading-edge slat component of the high-lift system for transport aircraft has been identified as a prominent source of airframe noise. Noise production mechanisms and potential treatments to mitigate airframe noise have been areas of active research for nearly three decades. Two promising technologies for slat noise reduction are the slat-gap filler (SGF) and slat-cove filler (SCF). Previous work was done using a 2D section of a transport class aircraft wing to demonstrate their feasibility via benchtop and computational models. A wind tunnel test campaign was recently undertaken to test the SGF and SCF technologies in a flight-like environment by installing dynamically scaled treatments on the 10%-scale High-Lift Common Research Model (CRM-HL), which is representative of a commercial twin-aisle transport aircraft. Advancement of the SGF and SCF technologies to overcome challenges of integration with 3D airframe structures, computational simulations of the CRM-HL predicting the aerodynamic and aeroacoustic performance and structural loads, and design of wind tunnel model hardware with features accommodating noise treatments were conducted in parallel. Details of innovations in the SGF and SCF technologies for 3D airframe feasibility will be described. The loads the treatments must sustain in flight and the influence of those loads on the design is also discussed. Finally, the design of wing leading-edge assemblies to treat the 10% CRM-HL with dynamically scaled SGF and SCF components is presented. The treatments were successfully tested in 2020/2021 providing a demonstration of leading-edge noise reduction using components that would allow slat articulation.

airframe noise reduction↗

Architecture Design for Remote Operation of Microreactors: Poster

The nuclear sector is pursuing a number of advanced reactor concepts, one of which is the microreactor, an advanced reactor characterized by a power output of less than 20 MWth. These reactors are intended for use in applications where traditional power solutions are economically or logistically impractical. One key feature required for the successful deployment of microreactors is a remote operation capability, which can dramatically cut staffing expenses by eliminating the need for licensed operators to be present at the site of each reactor and instead concentrate in a centrally located operations center. In moving to a remote operations framework for nuclear reactors, the number of potential attack surfaces for a cyber adversary looking to cause harm or disruption increases. Therefore, a robust cybersecurity architecture is required to mitigate these potential vulnerabilities. This paper explores the functional infrastructure, security, and communication requirements to adapt remote operations to nuclear applications. It then presents a reference architecture for remote operations of microreactors that applies best practices in cybersecurity and remote communications in the context of a digital twin remote operation system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Sweep-tracing algorithm: in silico slip crystallography and tension-compression asymmetry in BCC metals

Abstract Direct Molecular Dynamics (MD) simulations are being increasingly employed to model dislocation-mediated crystal plasticity with atomic resolution. Thanks to the dislocation extraction algorithm (DXA), dislocation lines can be now accurately detected and positioned in space and their Burgers vector unambiguously identified in silico, while the simulation is being performed. However, DXA extracts static snapshots of dislocation configurations that by themselves present no information on dislocation motion. Referred to as a sweep-tracing algorithm (STA), here we introduce a practical computational method to observe dislocation motion and to accurately quantify its important characteristics such as preferential slip planes (slip crystallography). STA reconnects pairs of successive snapshots extracted by DXA and computes elementary slip facets thus precisely tracing the motion of dislocation segments from one snapshot to the next. As a testbed for our new method, we apply STA to the analysis of dislocation motion in large-scale MD simulations of single crystal plasticity in BCC metals. We observe that, when the crystal is subjected to uniaxial deformation along its [001] axis, dislocation slip predominantly occurs on the {112} maximum resolved shear stress plane under tension, while in compression slip is non-crystallographic (pencil) resulting in asymmetric mechanical response. The marked contrast in the observed slip crystallography is attributed to the twinning/anti-twinning asymmetry of shears in the {112} planes relatively favoring dislocation motion in the twinning sense while hindering dislocations from moving in the anti-twinning directions.

36 MATERIALS SCIENCE↗

Multifidelity validation of digital surrogates using variable-density turbulent mixing models

In this study, ensembles of experimental data are presented and utilized to compare and validate two models used in the simulation of variable-density (Atwood = 0.22), compressible turbulent mixing. Though models of this kind (Reynolds averaged NavierStokes and large-eddy simulations) have been validated extensively with more canonical flows in previous studies, here the present approach offers novelty in the complexity of the geometry, the ensemble-based validation, and the uniformity of the computational framework on which the models are tested. Moreover, all experimental and computational tasks were completed by the authors which has led to a tightly coupled experimental configuration with its “digital twin.” The experimental divergent-shock-tube facility and its data acquisition methods are described and replicated in simulation space. A 2D Euler model which neglects the turbulent mixing at the interface is optimized to experimental data using a Gaussian process. This model then serves as the basis for both the 2D RANS and 3D LES studies that make comparisons to the mixing-layer data from the experiment. A relatively simple RANS model is shown to produce good agreement with experimental data only at late flow development times. The LES ensembles generally show good agreement with experimental data but display sensitivity to the characterization of initial conditions. Resolution-dependent behavior is also observed for certain higher-order statistics of interest. Overall, the LES model successfully captures the effects of divergent geometry, compressibility, and combined nonlinear instabilities inherent to the problem. The successful prediction of mixing width and its growth rate highlight the existence of three distinct regimes in the development of the instability, each with similarities to previously studied instabilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗

Health Monitoring and Prognostics for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously approved and published information.

Systems Health Managent↗

Eliminating Yield Anisotropy and Enhancing Ductility in Mg Alloys by Shear Assisted Processing and Extrusion

Solid phase processing techniques such as friction stir welding, Shear assisted processing and extrusion (ShAPE)/ friction extrusion and cold spray have been successfully demonstrated as promising thermomechanical methods to produce metallic materials with enhanced performance. In this study, AZ series with and without silicon, ZK60 Mg alloys in as-received forms (as-cast or as-extruded) were processed using Shear Assisted Processing and Extrusion (ShAPE). Microstructural characterization was performed using EBSD and TEM and revealed that as compared to the feedstock materials/ billets, friction extruded Mg alloys had more uniform microstructure, equiaxed grains, finer and homogeneously distributed precipitates and chemical homogeneity. It was also observed that basal planes were not oriented parallel to extrusion axis. As a result, rod products exhibited significantly reduced (in some cases eliminated) yield asymmetry and achieved enhanced ductility, which were uncommon or difficult to attain using conventional processing techniques. In addition, modified texture likely suppressed deformation twinning under compressive deformation.

Shear Assisted Processing and Extrusion, SHAPE, ma↗

Gear tooth stress measurements on the UH-60A helicopter transmission

The U.S. Army UH-60A (Black Hawk) 2200-kW (3000-hp) class twin-engine helicopter transmission was tested at the NASA Lewis Research Center. Results from these experimental (strain-gage) stress tests will enhance the data base for gear stress levels in transmissions of a similar power level. Strain-gage measurements were performed on the transmission's spiral-bevel combining pinions, the planetary Sun gear, and ring gear. Tests were performed at rated speed and at torque levels 25 to 100 percent that of rated. One measurement series was also taken at a 90 percent speed level. The largest stress found was 760 MPa (110 ksi) on the combining pinion fillet. This is 230 percent greater than the AGMA index stress. Corresponding mean and alternating stresses were 300 and 430 MPa (48 and 62 ksi). These values are within the range of successful test experience reported for other transmissions. On the fillet of the ring gear, the largest stress found was 410 MPa (59 ksi). The ring-gear peak stress was found to be 11 percent less than an analytical (computer simulation) value and it is 24 percent greater than the AGMA index stress. A peak compressive stress of 650 MPa (94 ksi) was found at the center of the Sun gear tooth root.

Oswald, Fred B.↗

Heat pipe dynamic behavior

The vapor flow in a heat pipe was mathematically modeled and the equations governing the transient behavior of the core were solved numerically. The modeled vapor flow is transient, axisymmetric (or two-dimensional) compressible viscous flow in a closed chamber. The two methods of solution are described. The more promising method failed (a mixed Galerkin finite difference method) whereas a more common finite difference method was successful. Preliminary results are presented showing that multi-dimensional flows need to be treated. A model of the liquid phase of a high temperature heat pipe was developed. The model is intended to be coupled to a vapor phase model for the complete solution of the heat pipe problem. The mathematical equations are formulated consistent with physical processes while allowing a computationally efficient solution. The model simulates time dependent characteristics of concern to the liquid phase including input phase change, output heat fluxes, liquid temperatures, container temperatures, liquid velocities, and liquid pressure. Preliminary results were obtained for two heat pipe startup cases. The heat pipe studied used lithium as the working fluid and an annular wick configuration. Recommendations for implementation based on the results obtained are presented. Experimental studies were initiated using a rectangular heat pipe. Both twin beam laser holography and laser Doppler anemometry were investigated. Preliminary experiments were completed and results are reported.

Issacci, F.↗

Cantor-derived medium-entropy alloys: bridging the gap between traditional metallic and high-entropy alloys

The year 2004 marked the beginning of a new era in the design of metallic materials, as the concept of multiple principal component alloys, commonly known as High-Entropy Alloys (HEAs), was proposed by Cantor and Yeh. The unexpected single-phase microstructure, instead of the expected brittle intermetallic compounds, was attributed to the large entropy of mixing and immediately caught the attention of the scientific community. Today, HEAs are considered important advanced materials and a broad range of alloys using nominally the same design principle have been investigated. Despite that, the CrMnFeCoNi (Cantor) alloy stands out as the most successful HEA due to its outstanding mechanical properties and microstructure. In this scenario, variants of the Cantor alloy, named medium-entropy alloys (MEAs), are gaining significant interest as they display a better industrial potential than both HEAs and traditional alloys. These variants of the Cantor alloy with only three or four main elements result in 15 possible combinations. The microstructure of these alloys is discussed in terms of advanced characterization as well as thermodynamic parameters and computational simulation. Their phase stability is addressed over a wide range of temperatures and strain rates. The mechanical properties, especially the fracture toughness, of the CrFeCoNi and CrCoNi alloys have been reported to be even superior to those of the Cantor alloy and most modern engineering alloys. This is associated with the formation of a continuous sequence of strengthening mechanisms, including hierarchical twin networks, which serve to prolong the strain hardening. The present article reviews and critically assesses, for the first time, recent advances in these Cantor-derived MEAs.

36 MATERIALS SCIENCE↗

FY25 Electric Grid Security Annual Report

Sandia’s Electric Grid Security program advances a national vision of energy dominance and accessibility, while applying our national security -emphasis on ensuring of a secure, resilient, and affordable electric system for all users. Our achievements reflect a strategic approach combining technology development; modeling, simulation, and data analytics; and partnered demonstrations and outreach to further the adoption of advanced grid and storage technologies. Our FY25 efforts leverage the strengths of our partnerships—spanning Sandia’s core science and technology competencies as well as external technology leaders—to develop the solutions today which enable the grid of tomorrow. Key accomplishments in this report that support our strategy span our technical program areas and include: • New open-source analytical tools for systems -level planning and optimization, including significant advances to the QuESt analytical environment; • Further advancement of artificial intelligence and machine learning to enhanced grid operations and planning as we rise to the challenge of new large loads; • Development of solid-state power conversion technologies and a new medium-voltage research lab; • New technologies to assess wildfire vulnerabilities and mitigate potential impacts; • Advanced applications of new cybersecurity technologies with industry partners; • Contributions to understanding the impacts of electromagnetic pulses and geomagnetic disturbances on grid components; and • Digital twin development for hybrid microgrids with multiple generators, storage, and loads. This report indicates key areas of research and engagement and summarizes the impact of Sandia’s contributions through notable accomplishments, journal publications, patents, and technical conferences and presentations. It is provided with the hope that readers discover ways we can further team to create our modern grid and apply the outcomes of our efforts. The bulk of work described herein is funded by several offices within the U.S. Department of Energy (USDOE), including the Office of Electricity (OE); Cybersecurity, Energy Security, and Emergency Response (CESER); former offices such as the Office of Energy Efficiency and Renewable Energy (EERE), the Grid Deployment Office (GDO), the Office of Clean Energy Demonstrations (OCED), and other key programs at USDOE. As we continue to state in these annual reports, the contributors to our successes are too numerous to name here, though our team wishes to express our deep gratitude to the numerous program and project sponsors at the US Department of Energy, who often function equally as technical collaborators; our many partners in industry, academia, utilities, and other national labs; and fellow researchers and business partners at Sandia whose leadership and creativity have enabled the accomplishments described herein.

24 POWER TRANSMISSION AND DISTRIBUTION↗