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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An Update on the Status of a Reduced Flow Test of a 10MW 700°C sCO2 Integrally Geared Compander

. In order to maintain viability as a future power-generating technology, concentrating solar power (CSP) must reduce its levelized cost of electricity (LCOE). One component of solving this problem is reducing the cost of the power block while simultaneously increasing the efficiency of the thermodynamic cycle. One disruptive technology that has the promise to accomplish this is supercritical CO2 based power cycles. These cycles are conceptually similar to steam cycles; however, they have substantially smaller turbomachinery at equivalent power while also delivering more efficiency at turbine inlet temperatures of 500-700 degrees Celsius. This paper will summarize the current status of a US Department of Energy project to develop machinery to support a 10 MW sCO2 power cycle. The team of Southwest Research Institute® (SwRI®) and Hanwha Power Systems America, proposed to develop an integrally-geared (IG) compressor-expander (compander) for use in a nominal 10 MW-scale concentrated solar power (CSP) supercritical carbon dioxide (sCO2) plant application. This integrally-geared compander (IGC) comprises multiple pinion shafts interconnected on a single bull gear to create a compact package, and utilizes a low-cost, low-speed driver. In addition, the integrally geared architecture allows each pinion to operate at different rotational speeds to optimize performance and easily allow for inter-stage cooling and turbine re-heat to further enhance both stage and cycle efficiency. The close integration of all turbomachinery elements into a single integrally-geared (IG) machine creates a design that lends itself to power block modularization, which makes it suitable for waste heat recovery, fossil fuel power plants, and especially CSP applications. As part of the commercialization of this technology, it is necessary to reduce risk by validation testing of key components. In the current work, the focus is developing a test loop to enable safe testing of the main compressor stage across a wide range of operating conditions, and to validate the mechanical integrity of the turbine at full pressure, temperature, and speed. Developing a test loop for sCO2 requires balancing a number of design alternatives that impact cost, lead time, safety, and performance. The current work discusses the design process for the reduced flow test loop for the compander, and supporting documentation supporting the viability of the test loop

wilkes, jason↗
DOE OSTI2026

Toward an AI-Powered Software Pipeline for Real-Time Tracking and Analysis of Wildfire and Smoke

Real-time tracking of wildfires and smoke is crucial for effective response, minimizing damage, protecting lives, and efficiently managing resources during fire emergencies. We develop a web-based AI-powered pipeline that detects wildfires in aerial video and estimates deployment-relevant behavior metrics, including cumulative burned area, burned-area growth rate, fire spread direction, and smoke dispersion. The system combines a YOLO-based detector with YCbCr-based fire segmentation, HSV-based smoke segmentation, Farneback optical flow, and centroid-based spatiotemporal tracking. Using ground sampling distance (GSD), pixel-level fire masks are converted to physical burned-area measurements by correlating fire pixel counts with camera altitude and tilt angle. We benchmark YOLO variants and non-YOLO baselines (GoogLeNet, CNN, DBN, Autoencoder, U-Net, and AlexNet) on the IEEE FLAME dataset and a newly created aerial frame dataset, Wildfire-DB. Cross-dataset evaluation uses a strict threshold-transfer protocol: decision thresholds are selected on FLAME validation and transferred unchanged to Wildfire-DB to quantify generalization under domain shift. YOLOv6 achieves the strongest cross-dataset frame-level fire detection on Wildfire-DB (ROC-AUC 0.8200, PR-AUC 0.8044, and transferred-threshold F1 0.7596). For tracking-oriented deployment requiring oriented localization, YOLO11-OBB provides the most reliable cross-dataset behavior among OBB-capable models while remaining computationally feasible. To analyze the feasibility of UAV deployment, we further measure inference efficiency using synchronized GPU and CPU power logs on a fixed workload of 1569 frames. YOLO-family models process the video in 5.73–12.47 seconds with net energy of 1247.28–1775.39 J, substantially lower latency and energy than heavier classification and reconstruction baselines. Overall, model optimality depends on operational objectives: YOLOv6 is best for cross-dataset detection robustness, whereas YOL...

Color segmentation↗
DOE OSTI2022

Comparative Analysis of Direct and Indirect Cooling of Wide-Bandgap Power Modules and Performance Enhancement of Jet Impingement-Based Direct Substrate Cooling

With the development of high-power and high-torque machines, requirements for high-power density electronics are increasing. Thermal management of such systems requires high heat extraction. Conventional air cooling based heat sinks and cold plate based liquid cooling have their own benefits for various applications but has limitations for high power density applications. The current study explores a jet impingement based direct substrate cooling system that was implemented for a SiC based direct bonded Cu substrate for various power losses. Numerical comparison between jet impingement cooling and conventional horizontal/indirect cooling (pin fin heat sink and genetic algorithm-optimized heat sink) showed that the area weighted average of the heat transfer coefficient (HTC) is high for both horizontal cooling designs, and the local HTC is higher for jet impingement. Design iterations were undertaken to resolve the bottleneck of this cooling system. Increasing the number of nozzles helped to cover more area at the direct bonded Cu bottom plate, which drops the chip temperature considerably. With a constant flow rate, increasing the number of nozzles would decrease local jet velocity, which reduces the heat extraction by jet impingement. This issue can be addressed by reducing the diameter of nozzle but doing so results in a high pressure drop where the design constraint is 2 psi. A flared nozzle design is proposed, which has a higher spreading angle of the jet that increases the flow coverage and reduces the pressure drop of the coolant loop.

Barua, Himel↗
DOE OSTI2020

Portable interactive visualization of large-scale simulations in geotechnical engineering using Unity3D

Development in large-scale geotechnical engineering simulation places tremendous demand for efficient visualization of such simulation data. This study presents a lightweight software tool, i.e. Geotechnical Interactive Visualization (GIV), as a solution to this challenge, which achieves efficient interactive visualization of large-scale simulations data in geotechnical engineering. Visualization data flow and algorithms specifically optimized for common geotechnical engineering applications are implemented in GIV. GIV can visualize geotechnical structure models with time-varying attributes attached to mesh with fixed topology, and also models with time-varying mesh topologies but no attributes attached, the two most common visualization tasks in geotechnical engineering. Furthermore, challenges for large-scale simulation data visualization, including parallel simulation data redundancy, massive data size, dynamic user interaction, and portability are overcome via specifically designed algorithms for simulation data preprocessing and optimized visualization modules using the powerful 3D rendering and interactive game engine Unity3D. Comparison of GIV with several widely used visualization tools for the visualization of large-scale idealized datasets and realistic geotechnical simulations highlights the visualization efficiency, smooth interactivity, and lightweight features of GIV.

42 ENGINEERING↗
DOE OSTI2022

Going mobile to address emerging climate equity needs in the heterogeneous urban environment

We report the Brookhaven National Laboratory Center for Multiscale Applied Sensing (CMAS) aims to address environmental equity needs in the context of a changing climate. As a first step towards this goal, the center developed a one-of-a-kind observatory tailored to the study of highly heterogeneous urban environments. This article describes the features of the mobile observatory that enable its rapid deployment either on or off the power grid, as well as its instrument payload. Beyond its unique design, the observatory optimizes data collection within the obstacle-laden urban environment using a new smart sampling paradigm. This setup facilitated the collection of previously poorly documented environmental properties including wind profiles throughout the atmospheric column. The mobile observatory captured unique observations during its first few intensive observation periods (IOPs).. Vertical air motion and infrared temperature measurements collected along the faces of the supertall One Vanderbilt skyscraper in Manhattan, NY reveal how solar and anthropogenic heating affect wind flow and thus the venting of heat, pollution, and contaminants in urban street canyons. Also, air temperature measurements collected during travel along a 150-km transect between Upton and Manhattan, NY offer a high-resolution view of the urban heat island and reveal that temperature disparities also exist within the city across different neighborhoods. Ultimately, the datasets collected by CMAS are poised to help guide equitable urban planning by highlighting existing disparities and characterizing the impact of urban features on the urban microclimate with the goal of improving human comfort.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Titanium-Cerium Electrode-Decoupled Redox Flow Batteries Integrated With Fossil Fuel Assets For Load-Following, Long-Duration Energy Storage

Operation of fossil plants at partial capacity with frequent cycling results in decreased efficiency, increased emissions and increased wear and maintenance. The objective of this project is to advance the integration of a titanium-cerium electrode-decoupled redox flow battery (RFB) system with conventional fossil-fueled power plants through technical and economic system-level studies and component scale-up and R&D. The Ti-Ce chemistry has a pathway to meet the DOE cost targets of $\$$100/kWh and $\$$0.05/kWh-cycle owing to the use of low-cost, earth abundant elemental actives and incorporation of inexpensive carbon felt electrodes and non-fluorinated anion exchange membrane (AEM) separators. The initial unit cell design was scaled up, with some modifications made to improve ease of manufacturing, from 25 cm 2 cell area to 400 cm 2 . Electrochemical tests demonstrated operation at a current density up to 50 mA/cm 2 , which is on par with other commercial RFB offerings. Furthermore, the Ti-Ce technology developed by WashU was evaluated and tested by industrial team partner, Giner, Inc., in their modular 3-cell stack. Several cell design modifications and alternate component material selections were successfully implemented to accommodate this chemistry while reducing polarization and leakage. Results from stack testing show high columbic efficiency and indicate that further optimization of cell compression and components will lead to successful operation of the Ti-Ce ED-RFB over longer duration at the multi-cell stack level. Engineering and cost analysis showed that an RFB system with power output on the order of 100 MW and with a charge/discharge duration of approx. 12 hours is the most cost effective for integration with fossil plants. At this scale, projected cycling of fossil fuel power plants can be significantly reduced. The use of a storage system is shown to reduce the fossil plant standalone cost of electricity by $\$$7/MWh, through increased capacity factor and improved average efficiency, in the scenario of high penetration of renewable power.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Evaluating the Grid Impact of Oregon Offshore Wind [Slides]

This analysis used high-resolution offshore wind data and a detailed production cost model (PCM) of the Western Interconnection to explore the value and operational impact of integrating offshore wind along Oregon's coastline. Leveraging local technical stakeholder expertise and input, we determined a set of scenarios to explore. These scenarios vary both offshore wind capacities and the Western Interconnection generation and transmission infrastructure. From the scenario modeling and analysis, we identified the following key findings. In addition, we simulated a subset of the scenarios for a range of historical weather years (2007-2013), to understand the robustness of our findings to different weather conditions. Trans-coastal transmission constraints and congestion are the key drivers to the curtailment of Oregon offshore wind. Once power can be delivered into the Willamette Valley, there are few system constraints that lead to a significant curtailment of offshore wind off the coast of Oregon. Approximately 2.6 GW of installed offshore wind capacity can be integrated into Oregon's power system without major upgrades to trans-coastal transmission while avoiding significant curtailment. The system value provided by offshore wind ranges between $\$65$ /MWh and $\$85$ /MWh across the various scenarios considered. Offshore wind heavily influences the flow of the cross Cascade transmission. Across all scenarios, we found a robust relationship of approximately 500-550 MW decrease in the hourly flow of the cross Cascade transmission for every 1,000 MW of hourly offshore wind generation. However, we also found there was not a strong relationship between the highest cross-Cascade transmission flow hours and high offshore wind generation, limiting the extent to which offshore wind can be considered a non-wires alternative to cross cascade transmission. Depending on the meteorological year, 880-1,580 MW and 1,650-3,100 MW can be counted on to serve coastal loads with 2.6 GW and 5 GW of offshore wind capacity, respectively. Offshore wind allows for more optimal daily and hourly scheduling of hydropower, while still complying with various technical and regulatory constraints on the water resource. Oregon offshore wind has the potential to contribute to the evening net load peak in California (i.e., mitigate duck curve challenges), however transmission congestion between California and Oregon limits this contribution. Co-located storage at the point of interconnection for offshore wind reduces curtailment when trans-coastal transmission is not upgraded, providing a non-wires alternative to increase offshore wind capacity beyond 2.6 GW.

17 WIND ENERGY↗
DOE OSTI2022

Effects of frequency and pulse width on electron density, hydrogen peroxide generation, and perfluorooctanoic acid mineralization in a nanosecond pulsed discharge gas-liquid plasma reactor

Plasma electron density and temperature were characterized in a continuous flowing gas-liquid film reactor with argon carrier gas by time-resolved optical emission spectroscopy. The plasma parameters were studied as a function of time for varying pulse widths and frequencies. Pulse frequency was varied between 1 and 10 kHz at 16 kV (input voltage) and 40 ns (pulse width) using an Eagle Harbor Technologies, Inc. (EHT) power supply and 5–100 kHz using an Airity Technologies, LLC (AT) power supply. The pulse width was varied between 40 and 200 ns at 16 kV, 2 kHz with the EHT power supply. Optimal frequencies of 5 and 20 kHz were observed for peak electron density with EHT and AT power supplies, respectively. The peak electron density increased with increasing pulse width between 40 and 200 ns using the EHT power supply. Hydrogen peroxide exiting the reactor in the liquid phase increased with discharge power irrespective of the power supply or pulse parameters. Mineralization of 12.5, 50, and 200 ppm perfluorooctanoic acid (PFOA) dissolved in DI water to fluoride (F - ) correlated to the peak electron density. Glycerol, a liquid-phase hydroxyl radical scavenger, depleted hydrogen peroxide but did not affect PFOA mineralization. CO, a gas-phase hydroxyl radical scavenger, led to a reduction in the formation of F - production, suggesting hydroxyl radicals in the gas-liquid film play a necessary, but not singular, role in mineralization of PFOA.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Bipolar plate flow channel designs for vanadium redox flow battery: a review

Vanadium redox flow battery is one of the preferred systems for grid scale energy storage due to long service life (>20000 cycles), higher efficiency (>85 %), deep discharge capability (>95 % DoD), and inherent scalability and safety. Among system components, flow field is most critical as it governs electrolyte distribution, mass transport and hydraulic performance. Here, this review examines emerging flow channel geometries, highlighting the impact of channel width (0.66 to 1.5 mm), depth (1 to 1.5 mm) and land width (0.5 to 1.5 mm) can reduce pressure drop to <10 kPa while enabling power densities above 600 mW.cm -2 . A key finding is that low channel width to depth ratio (<1) enhances voltage and energy efficiencies by improving under rib convection. The review also provides an overview of progress and perspective in bipolar plate materials, manufacturability, and shunt current mitigation strategies for stack scaling up. Cost analysis emphasizes the influence of flow field designs on the levelized cost of storage and pathways towards the US DOE's $\$$0.05 per kWh energy generation cost target. In addition, opportunities for AI/ML/DT tools assisted design and data driven optimization strategies are also outlined to accelerate next generation flow field development.

Efficiency optimization↗
DOE OSTI2021

Experimental study on rheological and settling properties of shape memory polymer for fracture sealing in geothermal formations

This article studies the rheology and annular flow of a smart lost circulation material (LCM) at 160 °C, using a high-temperature flow loop. The smart LCM is a shape memory polymer (SMP) that activates at high temperatures, and its particle size increases to seal fractures in geothermal formations. SMP was mixed with the base fluid in two different concentrations, 1.0 and 3.0 wt%, to study rheology, wellbore hydraulics, activation process, and settling behavior under different testing conditions. The results of this study showed that the SMP could be activated at high temperatures. An increase of 80–100% in the particle size was observed at 160 °C. The mud samples showed a high shear-thinning behavior at the two concentrations with a Power-law flow index (n) ranging between 0.025 and 0.101. No additional frictional pressure losses were observed when SMP was added to the base fluid. SMP particles showed an excellent suspension at 1.0 wt% while, at 3.0 wt%, a bed was formed at a low flow rate and without pipe rotation. Increasing the drill pipe rotational speed or flow rate effectively removed the bed and homogeneously dispersed the SMP particles, ensuring a better sealing efficiency. SMP particle dispersion in inclined wells was better than in horizontal wells. Moreover, the findings of this study help optimize the lost circulation treatment by considering a wide range of operating parameters that can further be extended to different systems and geometries.

02 PETROLEUM↗
DOE OSTI2022

Fast simultaneous feasibility testing and applications for electrical power grids

Fast simultaneous feasibility testing (SFT) for management of an electrical power grid is achieved through various innovations. The computation problem relates to evaluation of candidate solutions for external power flows into a power grid, with respect to predetermined constraints and contingencies. Storage and computations are reduced by formulating the problem in terms of transactional nodes (e.g. third party connections for generators and loads) instead of the larger number of bus nodes. Further advantages are achieved by precomputing matrices that can be reused across multiple SFT invocations, organizing matrices and operations to reduce storage and computation, and eliminating branches that have no contingency violations. The fast SFT further enables new applications for integrating SFT with a Security-Constrained Unit Commitment (SCUC) optimizer for efficient single pass solutions; operating SFT as a service to multiple SCUC solvers; or integration with Security-Constrained Economic Dispatch (SCED) optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Fuel cells for single-aisle regional aircraft: System configuration, performance and cost

A hydrogen fuel cell propelled electric aircraft can compete with incumbent turbofan technologies for single-aisle regional aircraft by coupling design of stack, air handling, thermal management, propulsion, and airframe to optimize performance. The stack operates at 95°C to facilitate heat rejection during take-off and below 75°C during cruise to extend lifetime and is oversized to satisfy power requirements at end of life. A multi-stage turbocompressor with a compression ratio >10 is selected to reach high stack power density at 11,300-m cruise altitude. The propulsion system is configured to accommodate air handling within the core duct, an inclined heat exchanger in the outer duct to limit the nacelle size, and variable area nozzles to independently control mass flows through the core and bypass ducts. The airframe is modified for maximum lift coefficient and longer balanced field length for dramatically reduced thrust during take-off, and the fuselage is stretched by 20% to store liquid hydrogen (LH 2 ). Modularization of power systems promotes safety in one engine inoperative scenarios and allows reaching specific power metrics for stack, balance-of-plant and fuel cell system (FCS), necessary for acceptable take-off weight. In conclusion, cost parity requires increase in FCS lifetime, LH 2 cost reduction, and improved FCS specific power.

Catalyst durability↗
DOE OSTI2021

Chapter 2: Global Value Chain and Manufacturing Analysis on Geothermal Power Plant Turbines

The global geothermal power market has shown significant growth since the last decade and is expected to reach a total installed capacity of 18.4 gigawatts electric (GWe) by the end of 2021 (GEA, 2016). The global geothermal power plant turbine market is dominated by a small number of manufacturers. Between 2005 and 2015, 82% of the geothermal steam turbines were manufactured in Japan, and 74% of the geothermal binary cycle turboexpanders were manufactured in Israel. During this period, the United States played an important role in the global trade flow of fully assembled turbine units and turbine parts, with a high volume of imports and exports. Another significant growth area was in Italian turbine/turboexpander manufacturers, who have increased their market share in the last couple of years. One other important change in the manufacturing market was in Turkey, where the bonus on feed-in-tariff (FIT) for domestic hardware components boosted the national manufacturing sector between 2010 and 2020. When planning geothermal power projects, developers customize their power plant size to fit the available geothermal resource capacity. The turbine is designed and sized to optimize the efficiency and utilization of resource and revenue production. The rest of the power plant components such as heat exchangers (HX), water-cooled cooling towers (WCCT), or air-cooled condensers (ACC) are then chosen to complement the turbine size and design. These one-off manufacturing custom design turbines have relatively higher manufacturing set-up costs, longer lead times, and higher capital costs than the standard design turbines manufactured in larger volumes. However, turbines produced in standard increments and in larger manufacturing volumes could result in lower costs per turbine, but potentially lower efficiency. Based on pipeline projects and resource assessments, there is significant potential value in creating standard turbine sizes that could offer an economic advantage, as is done for modular microturbines.

40 EE - Geothermal Technologies Office (EE-4G)↗
DOE OSTI2022

Hydrokinetic energy conversion using flow induced oscillations of single-cylinder with large passive turbulence control

Various types of flow-induced oscillations (FIOs) have been implemented in development of marine hydrokinetic (MHK) energy converters. With passive turbulence control (PTC), energy harvesting starts at a flow speed of about 0.5 m/s. However, there is worldwide MHK energy available in even slower currents. In the present study, the effect of damping on FIO and power extraction is investigated for a converter with large turbulence stimulation (PTC) consisting of straight strips with a height of 15% of the cylinder diameter and placed symmetrically on the cylinder surface. The oscillating amplitude decreases, as the damping ratio increases, with unchanged sinusoidal pattern of the displacement time-history. The frequency ratio is also affected by damping especially in the VIV initial branch and transition region between VIV and galloping. An important flow characteristic of the large-PTC cylinder is that a recirculation region is formed behind the PTC, causing appreciable disturbance to the flow past the cylinder. Power can be harvested in the whole FIO range and the harnessed power maximum appears at the largest inflow velocity tested. However, the optimum of harnessing efficiency is located at the beginning of the VIV upper branch. The gap between VIV and galloping is bridged when large PTC is used, eliminating the drop in power and efficiency even at higher damping, which would be a weakness of regular-PTC cylinder for energy harvesting. Finally, the mechanism behind the variation of harnessing efficiency with inflow velocity and damping ratio is revealed, and the optimality criterion for the converter design is discussed.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

The formation of an radial edge electric field due to finite ion orbit width effects is the possible root cause of the H-mode edge

Abstract Full orbit-following simulations of thermal ions show that finite ion-orbit width effects create charge separation near the last closed flux surface (LCFS) which generates a localized radial electric field. Experimentally, edge electric fields are observed in H-mode plasmas and they are necessary for the edge turbulence suppression via the E × B flow shear mechanism. Confined trapped (and to a lesser extent co-passing) ions near the plasma edge form a positive charge distribution outside the LCFS, while thermal electrons are tied more tightly to field lines owing to their small mass and are poorly confined outside the LCFS, hence charge neutrality is violated outside the LCFS. A large number of reported observations from spherical and conventional tokamaks support the results from the simulations although the simulations were not performed fully self consistently. The results suggest ways to lower the H-mode power threshold and optimize the H-mode plasma edge.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Techno-economic implications and cost of forecasting errors in solar PV power production using optimized deep learning models

Accurate solar Photovoltaic (PV) power forecasting is important for enhancing both the performance and economic feasibility of PV systems. This study evaluates several deep learning models, including Dense Neural Networks (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid LSTMCNN model, for predicting PV power production one day in advance. Prior to optimization, the models exhibited relatively high errors, with the best model (DNN) achieving a Root Mean Square Error (RMSE) of 31.13 kW and a coefficient of determination (R 2 ) of 62.15 %. After employing Bayesian optimization, the LSTM-CNN model demonstrated the best performance, with the RMSE reduced to 9.79 kW and R 2 improved to 97.62 %, showcasing significant enhancement in predictive accuracy. Here, the economic evaluation considered three cases: rewards for underestimation (0.08 USD/kWh), no rewards, and penalties for both over-and underestimation (120 % of the utility tariff). In the rewards scenario, the LSTM-CNN model reduced the Levelized Cost of Electricity (LCOE) by 4 %, while in the penalty scenario, a backup diesel generator would have increased the LCOE by 49 %. Additionally, the LSTM-CNN model minimized financial losses, achieving the lowest penalties and maximizing net cash flow compared to other models, demonstrating its overall technical and economic superiority.

Deep learning↗
DOE OSTI2021

Multi-Fidelity Active Subspaces for Wind Farm Uncertainty Quantification

Wind plants operate in stochastic environments characterized by complex turbulent flow dynamics and high-dimensional random variables. A key step in uncertainty quantification studies is sensitivity analysis and dimension reduction that can facilitate the development of surrogate models to be used for forward and inverse propagation or optimization under uncertainty. Prior work has shown active subspaces are an effective tool for identifying important directions in the space of stochastic inputs; however, they have only been applied to single-fidelity wind plant models. In this study, we investigate the efficacy of a multi-fidelity active subspace method for analyzing the uncertainty in wind plant power output. The multi-fidelity active subspace estimator offers the promise of increased accuracy in identifying active subspaces as compared to a single-fidelity estimator for the same computational cost, or a reduction in cost for the same accuracy. This makes the study of uncertainty in larger wind plants and with higher fidelity physics tractable. The multi-fidelity active subspace method is applied to gridded and existing wind plant layouts with single and multiple inflow conditions and its performance for surrogate modeling and uncertainty propagation is compared against a single-fidelity active subspace method. This multi-fidelity approach yields substantial computational speedups of 2x - 3.4x across the test cases along with acceptable accuracy in surrogate modeling and computing statistical moments.

active subspace↗
DOE OSTI2026

Towards Generalizable and Efficient Circuit Topology Design: A Graph-Transformer-based Surrogate Model with Curriculum Learning

Unlike circuit parameter and sizing optimizations, the automated design of analog circuit topologies poses significant challenges for learning-based approaches. One challenge arises from the combinatorial growth of the topology space with circuit size, which limits the topology optimization efficiency. Moreover, traditional circuit evaluation methods are time-consuming, while the presence of data discontinuity in the topology space makes the accurate prediction of circuit performance exceptionally difficult for unseen topologies. To tackle these challenges, we design a novel Graph-Transformer-based Network (GTN) as the surrogate model for circuit evaluation, offering a substantial acceleration in the speed of circuit topology optimization without sacrificing performance. Our GTN model architecture is designed to embed voltage changes in circuit loops and current flows in connected devices, enabling accurate performance predictions for circuits with unseen topologies. To address the cold start problem when scaling GTN to large-scale circuits, we further introduce a curriculum learning strategy that progressively trains GTN from small-scale to large-scale circuits. This approach enables the model to first learn fundamental physical principles from simpler topologies and gradually adapt to complex configurations, effectively bridging the circuit complexity gap and improving prediction accuracy. Taking the power converter circuit design as an experimental task, our GTN model significantly outperforms an analytical approach and baseline methods directly utilizing graph neural networks. Furthermore, GTN achieves less than 5% relative error and 196× speed-up compared with high-fidelity simulation. Notably, our GTN surrogate model empowers an automatic circuit design framework to discover circuits of comparable quality to those identified through high-fidelity simulation while reducing the time required by up to 98.2%. With curriculum learning, the enhanced GTN achieves a 51% improvement for performance prediction of large-scale circuits compared to the GTN model without this strategy. These advancements establish GTN as a scalable framework for automated analog circuit design across varying circuit complexity levels.

Lu, Haoshu [New Jersey Institute of Technology (NJ↗