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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 55 records · Page 3

Numerical Modeling of a Two-Stage Ocean Current Turbine

The Equinox Ocean Turbines (EQOT) current energy converter has a unique design with power generation in two small-diameter turbines attached to the tips of a large-diameter passive rotor. This configuration offers some key advantages for capturing ocean currents. With no centrally placed generator, almost no reaction torque is required at the nacelle of the main large-diameter rotor, and the small-diameter tip turbine generators operate at a higher speed and lower torque. The physics that determine the performance and loads on the turbine are also unique. The interactions of the flow field between the two stages and the general architecture of the system cannot be captured with traditional mid-fidelity modeling tools. For design iterations and large sets of load cases, it is important to have mid-fidelity models that can capture the important phenomenon with enough accuracy to identify global trends. This work uses a limited set of high-fidelity computational fluid dynamics (CFD) simulations to help inform the selection of and construction of a custom mid-fidelity model. Mid-fidelity modeling approaches were verified by comparing key turbine performance quantities to those found with the CFD model. Hydrodynamic interactions of the two-stage rotor were identified through high-fidelity CFD modeling. This highlighted the impact of the main rotor tip vortex and wake on the secondary rotor apparent inflow. This results in a relative flow rotation and sharp deficit, that change the optimal secondary rotor rotation speed and adds unsteadiness to the blade loading respectively. Multiple mid-fidelity approaches were evaluated for their ability to capture these effects. A simple approximation of the combined-stage performance based on single-stage BEM provides a reasonable rough prediction, especially near the peak TSR values, with some larger discrepancy at higher TSRs. Predicting the combined-stage performance based on single-stage CFD data improves this prediction across the TSR range. Although the combined-stage modeling in OLAF was not successful in this stage of the project, it showed promise as a mid-fidelity method, assuming the parameters can be tuned to account for the significant differences in time and length scales between the main and secondary rotors. This may be addressed through code changes in future work. A significant finding from the OLAF work was the agreement between the vortex core radius values found independently via a parameter space search and via CFD. The technique of using single-stage secondary rotor BEM, with a custom inflow taken from single-stage main rotor CFD or OLAF, provides an efficient method to capture one-way coupled flow interactions. This method provided generally good predictions of the impact of the flow rotation on the secondary rotor but struggled to accurately predict the peaks of the unsteady load progression. Future work could include some superposition of a tuned main rotor trailing edge viscous wake into the custom inflow to better predict this interaction.

16 TIDAL AND WAVE POWER↗

A tri-level distribution locational marginal price-based demand response framework

Here, in this paper, we propose a tri-level, nested, two-stage price-based demand response (PBDR) framework that considers distribution locational marginal price (DLMP) as DR enabler between load-serving entities (LSE), demand response providers (DRPs), and customers in the day-ahead distribution market. It enables LSE and customer interactions by using multiple DRPs, positioned in-between, and independently optimizes their objectives. The problem is formulated using linear power flow with approximated power losses and its application in DLMP as DR pricing. The tri-level problem is solved using a nested reformulation & decomposition (R&D) method and tested on the real Indian-108 bus distribution system under various dynamic pricings. Further, the temporal–spatial variations in DLMPs are assessed using fairness criteria. Numerical analyses demonstrate that DLMP applications can effectively improve economic efficiency, and transparency in DR programs valuation with a favorable fairness margin. The results show that DLMP as DR pricing signal induces (0-2) % variation in DLMP for DR participation up to 10 %. Further, it gives over 90 % fairness over temporal–spatial variation for all the customers.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Refractory-based thermal energy storage for industrial process heat: one-dimensional modeling, control, and optimization

The variable and weather-dependent output of wind and solar power plants present a substantial challenge for planning and operating electricity-systems, particularly in the absence of cost-effective and dispatchable energy storage technologies. This study investigates a high-temperature, electrically heated, refractory-based thermal energy storage (RTES) system that stores electrical energy as sensible heat in dense ceramic bricks over the 950–1800 °C range. The stored heat can be discharged as a controlled hot-gas stream for industrial heating, fuel substitution in high-temperature processes, or electricity generation. The main novelty is a comprehensive modelling, control, mapping, and optimization framework that integrates one-dimensional transient gas–solid heat transfer, fan-assisted discharge, bypass-flow regulation, reheating logic, fan-power evaluation, insulation-loss assessment, and genetic-algorithm-based design optimization. The model uses feedback from outlet temperature and delivered power to regulate discharge, while a two-stage genetic algorithm optimizes brick-channel geometry, gas-flow operation, and multilayer insulation thicknesses. Storage capacities below 50 MWh and discharge powers of 5–30 MW are analyzed to evaluate hold time, thermal delivery, fan-power penalty, heat loss, state-of-charge evolution, and indicative capital cost. Results demonstrate that optimized and well-insulated refractory-based thermal energy storage units can provide stable, efficient, and repeatable heat delivery over multiple discharge cycles. The generated performance and cost maps support modular refractory thermal energy storage as a practical option for large-scale integration of wind and solar generation and for high-temperature industrial process heat.

25 ENERGY STORAGE↗

Cooling Design and Thermal Analysis for Thermal Shields of a Cryocooler-Cooled Superconducting ECR Ion Source MARS-D Magnet

Here, a demonstrative NbTi based Mixed Axial and Radial field System (MARS-D) is being developed for a next-Generation Electron Cyclotron Resonance Ion Source (ECRIS) at Lawrence Berkeley National Laboratory (LBL), which employs a novel closed-loop coil design scheme that more efficiently utilizes conductor fields and extend the application of NbTi for high frequency (up to 45 GHz) ECR operation. The NbTi MARS-D magnet consists of a single hexagonally shaped closed-loop coil and a set of auxiliary solenoids. A cryostat for cooling the MARS-D magnet is under design at LBL. The MARS-D magnet working around 4.2 K will be bath-cooled in liquid helium using multiple two-stage cryocoolers. An intermediate temperature thermal radiation shield is adopted to reduce the heat leakage imposed on 4.2 K coil cold mass from room temperature. The thermal shield is conduction-cooled by the first-stage cold heads of four two-stage cryocoolers and the cold head of a single-stage cryocooler shared with nine binary leads. The temperature in the area of the shield that warm ends of HTS leads are mounted on is expected no higher than 60 K, which is limited by maximum allowable working temperature of HTS leads. The paper presents thermal analysis on the thermal radiation shield including heat loads and effects of eddy current induced during quench on its material selection.

ECRIS↗

Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography

Obtaining high-quality 3D reconstructions from electron tomography of crystalline particles embedded in lighter support elements is crucial for various material systems such as catalysts for fuel cell applications. However, significant challenges arise due to the limited tilt range, sparse and low signal-to-noise ratio of the measurements. In addition, small metal particles can cause strong streaking and shading artifacts in the 3D reconstructions when using conventional reconstruction algorithms due to the presence of Bragg diffraction and the large scattering cross-section difference between the materials of the particles and the background support regions. These artifacts lead to errors in the downstream characterization affecting extraction of critical features such as the size of the metal particles, their distribution and the volume of the lighter support regions. In this paper, we present a two-stage algorithm based on metal artifact reduction, utilizing model-based iterative reconstruction methods with adaptive adjustment of regularization parameters. Our approach yields high-quality 3D reconstructions compared to traditional algorithms, accurately capturing both the metal particles as well as the background support. We demonstrate the effectiveness of our algorithm through simulated and experimental bright-field electron tomography data, showing significant improvements in reconstruction quality compared to traditional methods.

97 MATHEMATICS AND COMPUTING↗

Endogeneity of pedestrian survival time and emergency medical service response time: Variations across disadvantaged and non-disadvantaged communities

The Vision Zero-Safe Systems Approach prioritizes fast access to Emergency Medical Services (EMS) to improve the survivability of road users in transportation crashes, especially concerning the recent increase in pedestrian-involved crashes. Pedestrian crashes resulting in immediate or early death are considerably more severe than those taking longer. The time gap between injury and fatality is known as survival time, and it heavily relies on EMS response time. The characteristics of the crash location may be associated with EMS response and survival time. A US Department of Transportation initiative identifies communities often facing challenges. Six disadvantaged community (DAC) indicators, including economy, environment, equity, health, resilience, and transportation access, enable an analysis of how survival and EMS response times vary across DACs and non-DACs. To this end, this study created a unique and comprehensive database by linking DACs data with 2017–2021 pedestrian-involved fatal crashes. This study utilizes two-stage residual inclusion models with segmentation for DACs and non-DACs accounting for the endogenous relationship between EMS response and pedestrian survival time. The results indicate that EMS response time is higher and pedestrian survival time is lower in DACs than in non-DACs. A delayed EMS response time is associated with a greater reduction in survival time in DACs compared to non-DACs. Factors, e.g., nighttime and interstate crashes, contribute to higher EMS response time, while pedestrian drugs, driver speeding, and hit-and-run behaviors are associated with a greater reduction in survival time in DACs than non-DACs. Finally, the implications of the findings are discussed in the paper.

60 APPLIED LIFE SCIENCES↗

Conduction-Cooled Operation of an SRF Multi-Cell Cavity

The development of compact, SRF-based accelerators for applications beyond research is experiencing notable advancements due to the use of cryocoolers for conduction-cooling instead of traditional liquid cryogens. Following the successful demonstration of a single-cell cavity operated through conduction cooling with three two-stage cryocoolers, Jefferson Lab (JLab) has made strides in the operation of a multi-cell resonator. This milestone paves the way for high-energy applications of compact, conduction-cooled SRF machines. The demonstration, carried out in collaboration with General Atomics, will take place in a dedicated horizontal test cryostat (HTC) at their San Diego facility. This presentation will highlight the technological developments, the latest results, and valuable lessons learned.

Vennekate, J. [Thomas Jefferson National Accelerat↗

Conduction-Cooled Operation of an SRF Multi-Cell Cavity

The development of compact, SRF-based accelerators for applications beyond research is experiencing notable advancements due to the use of cryocoolers for conduction-cooling instead of traditional liquid cryogens. Following the successful demonstration of a single-cell cavity operated through conduction cooling with three two-stage cryocoolers, Jefferson Lab (JLab) has made strides in the operation of a multi-cell resonator. This milestone paves the way for high-energy applications of compact, conduction-cooled SRF machines. The demonstration, carried out in collaboration with General Atomics, will take place in a dedicated horizontal test cryostat (HTC) at their San Diego facility. This presentation will highlight the technological developments, the latest results, and valuable lessons learned.

Vennekate, J. [Thomas Jefferson National Accelerat↗

Tailoring Molecular Space to Navigate Phase Complexity in Cs-Based Quasi-2D Perovskites via Gated-Gaussian-Driven High-Throughput Discovery

Cesium-based quasi-2D halide perovskites (HPs) offer promising functionalities and low-temperature manufacturability, suited to stable tandem photovoltaics. However, the chemical interplays between the molecular spacers and the inorganic building blocks during crystallization cause substantial phase complexities in the resulting matrices. To successfully optimize and implement the quasi-2D HP functionalities, a systematic understanding of spacer chemistry, along with the seamless navigation of the inherently discrete molecular space, is necessary. Herein, by utilizing high-throughput automated experimentation, the phase complexities in the molecular space of quasi-2D HPs are explored, thus identifying the chemical roles of the spacer cations on the synthesis and functionalities of the complex materials. Furthermore, a novel active machine learning algorithm leveraging a two-stage decision-making process, called gated Gaussian process Bayesian optimization is introduced, to navigate the discrete ternary chemical space defined with two distinctive spacer molecules. Through simultaneous optimization of photoluminescence intensity and stability that “tailors” the chemistry in the molecular space, a ternary-compositional quasi-2D HP film realizing excellent optoelectronic functionalities is demonstrated. Finally, this work not only provides a pathway for the rational and bespoke design of complex HP materials but also sets the stage for accelerated materials discovery in other multifunctional systems.

36 MATERIALS SCIENCE↗

Optimized Illuminance Operation-A Light-Driven Dilution Strategy to Improve Microalgae Biomass Productivity

Periodic dilution is a necessary operation to avoid light limitation due to self-shading as the culture grows dense. However, environmental conditions are constantly changing in outdoor cultivation systems, making it difficult to determine the optimal dilution rate. To address this challenge, this study evaluated a dilution approach based on light penetration to optimize illuminance (OptiLum) in the culture. Biomass concentration was controlled via sensor-feedback directed dilution to ensure that light reaching the bottom of the culture was maintained above the compensation intensity. Under replicated outdoor pond conditions, by keeping the entire culture within a net-positive photosynthetic zone, the OptiLum operation improved the biomass productivities of two top-performing strains, Picochlorum celeri and Tetraselmis striata, by 95 % and 86 %, respectively, compared to conventional semi-continuous batch cultivation. The dilution rate varied daily and was dynamically adjusted based on the light status within the culture, which is concurrently influenced by weather, culture density, and growth rate. The techno-economic analysis showed that the OptiLum operation could reduce biomass production cost by as much as 24 % and 33 % for P. celeri and T. striata, respectively, assuming a low-cost dewatering approach with initial gravity settling of biomass can be realized for both strains. However, a more costly two-stage dewatering strategy, comprising only membranes and centrifuges, may be necessary for non-settling strains, such as P. celeri, which would alternatively increase production costs by 23 % for the OptiLum case. The results demonstrated that the proposed OptiLum operation is a promising approach to improve biomass productivity and lower production cost via weather-responsive and self-adjusting dilution.

09 BIOMASS FUELS↗

Extraction of lithium from battery recycling wastewater using synergistic D2EHPA and TBP

The recycling of spent lithium-ion batteries (LIBs) poses significant challenges, including the generation of large volumes of chemically complex wastewater. The composition of this wastewater is influenced by both the intrinsic chemistry of the batteries and the specific recycling processes employed. Notably, this wastewater contains economically valuable components, such as lithium, which can be recovered. Here, in this study, a solvent extraction (SX) process was investigated as a method to recover lithium from battery recycling wastewater, especially from anode-washing stage. Initially, various commercial extractants were evaluated, including di(2-ethylhexyl)phosphoric acid (D2EHPA), mono-2-ethylhexyl (2-ethylhexyl)phosphonate (PC88A), bis(2,4,4-trimethylpentyl)phosphinic acid (Cyanex 272), 2-hydroxy-5-nonylacetophenone oxime (LIX 84-I), tri-butyl phosphate (TBP), and their combinations. Among these, D2EHPA + TBP demonstrated a synergism to advance and maximize the lithium extraction. Subsequently, the effects of key parameters, including D2EHPA concentration, TBP concentration, contact time, initial pH, and aqueous-to-organic (A/O) phase ratio, were systematically investigated and optimized. A two-stage SX approach was employed to enhance lithium recovery. Under the optimized conditions of 30.0 vol% D2EHPA, 10.0 vol% TBP, 10 min of contact time, and a 1:1 A/O phase ratio, a lithium extraction efficiency of more than 88% in a two-stage solvent extraction was achieved. Lithium was subsequently stripped from the loaded organic solution using sulfuric acid (H 2 SO 4 ). Using 2.0 M H 2 SO 4 , lithium stripping was achieved after two counter-current stripping stages at an organic-to-aqueous (O/A) phase ratio of 6:1. This stripping process enriched the lithium concentration by a factor of four compared to the original lithium concentration in the anode-washing wastewater. The recyclability of the synergistic D2EHPA + TBP system was also evaluated over four extraction-stripping cycles. The results demonstrated that the system maintained high extraction and stripping efficiencies.

D2EHPA↗

A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particles

Accurate and efficient characterization of nanoparticles (NPs), particularly regarding particle size distribution, is essential for advancing our understanding of their structure-property relationship and facilitating their design for various applications. In this study, we introduce a novel two-stage artificial intelligence (AI)-driven workflow for NP analysis that leverages prompt engineering techniques from state-of-the-art single-stage object detection and large-scale vision transformer (ViT) architectures. This methodology is applied to transmission electron microscopy (TEM) and scanning TEM (STEM) images of heterogeneous catalysts, enabling high-resolution, high-throughput analysis of particle size distributions for supported metal catalyst NPs. The model's performance in detecting and segmenting NPs is validated across diverse heterogeneous catalyst systems, including various metals (Ru, Cu, PtCo, and Pt), supports (silica (SiO 2 ), γ-alumina (γ-Al 2 O 3 ), and carbon black), and particle diameter size distributions with mean and standard deviations ranging from 1.6 ± 0.2 nm to 9.7 ± 4.6 nm. The proposed machine learning (ML) methodology achieved an average F1 overlap score of 0.91 ± 0.01 and demonstrated the ability to disentangle overlapping NPs anchored on catalytic support materials. The segmentation accuracy is further validated using the Hausdorff distance and robust Hausdorff distance metrics, with the 90th percent of the robust Hausdorff distance showing errors within 0.4 ± 0.1 nm to 1.4 ± 0.6 nm. In conclusion, our AI-assisted NP analysis workflow demonstrates robust generalization across diverse datasets and can be readily applied to similar NP segmentation tasks without requiring costly model retraining.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A flexible and differentiable coil proxy for stellarator equilibrium optimization

Balancing plasma performance and coil cost is a significant challenge when designing a stellarator power plant. Most current stellarator designs are produced through two-stage optimization: stage-1 for the equilibrium and stage-2 for a coil design that reproduces its magnetic configuration. Because few proxies connect both stages, two-stage optimization can produce plasmas that have high-quality physical properties but overly complex coils. In recent years, single-stage optimization has increasingly been used to optimize the plasma and coils simultaneously in order to improve the plasma–coil balance. However, all existing single-stage tools are specialized for filament coils, cannot model coil systems containing permanent magnets or dipole arrays, and continue to be challenged by numerical problems. The quasi-single-stage optimization finds a middle ground by integrating a coil optimization subproblem into stage-1 optimization. We present a flexible, differentiable coil complexity proxy based on the newly developed QUADCOIL coil optimization code. QUADCOIL is fast and can target realistic coil metrics and constraints that are unavailable to codes with comparable speed. We demonstrate the effectiveness and flexibility of the QUADCOIL proxy by presenting two QSS optimization studies. The first study produces a permanent magnet solution for the MUSE stellarator with 34% fewer magnets than previous solutions. The second study produces a coil solution for the ARIES-CS stellarator with 30% reductions in both peak and root-mean-square force.

Fu, Lanke [Princeton Plasma Physics Laboratory (PP↗

A Two-Stage Approach for PV Inverter Engagement in Power Factor Correction and Voltage Regulation

The rapid integration of distributed energy resources, like solar photovoltaics (PVs), can lead to overvolt-age challenges due to reverse power flow and a noticeable decrease in power factor at the substation interface. While existing literature extensively explores utilizing smart inverter capabilities for reactive power flexibility using a volt-var curve (VVC), obtaining time-varying operating points of such curves in real-time is challenging due to computational demands and communication requirements. Similarly, employing optimization-based approaches for reactive power control and active voltage regulation in large-scale distribution feeders is difficult due to the complexity of the problem and the challenges in effectively engaging customer-owned resources. This paper proposes a two-stage strategy to harness smart inverters for reactive power support. The first stage formulates short-term planning by optimally designing VVCs (on a daily or hourly basis) for large-scale solar PVs based on projected system needs and communicating optimal curves to smart inverters in advance. Subsequently, the second stage employs a transactive-based method to involve customer-owned PVs for reactive power support, effectively enhancing overall system performance and addressing real-time demands. In conclusion, the efficacy of this approach will be demonstrated using real-world distribution circuits provided by Vermont Electric Power Company (VELCO) and Vermont Electric Cooperative (VEC).

Poudel, Shiva [Pacific Northwest National Laborato↗

A Two-Stage Current Limiting Control Strategy for Direct-Droop-Controlled Grid-Forming Inverters

This paper presents a two-stage current limiting control strategy with fault ride-through capability for the direct-droop-controlled grid-forming (GFM) inverters. The proposed approach comprises Stage 1 and Stage 2 current limiting controls. During faults, Stage 1 current limiting control instantly clamps the inverter output current magnitude to its maximum transient limit by blocking the switching pulses to the insulated-gate bipolar transistors (IGBTs) with the help of a hysteresis loop control, and Stage 2 current limiting control limits the inverter output current magnitude to its steady-state limit by regulating the amplitude and frequency of the modulating waveform utilizing the active and reactive current limiting control loops. By implementing the current limiting actions in two stages, this approach can effectively limit the over-current within a few cycles after the fault occurrence and ensure synchronism to the grid after long-term fault events. The efficacy of the proposed control approach is validated through electromagnetic transient (EMT) simulation results conducted on a single GFM inverter-based test system and the IEEE 39-bus test system with multiple GFM inverters in the PSCAD platform.

Mohiuddin, Sheik Mohammad↗

Distributed Online Voltage Control in Distribution Network: A Two-Stage Real-Time Implementation

The increasing integration of renewable-based distributed generation (DG) brings growing challenges to distribution network (DN) voltage control. To address this issue, a two-stage distributed online voltage control framework (TDO-VC) is proposed in this paper. In the proposed method, legacy voltage control devices are controlled in the upper stage on an hourly timescale, while DGs operate autonomously online in the lower stage to remove instantaneous voltage violations. The idea of receding horizon control is applied in the upper stage to comprehensively consider the current and future renewable generation, and the generalized fast dual ascent (Gf-DA) is employed in the lower stage to effectively manage DGs through near real-time optimization. The effectiveness of the proposed TDO-VC is demonstrated by rigorous theoretical analysis and case studies on IEEE-123 bus system.

Distributed generation↗

Applications of fuzzy logic and best-worst method for tritium sensor selection

Accurate assessment of tritium as a fuel source is critical in fusion reactions, necessitating effective sensor evaluation methods. This study investigates a multi-criteria decision-making framework for selecting tritium sensors, integrating fuzzy logic to enhance decision quality. Initial attempts at applying fuzzy logic were found to be too elementary and failed to capture the complexity of multi-criteria selection; this prompted a refined approach that incorporated expert insights and advanced ranking techniques for sensor evaluation. The research used a two-stage methodology. In the first stage, important criteria and sub-criteria for sensor performance were identified and defined. These criteria were then weighted and scored using a fuzzy best-worst method, drawing upon expert opinions to ensure relevance and validity. The second stage involved interpreting information about varying sensors to rank them based on their overall criteria scores, encouraging the selection of the most suitable options. The result of the study is a proposed method for effective sensor selection in fusion reactors, which in turn will significantly improve the reliability of tritium monitoring in fusion applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Long-Term Impacts of Constrained Transmission Deployment on the Cost-Reliability Tradeoff

Traditional Resource Adequacy (RA) frameworks in the U.S. undervalue the contributions of inter-regional transmission to resource adequacy during stress periods, focusing on the availability of nameplate capacity instead. However, availability of nameplate capacity does not always translate into electricity delivery, especially during tail events. Moreover, the rapid deployment of energy-limited resources and increasing electricity demand challenge existing resource adequacy frameworks and couple regional electricity demand and availability of supply via transmission. We propose a two-stage framework that goes beyond the existing capacity-centered approaches to reveal the RA contributions of transmission. In the first stage we introduce a multi-objective optimization framework to quantify the merits of transmission expansion via Pareto Frontiers under alternative futures of no transmission investment, primary energy resources availability and demand growth. The second stage focuses on tail events and leverages the results of the first stage to characterize the risk profile of regional consumers across the U.S. under the alternative energy futures. We find that no new transmission can lead to a more expensive and less reliable national grid across scenarios, however, the impact on regional RA can vary. The probabilistic analysis reveals that transmission investments can alleviate the tail risk of consumers, however, the availability of fuel resources does not always alleviate regional tail risks. Our findings inform policymakers and utilities on the prioritization of transmission investments to mitigate the risk of widespread outages, also for tail events, and ensure reliable and affordable electricity delivery to all.

24 POWER TRANSMISSION AND DISTRIBUTION↗