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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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101 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Li-Ion Battery Thermal Characterization for Thermal Management Design

Battery design efforts often prioritize enhancing the energy density of the active materials and their utilization. However, optimizing thermal management systems at both the cell and pack levels is also key to achieving mission-relevant battery design. Battery thermal management systems, responsible for managing the thermal profile of battery cells, are crucial for balancing the trade-offs between battery performance and lifetime. Designing such systems requires accounting for the multitude of heat sources within battery cells and packs. This paper provides a summary of heat generation characterizations observed in several commercial Li-ion battery cells using isothermal battery calorimetry. The primary focus is on assessing the impact of temperatures, C-rates, and formation cycles. Moreover, a module-level characterization demonstrated the significant additional heat generated by module interconnects. Characterizing heat signatures at each level helps inform manufacturing at the design, production, and characterization phases that might otherwise go unaccounted for at the full pack level. Further testing of a 5 kWh battery pack revealed that a considerable temperature non-uniformity may arise due to inefficient cooling arrangements. To mitigate this type of challenge, a combined thermal characterization and multi-domain modeling approach is proposed, offering a solution without the need for constructing a costly module prototype.

25 ENERGY STORAGE↗
DOE OSTI2025

Spatially Patterned Architectures to Modulate CO 2 Reduction Cascade Catalysis Kinetics

Electrochemical CO 2 reduction using renewable sources of electrical energy holds promise for converting CO 2 into fuels and chemicals. The complex interactions among chemical/electrochemical reactions and mass transport make it difficult to analyze the effect of an individual process on electrode performance based only on experimental methods. Here, we developed a generalized steady-state simulation to describe an electrode surface in which sequential cascade catalysts are patterned in a periodic trench design. If appropriately constructed, this trench geometry is hypothesized to be able to yield a higher net current density for a CO 2 reduction (CO 2 R) cascade reaction. We have used realistic experimental reaction kinetics to investigate the role of trench geometry in mass transport, local microenvironments, and selectivity for a model CO 2 R cascade reaction. The model considers local concentration gradients of bicarbonate species at quasi-equilibrium and catalytic surface reactions based on concentration-dependent Butler–Volmer kinetics. Our results suggest that varying the spatial distribution of active sites plays a significant role in facilitating effective mass transport between active sites, modulating selectivity for the cascade reaction, and enhancing the yield of desirable cascade products. Moreover, we observe that this trench geometry significantly alters the cascade reaction rate by affecting the local pH, which can cause inadvertent depletion of available aqueous CO 2 to limit the CO 2 R cascade kinetics and modest suppression of the hydrogen evolution reaction (HER). The results highlight the trade-offs between mass transport, pH, and reaction kinetics that become apparent only when considering the coupled physics of all processes at the electrode surface. Here, this model can thus serve as a primary tool to build more selective and efficient patterned architectures for the CO 2 R cascade catalysis.

CO2 reduction↗
DOE OSTI2026

Human Factors and Technologies Design to Improve User Acceptance of Pooled Rideshare for Increasing Transportation System Energy Efficiency

This multi-year project delivered a comprehensive, human-factors-driven framework to understand, model, and improve pooled rideshare (PR) adoption in the United States. Through three large-scale national survey studies involving more than 16,000 participants across multiple cities and demographic groups, the research established one of the most extensive datasets to date on user perceptions, behavioral barriers, and service expectations related to pooled rideshare. These data revealed key human factors barriers of user acceptance of PR and suggested potential actionable experience optimizations that could lead to increased PR usage. This foundational knowledge guided the development of novel human-factors models and behavioral choice models that quantify how psychological, demographic, and trip-level factors influence willingness to pool. Building on these empirical insights, the project developed advanced behavioral modeling tools, including mixed logit and integrated choice and latent variable models, to capture both observable and latent influences on PR adoption. These models significantly improved the ability to predict riders’ acceptance of pooled trips, explaining choice heterogeneity through latent constructs such as safety, service experience, privacy concerns, time sensitivity, and environmental attitudes. Together, these models provide a robust analytical foundation for designing PR systems that more effectively meet user needs. The project translated human-factors insights and behavioral models into actionable technology innovations by extending POLARIS—an agent-based, activity-based travel simulation platform—into a fully functional pooled rideshare simulation environment. New PR modules, acceptance models, and regional scenarios were implemented for Greenville, SC and Austin, TX, enabling high-fidelity validation of algorithmic strategies under realistic demand and traffic conditions. The simulation platform supported the development and evaluation of adaptive discount-based assignment algorithms, enhanced willingness-to-pay formulations, demographic-aware incentive mechanisms, and a proactive joint assignment and repositioning strategy. Simulation results demonstrated substantial gains in pooling uptake, average vehicle occupancy, energy efficiency, and fleet profitability. In Greenville, pooling adoption more than doubled, while reductions in vehicle-miles traveled and energy consumption were significant. In Austin, pooling improvements were achieved with minimal service-quality trade-offs, and profitability increased across all fleet sizes. Through this research, we developed a comprehensive understanding of the human factors barriers that limit user acceptance of pooled rideshare services. These insights enabled the design of human-factors-aware pooled rideshare technologies that more effectively address user concerns and improve adoption rates. By integrating these models into an advanced agent-based simulation framework, we demonstrated that higher adoption of pooled rideshare can lead to measurable improvements in energy efficiency and system performance. Together, these contributions establish a validated pathway from human-centered analysis to technology development and energy-saving outcomes, supporting national goals for more sustainable and efficient mobility systems.

Jia, Yunyi↗
DOE OSTI2022

Scalable membrane-less microbial electrolysis cell with multiple compact electrode assemblies for high performance hydrogen production

Bioelectrochemical hydrogen production via microbial electrolysis cells (MECs) is a promising method for sustainable energy production and decarbonization of energy systems. However, the application of MECs is limited by the electrochemical performance, scalability, and the cost associated with expensive materials. Here, in this study, a scalable MEC (500 mL) with novel compact electrode assemblies and high electrode surface area to volume ratio (160 m 2 /m 3 ) was designed and constructed. The use of membranes, precious metal catalyst, and current collectors with high costs was avoided. A high current density at the steady state of 49.5 ± 5.3 A/m 2 was achieved using acetate as the substrate with phosphate buffer under the applied voltage of 1.01 V. The corresponding volumetric current density was 3948 ± 422 A/m 3 . The compact electrode assembly design limited methane production rate to 3.9 ± 0.2 L/L/D, while achieving a hydrogen production rate of 33.7 ± 1.7 L/L/D. With the suppression of microbial hydrogen consumption, the hydrogen production rate was 39.8 ± 1.9 L/L/D, higher by almost one order of magnitude than those of MECs with scaling up attempts. The compact electrode configuration reduced internal resistance to 88.5 ± 4.4 Ω cm 2 . The energy efficiency based on input electricity was 146 ± 7 % to 189 ± 9 % within the applied voltage range of 0.71 to 1.05 V. The results in this study demonstrated successful scaling up of high performance small MECs and offered a new possible approach of scaling up MECs by stacking high-performance subunits, with no trade-offs on electrochemical performance.

08 HYDROGEN↗
DOE OSTI2026

Cradle-to-gate life cycle assessment of advanced composite panels incorporating CO 2 -derived multi-walled carbon nanotubes and hemp fiber for sustainable building applications

Advanced composite panels represent a promising pathway to reducing carbon emissions in the construction industry, yet comprehensive environmental impact assessments remain limited. Here, in this study, we conduct a life cycle assessment (LCA) to evaluate the environmental impacts of innovative composite panels produced from multi-walled carbon nanotubes (MWCNTs), hemp fiber (HF), recycled carbon fiber (rCF), and recycled polypropylene (PP), exploring their potential as baseline structural equivalents to conventional gypsum board. MWCNTs and HF play a critical role in sequestering carbon during raw material production, while the recycling processes for CF and PP generally require less energy compared to virgin material production. The LCA evaluates environmental performance using the TRACI 2.1 method, covering global warming potential (GWP), ozone depletion, smog formation, acidification, eutrophication, carcinogenic and non-carcinogenic effects, respiratory impacts, ecotoxicity, and fossil fuel depletion. Compositional variations—resin type (virgin vs. recycled), rCF content (9–29 wt%), and HF content (10–30 wt%)—are introduced for sensitivity and hotspot analyses. Results demonstrate that, when compared on the basis of preliminary structural equivalence, increasing recycled PP, rCF, and HF content can significantly reduce global warming potential compared to gypsum board. Beyond carbon reduction, the composite panels show trade-offs across other environmental categories. With the growing demand for composite materials in interior panels, ceiling systems, and exterior claddings, these findings highlight the environmental benefits and potential trade-offs of the proposed composites, establishing a foundational framework to support their continued development toward full building-system integration.

Advanced composite manufacturing↗
DOE OSTI2024

Durable and High-Performance SOECs Based on Proton Conductors for Hydrogen Production

Proton-conducting solid oxide electrolysis cells (P-SOECs) are a promising technology for cost-effective and efficient production of green hydrogen. Breakthroughs in materials development, optimization of cell structure, and achievement of high performance and durability are essential to significantly increase the commercial competitiveness of these technologies. The main objective of this project is to gain scientific knowledge for the rational design, fabrication, and demonstration of a robust, highly efficient, and low-cost SOEC technology based on a proton-conducting electrolyte membrane for hydrogen production. We focused on better understanding the degradation mechanisms of proton-conducting electrolytes, air electrodes, and catalyst materials under electrolysis mode to develop an effective strategy for rationalizing new materials that are vital for enhancing cell performance and durability. The scope includes enhancing the performance and durability of the electrolyte and electrode materials under realistic operating conditions, developing highly active and robust catalysts to minimize electrode losses while improving tolerance to contaminant poisoning, revealing the mechanism of enhanced activity and stability of the catalyst, and understanding the underlying degradation mechanisms. In addition, various characterization techniques were employed to gain a fundamental understanding of the materials’ behavior and their impact on cell performance, providing vital information to guide materials discovery and cell design. After defect chemistry engineering, the optimized donor and acceptor co-doped electrolytes BaMo/W 0.03 Ce 0.71 Yb 0.26 O 3-δ (BM/W03) showed substantially improved chemical stability against high concentrations of CO 2 and H 2 O compared to the state-of-the-art electrolyte (BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3-δ , BZCYYb1711) while maintaining comparable ionic conductivity and ionic transference number. To bypass the inherent trade-off between conductivity and chemical stability, we fabricated a bi-layer electrolyte composed of BZCYYb1711 coated with a highly-stable thin layer of BaHf 0.83 Yb 0.17 O 3-δ (BHYb). This bi-layer electrolyte displayed excellent chemical stability against high concentration CO 2 ; there was no detectable formation of BaCO 3 after exposure to 97% CO 2 (with 3% H 2 O) at 500 °C for 1000 hours and the rate of degradation in resistance was about 0.4% per 1,000 hours (kh). In contrast, the same BZCYYb1711 electrolyte without a BHYb coating degraded significantly under the same testing conditions; the degradation rate was increased to 5.1%/kh. In addition, a triple conducting air electrode Ba 0.9 Pr 0.1 Hf 0.1 Y0.1Co 0.8 O 3-δ (BPHYC) was developed by heavily doping transition metal ions into a proton-conducting material. This air electrode material, composed of 3 distinct phases, exhibits superior electrocatalytic activity due to the synergistic effect from the three component phases. Moreover, an active and durable catalyst, La 2 Ni 0.5 Fe 0.5 O 4+δ (LNF), was developed, showing excellent catalytic activity and contaminant tolerance, with a degradation rate of only 0.49%/kh when exposed to high concentrations of steam and Cr. Finally, single cells were constructed from the best electrolytes, electrodes, and catalyst coatings developed in this project. These cells demonstrated superior high current density at a given cell voltage, high roundtrip efficiency, and remarkable durability (up to 1000 hours of operation).

08 HYDROGEN↗
DOE OSTI2026

Predicting Li-Ion Battery Capacity Fade Using Early-Life Data and a Hybrid Data-Driven Gaussian Process-Bayesian Regression Approach

Accurately predicting Li-ion battery capacity trajectories using early-life data can dramatically improve battery-life understandings and be used to rapidly evaluate design/cost/performance trade-offs when developing new battery materials. Accurate early-life predictions enable researchers to quickly iterate over cell designs and material precursor properties without consistently cycling cells to failure. To this end, we present a toolbox that uses a combined Gaussian Process and Bayesian regression approach that capitalizes on signals other than just capacity (e.g., dQ/dV, voltage drops) to rapidly predict capacity-fade trajectories. The prediction tool uses Bayesian regression to fit functional forms, e.g., power law, sigmoids, etc., to predict capacity-fade dynamics. By fitting functional forms, the capacity fade can be interrogated at any point in the future, allowing for early cell-failure prediction. Additionally, Bayesian regression allows for accurate uncertainty estimates that account for cell-to-cell variability (aleatoric uncertainty) and the lack of observation data (epistemic uncertainty). By only using early cycle data to predict the capacity fade trajectory, uncertainty bounds at end-of-life can be extremely large. The large uncertainty bounds are further exacerbated because there is no systematic way to define the prior distribution of the functional forms' parameters. We improve our the predicted trajectory confidence interval of our predicted trajectory using two methods. First, we shows that a small amount of held-out cycling data is sufficientuse some train cells, that have been cycled to failure to derive information regarding the appropriate prior distributions for the functional forms' parameters of the functional form, effectively leading to data-driven priors.. We propose constructing the data-driven priors by first running a Bayesian regression starting with uninformed priors to generate intermediate cell-specific posterior parameter distributions. These posterior distributions are combined using a Ggaussian mixture model for each parameter to create the data-driven priors. These mixture models serve as the data-driven prior distributions for the parameters for. Second, we derive multiple features, e.g., C_dchg 0.5 DoD 0.5, log (|mean(dQ/dV_(w_3-w_0 ) (V)|), etc., from the train cellsheld-out cycling data, identify which the features are that best predicting capacity at early/mid-life cycles, and then create Ggaussian process regression models that are used for predicting capacity at early/mid-life cycles for the test cells (see blue dots with error bars in Fig 1b). Finally, these predicted data-points are used in addition to the actual early cycle data capacity fade to construct the Bayesian regression trajectory for the test cell s. Notably. We note that these two methods are complementary and can be combined with each other. We evaluate the performance of our proposed method on an testing open-source dataset from Iowa State University and Iowa Lakes Community College (ISU-ILCC). This dataset comprises of 251 nickel-manganese-cobalt/graphite Lithium-ion cells that are cycled under 63 different conditions. We compute the mean average percentage error (MAPE) and negative log predictive density (NLPD) to quantify the efficacy of our method. Our initial findings suggest that, when only few observations are available, for test cells, when using only Bayesian regression with uninformed priors, a power law functional provides the most accurate predictions. with very few data points. However, asHowever, a the number of data points increases, a twin sigmoidal function becomes more accurate as the number of observations further increases. We also find that using as little as 10% of the data set towards generating data-driven priors can lead to significant improvement in prediction accuracy when using early cycle data. Lastly, we found that augmenting early-cycle data with Gaussian process-predicted capacity data for Bayesian regression greatly improves the prediction accuracy. We will present a comprehensive comparison of our methods to other methods available in the literature and apply this method to additional battery datasets.

42 ENGINEERING↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2025

Multi-physics Topology OPtimization and Additive Manufacturing for High-temperature Heat Exchangers

This research significantly advances the understanding of high-temperature heat exchanger design through an integrated approach that combines topology optimization (TO), triply periodic minimal surface (TPMS) structures, additive manufacturing (AM) and thermohydraulic testing. Each of these components contributes uniquely to a unified, high-performance design, fabrication and testing workflow. Topology optimization serves as the foundation of the design methodology by providing a systematic way to determine the most effective material layout for separating hot and cold fluids while maximizing thermal performance. The researchers introduced a novel three-material optimization framework using two density fields to represent hot fluid, cold fluid, and solid domains. This approach enables automated discovery of optimal shapes and flow paths that cannot be intuitively designed, especially under constraints imposed by manufacturing technologies. Furthermore, constraints such as minimal wall thickness and overhang angles were embedded into the optimization process, ensuring that resulting designs are not only thermally efficient but also manufacturable using modern additive techniques. In parallel, the study delves into the use of Gyroid-based TPMS geometries for constructing the core of the heat exchanger. TPMS structures are known for their high surface area, excellent fluid mixing capabilities, and minimal pressure drop characteristics. The researchers applied a data-driven modeling framework using Heteroscedastic Sparse Gaussian Process Regression (HSGPR) combined with genetic algorithms. This allowed for the rapid evaluation and optimization of key geometric parameters such as frequency, iso-value, and phase shift. The result was a set of Gyroid structures tailored for high heat transfer and low flow resistance, demonstrating clear improvements over conventional straight-channel designs. After the designing process, additive manufacturing played a critical role by turning these highly complex, optimized geometries into physical components. Utilizing Laser Powder Bed Fusion (LPBF) with Haynes 282, the study demonstrated the feasibility of fabricating these heat exchangers at high precision. Post-processing methods, including dilation-erosion operations, were applied to ensure local features adhered to self-supporting constraints. The fabricated structures were then subjected to thermohydraulic testing under conditions representative of supercritical CO 2 Brayton cycles, validating the predicted performance and confirming the viability of the full design-to-fabrication pipeline. Finally, thermohydraulic testing across the above studies served as a crucial experimental validation of advanced heat exchanger. Under consistent high-temperature and high-pressure conditions using supercritical CO 2 , the testing demonstrated that both TO and Gyroid-based TPMS designs significantly outperformed conventional straight-channel HXs. The TO design achieved a 115% increase in UA and NTU and a 27.6% boost in gravimetric power density, while the data-driven optimized Gyroid design delivered a 166% increase in UA and NTU and improved effectiveness from 68.7% to 86.1%. These results validate the simulation models, confirm the manufacturability of complex geometries under AM constraints, and provide key insights into design-performance trade-offs, thereby advancing the development of high-efficiency, compact heat exchangers for extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Wind Energy Accomplishments and Year-End Performance Report: Fiscal Year 2022

Four decades ago, construction was just beginning on experimental turbines at the National Wind Technology Center (NWTC). Today, the U.S. Department of Energy's (DOE's) National Renewable Energy Laboratory (NREL) facility is the centerpiece of the laboratory's Flatirons Campus, a world-class hub for renewable energy research. The nation's shift to 100% clean electricity by 2035 will require a mix of renewable energy sources and strategies - and together, wind and solar energy could account for 60% to 80% of that clean energy resource. In Fiscal Year (FY) 2022, NREL scientists, engineers, and analysts contributed to these visionary goals through their wind energy research. As wind innovations push into new areas, NREL continues to play a vital role in advancing technology and addressing deployment barriers in pursuit of more efficient, reliable, and predictable wind energy systems. FY 2022 wind research and development explored the potential for dramatic growth in land-based systems, the launch of the nation's first commercial-scale offshore installations, and transmission infrastructure buildout. Land-based wind energy is one of the most cost-effective electricity supply options - but utility-scale deployment will require up to 10 times the current number of turbines. An NREL plan addressed this need to accelerate U.S. wind technology rollout at distributed and utility scales. Another project conducted by NREL and the Pacific Northwest National Laboratory (PNNL) helps position the nation's first major offshore wind corridor for success. The WETO-funded Atlantic Offshore Wind Transmission Study is evaluating options for balancing electricity supply and demand, while supporting resilience of the grid and marine industries. WETO, NREL, and other partners are working to enable the enormous supply chain and workforce changes the U.S. wind energy industry will need to meet net-zero-carbon-emissions targets. As part of a seminal series of DOE-funded supply chain studies, NREL analysts reported on the trade-offs involved in manufacturing large volumes of wind technologies, while addressing cost, workforce, and logistics issues. All of this research is supported by NREL's outstanding research teams, tools, data, and facilities. A WETO-funded international wind energy field campaign, the American WAKE experimeNt (AWAKEN), has brought together experts from NREL, PNNL, and Sandia National Laboratories to create the world's most comprehensive set of high-resolution data on wind energy atmospheric phenomenon. This study could lead to more accurate predictions of losses from turbine-to-turbine wake interactions, eventually helping wind plants capture more energy and operators save millions of dollars. In addition, NREL researchers developed testing, modeling, and analysis tools to improve the security of power grids by identifying wind power plant dynamic stability problems. A new Stochastic Soaring Raptor Simulator (SSRS) protects golden eagles from turbine encounters by predicting flight paths. This report provides more detail on these top achievements and other accomplishments made by NREL and its partners during FY 2022 (between October 1, 2021, and September 30, 2022).

accomplishments↗
DOE OSTI2022

Magnetic Gears: The Key to Robust, Cost-Effective Hydropower Drivetrains

Based on previous demonstrated success at fabricating 5 and 10 kW scale magnetic gearbox (MGB) prototypes, Emrgy and its partners (the project team) proposed to design and construct a 100 kW scale MGB with a 30:1 gear ratio for the low-head hydro applications. The Statement of Project Objectives included tasks covering: 1) Market Applicability; 2) Technical Metrics; 3) Design (initial); 4) Electromagnetic (EM) Load and Structural Analysis; 5) Modal Analysis; 6) Sealing Design and 7) Final Design during Budget Period 1. Budget Period 2 included tasks covering: 1) Materials Procurement and Test Plan Development; 2) Assembly; and 3) Testing. The Market Applicability study (Task 1) led to a clear conclusion and recommendation toward “Low Head” technologies for maximum market share of both New Stream Reach development as well as powering Non-Powered Dams. The findings of this study also identified the opportunity for a larger scale magnetic gearbox-based drive train as a function of increased torque, as opposed to increased speed. The Technical Metrics Study (Task 2) concluded a horizontal orientation was preferred, examined potential loss mechanisms, concluded that a Halbach Array magnetic design was preferred, established a 30:1 gear ratio as optimal, and established a power rating of 100 kW as optimal. The subsequent initial and final detailed design process included electro-magnetic (EM) load and structural analysis (Task 4), a Modal analysis (for vibration) (Task 5), and a sealing design (Task 6) to assure water impermeability. The final design package (Task 7) included 729 individual parts, 117 unique part numbers, and 15 assemblies. In order to facilitate procurement, the full bill of materials was broken down into several sub-components: 1) custom magnetic parts; 2) custom machined parts; 3) custom casted parts; and 4) commercial off the shelf (COTS) parts. The casted parts were fabricated by Oak Ridge National Laboratory (ORNL) via a Cooperative Research and Development Agreement (CRADA) with Emrgy and funded by the Advanced Manufacturing Office (AMO). The procurement effort (Task 8) ultimately covered three time periods based on challenges encountered in meeting the budgeted cost for the prototype. Following the first effort in the early stages of Budget Period 2 in 2017, a no-cost time extension was granted to seek alternative fabrication and procurement options. The project was re-booted in 2020 based on the new ORNL CRADA that would focus on five (5) of the more difficult and expensive parts using their advanced manufacturing expertise. Procurement efforts for the other custom machined parts resulted in quotations that still exceeded the budget by more than $\$$100k. This was, in part, also due to the concurrent COVID-19 pandemic that caused both supply chain disruptions and labor shortages. As the project continued, pricing and availability degraded further. In Q2 FY’22, it was decided to not proceed with the fabrication of the prototype (Task 9) based on budgetary limitations. Outcomes included a full and detailed design of a 100 kW magnetic gearbox and associated indented bill of materials (BOM) and CAD drawings, a full assembly instruction manual with an associated BOM for materials necessary to support assembly, the fabrication of the double Halbach magnetic array for the rotor/stator system, fabrication of five (5) sand-casted/machined parts (via CRADA with ORNL) and an initial draft of a comprehensive testing plan. The most significant non-outcome was the actual fabrication and testing of the prototype gearbox based on budget limitations. Lessons learned included the need for an Application / Design / Cost trade analysis to better elucidate the cost potential of the MGB in the projected volumes anticipated for future demand. This would better establish the efficacy of the original cost target ($\$$0.80/Watt) and/or the need for reconsideration of designs and applications. Likewise, additional consideration of the prototype nature of the gearbox – single use, short lifetime, etc. - either as a separate exercise or in place of the design process completed, to reduce the cost of the demonstration prototype device. Additionally, project continuity was cited as a significant risk based on the loss of the primary design engineering firm after Budget Period 1. A design analysis exercise was conducted at the conclusion of the project to identify potential areas for cost reduction. One concept considered was the removal of the inner ring of magnets (with associated changes in the outer ring magnets) to enable a horizontal collapse of the design. It was estimated this could reduce cost by 10-25% without impacting performance.

13 HYDRO ENERGY↗