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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗
DOE OSTI2021

Valence Effects of Fe Impurity for Recovered LiNi 0.6 Co 0.2 Mn 0.2 O 2 Cathode Materials

Iron impurities are generally included in the obtained leaching liquor solution during the hydrometallurgical recycling method of spent lithium-ion batteries (LIBs) due to the usage of iron in battery casings and machinery parts of recycling equipment, which would definitely affect the physical and electrochemical features of the recovered active materials. In this paper, the effects of iron impurity with different valence states (Fe 2+ and Fe 3+ ) and gradient concentrations (0.2, 1.0, and 5.0 at. %) for the obtained LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) cathodes are fully studied. It is found that Fe3+ impurity could easily lower the tap density and average size of NCM622 particles and even introduce some impurity phases in the NCM622 structure at high concentration (5.0 at. %), leading to much lower specific capacity, worse rate capability, and cycling performance of the Fe 3+ -based NCM622 cathode. On contrast, with certain concentrations of Fe 2+ impurity (0.2 and 1.0 at. %), the NCM622 cathode material exhibits comparable and much better electrochemical properties compared with the virgin NCM622 materials. Based on these results, the valence of Fe impurity should be considered and controlled as well as its concentration during the recycling process design for spent LIBs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Using Co-Optimized Machine Learned Manifolds for Modeling Chemically Reacting Flows

Chemically reacting flows play a key role in a wide range of engineered systems, from chemical and polymer processing to combustion-based energy conversion technologies. Simulations of these flows involve solving a coupled set of partial differential equations for mass, momentum, energy, and all relevant chemical species in the system. Chemical reaction pathways may be extremely complex and involve hundreds or more intermediate species, with reactions that occur over timescales varying by several orders of magnitude - presenting a significant numerical stiffness challenge. The combination of these factors makes simulation of chemically reacting flows vastly more expensive than nonreactive simulations, and often makes direct solution of the governing equations intractable. It is necessary to apply lower-fidelity models in place of the detailed governing equations in order to reduce computational cost to enable reacting flow simulation tools to be used in the engineering design process. Many of the models employed for this purpose are based on reducing the dimension of the thermochemical state, motivated by the observation that the observed thermochemical states in a system lie on a low-dimensional manifold in thermochemical state space. This behavior occurs due to the fast equilibration of certain reactive and transport processes, and physics-based manifold models rely on idealized assumptions about the balance of timescales and the way in which chemistry and transport are coupled. In this work, we apply a novel method for data-driven manifold-based modeling that can leverage data from high-fidelity reacting flow simulations to improve model accuracy in cases where the physics-based modeling assumptions break down. The approach is designed to be broadly applicable across chemically reacting flow systems but is applied here to turbulent combustion modeling.

machine learning↗
DOE OSTI2023

Physics-informed machine-learning model of temperature evolution under solid phase processes

We model temperature dynamics during Shear Assisted Proccess Extrusion (ShAPE), a solid phase process that plasticizes feedstock with a rotating tool and subsequently extrudes it into a consolidated tube, rod, or wire. Control of temperature is critical during ShAPE processing to avoid liquefaction, ensure smooth extrusion, and develop desired material properties in the extruded products. Accurate modeling of the complicated thermo-mechanical feedbacks between process inputs, material temperature, and heat generation presents a significant barrier to predictive modeling and process design. In particular, connecting micro-structural scale mechanisms of heat generation to macro-scale predictions of temperature can become computationally intractable. In this work we use a neural network (NN) model of heat generation to bridge this gap, by combining it with a simplified model of the temperature dynamics due to conduction and convection to capture the macro scale evolution of temperature. We inform the construction of the NN heat generation model using crystal plasticity simulations at the micro-structural scale to model the effects of process inputs on generation of heat. We achieved close fits of the temperature dynamics model to a diverse experimental data-set. Further, the relationships learned by the NN model between process inputs and heat generation showed qualitative agreement with those predicted by crystal plasticity simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Economic and Environmental Assessment of Biological Conversions of Agile BioFoundry (ABF) Bio-Derived Chemicals

Bio-derived chemicals are an essential part of the growing bioeconomy. They possess the potential to boost a new domestic bioproduct industry, improve the sustainability of integrated biomanufacturing, and reduce U.S. dependence on fossil energy. The Agile BioFoundry (ABF) consortium, a Department of Energy (DOE)-sponsored collaboration, is investigating biobased pathways to produce advantaged products including advanced biofuels, fuel intermediates, and bioproducts. ABF is integrating advanced computational tools for biological engineering, process design and data analysis, and economic/sustainability modeling tools into a comprehensive and dynamic platform for biomanufacturing of microbes and using them to produce key metabolic intermediates or beachhead molecules which may be derivatized into several distinct bioproducts of industrial interest. In this presentation, we first discuss a methodology to select a single exemplar product molecule to represent each beachhead pathway based on similarities with other end-molecule options (yields/titers/rates, fermentation operation mode, oxygen requirements, general separations challenges, etc.), in order to maintain a reasonable number of cases for rigorous process simulation. We then use techno-economic analysis (TEA) and life-cycle analysis (LCA) to highlight sensitivities and trends around economic performance and environmental footprint, reflecting examples for two selected ABF technology pathways to bio-derived chemicals: 1) adipic acid production via muconic acid fermentation from mixed sugars with Pseudomonas putida and 2) cineole via geranyl diphosphate with Rhodosporidium toruloides. We present multi-variable scan plots highlighting key drivers on costs and greenhouse gas emissions in order to identify the parameter space in which each pathway could ultimately achieve economic and environmental sustainability meeting or exceeding commodity product benchmarks, thus prioritizing future R&D focus areas.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching

Acoustic waves play a crucial role in various applications, including medical imaging, non-destructive testing, and sonar systems. One of the significant challenges in these applications is impedance matching, which is essential for minimizing reflections and maximizing the transfer of acoustic energy between different media. Acoustic metamaterials offer a promising solution to this challenge. In addition to impedance control, gradient stiffness can enhance structural efficiency and enable spatial control of wave propagation, making it a valuable feature in acoustic metamaterial design. In this pa- per, we present our developed computational method to design 3D stiffness gradient acoustic metamaterials for impedance matching. The key steps in our approach include generating initial designs using a periodic covariance function to provide unit cells that are both periodic on the boundaries and randomly formed inside the unit cell. Furthermore, we integrated manufacturing constraints into the design process, ensuring that the structures are interconnected for fabrication. We propose two computational optimization algorithms: GenUnit, based on a non-dominated sorting genetic algorithm (NSGA-II), and MLMatch, which leverages differentiable machine learning. The two approaches are not separate contributions but complementary com- ponents of a unified framework. GenUnit requires no training data and directly interfaces with physics-based simulations, making it highly accurate but slower for large-scale exploration. In contrast, MLMatch is data-hungry during training but, once trained, enables near-instantaneous inference and broad design-space coverage. Together, they form a hybrid strategy: ML- Match rapidly explores the global design space, and GenUnit provides local refinement with high-fidelity accuracy. This balance between training cost, inference time, and precision is the motivation for including both methods in the same study. We applied this dual-algorithm framework to generate two metallic-based metamaterial designs that match the acoustic impedance of water while exhibiting a controlled gradient in stiffness (from stiff to soft). The stiffness gradient is particularly advantageous in applications where one side of the structure must interface with soft or sensitive surfaces, such as human tissue or delicate components. Here, this work paves the way for improved materials in various acoustic applications, particularly in ultrasound devices, by providing better impedance.

Metamaterial↗
DOE OSTI2022

Data Mining and Visualization of High-Dimensional ICME Data for Additive Manufacturing

Integrated computational materials engineering (ICME) methods combining CALPHAD with process-based simulations can produce rich, high-dimensional data for alloy and process design. In ICME methods for metallurgical applications, the visualization and interpretation of such high-dimensional data has previously been through heat maps represented in 2 or 3 dimensions. While such an approach is ideal when one variable is varied at a time, in the case of high-dimensional data with multiple variables varied simultaneously, as is the case in additive manufacturing, interpreting the trends through two- or three-dimensional heat maps becomes challenging. Here, we propose a strategy of mixed visual data mining and quantitative analysis for high-dimensional metallurgical and process data using high-throughput thermodynamic calculations. Two case studies show the application of the proposed approach. The first case study investigated the effects of feedstock chemistry on the δ ferrite formation in 316L stainless steel powders used for binder jet additive manufacturing. The second case study linked Scheil–Gulliver calculations to a process model for dissimilar joining of aluminum alloys 5356 and 6111 during laser hot-wire additive manufacturing. Both cases contained thousands of calculated data points, showcasing the utility of visual data analysis through parallel coordinate plotting, Pearson correlation coefficient matrices, and scatter matrices compared to traditional process maps. These visualization techniques can be extended to many additive manufacturing problems to capture process–structure–property relationships for additively manufactured components.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Design Advancement of an Indoor Soil Deposition Chamber: A Road to Standardization

An indoor soil deposition chamber has been designed and constructed at the Arizona State University Photovoltaic Reliability Lab (ASU-PRL) to simulate photovoltaic (PV) module soiling in the field. This paper discusses the next set of revisions to the design, which are organized into three categories: design improvements, cost reduction, and new features. The design process was carried out with the intention of working toward the development of a national or international soiling standard. The improvements made to the chamber are described in detail to provide a guide for other laboratories or research facilities who wish to study photovoltaic (PV) soiling.

14 SOLAR ENERGY↗
DOE OSTI2025

Transformational Nano-confined Ionic Liquid Membrane for Greater than or Equal to 97 Percent Carbon Dioxide Capture from Natural Gas Combined Cycle Flue Gas

A transformational process based on nano-confined ionic liquid (NCIL) membranes was developed for capturing ≥97% CO 2 from natural gas combined cycle (NCCC) flue gas. The NCIL membranes were prepared by loading amino acid ionic liquid into a framework composed of single-walled carbon nanotube mesh filled with graphene oxide quantum dots. The membranes exhibited CO 2 permeance as high as 2,000 GPU with a CO 2 /N2 selectivity of 2,300 for a typical NGCC flue gas composition. When H 2 O vapor sweep was applied in the permeate side, 96.6% CO2 dry-basis purity and 97.6% CO 2 capture rate were achieved for a simulated NGCC flue gas with single stage. In the process design, a highly H 2 O-selective membrane would be needed to recover majority of the H 2 O vapor, and the recovered H 2 O vapor could be recycled to the permeate side of the NCIL membrane. Sulfonated poly(ether ether ketone) membranes were successfully developed for this purpose. These membranes exhibited H 2 O permeance great than 11,000 GPU and H 2 O/CO 2 selectivity greater than 1,000 at 70ºC for a feed mixture consisting of 14.5 vol% H2O and balanced CO 2 . A standalone membrane model using MATLAB platform was developed for process simulation. The model was validated with experimental data. Techno-economic analysis based on the testing data collected during the current program suggests this transformational membrane process can achieve 97% CO 2 capture efficiency with a cost of $47.8/tonne of CO 2 , which is a 21% reduction versus DOE’s reference case B31B.97.

03 NATURAL GAS↗
DOE OSTI2022

Estimating List-Mode Data Sensitivities to Nuclear Data with MCNP6

Nuclear data are a vital component of predictive simulations used in applications like experiment design, stockpile stewardship, nuclear nonproliferation/safeguards, health physics, and criticality safety. A singular simulation requires the coalescence of different areas of nuclear data such as cross sections, angular distributions, and energy distributions of emitted neutrons for different materials and energy ranges. Improving nuclear data and thus reducing the uncertainty in simulated parameters could enable smaller, better-informed safety factors and ultimately reduce operational and procedural costs. There is a constant effort to garner a better understanding of the physical quantities represented by nuclear data through experiments. Integral experiment benchmarks use simulated and measured results to validate current nuclear data values. In the past, benchmarks primarily focused on the effective multiplication factor (k eff ); however, this limited scope has caused compensating errors and areas of nuclear data that lack validation. Compensating errors are inaccuracies in nuclear data that are obfuscated by cancellation when observing integrated values such as k eff . Diverse integral benchmark experiments that look for quantities of interest other than k eff and include multiple responses minimize the possibility of compensating errors and provides validation to areas of nuclear data previously lacking experimental validation. Benchmark experiments can be optimized during the design process to be highly dependent on specific areas of nuclear data. The dependence of a response in an experiment to a specific area/type of nuclear data is defined as sensitivity. A larger sensitivity means that nuclear data uncertainties will play a larger role in the response(s) resulting in larger bias. Currently, the sensitivity capabilities of the Monte Carlo N-Particle (MCNP ®1 ) transport code are limited to responses of k eff and tallied values (e.g., flux, surface current). As a part of the EUCLID project, this work explores estimating list-mode nuclear data sensitivities that can be used to design experiments aimed to constrain and reduce compensating errors in nuclear data by focusing on responses other than k eff . Tallied values are ideal quantities that are estimated with detectors during experiments. List-mode data (a list of neutron collection times) are the direct output of detector systems in subcritical neutron noise experiments. Expanding MCNP sensitivity capabilities to include the sensitivity of responses estimated from list-mode data, such as the prompt neutron decay constant (α) and multiplicity estimates (S and D), enables more direct comparison of simulated and measured experimental quantities. Additionally, deterministic tools such as SENSMG are capable of obtaining sensitivities to a wide variety of responses; however, these tools cannot handle complex geometries due to the assumptions made in discretizing the phase-space variables of the Boltzman transport equation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Jacobian-based Model Diagnostics and Application to Equation Oriented Modeling of a Carbon Capture System

Equation-oriented (EO) modeling has the potential to enable the effective design and optimization of the operation of advanced energy systems. However, advanced modeling of energy systems results in a large number of variables and non-linear equations, and it can be difficult to search through these to identify the culprit(s) responsible for convergence issues. The Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES-IP) contains a tool to identify poorly scaled constraints and variables by searching for rows and columns of the Jacobian matrix with small L2-norms so they can be rescaled. A further singular value decomposition can be per-formed to identify degenerate sets of equations and remaining scaling issues. This work presents an EO model of a flowsheet developed for post-combustion carbon capture using a monoethanolamine (MEA) solvent system as a case study. The IDAES diagnostics tools were successfully applied to this flowsheet to identify problems to improve model robustness and enable the optimization of process design and operating conditions of a carbon capture system.

Allan, Douglas↗
DOE OSTI2020

Design and evaluation of large area strip sensor prototypes for the ATLAS Inner Tracker detector

The ATLAS community is facing the last stages prior to the production of the upgraded silicon strip Inner Tracker for the High-Luminosity Large Hadron Collider. An extensive Market Survey was carried out in order to evaluate the capability of different foundries to fabricate large area silicon strip sensors, satisfying the ATLAS specifications. The semiconductor manufacturing company, Infineon Technologies AG, was one of the two foundries, along with Hamamatsu Photonics K.K., that reached the last stage of the evaluation for the production of the new devices. The full prototype wafer layout for the participation of Infineon, called ATLAS17LS-IFX, was designed using a newly developed Python-based Automatic Layout Generation Tool, able to rapidly design sensors with different characteristics and dimensions based on a few geometrical and technological input parameters. Here we present the layout design process and the results obtained from the evaluation of the new Infineon large area sensors before and after proton and neutron irradiations, up to fluences expected in the inner layers of the future ATLAS detector.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A Process with Decoupled Absorber Kinetics and Solvent Regeneration through Membrane Dewatering and In-Column Heat Transfer (Final Report)

This report summarizes the work conducted on project DE-FE0031604 where University of Kentucky Center for Applied Energy (UK CAER) has validated its intensified CO 2 capture process through substantial enhancements to the kinetics of the absorption process and energy reductions by absorber temperature profile modification, dewatering and heat integration technologies for achieving significant capital and operating cost reductions. To address DOE’s objective of improving post-combustion CO 2 capture technology and reducing associated cost, UK CAER employed an intensified process which combined three key aspects targeted at overcoming inherent limitations or barriers in the conventional CO 2 capture and desorption process. The process designed to be independent of the type of solvent used, included (1) the use of 3-D printed two-channel structured packing material to control the temperature profile and increase the CO 2 absorption rate in the absorber, (2) a zeolite membrane dewatering unit for dewatering of the carbon-rich solvent to decouple solvent concentration needs for CO 2 absorption and desorption, and (3) a rich-split feed with two-phase flow heat transfer prior to the stripper that provided a secondary point of vapor generation to provide energy savings in steam extraction and solvent regeneration. The project was executed over two budget periods. This involved testing of individual process components which included the advanced heat transfer packing and the dewatering membrane on UK CAER’s 30 liter per minute (L/min) CO 2 (3” Column) capture bench unit with simulated flue gas in the first budget period. Subsequent scaled-up testing of these components together with the split-feed configuration were also tested in UK CAER’s 0.1 MWth CO 2 capture unit with coal-derived flue gas in the second-budget period. Long term studies were done during this period to assess process and solvent performance over extended duration. Project partners Lawrence Livermore National Laboratory (LLNL) and Media & Process Technology (MPT) led the development efforts for the advanced packing material and dewatering membrane respectively. Data from the long term testing was used as input for an environmental, health and safety (EH&S) assessment for the process and scaled technology performed by ALL4 LLC. Trimeric Corporation also completed a techno-economic analysis (TEA) for the UK CAER technology which was compared to the DOE reference Case B12B. Tests on the 3” column capture unit showed that the advanced heat transfer packing could be used to lower the bulge temperature in the absorber, and this was also proven in the scaled testing in the 0.1 MWth CO 2 capture unit. The bulge temperature could be lowered by >10 °C, changing the temperature profile in the absorber, and showed potential to enhance absorption with the ability to tailor the profile to provide conditions suitable for a solvent’s properties and kinetics. Conditions for short term evaluation of a 19” zeolite dewatering membrane on the 3” column capture unit yielded desirable fluxes and sustained rejection rates of >80%. However, for the scaled testing of six membrane modules consisting of 21 parts of 31-inch-long membrane tubes in each bundle (surface area 0.3 m 2 ), over a more extended duration, similar rejection rates could not be achieved. With the split-feed of the rich stream to the stripper, improved heat recovery minimized waste heat exiting the top of the stripper. The stripper exhaust temperatures could be reduced by >10 °C; reducing the amount of water vaporization contributed to lowering the regeneration energy by ~ 15%. The energy benefit could be sustained from the long term monitoring of the solvent performance. The solvent properties were not significantly impacted over the long-term operations. The benefits of the UK CAER process demonstrated experimentally were mostly validated from the TEA comparing a commercial scale application of the technology to DOE reference Case B12B. The cost of CO 2 capture for the UK CAER technology was estimated to be ~$34.97/tonne of CO 2 captured; a reduction of 23% compared to Case B12B. The increase in cost of electricity was also shown to be 16% lower than that of Case B12B. The total parasitic demand was also shown to be 11% lower. The key drivers for the benefits are a result of the process intensification approaches employed in the UK CAER technology for enhanced solvent performance, effective heat recovery and improved energy performance. The EH&S assessment did not find any major environmental concerns or barriers to the full scale implementation of the technology.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

LH CO 2 MENT Colorado Project (Final Report)

The objective of Electricore’s pre-FEED project “LH CO 2 MENT COLORADO PROJECT” is to accelerate the implementation of a 1.5 million tonnes per year (TPY), and first-of-a-kind (FOAK) at world scale, Svante VeloxoTherm™ carbon capture plant. This project represents a quantum leap to a large-scale facility that will launch Svante’s carbon capture technology into the next era of accomplishments and market acceptance. By completing the Front-End Loading (FEL) Feasibility Study Report (FEL-2) for a fit-for-purpose design at the HOLCIM cement plant, located near Florence Colorado, USA, this technology can be proven as the future of large-scale deployment for carbon capture and storage. This carbon capture plant was designed with the goal of reaching a target of near Net Zero Emissions by capturing 90% of the carbon dioxide (CO 2 ) emissions from the HOLCIM cement plant and from the boiler which produces steam required to regenerate the adsorbent. Additionally, this project will be leveraging a renewable Power Purchase Agreement (PPA) using solar energy to acquire power at the target price of 0.04 $/kWh or less. In its current configuration CO 2 emissions from the HOLCIM cement plant is around 700 – 800 kg/ton of clinker produced. The proposed new carbon capture plant will allow a reduction of CO 2 emissions to about 100 kg/ton of clinker produced. The scope of work consists of the process design and capital & operating cost estimation (Class IV) for a total plant capacity of 4,750 TPD of pipeline grade CO 2 . The Svante VeloxoTherm™ technology is comprised of a Rotary Adsorption Machine (RAM) for intensified Thermal Swing Adsorption (TSA) using Structured Adsorbent Beds (SABs) and related Balance of Plant (BOP), including CO 2 compression. A business case (financial analysis) evaluation has been undertaken for the Owner’s management review. Recommendations on how best to proceed to the next stage of the project have been conveyed and are documented within this report. This analysis has relied on a detailed and comprehensive Project Financial Model, evaluating the Total Project IRR (after tax, unlevered, and including all forecast 45Q PTCs and 100% tax efficiency) – the project financial analysis (as opposed to standard TEA analysis) only considered a 12 year plant economic lifetime as a result of 45Q being the sole driver considered at this stage. The Total Project IRR was evaluated across a large number of potential scenarios. The evaluation demonstrated that if the 45Q PTD is increased to $85/MT for sequestration, there are a large number of feasible scenarios which demonstrate economic returns.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Studies as a function of different ALD capping layers on cavity losses for QIS and accelerators

Niobium-based bulk SRF cavities have demonstrated exceptional performance. To further improve niobium cavity performance, we present studies involving a novel surface engineering process designed to prevent the formation of amorphous niobium oxides on the surface. This is achieved by encapsulating the niobium surface using thermal Atomic-Layer-Deposition (ALD). This technique has been shown to enhance the properties of niobium cavities and 2D resonators. This study not only aims to improve SRF performance at high fields but also has the potential to enhance the quality factor in low-field regimes, particularly for quantum applications. For the method to be effective, it is essential to have a clean interface between the encapsulating layer and the bulk niobium. Achieving this requires a uniform coating across the entire cavity surface and efficient removal of the underlying niobium oxides. To optimize the process, a variety of material characterization tools have been utilized to refine parameters such as thin film thickness and annealing conditions. Cavity RF measurements were performed at the vertical test facilities at FNAL to assess the Q vs. Eacc curves for accelerator applications, as well as at the SQMS dilution refrigerators to investigate the full two-level system (TLS) losses at milliKelvin and single-photon levels. Based on the results, we characterized the loss of different oxides in various regimes and applications, comparing them to natural niobium pentoxide.

Grassellino, L. [Fermilab]↗
DOE OSTI2024

Modeling Reversible Volume Change in Automotive Battery Cells with Porous Silicon Oxide-Graphite Composite Anodes

Automotive battery manufacturers are working to improve the individual cell and overall pack design by increasing durability, performance, and range, while reducing cost, and active material volume change is a key aspect that needs to be considered during this design process. Recently, silicon oxide-graphite composite anodes are being explored to increase total anode capacity while maintaining a tolerable amount of cell level reversible volume expansion due to the relatively lower reversible volume change of the silicon oxide compared to pure battery grade or metallurgical grade silicon. To predict the blended anode response and contribution to the overall cell volume change, we integrated the mechanical behavior of the individual active materials with the multi-species, multi-reaction model to predict the state-of-lithiation of the active materials in the cell at a given potential. The resulting simulations illustrate the tradeoff in volume change between the silicon oxide and the graphite during cell operation. This type of modeling approach will allow designers to virtually consider the impact of cell level and pack level design changes on overall system mechanical performance for automotive and grid storage applications, namely that relatively small addition of silicon containing materials can drive a significant increase in the volume change at the cell level, as demonstrated by the 5 wt% addition of silicon oxide accounting for half of the overall volume change in the cell.

Garrick, Taylor R. (ORCID:0000000322518129)↗
DOE OSTI2020

Innovative rail transport of a supersized land-based wind turbine blade

Wind turbine blade logistic providers are being challenged with escalating costs and routing complexities as one-piece blade approach lengths of 75 m in various regions of the U.S. land-based market. New lower cost solutions are needed to enable further reductions in the levelized cost of energy (LCOE) and continued market expansion. In this paper, a novel method of using existing U.S. rail infrastructure to deploy 100-m, one-piece blades to U.S. land-based wind sites is numerically investigated. The study removes the constraint that blades must be kept rigid during transport, and it allows bending to keep blades within a clearance profile while navigating horizontal and vertical curvatures. Novel system optimization and blade design processes consider blade structural constraints and rail logistic constraints in parallel to develop a highly flexible, rail-transportable blade. Results indicate maximum deployment potential in the Interior region of the United States and limited deployment potential in other regions. The study concludes that innovative rail transportation solutions combined with advanced rotor technologies can provide a feasible alternative to segmentation and support continued LCOE reductions in the U.S. land-based wind energy market.

17 WIND ENERGY↗