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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Flexible and Generic Functional Mock-up Unit Based Threat Injection Framework for Grid-interactive Efficient Buildings: A Case Study in Modelica

Grid-interactive efficient buildings (GEBs) have been considered as an important asset to support the power grid reliability by utilizing the demand flexibility offered by GEBs. GEBs are enabled by advances in sensors and controls, and the communication between building equipment, whole buildings, and the grid. The integration of different building technologies and network-based communication system makes GEBs vulnerable to passive threats such as equipment failure and active threats such as cyber-attacks. Modeling and simulation is an effective way to evaluate the impact of threats on the system performance. This paper proposes a generic and flexible threat injection framework for commonly-used building energy simulators such as EnergyPlus and Modelica to support threat modeling and evaluation. This framework leverages functional mock-up unit (FMU) to develop a general modeling interface for threat injection and simulation. A numerical case study using Modelica as a building energy simulator is conducted to demonstrate the capability of the framework for supporting single/multiple-order threat modeling and simulation of a GEB. Four threats and their combinations are injected on a Modelica-based threat-free building energy and control system, including operating supply fan at its full speed, remotely cycling the chiller on and off, blocking the chiller from receiving the chilled water supply temperature setpoints, and hijacking the global zone air temperature setpoint. Simulation results show that the cyber-attack that leads to short-term signal blocking has small effects on the system operation due to the "self-healing" feature of the heating, ventilation, and air-conditioning (HVAC) interactive control system. The threat that takes control of resetting the global zone air temperature setpoints has the most adverse impact on the system energy use, peak power demand, thermal comfort and the provision of demand flexibility. The combination of four threats have aggregative effects on the system but the effects are less than the additive effects of the individual threat.

Fu, Yanyang↗
DOE OSTI2023

Developing and tuning a community scale energy model for a disadvantaged community

This work describes the development of a community-scale energy model for a mixed-use low-income community located in Huntington Beach, CA. An accurate community-scale energy model is useful for evaluating the use of limited capital resources used to invest in clean energy technologies. This work lays out the process of developing such a model while relying primarily on publicly available data and highlighting critical partnerships necessary for model development success. The primary contribution of this work is the demonstration of the process used to develop an accurate energy model for a disadvantaged community when minimal building and energy use data is available. The heart of the model is the physics-based community scale energy modeling platform URBANopt. Using a bottom-up load modeling approach, energy simulated energy use falls within 3% or less of aggregate annual utility data, and within 10% or less aggregate monthly utility data. The demonstrated model development and tuning process can be used by others to characterize other atypical communities, which may differ significantly from prototypical models.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗
DOE OSTI2023

Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting

Here, the growing usage of decentralized renewable energy sources has made accurate estimation of their aggregated generation crucial for maintaining grid flexibility and reliability. However, the majority of distributed photovoltaic (PV) systems are behind-the-meter (BTM) and invisible to utilities, leading to three challenges in obtaining an accurate forecast of their aggregated output. Firstly, traditional centralized prediction algorithms used in previous studies may not be appropriate due to privacy concerns. There is therefore a need for decentralized forecasting methods, such as federated learning (FL), to protect privacy. Secondly, there has been no comparison between localized, centralized, and decentralized forecasting methods for BTM PV production, and the trade-off between prediction accuracy and privacy has not been explored. Lastly, the computational time of data-driven prediction algorithms has not been examined. This article presents a FL power forecasting method for PVs, which uses federated learning as a decentralized collaborative modeling approach to train a single model on data from multiple BTM sites. The machine learning network used to design this FL-based BTM PV forecasting model is a multi-layered perceptron, which ensures privacy and security of the data. Comparing the suggested FL forecasting model to non-private centralized and entirely private localized models revealed that it has a high level of accuracy, with an RMSE that is 18.17% lower than localized models and 9.9% higher than centralized models.

14 SOLAR ENERGY↗
DOE OSTI2021

An assessment of power flexibility from commercial building cooling systems in the United States

Understanding varying characteristics and aggregate potential of power flexibility from different building types considering regional diversity is critically important to actively engaging building resources in future eco-friendly, low-cost, and sustainable power systems. This paper presents a comprehensive characteristics analysis and potential assessment of the power flexibility from heating, ventilation, and air conditioning (HVAC) loads in commercial buildings in the U.S. using a simulation-based method. In this method, commercial buildings are first grouped by building types and climate regions. The U.S. Department of Energy Commercial Prototype Building Models are used to represent an average building in each group and are simulated to characterize corresponding power flexibility. Based on building survey data, the number of commercial buildings in each group is estimated and used to calculate aggregate power flexibility. It is found that HVAC loads in commercial buildings offer more flexibility for increasing power consumption than for decreasing it. The power consumption of commercial buildings in the U.S. can be increased by 46 GW and decreased by 40 GW on peak summer days. Among all commercial building types, standalone retail buildings provide the most absolute flexibility while the medium office buildings have the most flexibility as a percentage of the rated power consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Identification of engine oil-derived ash nanoparticles and ash formation process for a gasoline direct-injection engine

Engine oil-derived ash particles emitted from internal combustion (IC) engines are unwanted by-products, after oil is involved in in-cylinder combustion process. Since they typically come out together with particulate emissions, no detail has been reported about their early-stage particles other than agglomerated particles loaded on aftertreatment catalysts and filters. To better understand ash formation process during the combustion process, here differently formulated engine oils were dosed into a fuel system of a gasoline direct injection (GDI) engine that produces low soot mass emissions at normal operating conditions to increase the chances to find stand-alone ash particles separated from soot aggregates in the sub-20-nm size range. In addition to them, ash/soot aggregates in the larger size range were examined using scanning transmission electron microscopy (STEM)-X-ray electron dispersive spectroscopy (XEDS) to present elemental information at different sizes of particles from various oil formulations. The STEM-XEDS results showed that regardless of formulated oil type and particle size, Ca, P and C were always contained, while Zn was occasionally found on relatively large particles, suggesting that these elements get together from an early stage of particle formation. The S, Ca and P K-edge X-ray absorption near edge structure (XANES) analyses were performed for bulk soot containing raw ash. The linear combination approach & cross-checking among XANES results proposed that Ca 5 (OH)(PO 4 ) 2 , Ca 3 (PO 4 ) 2 and Zn 3 (PO 4 ) 2 are potentially major chemical compounds in raw ash particles, when combined with the STEM-XEDS results. Despite many reports that CaSO 4 is a major ash chemical when ash found in DPF/GFP systems was examined, it was observed to be rarely present in raw ashes using the S K-edge XANES analysis, suggesting ash transformation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

In situ deformation of antigorite-olivine two-phase mixtures: Implications for dynamics and seismic anisotropy in the mantle wedge

Water released from hydrous minerals in subducting slabs reacts with the overlying plate, resulting in widespread serpentinization in the mantle wedge. Deformation of serpentinized peridotites has been invoked to explain forearc seismic anisotropy, yet studies of the mechanical properties and deformation behaviors of serpentine-bearing multiphase aggregates remain limited. Here we deformed olivine-antigorite mixtures containing 70, 50, and 20 vol.% of antigorite at 2.5 – 7.6 GPa, 673 K and strain rates of ∼10 –5 –10 –4 s –1 . Elasto-viscoplastic self-consistent simulations, constrained by synchrotron X-ray diffraction (XRD) data, were used to estimate lattice strain, stress–strain partitioning, crystallographic preferred orientations (CPO) development, and aggregate strength. Selected run products were also analyzed by electron backscatter diffraction for comparison with the CPO results obtained from XRD experiments. We found olivine transitions from A- or B-type to C-type when antigorite fraction drops to 20 vol.%, coinciding with a microstructural change from interconnected weak layers to a load-bearing framework (LBF). An additional run on a sample Atg50/Ol50 with preexisting microstructures suggested the formation of LBF was promoted by these microstructures, although the preexisting antigorite CPO has been overprinted at 20.8 % strain and could be erased completely by subsequent deformation in nature. Estimated viscosity of the two-phase mixtures suggests that low-degree serpentinization (≤20 %) in the mantle wedge may increase the strength of olivine-rich peridotite and hinder slab-mantle decoupling, whereas high-degree serpentinization (≥50–70 %) weakens the peridotite and favors decoupling if sufficient viscosity contrast (>10) develops. Seismic anisotropy shows a nonlinear dependence on antigorite fraction: antigorite CPO governs the anisotropy of the mixtures with ≥50 vol.% antigorite, whereas olivine CPO dominates at low fractions (∼20 vol.%). The presence of pre-existing microstructures reduces seismic anisotropy of the deformed mixtures, however the persistence of pre-existing CPO in actively subducting slabs remains uncertain, making their significance over geological timescales questionable.

Crystallographic preferred orientation↗
DOE OSTI2024

Levelized cost of charging of extreme fast charging with stationary LMO/LTO batteries

Extreme DC fast charging for electric vehicles (EVs) could be competitive with the internal combustion engine refueling experience and enable longer-distance travel, which could help with EV adoption and decarbonization, but these systems have high capital costs and extremely variable high-power demands. Behind-the-meter systems (BTMS) could support extreme-fast-charging (XFC) stations to increase nationwide adoption of EVs. Here, this study examines the optimal break-even levelized cost of charging (LCOC) across 96 BTMS scenarios to enable low-wait XFC stations providing 200 miles of charge in 10 min. This research simulates LCOC via synthetic XFC-capable EV loads, machine-learned battery life models from testing data, and nonlinear optimal controls, co-minimizing complex utility costs and battery replacements. An aggregate optimal BTMS design treating each EV load as equal likely gives an optimal LCOC per utility rate, the average of which is $\$$0.59/kWh. In addition, the sensitivity of optimal and off-optimal design factors, the long-life LMO/LTO chemistry, and optimized controls are analyzed. The battery control model, based on battery stressors to compare chemistries, optimizes LMO/LTO resting state of charge and cycle depth without compromising cost reduction, which enables greater flexibility in operation. The LCOC savings due to replacement reduction are small, up to $\$$0.035/kWh (6%), with an average of $\$$0.02/kWh (3.5%). Compared with gasoline stations, the aggregate XFC station design achieves comparable speed, experience of service, and cost at $\$$3.81/gal gasoline, showing that EVs can replace gasoline vehicles even for longer-distance travel.

25 ENERGY STORAGE↗
DOE OSTI2026

Roadmap and Benchmarking: Privacy in Federated Load Forecasting

Data-driven techniques for energy demand forecasting continue to emerge with promising impacts on distribution grid planning. However, the development of robust and generalizable machine learning models requires that representative high quality training data are available. Distributed energy resources have begun to embed intelligence, gathering large amounts of data on customer demand, behavior, and household devices that are connected to the grid. Though utilities aggregate meter-level demand data for load shaping, demand response, outage management, reliability planning, and billing applications, there lies an inherent privacy concern in sharing consumption data that may identify individual consumer behavioral patterns. Hence, while sharing the data is crucial, the private sensitive customer data must be safeguarded from being exposed or manipulated. In this study, we propose a roadmap for implementing a based privacy preserving framework to support the advancement of data-driven analytics in data-sensitive distributed energy resources environments. The roadmap incorporates federated learning–a distributed training framework, differential privacy–a statistical framework that provides guarantees to safeguard the leakage of sensitive data, secure multiparty computation and homomorphic encryption– techniques for encrypting model gradients and applying secure aggregation on the server. Moreover, we perform baseline experiments on the federated short-term load forecasting (STLF) task using open-source residential load profile datasets, offering insights into the challenges of integrating differential privacy into federated learning.

Abebe, Waqwoya [Oak Ridge National Laboratory (ORN↗
DOE OSTI2022

Tri-level hybrid interval-stochastic optimal scheduling for flexible residential loads under GAN-assisted multiple uncertainties

Various building loads, such as heating, ventilation, and air conditioners (HVACs), electric water heaters (EWHs), and electric vehicles (EVs), can introduce opportunities for improving the flexibility of electricity consumption while satisfying the needs of building owners as well as benefiting the resilience of distribution system. To utilize such flexibility, a tri-level distribution market framework is established, including residential consumers, load aggregators (LAs), and the distribution system operator (DSO). In this work, the uncertainties from all three levels are considered. The random consumption behavior at the consumer level is modeled as a Gaussian noise that is also aggregated and transmitted to the LA level. The weather temperature in the LA level is forecasted as an interval, and the photovoltaic (PV) power in the market-clearing level is modeled by a set of power scenarios generated by Generative Adversarial Networks (GANs). Then, a hybrid interval-stochastic programming is proposed to transform the uncertain problems in the first two levels into deterministic ones. For real-time implementations, a rolling horizon optimization (RHO) scheme is employed to continuously optimize the power consumption based on the latest operating information. Finally, case studies on a modified IEEE 69-bus system validate the effectiveness of the proposed uncertainty modeling strategies and the RHO scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Conservation management decreases surface runoff and soil erosion

Conservation management practices – including agroforestry, cover cropping, no-till, reduced tillage, and residue return – have been applied for decades to control surface runoff and soil erosion, yet results have not been integrated and evaluated across cropping systems. In this study we collected data comparing agricultural production with and without conservation management strategies. We used a bootstrap resampling analysis to explore interactions between practice type, soil texture, surface runoff, and soil erosion. We then used a correlation analysis to relate changes in surface runoff and soil erosion to 13 other soil health and agronomic indicators, including soil organic carbon, soil aggregation, infiltration, porosity, subsurface leaching, and cash crop yield. Across all conservation management practices, surface runoff and erosion had respective mean decreases of 67% and 80% compared with controls. Use of cover cropping provided the largest decreases in erosion and surface runoff, thus emphasizing the importance of maintaining continuous vegetative cover on soils. Coarse- and medium-textured soils had greater decreases in both erosion and runoff than fine-textured soils. Changes in surface runoff and soil erosion under conservation management were highly correlated with soil organic carbon, aggregation, porosity, infiltration, leaching, and yield, showing that conservation practices help drive important interactions between these different facets of soil health. This study offers the first large-scale comparison of how different conservation agriculture practices reduce surface runoff and soil erosion, and at the same time provides new insight into how these interactions influence the improvement or loss of soil health.

58 GEOSCIENCES↗
DOE OSTI2024

Expression, purification, and characterization of diacylated Lipo-YcjN from Escherichia coli

YcjN is a putative substrate binding protein expressed from a cluster of genes involved in carbohydrate import and metabolism in Escherichia coli. Here, we determine the crystal structure of YcjN to a resolution of 1.95 Å, revealing that its three-dimensional structure is similar to substrate binding proteins in subcluster D-I, which includes the well-characterized maltose binding protein. Furthermore, we found that recombinant overexpression of YcjN results in the formation of a lipidated form of YcjN that is posttranslationally diacylated at cysteine 21. Comparisons of size-exclusion chromatography profiles and dynamic light scattering measurements of lipidated and nonlipidated YcjN proteins suggest that lipidated YcjN aggregates in solution via its lipid moiety. Additionally, bioinformatic analysis indicates that YcjN-like proteins may exist in both Bacteria and Archaea, potentially in both lipidated and nonlipidated forms. Together, our results provide a better understanding of the aggregation properties of recombinantly expressed bacterial lipoproteins in solution and establish a foundation for future studies that aim to elucidate the role of these proteins in bacterial physiology.

Escherichia coli↗
DOE OSTI2020

Connecting particle interactions to agglomerate morphology and rheology of boehmite nanocrystal suspensions

Rheology imposes significant challenges on processing of complex suspensions such as nuclear waste slurries at the Hanford and Savannah River sites. Understanding rheology connecting to microstructures and underlying particle interactions in complex slurries is therefore important for both fundamental knowledge and practical applications. Here, we use suspensions of aluminum oxyhydroxide minerals in the form of boehmite as an analog of the radioactive waste slurry to gain physical insights on the correlation between particle interactions, microstructures, and slurry rheology. Specifically, we use a combination of Couette rheometry and small-angle scattering techniques (independently and simultaneously) to understand how the slurry microstructure changes under flow and how these structural changes manifest themselves in the bulk rheology of the suspensions. Our experiments show that the boehmite slurries are thixotropic, with the rheology and structure of the suspensions changing with increasing exposure to flow. In the slurries, particle aggregates begin as loose, system-spanning clusters, but exposure to moderate shear rates causes the aggregates to irreversibly consolidate into denser clusters of finite size. The microstructural changes directly influence the rheological properties of the slurries such as viscosity and viscoelasticity. More importantly, our study shows that solution pH affects the amount of structural rearrangement and the kinetics of the rearrangement process, with an increase in pH leading to faster and more dramatic changes in the bulk rheology. Such dynamic microstructural changes and resultant rheology are understood via correlations between particle interactions and strength of particle network, coupled with surface chemistry and anisotropic nature of particle interactions. Nearly identical structural changes are also observed in Poiseuille flow geometries, implying that the observed changes are relevant in the pipe flow conditions present during waste processing.

Weston, Javen S.↗
DOE OSTI2023

Understanding the trade-offs of national municipal solid waste estimation methods for circular economy policy

Policies embracing circular economy concepts have taken hold in national legislation around the world. As the number of governments and organizations adopting circular economy policies increases, so does the need for accurate and timely measurement of material resource flows. Since many countries do not have access to centrally reported municipal solid waste (MSW) data, estimation and modeling are critical in evaluating circular economy policy effectiveness. The purpose of this paper is to examine three modeling approaches estimating national MSW data in the United States, including industry-based material flow analysis, waste-extended input-output modeling, and aggregated regional waste reporting. We establish five criteria to guide the analysis through the context of policy monitoring (data quality, flow totality, update frequency, sensitivity to disruption, and product granularity) and use these criteria to analyze and score each model. We then use a literature search to identify five, internationally-implemented options for circular economy policy and determine the data and modeling components that are most helpful in evaluating policy effectiveness. Finally, we provide a crosswalk of the model scores and policy needs to inform the suitability of model selection by policy type. We found that data quality and update frequency are identified as critical components for evaluating circular economy policies within the models evaluated, and can both be fulfilled by aggregated regional waste reporting. Flow totality, sensitivity to disruption, and product granularity requirements vary by both model and policy types. While none of the evaluated models satisfy the combination of requirements for any of the five policies, industry-based material flow analysis offers flow totality for extended producer responsibility, landfill bans, and recycling rate target policies that typically require it. Here, the waste-extended input-output model can provide disruption sensitivity and product granularity as needed for policies like minimum recycled content and market restrictions. Policy developers in areas where strong centralized data collection is not an option should design policy action(s) with modeling tradeoffs in mind, including the potential hybridization of modeling approaches that may provide the most accurate national MSW estimates.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Sintering effects on NpO 2 grain size and morphology: The role of precursor Np phase

Neptunium dioxide (NpO 2 ) is a key phase in nuclear material processing as a target material for the production of plutonium-238 ( 238 Pu) and has been historically synthesized via the calcination of a Np oxalate precursor. Alternative synthesis methods for NpO 2 are now more prevalent, necessitating their study and comparison with the more common oxalate route. The purpose of this work was to investigate the microstructural properties of NpO 2 synthesized from a nitrate-based Np precursor phase via the assessment of NpO 2 particle size and morphology as a function of calcination temperature and time. Scanning electron microscopy (SEM) was used to probe the primary grain size and morphology of NpO 2 after calcination at temperatures ranging from 700 to 1100 °C and hold times ranging from 1 to 10 h. Post-image analysis using ImageJ software enabled the quantification of mean particle diameter. This analysis indicated that particle diameter increases with both increasing calcination temperature and hold time. Primary particles were shown to be clumped in irregular patterns into the overall rough, blocky aggregates, but this macroscopic morphology was not affected by calcination time or temperature. Although trends in primary grain size of NpO 2 were consistent with available literature from other Np precursor phases, the macroscopic morphology of the NpO 2 aggregates was quite different than reported for other precursors. Through comparison with historical literature on Np oxalate, this work emphasizes the importance of Np precursor on the physical properties of NpO 2 .

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

A highly stable Cu(OH) 2 -Poly(vinyl alcohol) nanocomposite membrane for dramatically enhanced direct borohydride fuel cell performance

Nano-additive aggregation, limited performance, and unclear modification mechanisms are the main obstacles in developing nanocomposite anion exchange membranes (AEMs). In this work, for the first time, an effective and highly stable Poly(vinyl alcohol) (PVA)-based AEMs with dispersive Cu(OH)$_2$ nanoclusters (Cu-AEMs) are prepared by a simple and eco-friendly three-step method: ‘CuCl$_2$ doping-casting-KOH immersing’. The doped Cu$^{2+}$ ions chemically combine with OH- ions to form anionic conductive Cu(OH)2 nanoclusters intermediated by attaching resins. The PVA skeletons wrap around Cu(OH)$_2$ while the hydroxyl groups expose to bulk water, forming Cu(OH)$_2$-PVA complex, which avoids nano-additive aggregation, increases anionic channels, and strengthens additive-matrix connection. A direct borohydride fuel cell using Cu-AEM with 0.56 wt % CuCl$_2$ possess the highest power density of 403.3 mW cm$^{-2}$ at 60 °C and a life span of over 200 h. The high-performance and durability come from the unique structure of Cu(OH)$_2$ nanoclusters-PVA complex. ‘Vehicle’ theory is considered to be the dominant mechanism for enhancing such nanocomposite Cu-AEMs. This work demonstrates a new concept for preparing the stable AEMs toward high-performance fuel cells. The synthetic chemistry involved can be broadly extended for fabricating versatile AEMs.

25 ENERGY STORAGE↗
DOE OSTI2020

Tailoring electrode microstructure via ink content to enable improved rated power performance for platinum cobalt/high surface area carbon based polymer electrolyte fuel cells

Improvements in polymer electrolyte fuel cell (PEFC) electrode performance have primarily focused on catalyst and ionomer developments, marginalizing the importance of catalyst ink formulation. In this study, the effect of ink formulation is examined across a series of cathodes comprised of PtCo supported on high surface area carbon (PtCo/ HSC ) and Nafion ionomer using an array of in situ electrochemical and ex situ characterization techniques. In contrast to prior work on Pt/Vu systems, ink water content had little effect on the electrochemically determined ionomer coverage for the PtCo/HSC electrocatalyst examined here. Characterization using nano-scale resolution X-ray computed tomography (nano-CT) demonstrated that water-rich ink formulations lead to a reduction in aggregate size (ionomer + PtCo/HSC), improving local O 2 transport. This understanding, combined with the use of a commercially-available electrocatalyst was used to produced state-of-the-art membrane electrode assemblies with Pt loadings of 0.03/0.08 mg Pt /cm 2 on the anode and cathode respectively, having; i) > 1 A/mg Pt (0.9 V iR-free , 150 kPa, 80 °C, 100% RH, H 2 /O 2 ), ii) 320 mA/cm 2 at 0.8 V, 150 kPa, 80 ° C, 100% RH, H 2 /Air), and iii) > 1 W/cm 2 elec at rated power (0.67 V, 250 kPa, 94 °C, 65% RH, H 2 /Air) or < 0.11 g Pt /kW rated .

25 ENERGY STORAGE↗
DOE OSTI2025

Microstructure and CPO evolution of dynamically recrystallized olivine during complex deformation conditions: a full-field numerical modeling approach

The rheological properties of mantle rocks are strongly dependent on their crystallographic preferred orientation (CPO). Olivine CPO, defined by the orientation of seismically fast [100] axes parallel to flow direction, is also thought to be a dominant contributor to seismic anisotropy in the Earth's upper mantle. However, the amount of deformation needed to overprint a new CPO on a pre-existing fabric and the impact of the inherited CPOs on the transient microstructure evolution, remain unknown. This study employs a full-field numerical approach (VPFFT-ELLE) to explore the dynamic recrystallization and microstructural evolution of olivine polycrystalline aggregates under complex deformation conditions. We test four combinations of successive pure shear and simple shear boundary conditions. Findings indicate that inherited CPOs influence subsequent deformation in a manner dependent on the kinematic relationship between successive stages. In all cases, a minor strain increment (ε ∼0.3–0.6) is sufficient to erase the previous microstructure and CPO. However, when deformation conditions change dramatically (e.g., stretching direction changes orthogonally), the intensity of the new CPO developed is significantly lower and strain distribution are specially altered. During a transient strain stage, pre-existing microstructures undergo extensive reworking, especially when deformation conditions are changed dramatically, such as switching from simple shear to a pure shear condition with a parallel shortening direction relative to the stretching direction. We estimate the significance of these results in interpreting observations of seismic velocity anisotropy, concluding that P-wave seismic anisotropy is significantly and positively correlated with the evolution of olivine CPO with deformation history. This research underscores the transient nature of microstructural rearrangement in olivine aggregates and the necessity for caution in interpreting seismic anisotropy in regions with complex deformation histories, as inherited CPOs can influence current fabric development and induce deviation to the present deformation conditions.

36 MATERIALS SCIENCE↗