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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Spatially resolved performance and degradation in a perfluorinated anion exchange membrane fuel cell

Anion exchange membrane fuel cells may enable future operation with non-precious metal-based catalysts. These systems have a delicate sensitivity to operating conditions such as humidification levels and the presence of CO2 in the air oxidant stream. We present spatially resolved in-situ performance results that shed light on phenomena that are unique to anion exchange membrane fuel cells. For cell construction, a highly conductive perfluorinated anion exchange polymer was used as the membrane and the material in powder form as the ionomer. Experiments were conducted to investigate the effects of humidification, fuel/oxidant concentration, and carbonation effects on the performance and its distribution in the cell. The results indicated that (i) dry conditions at the cathode have a stronger effect than at the anode on overall cell performance, (ii) performance significantly suffered when humidification was below 90%, (iii) fuel and oxidant dilution effects lead mass-transport losses and were stronger than flow rate effects, (iv) CO2 in the cathode feed stream creates an equilibration disparity between the inlet and outlet sections and CO2 purging is affected by flooding conditions, and (v) after >500 h of operation, performance deteriorates predominantly at the inlet.

2+1D fuel cell model↗
DOE OSTI2025

Study of hydride reorientation and ductility of SRA ZIRLO and PRXA ZIRLO using ring compression testing (RCT)

Before dry storage, spent nuclear fuel is dried at high temperatures, which induces hydride dissolution. Radial hydride particles precipitate when cooled under a stress above a threshold stress. This process can severely degrade the ductility of cladding tubes. Zirconium alloys with different microstructures, such as grain boundary density, were quantified and studied to examine their effect on hydride reorientation. The ring compression loading mode creates a range of stress states depending on the location within the ring with respect to the applied load, producing various hydride morphologies that can be used to study the effects of microstructures on hydride reorientation. Finite element modeling was performed to calculate the hoop stress, which is the dominant stress, during hydride reorientation. These results were coupled with hydride morphology to determine the threshold stress for hydride reorientation, depending on the microstructure. In conclusion, the samples were then mechanically tested using ring compression to evaluate the ductility of zirconium alloys as a function of different hydride morphologies.

Hydride embrittlement↗
DOE OSTI2024

Modeling Diurnal and Annual Ethylene Generation from Solar-Driven Electrochemical CO 2 Reduction Devices

Integrated solar fuels devices for CO 2 reduction (CO 2 R) are a promising technology class towards achieving net-negative carbon emissions. Designing integrated CO 2 R solar fuels devices requires careful co-design of electrochemical and photovoltaic components as well as consideration of the diurnal and seasonal effects of solar irradiance, temperature, and other meteorological factors expected for ‘on-sun’ deployment. Here, using a photovoltaic-electrochemical (PV-EC) platform, we developed a temperature and potential-dependent diurnal and annual model using experimental CO 2 R performance of Cu-based electrocatalysts, local meteorological data from the National Solar Radiation Database (NSRD), and modeled performance of commercial c-Si PVs. We simulated diurnal product outputs with and without the effects of ambient temperature to determine gaseous product temperature sensitivity. From these outputs, we observed seasonal variation in gaseous product generation, with up to two-fold increases in ethylene productivity between the Winter and Summer, analyzed the consequences of dynamic cloud coverage, and identified periods where device cooling/heating mechanisms could be implemented to maximize ethylene generation. Finally, we modeled the annual ethylene generation for a scaled 1 MW solar farm at three different locations (Beijing, CN; Sydney, AUS; Barstow, CA) to determine the consequences of local meteorological climates on PV-EC CO 2 R product output, recording a maximum ethylene output of 18.5 tonne/yr at Barstow. Overall, this model presents a critical tool for streamlining the translation of experimental solar-driven electrochemical research to real-world implementation.

Yap, Kyra M. K.↗
DOE OSTI2023

Quantitative Insight to Fission Gas Bubble Distribution in Irradiated Annular U-10Zr Metallic Fuel Using Deep Learning

U-10Zr Metal fuel is a promising nuclear fuel candidate for next-generation sodium-cooled fast spectrum reactors. Porosity is one of the key facts to impact the performance of metallic fuel. Additionally, a mechanical understanding of fission gas bubbles evolution behavior is a prerequisite for fuel development and qualification. Previous study of fission gas bubbles relied on a simple threshold method working on low resolution optical microscopy images, which has challenges in recognizing bubble boundaries, and caused inaccurate statistics of bubble properties. In this paper, a pre-trained deep learning model on Scanning Electron Microscopy (SEM) images from an annular U-10Zr fuel (AF1), was applied to another U-10Zr annular fuel (AF2). More accurate fission gas bubble segmentation results were generated, which leads to more precise qualitative analysis on the morphology, size, density, and orientation of bubbles. Furthermore, we investigated the lanthanide movement along the radial temperature gradient and obtained conclusive findings.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Kinetics and Reactor Modeling of VFA Ketonization for Sustainable Aviation Fuel Production

Ketonization of volatile fatty acids (VFAs) produced via arrested methanogenesis of wet waste represents the only unit operation of wet waste upgrading to sustainable (net-zero or negative life cycle CO2 emissions) aviation fuel not currently in industrial practice. Ketone product yields of close to 100% have been obtained during gas-phase reactions of VFAs over oxide catalysts at the laboratory scale, but design of an industrial ketonization reactor requires understanding of the impacts of reactant and product partial pressures, deactivation, and heat and mass transport phenomena on observed ketonization rates. Our work leverages rigorous kinetic analysis of ketonization processes to inform reactor scale-up efforts through packed-bed reactor modeling. We first present results of a kinetic study of ketonization of a model VFA, hexanoic acid, over an industrial ZrO2 catalyst performed in a packed-bed microreactor in conditions free of significant heat or mass transfer gradients. Major findings of the analysis include: (i) hexanoic acid saturates all catalyst active sites at relatively low partial pressure (~10 kPa) and (ii) ketonization products 6-undecanone, water, and CO2 inhibit reaction rates. Kinetic data are used to fit a rate expression quantifying the functional dependence of ketonization rate on partial pressures of VFA reactants and ketone, water, and CO2 products. A packed-bed reactor model describing vapor-phase hexanoic acid ketonization over ZrO2 extrudate catalysts is developed based on the kinetic model. The effects of (i) bed- and pellet-scale mass- and heat-transfer limitations and (ii) axial pressure drop guide our development of recommendations for optimal sizing, temperature, and influent flow composition of an industrial-scale ketonization reactor. The quantitative understanding of VFA ketonization developed in this study represents an advance toward derisking the VFA ketonization step of wet waste upgrading to sustainable aviation fuel.

biojet↗
DOE OSTI2020

Bipolar membrane polarization behavior with systematically varied interfacial areas in the junction region

The palette of applications for bipolar membranes (BPMs) has expanded recently beyond electrodialysis as they are now being considered for fuel cell and electrolysis applications. Their deployment in emerging electrochemical technologies arises from the need to have a membrane separator that provides disparate pH environments and to prevent species crossover. Most materials research for BPMs has focused on water dissociation catalysts and less emphasis has been given to the design of the polycation–polyanion interface for improving BPM performance. Here, soft lithography fabricated a series of micropatterned BPMs with precise control over the interfacial area in the bipolar junction. Polarization experiments showed that a 2.28× increase in interfacial area led to a 250 mV reduction in the onset potential. Additionally, the same increase in interfacial area yielded marginal improvements in current density due to the junction region being under kinetics-diffusion control. A simple physics model based on the electric field of the junction region rationalized the reduction in the overpotential for water dissociation as a function of interfacial area. Finally, the soft lithography approach was also conducive for fabricating BPMs with different chemistries ranging from perfluorinated polymer backbones to alkaline stable poly(arylene) hydrocarbon polymers. These polymer chemistries are better suited for fuel cell and electrolysis applications. The BPM featuring the alkaline stable poly(terphenyl) anion exchange membrane had an onset potential of 0.84 V, which was near the thermodynamic limit, and was about 150 mV lower than a commercially available variant.

25 ENERGY STORAGE↗
DOE OSTI2025

Pressure Gain, Stability, and Operability of Methane/Syngas Based RDEs Under Steady and Transient Conditions (Final Project Report)

The scope of this work addresses key issues associated with losses associated with the detonation wave and other processes internal to the RDE operation, as well as it develops modeling tools for the evaluation of these losses and exhaust emissions in RDEs. The main challenge in studying RDEs is that RDE performance is highly reliant on the specifics of the design so much so that simple/canonical systems alone cannot provide useful engineering information, but practical RDE designs are sufficiently complex and involve extreme operational environments that detailed access either experimentally (laser diagnostics, for instance) or computationally (direct numerical simulations) are as yet to become practical. To overcome this challenge, we have conducted a combined experimental/simulation/analytical study investigating key phenomena that control the characteristics of operation of RDEs. As a result, the study has developed tools and methods that can be used to evaluate performance and design approaches using reduced-physics models, with the assumptions validated using detailed simulations, and the model prediction tested using experimental observations. The specific objectives of the research were: (1) Develop and demonstrate a low-loss fully axial injection concept, taking advantage of stratification effects to alter the detonation structure and position the wave favorably within the combustor; (2) Obtain stability and operability characteristics of an RDE across operating conditions to aid in the development of operability and performance rules for the operations of other systems; and (3) Develop quantitative metrics for performance gain as well as quantitative description of the loss mechanisms through a combination of diagnostics development, reduced-order modeling, and detailed simulations. The work conducted here has made contribution on design of low-loss inlets that has broad application within the power generation industry for use with pressure gain combustion. The operability and stability of different designs, while focusing on axial air inlet designs, has been analyzed. The effect of nozzle and injection conditions was studied. Models and simulations of exhaust emissions, focusing on NOx emission has been developed and used to investigate how operation of the RDE affect NOx production using Lagrangian analysis of RDE simulations. This work has built on previous programs, with the goal of further understanding operation of RDEs and elevate the readiness of design consideration. In addition, a suite of diagnostic and modeling tools have been developed to obtain quantitative metrics on performance based on measurements, which can be readily transferred to other experimental configurations.

08 HYDROGEN↗
DOE OSTI2024

Uniaxial compressive creep tests by spark plasma sintering of 70% theoretical density α -uranium and U-10Zr

Metallic fuels hold numerous advantages over conventional uranium dioxide fuels and are a key component of several liquid metal-cooled advanced reactor concepts including sodium fast reactors. These fuels undergo rapid swelling during early burnup; consequently, they spend most of their reactor lifetime in a porous state. The presence of this porosity alters many of the mechanical properties of the fuel including creep impacting fuel deformation during axial swelling. This work investigates the creep behavior of the porous fuel using a spark plasma sintering technique. Creep tests were performed for the first time on porous α-phase uranium and uranium with 10 wt. % zirconium (U-10Zr) samples. The samples of α-phase uranium and U-10Zr were fabricated from depleted uranium by spark plasma sintering and subjected to uniaxial compressive creep testing. Calculated stress exponents were found to be 2.6±1.6 and 5.7±1.4 for α-U and U-10Zr, respectively, and calculated activation energies were found to be 61.6±1.1kJ/mol for α-U. The creep data were also used to evaluate existing porosity inclusive in creep models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

RU Net for Automatic Characterization of TRISO Fuel Cross Sections

TRistructural ISOtropic (TRISO) particle fuel is a type of nuclear fuel known for its high-temperature and high-burnup performance. Each sub-millimeter diameter TRISO particle consists of uranium-oxycarbide (UCO) or UO2 fuel kernel, coated with buffer, inner pyrolytic carbon (IPyC), silicon carbide (SiC), and outer pyrolytic carbon (OPyC) layers. The SiC layer acts as the main containment barrier for the TRISO particle to retain the fission products, while the IPyC and OPyC layers provide additional barriers to the release of fission products, especially fission gases. During irradiation, phenomena like kernel swelling, buffer densification, and IPyC fracture may impact fuel performance. Post-irradiation microscopy on entire compact cross sections or samples of individual particles deconsolidated from compacts is often used to identify these irradiation-induced changes in morphology. However, each fuel compact generally contains thousands of TRISO particles. To get statistical information on these phenomena, it is cumbersome work if done manually. For example, to get information about swelling/densification behaviors of different layers or kernels after irradiation, researchers previously manually measured the perimeter of each TRISO layer in hundreds of particles after four rounds of iterative grinding and polishing encompassing more than 2000 cross-section images for a total of four fuel compacts. To attempt to reduce the subjectivity inherent in that process and accelerate data analysis, we conducted a study on the automatic TRISO layer segmentation on cross-sectional microscopic images using Convolutional Neural Networks (CNNs). CNNs are a class of machine learning algorithms specifically designed for processing structured grid data that have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we have generated the large irradiated TRISO layer dataset with more than 2000 cross-section TRISO microscopic images and the corresponding annotated images. Based on these annotated images, we have employed different CNNs for automatic segmentation of different TRISO layers. These include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net has the best performance in terms of intersection-over-union (IoU). Through the aid of these CNN models, we can expedite the analysis of TRISO particle cross-sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.

Convolutional Neural Networks↗
DOE OSTI2024

Feasibility and performance of the staged Z-pinch: A one-dimensional study with FLASH and MACH2

Z-pinch platforms constitute a promising pathway to fusion energy research. Here, we present a one-dimensional numerical study of the staged Z-pinch (SZP) concept using the FLASH and MACH2 codes. We discuss the verification of the codes using two analytical benchmarks that include Z-pinch-relevant physics, building confidence on the codes' ability to model such experiments. Then, FLASH is used to simulate two different SZP configurations: a xenon gas-puff liner (SZP1*) and a silver solid liner (SZP2). The SZP2 results are compared against previously published MACH2 results, and a new code-to-code comparison on SZP1* is presented. Using an ideal equation of state and analytical transport coefficients, FLASH yields a fuel convergence ratio (CR) of approximately 39 and a mass-averaged fuel ion temperature slightly below 1 keV for the SZP2 scheme, significantly lower than the full-physics MACH2 prediction. For the new SZP1* configuration, full-physics FLASH simulations furnish large and inherently unstable CRs (>300) but achieve fuel ion temperatures of many kilo-electron volts. While MACH2 also predicts high temperatures, the fuel stagnates at a smaller CR. The integrated code-to-code comparison reveals how magnetic insulation, heat conduction, and radiation transport affect platform performance and the feasibility of the SZP concept.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Insights into Constraining Rate Coefficients in Fuel Oxidation Mechanisms Using Genetic Algorithm Optimization

Accurate fuel oxidation mechanisms can enable predictive capabilities that aid in advancing combustion technologies. High-level computational kinetics can yield reasonable rate coefficients with uncertainties, in some cases, below a factor of 2. Computed rate coefficients can be constrained further by optimizing against experimental data. Here, we explore the application of genetic algorithm (GA) optimization to constrain computed rate coefficients in complex fuel oxidation mechanisms in conjunction with temperature-dependent species mole fractions from jet-stirred reactor (JSR) measurements. Cyclohexane is a model candidate for understanding the reactivity of cyclic fuels. In this work, we optimize the rate coefficients of the most recent literature cyclohexane mechanism, which incorporates theoretically computed rate coefficients for the reaction networks stemming from the first and second O 2 addition pathways, against the experimental results of two separate literature JSR studies. Optimization consistency is evaluated by carrying out three GA optimizations: fitting to the temperature-dependent species mole fractions in each JSR experiment separately and simultaneously fitting the species mole fractions in both experiments. Local sensitivity analyses are used to identify five influential low-temperature oxidation reactions for optimization. Although the three optimizations do not yield identical rate coefficients, the direction of change in all five rate coefficients is consistent among the three optimizations. Performance of the models from the three optimizations is assessed against literature ignition delay times with differences in the level of agreement observed among the different optimizations. In conclusion, comparisons are made with our recent optimization work of a cyclopentane oxidation master-equation model against time-resolved species concentrations, and insights and improvements of the strategy for constraining rate coefficients using GA optimization are discussed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Modeling Nanoscale Ohmics in Carbon Supports of Fuel Cell Cathodes

Here, reducing platinum (Pt) loading in polymer electrolyte fuel cells (PEFCs) while meeting performace requirements is critical to their widespread deployment. However, significant polarization losses manifest at higher current densities in cathodes with lower Pt content. The morphology of the carbon supports in PEFC cathodes affects the location of Pt deposition into the micropits or onto the surface of the carbon support, translating into different kinetic and transport resistances. In this work, we derive an agglomerate scale model that differentiates the sink terms for Pt on the surface and in the pits of carbon supports. We develop an approach to assess the impact of nanoscale ohmic resistance to Pt in the micropits arising from weakly ionic solution in the carbon support on PEFC performance. Effectiveness factors relating the actual reaction rate to the maximum reaction rate (had all the Pt been exposed) are derived and embedded into a one-dimensional catalyst layer model. Parameters in the catalyst layer model are tuned based on experimental local oxygen transport resistances. Subsequently, we estimate bounds for the micropore resistances based on geometric and physical arguments. Lastly, polarization curves are simulated to assess the effect of the micropore resistance in fully-humidified and oxygen-rich environments.

25 ENERGY STORAGE↗
DOE OSTI2021

Measurement of radiolytic hydrogen generation and impact of drying treatments on reactor exposed and surrogate aluminum materials

Technical challenges associated with dry storage of Aluminum-clad Spent Nuclear Fuel (ASNF) include a need to better understand the potential extent of gaseous molecular hydrogen (H 2 ) production through radiolytic degradation of the aluminum (oxy)hydroxide films present on ASNF. The characterization of radiolytic gas generation from ASNF (oxy)hydroxide layers has been identified as a key knowledge gap which poses a technical challenge to the long-term storage of ASNF. Task 2 of the action plan for the extended (>50 years) dry storage of ASNF addresses this gap. Previous radiolysis studies performed under Task 2 established baseline estimates of H 2 generation rates from the attendant hydrated oxides. The results confirmed that net radiolytic H 2 production has a dependency on absorbed gamma dose, as well as relative humidity and cover gas composition (air, nitrogen, and argon). Further experiments revealed that the physisorbed water on the samples may significantly impact the radiolytic H 2 yield. This phenomenon complicates the determination of H 2 generation rates for hydrated oxides, which may lead to inaccurate modeling predictions of the long-term H 2 yields in sealed storage systems containing ASNF, particularly when compared to spent fuel casks which have undergone some drying process intended to remove physically- and chemically-bound water. This report describes the testing methods utilized and the hydrogen generation results obtained in an investigation of the effects of gamma irradiation on aluminum materials for a variety of (oxy)hydroxide surface compositions and drying conditions. The testing methods included small-area aluminum material testing in ampules, and large-area aluminum material testing in steel vessels. Test material preparation, irradiation, and radiolytic H 2 measurement methods are summarized. The measured H 2 concentrations, which reflect the variable initial hydrated inventory and drying treatments, are compared to one another as well as to previously published data, to identify the primary factors affecting radiolytic H 2 generation rates and potential equilibrium H 2 concentrations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Modeling diurnal and annual ethylene generation from solar-driven electrochemical CO 2 reduction devices

Integrated solar fuels devices for CO 2 reduction (CO 2 R) are a promising technology class towards reducing carbon emissions. Designing integrated CO 2 R solar fuels devices requires careful co-design of electrochemical and photovoltaic components as well as consideration of the diurnal and seasonal effects of solar irradiance, temperature, and other meteorological factors expected for ‘on-sun’ deployment. Using a photovoltaic-electrochemical (PV-EC) platform, we developed a temperature and potential-dependent diurnal and annual model using experimentally-determined CO 2 R performance of Cu-based electrocatalysts, local meteorological data from the National Solar Radiation Database (NSRD), and modeled performance of commercial c-Si PVs. Here we simulated gaseous diurnal product outputs with and without the effects of ambient temperature. From these outputs, we observed seasonal variation in gaseous product generation, with up to two-fold increases in ethylene productivity between the Winter and Summer, analyzed the consequences of dynamic cloud coverage, and identified periods where device cooling/heating mechanisms could be implemented to maximize ethylene generation. Finally, we modeled the annual ethylene generation for a scaled 1 MW solar farm at three different locations (Beijing, CN; Sydney, AUS; Barstow, CA) to determine the consequences of local meteorological climates on PV-EC CO 2 R product output, recording a maximum ethylene output of 18.5 tonne per year at Barstow. Overall, this model presents a critical tool for streamlining the translation of experimental solar-driven electrochemical research to real-world implementation.

14 SOLAR ENERGY↗
DOE OSTI2022

Nonlinear burn control in ITER using adaptive allocation of actuators with uncertain dynamics

Abstract ITER will be the first tokamak to sustain a fusion-producing, or burning, plasma. If the plasma temperature were to inadvertently rise in this burning regime, the positive correlation between temperature and the fusion reaction rate would establish a destabilizing positive feedback loop. Careful regulation of the plasma’s temperature and density, or burn control, is required to prevent these potentially reactor-damaging thermal excursions, neutralize disturbances and improve performance. In this work, a Lyapunov-based burn controller is designed using a full zero-dimensional nonlinear model. An adaptive estimator manages destabilizing uncertainties in the plasma confinement properties and the particle recycling conditions (caused by plasma–wall interactions). The controller regulates the plasma density with requests for deuterium and tritium particle injections. In ITER-like plasmas, the fusion-born alpha particles will primarily heat the plasma electrons, resulting in different electron and ion temperatures in the core. By considering separate response models for the electron and ion energies, the proposed controller can independently regulate the electron and ion temperatures by requesting that different amounts of auxiliary power be delivered to the electrons and ions. These two commands for a specific control effort (electron and ion heating) are sent to an actuator allocation module that optimally maps them to the heating actuators available to ITER: an electron cyclotron heating system (20 MW), an ion cyclotron heating system (20 MW), and two neutral beam injectors (16.5 MW each). Two different actuator allocators are presented in this work. The first actuator allocator finds the optimal mapping by solving a convex quadratic program that includes actuator saturation and rate limits. It is nonadaptive and assumes that the mapping between the commanded control efforts and the allocated actuators (i.e. the effector model) contains no uncertainties. The second actuator allocation module has an adaptive estimator to handle uncertainties in the effector model. This uncertainty includes actuator efficiencies, the fractions of neutral beam heating that are deposited into the plasma electrons and ions, and the tritium concentration of the fueling pellets. Furthermore, the adaptive allocator considers actuator dynamics (actuation lag) that contain uncertainty. This adaptive allocation algorithm is more computationally efficient than the aforementioned nonadaptive allocator because it is computed using dynamic update laws so that finding the solution to a static optimization problem is not required at every time step. A simulation study assesses the performance of the proposed adaptive burn controller augmented with each of the actuator allocation modules.

Physics↗
DOE OSTI2023

High-Fidelity Numerical Investigation on Elucidating Sodium Heat Transfer Characteristics for 37-Pin Wire-Wrapped Fuel Bundle in the PLANDTL Facility

This study involved a Reynolds-averaged Navier-Stokes- (RANS-) based computational fluid dynamics (CFD) analysis of the 37-pin wire-wrapped fuel bundle of the PNC Plant dynamics test loop (PLANDTL) facility. Previously, mainly the hydrodynamic phenomena of the wire-wrapped fuel bundle were analyzed, but the present study additionally included heat transfer analysis through conjugate heat transfer. The main purpose of the study was to benchmark the experimental data of the PLANDTL 37-pin wire-wrapped fuel bundle to investigate the heat transfer phenomena. In addition, the aim was to verify the accuracy of the RANS-based CFD analysis method using the STAR-CCM+ simulation software in comparison with the experimental data. The grid used for verification was an innovative grid system consisting of hexahedra using Fortran-based code. The development of the RANS-based CFD methodology included grid sensitivity analysis, turbulence model sensitivity analysis, and turbulent Prandtl number sensitivity analysis. Information on the temperature, mass flow rate, and area of the CFD results for each subchannel was provided for the top of the heated section and is expected to serve as a reference for future studies aiming to perform the validation and verification of a PLANDTL facility. In addition, the dependence of the peak temperature on the azimuth angle of each pin was analyzed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Task 12 Sustainability - Methodological Guidelines on Net Energy Analysis of Photovoltaic Electricity (2nd Edition)

Net Energy Analysis (NEA) is a structured, comprehensive method of quantifying the extent to which a given energy source is able to provide a net energy gain (i.e., an energy surplus) to the end user, after accounting for all the energy losses occurring along the chain of processes that are required to exploit it (i.e., for its extraction, processing and transformation into a usable energy carrier, and delivery to the end user), as well as for all the additional energy 'investments' that are required in order to carry out the same chain of processes. However, this general framework leaves the individual practitioner with a range of choices that can affect the results and thus, the conclusions of a NEA study. The current IEA PVPS guidelines were developed to provide guidance on assuring consistency, balance, and quality to enhance the credibility and reliability of the results from photovoltaic (PV) NEAs. The guidelines represent a consensus among the authors - PV NEA experts in North America and Europe - for assumptions made on PV performance, process inputs and outputs, methods of analysis, and reporting of the results. Guidance is given on photovoltaic-specific parameters used as inputs in NEA and on choices and assumptions in inventory data analysis and on implementation of modelling approaches. A consistent approach towards system modelling, the functional unit, the system boundaries and allocation aspects enhance the credibility of PV electricity NEA studies and enables balanced NEA-based comparisons. Specifically, "apples-to-oranges" comparisons of different energy carriers (e.g., fuels vs. electricity) are not methodologically sound and are to be avoided in all cases; also, any comparison across renewable and non-renewable electricity generation technologies must clearly point out the intrinsically short-term nature of the NEA viewpoint, which does not capture the long-term sustainability implications of renewable vs. non-renewable primary energy harvesting and use: non-renewable primary energy resources are depleted and finally exhausted (irrespective of the size of the EROI), while renewable primary energy resources are not. This document provides an in-depth discussion of a common metric of NEA, namely the energy return on investment (EROI), and how this is to be interpreted vis-a-vis the deceptively similar-sounding metrics in the field of Life Cycle Assessment (LCA): cumulative energy demand (CED) and non-renewable cumulative energy demand (nr-CED) per unit output. Specifically, a number of key differences are highlighted between these metrics as applied to electricity production systems, which are listed in Table S-1.

14 SOLAR ENERGY↗
DOE OSTI2022

Defect-driven anomalous transport in fast-ion conducting solid electrolytes

We report solid-state ionic conduction is a key enabler of electrochemical energy storage and conversion. The mechanistic connections between material processing, defect chemistry, transport dynamics, and practical performance are of considerable importance, but remain incomplete. Here, inspired by studies of fluids and biophysical systems, we re-examine anomalous diffusion in the iconic two-dimensional fast-ion conductors, the β- and β"-aluminas. Using large-scale simulations, we reproduce the frequency dependence of alternating-current ionic conductivity data. We show how the distribution of charge-compensating defects, modulated by processing, drives static and dynamic disorder, which lead to persistent sub-diffusive ion transport at macroscopic timescales. We deconvolute the effects of repulsions between mobile ions, the attraction between the mobile ions and charge-compensating defects, and geometric crowding on ionic conductivity. Our quantitative framework based on these model solid electrolytes connects their atomistic defect chemistry to macroscopic performance with minimal assumptions and enables mechanism-driven 'atoms-to-device' optimization of fast-ion conductors.

25 ENERGY STORAGE↗