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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Intra-Hour Dispatch and Automatic Generator Control Demonstration with Solar Forecasting - Final Report

In this project we address multiple resource integration challenges associated with increasing levels of solar penetration that arise from the variability and uncertainty in solar irradiance. We will model the SMUD service region as its own balancing region, and develop an integrated, real-time operational tool that takes solar-load forecast uncertainties into consideration and commits optimal energy resources and reserves for intra-hour and intra-day decisions. The primary objectives of this effort are to reduce power system operation cost by committing appropriate amount of energy resources and reserves, as well as to provide operators a prediction of the generation fleet’s behavior in real time for realistic PV penetration scenarios. The proposed methodology includes the following steps: clustering analysis on the expected solar variability per region for the SMUD system, Day-ahead (DA) and real-time (RT) load forecasts for the entire service areas, 1-year of intra-hour CPR forecasts for cluster centers, 1-year of smart re-forecasting CPR forecasts in real-time for determination of irreducible errors, and uncertainty quantification for integrated solar-load for both distributed and central stations (selected locations within service region) PV generation.

14 SOLAR ENERGY↗
DOE OSTI2013

Characterizing particle-scale equilibrium adsorption and kinetics of uranium(VI) desorption from U-contaminated sediments

Rates of contaminant U(VI) release from individual size fractions of a composite sediment from the seasonally saturated lower vadose zone of the Hanford 300-Area were examined in flow-through batch reactors to maintain quasi-constant chemical conditions. Variability in equilibrium adsorption among the various size fractions was determined in static batch reactors and analyzed using the surface complexation modeling approach. The estimated stoichiometric coefficients of U(VI) surface complexation reactions with respect to pH and carbonate concentrations varied with size fractions. This source of variability significantly increased the uncertainty in U(VI) conditional equilibrium constants over that estimated from experimental errors alone. A minimum difference between conditional equilibrium constants was established in order to evaluate statistically significant differences between sediment adsorption properties. A set of equilibrium and kinetic expressions for cation exchange, calcite dissolution, aerobic respiration, and silica dissolution were incorporated in a reaction-rate model to describe the temporal evolution of solute concentrations observed during the flow-through batch experiments. Parameters in the reaction-rate model, calibrated using experimental data for select size fractions, predicted the changes in solute concentrations for the bulk, <2 mm, sediment sample. Kinetic U(VI) desorption was well described using a multi-rate surface complexation model with an assumed lognormal distribution for the rate constants. The estimated mean and standard deviation were the same for all < 2mm size fractions, but differed in the 2-8mm size fraction. Micropore volumes in the varied size fractions were also similar as assessed using t-plots to analyze N2 desorption data. These findings provide further support for the link between microporosity and particle-scale mass transfer rates controlling kinetic U(VI) adsorption/desorption and for the utility of N2 desorption isotherms for characterizing pore networks that influence mass transfer rates.

Stoliker, Deborah L.↗
DOE OSTI2012

Measurement of the single-top-quark t-channel cross section in pp collisions at $ \sqrt{s}=7 $ TeV

A measurement of the single-top-quark t-channel production cross section in pp collisions at sqrt(s) = 7 TeV with the CMS detector at the LHC is presented. Two different and complementary approaches have been followed. The first approach exploits the distributions of the pseudorapidity of the recoil jet and reconstructed top-quark mass using background estimates determined from control samples in data. The second approach is based on multivariate analysis techniques that probe the compatibility of the candidate events with the signal. Data have been collected for the muon and electron final states, corresponding to integrated luminosities of 1.17 and 1.56 inverse femtobarns, respectively. The single-top-quark production cross section in the t-channel is measured to be 67.2 +/- 6.1 pb, in agreement with the approximate next-to-next-to-leading-order standard model prediction. Using the standard model electroweak couplings, the CKM matrix element abs(V[tb]]) is measured to be 1.020 +/- 0.046 (meas.) +/- 0.017 (theor.).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Shape Control of Colloidal Semiconductor Nanocrystals through Thermodynamically Driven Aggregative Growth

The optoelectronic properties of colloidal semiconductor nanocrystals (NCs) can be manipulated by changing their geometric shapes. The precise synthetic control over particle morphologies, however, has remained elusive. Conventional growth techniques rely on the kinetic assembly of atomic units, where supersaturation and precipitation processes can lead to a broad distribution of particle shapes. In this paper, we demonstrate that replacing atomic precursors with small-size nanocrystals as building blocks for larger colloids offers an easier, more predictive control over nanoparticle shape evolution. The reported growth strategy is illustrated via shape-selective syntheses of CdSe and CdS NC cubes, spheres, rods, as well as unprecedented “donut” and ring-like structures. Growth of desired particle morphologies was achieved by choosing an appropriate reaction solvent that minimizes the surface free energy through a unique shape formation. This so-called "aggregative growth mechanism" is explained using a thermodynamic model for interacting viscous colloids. We expect that this novel growth strategy will offer a viable platform for shape–selective synthesis of many inorganic colloids, providing pathways to optimizable optoelectronic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Combining physics-based and data-driven models for quantitatively accurate plasma profile prediction that extrapolates well; with application to DIII-D, AUG, and ITER tokamaks

For design, scenario planning, and control, ITER and all other envisioned tokamaks rely on a variety of statistical and physics-based models to extrapolate to unseen regimes; most notably from low plasma current to high. A 'meta-learning' methodology for combining the accuracy of data-driven models with the generalizability of physics-based models is described and tested, yielding a 5–10 percent improvement in performance beyond either alone for the task of extrapolating time-dependent plasma profile prediction from low- to high- plasma current DIII-D tokamak discharges. Meanwhile, it is shown that both machine learning models extrapolated far-distribution and state-of-the-art 'physics-based' profile predictors fare worse than merely assuming plasma profiles do not change from their initial values. Finally, a variety of other mechanisms for helping data-driven models generalize—transfer learning, adding contextual information from physics simulators, and adding data from the ASDEX Upgrade tokamak—are attempted for similar extrapolation tasks but, in the methodology used in this paper, yield no significant improvement beyond simple data-driven models. Results are summarized in figures 15 and 16.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Chemistry and Formation of the Beilby Layer During Polishing of Fused Silica Glass

The chemical characteristics and the proposed formation mechanisms of the modified surface layer (called the Beilby layer) on polished fused silica glasses are described. Fused silica glass samples were polished using different slurries, polyurethane pads, and at different rotation rates. The concentration profiles of several key contaminants, such as Ce, K, and H, were measured in the near surface layer of the polished samples using Secondary Ion Mass Spectroscopy (SIMS). The penetration of K, originating from KOH used for pH control during polishing, decreased with increase in polishing material removal rate. In contrast, penetration of the Ce and H increased with increase in polishing removal rate. In addition, Ce penetration was largely independent of the other polishing parameters (e.g., particle size distribution and the properties of the polishing pad). The resulting K concentration depth profiles are described using a two-step diffusion process: (1) steady-state moving boundary diffusion (due to material removal during polishing) followed by (2) simple diffusion during ambient postpolishing storage. Using known alkali metal diffusion coefficients in fused silica glass, this diffusion model predicts concentration profiles that are consistent with the measured data at various polishing material removal rates. On the other hand, the observed Ce profiles are inconsistent with diffusion based transport. Rather we propose that Ce penetration is governed by the ratio of Ce–O–Si and Si–O–Si hydrolysis rates; where this ratio increases with interface temperature (which increases with polishing material removal rate) resulting in greater Ce penetration into the Beilby layer. Calculated Ce surface concentrations using this mechanism are in good agreement to the observed change in measured Ce surface concentrations with polishing material removal rate. In conclusion, these new insights into the chemistry of the Beilby layer, combined together with details of the single particle removal function during polishing, are used to develop a more detailed and quantitative picture of the polishing process and the formation of the Beilby layer.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Are atmospheric updrafts a key to unlocking climate forcing and sensitivity?

Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, explicit control on cloud–aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction. Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climate and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying vertical velocities, and parameterizations that do provide vertical velocities have been subject to limited evaluation against what have until recently been scant observations. Atmospheric observations imply that the distribution of vertical velocities depends on the areas over which the vertical velocities are averaged. Distributions of vertical velocities in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models. New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of the scale dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Are Atmospheric Updrafts a Key to Unlocking Climate Forcing and Sensitivity?

Both climate forcing and climate sensitivity persist as stubborn uncertainties limiting the extent to which climate models can provide actionable scientific scenarios for climate change. A key, explicit control on cloud-aerosol interactions, the largest uncertainty in climate forcing, is the vertical velocity of cloud-scale updrafts. Model-based studies of climate sensitivity indicate that convective entrainment, which is closely related to updraft speeds, is an important control on climate sensitivity. Updraft vertical velocities also drive many physical processes essential to numerical weather prediction. Vertical velocities and their role in atmospheric physical processes have been given very limited attention in models for climate and numerical weather prediction. The relevant physical scales range down to tens of meters and are thus frequently sub-grid and require parameterization. Many state-of-science convection parameterizations provide mass fluxes without specifying vertical velocities, and parameterizations which do provide vertical velocities have been subject to limited evaluation against what have until recently been scant observations. Atmospheric observations imply that the distribution of vertical velocities depends on the areas over which the vertical velocities are averaged. Distributions of vertical velocities in climate models may capture this behavior, but it has not been accounted for when parameterizing cloud and precipitation processes in current models. New observations of convective vertical velocities offer a potentially promising path toward developing process-level cloud models and parameterizations for climate and numerical weather prediction. Taking account of scale-dependence of resolved vertical velocities offers a path to matching cloud-scale physical processes and their driving dynamics more realistically, with a prospect of reduced uncertainty in both climate forcing and sensitivity.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

An Advanced Buffet Load Alleviation System

This paper describes the development of an advanced buffet load alleviation (BLA) system that utilizes distributed piezoelectric actuators in conjunction with an active rudder to reduce the structural dynamic response of the F/A-18 aircraft vertical tails to buffet loads. The BLA system was defined analytically with a detailed finite-element-model of the tail structure and piezoelectric actuators. Oscillatory aerodynamics were included along with a buffet forcing function to complete the aeroservoelastic model of the tail with rudder control surface. Two single-input-single-output (SISO) controllers were designed, one for the active rudder and one for the active piezoelectric actuators. The results from the analytical open and closed loop simulations were used to predict the system performance. The objective of this BLA system is to extend the life of vertical tail structures and decrease their life-cycle costs. This system can be applied to other aircraft designs to address suppression of structural vibrations on military and commercial aircraft.

Burnham, Jay K.↗
DOE OSTI2023

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems

In recent years, the field of data-driven neural network-based machine learning (ML) algorithms has grown significantly and spurred research in its applicability to instrumentation and control systems. While they are promising in operational contexts, the trustworthiness of such algorithms is not adequately assessed. Failures of ML-integrated systems are poorly understood; the lack of comprehensive risk modeling can degrade the trustworthiness of these systems. In recent reports by the National Institute for Standards and Technology, trustworthiness in ML is a critical barrier to adoption and will play a vital role in intelligent systems' safe and accountable operation. Thus, in this work, we demonstrate a real-time model-agnostic method to evaluate the relative reliability of ML predictions by incorporating out-of-distribution detection on the training dataset. It is well documented that ML algorithms excel at interpolation (or near-interpolation) tasks but significantly degrade at extrapolation. This occurs when new samples are "far" from training samples. The method, referred to as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets, which is used to calculate a prediction's relative reliability. LADDR is demonstrated on a feedforward neural network-based model used to predict safety significant factors during different loss-of-flow transients. LADDR is intended as a "data supervisor" and determines the appropriateness of well-trained ML models in the context of operational conditions. Ultimately, LADDR illustrates how training data can be used as evidence to support the trustworthiness of ML predictions when utilized for conventional interpolation tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Effect of process variables on the quality attributes of briquettes from wheat, oat, canola and barley

Effect of process variables on the quality attributes of briquettes from wheat, oat, canola and barley straw Jaya Shankar Tumuluru*, L. G. Tabil, Y. Song, K. L. Iroba and V. Meda Biomass is a renewable energy source and environmentally friendly substitute for fossil fuels such as coal and petroleum products. Major limitation of biomass for successful energy application is its low bulk density, which makes it very difficult and costly to transport and handle. To overcome this limitation, biomass has to be densified. The commonly used technologies for densification of biomass are pelletization and briquetting. Briquetting offers many advantages at it can densify larger particles sizes of biomass at higher moisture contents. Briquetting is influenced by a number of feedstock and process variables such as moisture content, particle size distribution, and some operating variables such as temperature and densification pressure. In the present study, experiments were designed and conducted based on Box-Behnken design to produce briquettes using barley, wheat, canola and barley straws. A laboratory scale hydraulic briquette press was used for the present study. The experimental process variables and their levels used in the present study were pressure levels (7.5, 10, 12.5 MPa), three levels of temperature (90, 110, 130 C), at three moisture content levels (9, 12, 15% w.b.), and three levels of particle size (19.1, 25.04, 31.75 mm). The quality variables studied includes moisture content, initial density and final briquette density after two weeks of storage, size distribution index and durability. The raw biomass was initially chopped and size reduced using a hammer mill. The ground biomass was conditioned at different moisture contents and was further densified using laboratory hydraulic press. For each treatment combination, ten briquettes were manufactured at a residence time of about 30 s after compression pressure setpoint was achieved. After compression, the initial dimensions and the final dimensions after 2 weeks of storage in controlled environment of all the samples were measured. Durability, dimensional stability, and moisture content tests were conducted after two weeks of storage of the briquettes produced. Initial results indicated that moisture content played a significant role on briquettes durability, stability, and density. Low moisture content of the straws (7-12%) gave more durable briquettes. Briquette density increased with increasing pressure depending on the moisture content value. The axial expansion was more significant than the lateral expansion, which in some cases tended to be nil depending on the material and operating variables. Further data analysis is in progress in order to understand the significance of the process variables based on ANOVA. Regression models were developed to predict the changes in quality of briquettes with respect of the process variables under study. Keywords: Herbaceous biomass, densification, briquettes, density, durability, dimensional stability, ANOVA and regression equations

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Comparative Analysis of Model Predictive Control and MPC-Informed Rule-Based Control for Thermal Storage Operation in Ultra-Low Temperature 4th Generation District Heating Networks

The integration of thermal storage and heat pumps in district heating networks (DHNs) can significantly enhance operational flexibility and energy efficiency; however, the practical deployment of advanced control strategies is often hindered by forecasting requirements and computational complexity. This study presents a comparative analysis of thermal storage control strategies in an ultra-low-temperature fourth-generation DHN, focusing on the development of a simplified rule-based control (RBC) explicitly informed by Model Predictive Control (MPC) behavior. The proposed methodology systematically analyzes the charging and discharging decisions of an MPC-controlled system under ideal forecasting conditions and extracts recurrent control patterns as a function of key system variables, including outdoor temperature, thermal demand, and electricity price. These patterns are translated into a set of structured time- and condition-based rules, resulting in an MPC-informed RBC that embeds predictive insights while preserving implementation simplicity and operational transparency. The approach is validated on a realistic mixed-use urban district in Denver, Colorado, USA, equipped with a centralized air-source heat pump, distributed water-to-water heat pumps, and a central thermal storage unit. Results show that the tuned RBC attains approximately 96% of ideal MPC economic performance (-27% of costs), preserves values of technical and environmental indicators (reduction only of 2-3%), and substantially reduces complexity. Sensitivity analyses further demonstrate the robustness of the RBC under varying operational conditions (i.e., ambient temperature, electricity price). Overall, the study demonstrates that MPC-informed rule-based control represents an effective trade-off between control performance and real-world applicability, enabling the integration of additional system components while maintaining simplicity, robustness, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1979

Spatial-frequency response of the Limb Infrared Monitor of the Stratosphere /LIMS/ radiometer

The Limb Infrared Monitor of the Stratosphere (LIMS), an experiment on the Nimbus-7 satellite, is designed to scan the earth's limb vertically and measure spectral emission profiles of trace atmospheric gases believed to be important in processes controlling the stratospheric ozone distribution. Experiment objectives are reviewed and several analyses and measurements are described which were performed to determine the adequacy of the system for satisfying these objectives. Adequate spatial-frequency response for all the spectral channels is required to provide, through inversion of the measured limb-radiance profiles, important information about the temperature, structure, and composition of the atmosphere. From the LIMS design-model data, the MTF was calculated for the optical system, the detector field mask, the electronics, and the overall system, for each channel. The signal output performance of the instrument was predicted from the system MTF data model input radiance data for each channel. MTF measurements made on the flight sensor confirmed the analytical results. The predictions indicate that the instrument can satisfy the basic measurement objectives of the experiment.

Wilson, R. G.↗
NASA NTRS1987

Spacecraft particulate contaminant redistribution

This paper describes analyses that were performed in support of the Hubble Space Telescope (HST)) particulate contamination control effort. The specific problems addressed include extension of available particle removal data to launch acoustic and random vibration conditions, development of an engineering model for transport of suspended particulates by airflow and in the presence of vehicle acceleration, turbulent diffusion, and migration of particulates over vibrating surfaces, and integration of the various models into a code that could be used to generate contamination level estimates for the HST primary mirror and other critical surfaces for the HST mission phases. The overall redistribution calculations were made assuming a specified initial contaminant distribution in terms of MIL STD 1246A levels, and using predicted vibration data for the various HST surfaces and mission phases. As expected, the effects of airflow were found to be significant, particularly for the larger particles. Particles smaller than about 20 microns did not participate appreciably in the redistribution.

Klavins, A.↗
NASA NTRS2014

The Effect of Drive Signal Limiting on High Cycle Fatigue Life Analysis

It is common practice to assume a Gaussian distribution of both the input acceleration and the response when modeling random vibration tests. In the laboratory, however, shaker controllers often limit the drive signal to prevent high amplitude peaks. The high amplitudes may either be truncated at a given level (socalled brick wall limiting or abrupt clipping), or compressed (soft limiting), resulting in drive signals which are no longer Gaussian. The paper first introduces several methods for limiting a drive signal, including brick wall limiting and compression. The limited signal is then passed through a linear time-invariant system representing a device under test. High cycle fatigue life predictions are subsequently made using spectral fatigue and rainflow cycle counting schemes. The life predictions are compared with those obtained from unclipped input signals. Some guidelines are provided to help the test engineer decide how clipping should be applied under different test scenarios.

Kihm, Frederic↗
DOE OSTI2011

Fusion Simulation Program Execution Plan

The overall science goal of the FSP is to develop predictive simulation capability for magnetically confined fusion plasmas at an unprecedented level of integration and fidelity. This will directly support and enable effective U.S. participation in research related to the International Thermonuclear Experimental Reactor (ITER) and the overall mission of delivering practical fusion energy. The FSP will address a rich set of scientific issues together with experimental programs, producing validated integrated physics results. This is very well aligned with the mission of the ITER Organization to coordinate with its members the integrated modeling and control of fusion plasmas, including benchmarking and validation activities. Initial FSP research will focus on two critical areas: 1) the plasma edge and 2) whole device modeling including disruption avoidance. The first of these problems involves the narrow plasma boundary layer and its complex interactions with the plasma core and the surrounding material wall. The second requires development of a computationally tractable, but comprehensive model that describes all equilibrium and dynamic processes at a sufficient level of detail to provide useful prediction of the temporal evolution of fusion plasma experiments. The initial driver for the whole device model (WDM) will be prediction and avoidance of discharge-terminating disruptions, especially at high performance, which are a critical impediment to successful operation of machines like ITER. If disruptions prove unable to be avoided, their associated dynamics and effects will be addressed in the next phase of the FSP. The FSP plan targets the needed modeling capabilities by developing Integrated Science Applications (ISAs) specific to their needs. The Pedestal-Boundary model will include boundary magnetic topology, cross-field transport of multi-species plasmas, parallel plasma transport, neutral transport, atomic physics and interactions with the plasma wall. It will address the origins and structure of the plasma electric field, rotation, the L-H transition, and the wide variety of pedestal relaxation mechanisms. The Whole Device Model will predict the entire discharge evolution given external actuators (i.e., magnets, power supplies, heating, current drive and fueling systems) and control strategies. Based on components operating over a range of physics fidelity, the WDM will model the plasma equilibrium, plasma sources, profile evolution, linear stability and nonlinear evolution toward a disruption (but not the full disruption dynamics). The plan assumes that, as the FSP matures and demonstrates success, the program will evolve and grow, enabling additional science problems to be addressed. The next set of integration opportunities could include: 1) Simulation of disruption dynamics and their effects; 2) Prediction of core profile including 3D effects, mesoscale dynamics and integration with the edge plasma; 3) Computation of non-thermal particle distributions, self-consistent with fusion, radio frequency (RF) and neutral beam injection (NBI) sources, magnetohydrodynamics (MHD) and short-wavelength turbulence.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

Optimal control system design of an acid gas removal unit for an IGCC power plants with CO2 capture

Future IGCC plants with CO{sub 2} capture should be operated optimally in the face of disturbances without violating operational and environmental constraints. To achieve this goal, a systematic approach is taken in this work to design the control system of a selective, dual-stage Selexol-based acid gas removal (AGR) unit for a commercial-scale integrated gasification combined cycle (IGCC) power plant with pre-combustion CO{sub 2} capture. The control system design is performed in two stages with the objective of minimizing the auxiliary power while satisfying operational and environmental constraints in the presence of measured and unmeasured disturbances. In the first stage of the control system design, a top-down analysis is used to analyze degrees of freedom, define an operational objective, identify important disturbances and operational/environmental constraints, and select the control variables. With the degrees of freedom, the process is optimized with relation to the operational objective at nominal operation as well as under the disturbances identified. Operational and environmental constraints active at all operations are chosen as control variables. From the results of the optimization studies, self-optimizing control variables are identified for further examination. Several methods are explored in this work for the selection of these self-optimizing control variables. Modifications made to the existing methods will be discussed in this presentation. Due to the very large number of candidate sets available for control variables and due to the complexity of the underlying optimization problem, solution of this problem is computationally expensive. For reducing the computation time, parallel computing is performed using the Distributed Computing Server (DCS®) and the Parallel Computing® toolbox from Mathworks®. The second stage is a bottom-up design of the control layers used for the operation of the process. First, the regulatory control layer is designed followed by the supervisory control layer. Finally, an optimization layer is designed. In this paper, the proposed two-stage control system design approach is applied to the AGR unit for an IGCC power plant with CO{sub 2} capture. Aspen Plus Dynamics® is used to develop the dynamic AGR process model while MATLAB is used to perform the control system design and for implementation of model predictive control (MPC).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Predicted Impacts of Pt and Ionomer Distributions on Low-Pt-Loaded PEMFC Performance

Low-cost, high performance proton exchange membrane fuel cells (PEMFCs) have been difficult to develop due to limited understanding of coupled processes in the cathode catalyst layer (CCL). Low-Pt-loaded PEMFCs suffer losses beyond those predicted solely due to reduced catalyst area. Although consensus links these losses to thin ionomer films in the CCL, a precise mechanistic explanation remains elusive. In this publication, we present a physically based PEMFC model with novel structure-property relationships for thin-film Nafion, validated against PEMFC data with low Pt loading. Results suggest that flooding exacerbates kinetic limitations in low-loaded PEMFCs, shifting the Faradaic current distribution. As current density increases, protons travel further into the CCL, resulting in higher Ohmic overpotentials. We also present a parametric study of CCL design parameters. We find that graded Pt and ionomer loadings reduce Ohmic losses and flooding, but individually do not provide significant improvements. However, a dual-graded CCL (i.e., graded Pt and ionomer) is predicted to significantly improve the maximum power density and limiting current compared to uniformly loaded CCLs. This work highlights the importance of accurate transport parameters for thin-film Nafion and provides a pathway to low-cost PEMFCs via precise control of CCL microstructures.

08 HYDROGEN↗