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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Design of a graphical user interface for few-shot machine learning classification of electron microscopy data

The recent growth in data generation by modern electron microscopes requires rapid, scalable, and flexible approaches to image segmentation and analysis. Few-shot machine learning, which can richly classify images from a handful of user-provided examples, is a promising route to high-throughput analysis. However, current command-line implementations of such approaches can be slow and unintuitive to use, lacking the real-time feedback necessary to perform effective classification. Here we report on the development of a Python-based graphical user interface that enables end users to easily conduct and visualize the output of few-shot learning models. This interface is portable and can be hosted locally or on the web, providing the opportunity to reproducibly conduct, share, and crowd-source few-shot analyses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The impact of hot-press conditions on the durability of polymer electrolyte membrane fuel cells

The proton exchange membrane integrity can be compromised during hot-press fabrication of membrane electrode assemblies (MEAs) causing premature cell failures during operation. In this work, infrared (IR) thermography was used as a diagnostic tool to spatially visualize hydrogen (H 2 ) crossover and identify process-induced-membrane irregularities (PIMs). These irregularities were identified as seed locations for MEA failures. Fine tuning of hot-press conditions was used to mitigate premature cell failures informed by accelerated stress testing (AST). The impact of PIMs on the initial performance, high-frequency resistances, open-circuit voltage, and H 2 crossover are reported. Nafion XL and 212 membranes, hot-pressed with a force of 16 kg/cm 2 and temperature of 120°C, were found to be consistently irregularity-free. Irregularity-free MEAs using Nafion 211, 212, and XL membranes demonstrated AST lifetime improvements of 58, 64 and 400%, respectively, compared to those fabricated with non-optimized conditions. In conclusion, this work highlights the importance of fabrication parameters on premature cell failures.

08 HYDROGEN↗
DOE OSTI2020

Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials

Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. Finally, the ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Dissolution of Pt during Oxygen Reduction Reaction Produces Pt Nanoparticles

The loss of Pt during the oxygen reduction reaction (ORR) affects the performance and economic viability of fuel cells and sensors. Our group previously observed the dissolution of Pt nanoelectrodes at moderately negative potentials during the ORR. Here we report a more detailed study of this process and identify its product. The nanoporous Pt surface formed during the ORR was visualized by AFM and high-resolution SEM, which also showed ~5 nm sized Pt particles on the glass surface surrounding the electrode. Furthermore, the release of these nanoparticles into the solution was confirmed by monitoring their catalytically amplified collisions with a Hg-coated microelectrode used as the tip in the scanning electrochemical microscope (SECM).

36 MATERIALS SCIENCE↗
DOE OSTI2018

Top Efficacy Performers: An Investigation into High-Achieving LED Luminaires

This report documents a DOE study that evaluated the performance of several LED lighting products with claimed efficacies near or exceeding 200 lm/W. The purpose: to validate the claimed efficacy, determine the features or characteristics the products had in common, and identify any performance tradeoffs in luminaires reaching this very high efficacy level. All of the products were industrial luminaires with CCTs of 5000K, and all but one had exposed LED emitters. Overall, photometric testing yielded values reasonably close to manufacturer’ claims, yet evaluations by knowledgeable observers highlighted some key considerations – such as the importance of visual comfort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Transportation Deployment; NREL (National Renewable Energy Laboratory)

Automakers, commercial fleet operators, component manufacturers, and government agencies all turn to the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) to help put more green vehicles on the road. The lab’s independent analysis and evaluation pinpoint fuel-efficient and low-emission strategies to support economic and operational goals, while breaking down barriers to widespread adoption. Customized assessment of existing equipment and practices, energy-saving alternatives, operational considerations, and marketplace realities factor in the multitude of variables needed to ensure meaningful performance, financial, and environmental benefits. NREL provides integrated, unbiased, 360-degree sustainable transportation deployment expertise encompassing alternative fuels, advanced vehicles, and related infrastructure. Hands-on support comes from technical experts experienced in advanced vehicle technologies, fleet operations, and field data collection coupled with extensive modeling and analysis capabilities. The lab’s research team works closely with automakers and vehicle equipment manufacturers to test, analyze, develop, and evaluate high-performance fuel-efficient technologies that meet marketplace needs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

The Sensor Dilemma in Intelligent Transportation Systems: Evaluating Radar, Lidar and Camera: Preprint

Intelligent transportation systems (ITS) are at the forefront in advancing the way we interact with and perceive the transportation network. This revolution is fueled by the significant advancement in sensor perception technologies such as radar, lidar, and video imaging, which are the most popular modalities for ITS. Real-time perception data from these sensors allow intelligent infrastructure-side decision-making to improve the energy, efficiency, and safety at traffic intersections. As traffic departments across the United States transition from traditional loop detectors and emulators and embrace newer technologies, they are often left with a dilemma in choosing a sensor technology for infrastructure-based perception that is reliable, inexpensive, and easy to set up and that has robust performance in varying weather conditions. However, choosing a sensor that checks all these boxes is not straightforward, as every sensor type has unique benefits and drawbacks. Radar is excellent at detecting long-range vehicles and weather resistance but lacks high resolution. Lidar is expensive and weather-sensitive, while cameras provide rich visual data at a low cost but are constrained by lighting and visibility. This study examines radar, lidar, and camera sensor capabilities to ascertain whether any of these qualifies as the "best" sensor for ITS perception. While no single sensor can meet all the demands of ITS, a hybrid approach combining multiple sensor modalities like radar, lidar, and cameras offers the most robust solution for enhancing the safety and efficiency of ITS. Through this evaluation, we hope to draw attention to the necessity of the National Renewable Energy Laboratory's infrastructure perception and control framework, which presents a multisensor track data fusion engine to assimilate multiple data streams in order to provide robust and reliable perception.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

A Practical Comparison of Data-Driven Prognostics Methods for Energy Systems

This study explores data-driven prognostics for nuclear power plant (NPP) condensers, focusing on tube fouling. We utilized the Asherah nuclear power plant simulator (ANS) to compare four methods: Random Forest (RF), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory Neural Network (LSTM). By simulating various fouling scenarios in the ANS, we generated data with different degradation rates under transient operations. The models were trained and tested on these data, with performance evaluated visually and numerically including uncertainty assessment. The LSTM model excelled, exhibiting minimal prediction noise and the most accurate remaining useful life estimates across all degradation levels. Its ability to capture long-term dependencies and produce cleaner outputs makes it a strong candidate, although accurate training data across the entire component lifespan are crucial. The RF model emerged as a robust alternative, providing reliable predictions with high confidence. The FCNN and SVR models, while less effective overall, showed potential under specific conditions. FCNN offers a less complex alternative to LSTM and might benefit from larger datasets. SVR excels in precision when the quality of the training data is high. Furthermore, this study highlights the operational benefits of advanced prognostics in the energy sector and emphasizes the need for further research in NPP condenser health management through real-life experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2011

Comparative imaging study in ultrasound, MRI, CT, and DSA using a multimodality renal artery phantom

Purpose: A range of anatomically realistic multimodality renal artery phantoms consisting of vessels with varying degrees of stenosis was developed and evaluated using four imaging techniques currently used to detect renal artery stenosis (RAS). The spatial resolution required to visualize vascular geometry and the velocity detection performance required to adequately characterize blood flow in patients suffering from RAS are currently ill-defined, with the result that no one imaging modality has emerged as a gold standard technique for screening for this disease. Methods: The phantoms, which contained a range of stenosis values (0%, 30%, 50%, 70%, and 85%), were designed for use with ultrasound, magnetic resonance imaging, x-ray computed tomography, and x-ray digital subtraction angiography. The construction materials used were optimized with respect to their ultrasonic speed of sound and attenuation coefficient, MR relaxometry (T{sub 1},T{sub 2}) properties, and Hounsfield number/x-ray attenuation coefficient, with a design capable of tolerating high-pressure pulsatile flow. Fiducial targets, incorporated into the phantoms to allow for registration of images among modalities, were chosen to minimize geometric distortions. Results: High quality distortion-free images of the phantoms with good contrast between vessel lumen, fiducial markers, and background tissue to visualize all stenoses were obtained with each modality. Quantitative assessments of the grade of stenosis revealed significant discrepancies between modalities, with each underestimating the stenosis severity for the higher-stenosed phantoms (70% and 85%) by up to 14%, with the greatest discrepancy attributable to DSA. Conclusions: The design and construction of a range of anatomically realistic renal artery phantoms containing varying degrees of stenosis is described. Images obtained using the main four diagnostic techniques used to detect RAS were free from artifacts and exhibited adequate contrast to allow for quantitative measurements of the degree of stenosis in each phantom. Such multimodality phantoms may prove useful in evaluating current and emerging US, MRI, CT, and DSA technology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Microscopic nuclear mass table with high-performance computing

We overview the methodology behind the large-scale mass table calculations based on the nuclear density functional theory (DFT) with Skyrme energy density functionals (EDFs). The calculations employing massively parallel computers are done in a large configuration space of an axially deformed harmonic oscillator. Nuclear mass tables tabulating global nuclear properties such as binding energies, radii, shape deformations, and pairing gaps are obtained for several Skyrme EDFs augmented by a mixed-type pairing functional, using an approximate particle number projection before variation. Specialty visualization tools have been developed to analyze the results. As an illustrative example of our current capabilities, we show some results for a wide range of even-even nuclei with Z ≤ 120 and N ≤ 300.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Combined Microwave Ablation and Cementoplasty in Patients with Painful Bone Metastases at High Risk of Fracture

PurposeTo retrospectively evaluate the effectiveness of computed tomography-guided percutaneous microwave ablation (MWA) and cementoplasty in patients with painful bone metastases at high risk of fracture.Materials and MethodsThirty-five patients with 37 metastatic bone lesions underwent computed tomography-guided MWA combined with cementoplasty (polymethylmethacrylate injection). Vertebrae, femur, and acetabulum were the intervention sites and the primary end point was pain relief. Pain severity was estimated by visual analog scale (VAS) before treatment; 1 week post-treatment; and 1, 6, and 12 months post-treatment. Functional outcome was assessed by improved patient walking ability. Radiological evaluation was performed at baseline and 3 and 12 months post-procedure.ResultsIn all patients, pain reduction occurred from the first week after treatment. The mean reduction in the VAS score was 84, 90, 90 % at week 1, month 1, and month 6, respectively. Improved walking ability occurred in 100 and 98 % of cases at the 1- and 6-month functional outcome evaluations, respectively. At the 1-year evaluation, 25 patients were alive, and 10 patients (28 %) had died because of widespread disease. The mean reduction in the VAS score and improvement in surviving patients’ walking ability were 90 and 100 %, respectively. No patients showed evidence of local tumor recurrence or progression and pathological fracture in the treated sites.ConclusionOur results suggest that MWA combined with osteoplasty is safe and effective when treating painful bone metastases at high risk of fracture. The number of surviving patients at the 1-year evaluation confirms the need for an effective and long-lasting treatment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

Development of a New Analysis Tool for Evaluating and Correcting for Weather Conditions that Constrain Radiation Portal Monitor Performance

Researchers at Oak Ridge National Laboratory (ORNL) developed the Adaptable, Multiplatform, Real-Time Analysis Package (AMRAP) for the continuous measurement of environmental radionuclide decay. AMRAP is a completely open source visualization and analysis package capable of combining a variety of data streams into an array of real-time plots. Once acquired, data streams are analyzed to store static images and extract data based on previously defined thresholds. AMRAP is currently used at ORNL to combine data streams from an Ortec Detective high-purity germanium (HPGe) detector, a TSA Systems radiation portal monitor (RPM), and an Orion weather station. The combined data are used to study the rain-induced increase in RPM background radiation levels. RPMs experience an increase in background radiation during precipitation due to the deposition of atmospheric radionuclides on the ground. Using AMRAP results in a real-time analysis workstation specifically dedicated to the study of RPM background radiation levels. By means of an editable library of common inputs, AMRAP is adaptable to remote monitoring applications that would benefit from the real-time visualization and analysis of radiation measurements. To study rain-induced increases in background radiation levels observed in radiation portal monitors (RPMs), researchers at Oak Ridge National Laboratory (ORNL) developed a software package that allows data with different formats to be analyzed and plotted in near real time. The Adaptable, Multiplatform, Real-Time Analysis Package (AMRAP) was developed to operate in the background and capture plots of important data based on previously defined thresholds. After executing AMRAP, segments of a data stream can be captured without additional post-processing. AMRAP can also display previously recorded data to facilitate a detailed offline analysis. Without access to these capabilities in a single software package, analyzing multiple continuously recorded data streams with different formats is impractical. Commercially available acquisition software packages record and analyze radiation measurements but are not designed to perform real-time analysis in conjunction with data from other vendors. The lack of collaboration between vendors is problematic when research requires different data streams to be correlated in time and immediately analyzed. AMRAP was specifically developed to provide a solution to this problem. AMRAP is a completely open source visualization and analysis package capable of plotting and analyzing data from different vendors in near real time.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Effects of sub‐mm cylindrical voids on detonation performance in PBX 9501

Abstract Internal features of varying scale and geometry are always present in explosives systems. Below a critical length, dependent on the explosive, these features can operate as a driving force for energy concentration and reaction, known as hotspots. At larger length scales, internal features result in jetting and detonation wave shaping, allowing for bulk work to be done by the explosive as is seen in shape charges. To date, a large volume of work has been performed to simulate hotspot ignition and large‐scale wave shaping. However, little experimental data exists on the effects of features in intermediate length scales, 0.1 to 1 millimeter. It has been observed in many tests that these small‐scale features can influence the high explosive (HE) performance and in some cases cause substantial damage to adjacent systems. This work provides quantitative data on the effects of machined voids moderately above more typical hotspot lengths, 0.3–0.8 mm in diameter, in PBX 9501 pressed to 1.785 g cm −3 ±2.5 mg cm −3 . Streak imaging was used to visualize void collapse, jet velocity, re‐initiation time, and wave shape evolution. Delay in detonation front propagation time was found to be linearly dependent on the void diameter and jet velocity was found to be independent within the tested range and resolution. Cut‐back experiments were used to investigate wave shape distortion and evolution downstream of the void, showing consistent growth and decay shapes across all void sizes. Simulations using CTH, a hydrocode by Sandia National Laboratory, were used to investigate void collapse showing agreement with the trends of experimental results but yielded inaccurate wave shape development delay values. Both simulation and experimental results identified several re‐initiation mechanisms with jetting and subsequent double shocking of localized HE being the dominant mechanisms for the void sizes that were studied.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2013

How an integrated change programme has accelerated the reduction in high hazard nuclear facilities at Sellafield

For over five decades the Sellafield Site has been central to the UK's nuclear programme. Now operated by Sellafield Ltd, under the management of Parent Body Organisation Nuclear Management Partners (NMP), a consortium of URS Washington Division, AMEC and AREVA is focussed on the decommissioning of historical facilities. When Decommissioning commenced in the late 1980's the site focus at that time was on commercial reprocessing and waste management. Now through the implementation of a company change programme, emphasis has shifted towards accelerated risk and hazard reduction of degraded legacy plants with nuclear inventory whilst ensuring value for money for the customer, the Nuclear Decommissioning Authority. This paper will describe the management success by the Site owners in delivering a successful change programme. The paper will explain how the site has transitioned to the INPO Standard Nuclear Performance Model (SNPM) and how through the use of a change maturity matrix has contributed to the accelerated reduction in high risk high hazard nuclear facilities. The paper will explain in detail how the Decommissioning Programme Office has facilitated and coordinated the Governance and assured delivery of the change plan and how successful application of visual management has aided the communication of its progress. Finally, the paper will discuss how the Delivery Schedules have proved critical for presenting the change plan to Key Stakeholders, Government Owners and Powerful Regulators. Overall, this paper provides an insight into how a massive change programme is being managed within one of the world's highest regulated industries. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Design of a Combined Proportional Integral Derivative Controller to Regulate the Temperature Inside a High-Temperature Tubular Solar Reactor

Solar fuels are proven to be promising candidates for thermochemical energy storage. However, the transient nature of solar radiation is an obstacle to maintaining a stable operational temperature inside a solar reactor. To overcome this challenge, the temperature of a solar reactor can be regulated by controlling the incoming solar radiation or the feedstock flowrate inside the reactor. In this work, a combined proportional integral derivative (PID) controller is implemented to regulate the temperature inside a high-temperature tubular solar reactor with counter-current flowing gas/particles. The control model incorporates two control systems to regulate incoming solar radiation and gas flow simultaneously. The design of the controller is based on a reduced-order numerical model of a high-temperature tubular solar reactor that is vertically oriented with an upward gas flow and downward particle flow. The reactor receives heat circumferentially through its wall over a finite segment of its length. Formulation of the heat transfer model is presented by applying the energy balance for the reactor tube and considering heat and mass transfer inside. A set of governing differential equations are solved numerically by using the finite volume method to obtain reactor wall, particles, and gas temperatures along the reactor length with various boundary conditions. Simulation results are used to tune the PID controller parameters by utilizing the Ziegler–Nichols tuning method. Both the simulation results and the controller performance are visualized on the LABVIEW platform. The controller is challenged to track different temperature setpoints with different scenarios of transient solar radiation. Here, the performance of the PID controller was compared to experimental results obtained from an industrial PID controller embedded in a 7 kW electric furnace. Results show that the combined PID controller is successful in maintaining a stable temperature inside the reactor by regulating the incoming solar radiation and the flowrate via small steady-state error and reasonable settling time and overshoot.

14 SOLAR ENERGY↗
DOE OSTI2023

PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management

The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Control of Ionomer Distribution and Porosity in Roll-to-Roll Coated Fuel Cell Catalyst Layers

As fuel cells transition from the laboratory to industrial production for vehicles and other applications there is a need to understand how manufacturing processes impact the properties and, ultimately, the performance of materials. Roll-to-roll coating enables processing at meters per second speeds with physics that can be quite different from common laboratory coating methods. In this work, we have focused on the coating of catalyst layers for proton exchange membrane fuel cells. Commonly, laboratory samples are prepared using spray coating or hand painting, which effectively coat many thin layers to achieve the desired catalyst layer thickness. This creates catalyst layers with uniform distributions of materials throughout the thickness of the full catalyst layer. In contrast, roll-to-roll methods, like slot die and gravure, coat the catalyst layer as a single wet film. As the film dries, ink constituents are able to segregate, leading to a heterogeneous distribution of materials through the thickness of the layer. It is also known that solvent and drying rate can affect the morphology of the catalyst layer. Thus, there is a need to understand how ink formulation and the drying process influence the distribution of materials and catalyst layer morphology. Here, we have explored the influence of solvent and drying rate on morphology of roll-to-roll coated catalyst layers. We show the development of the Kelvin probe method as a rapid screening technique for qualitative analysis of ionomer distribution. We also utilize nano X-ray computed tomography to visualize electrode structure and to quantify particle-size and pore-size distributions, thickness-dependent ionomer distribution, tortuosity, and effective transport properties. We find that solvent has a strong influence on ionomer distribution, with less of an effect on porosity. Conversely, drying temperature has a strong influence on porosity, but less influence on ionomer distribution. Finally, we utilize in situ fuel cell performance testing and other advanced diagnostics to quantify the impact of catalyst layer properties on fuel cell performance and demonstrate that roll-to-roll coating is capable of coating high performance catalyst layers in multi-meter lengths. have focused on the coating of catalyst layers for proton exchange membrane fuel cells. Commonly, laboratory samples are prepared using spray coating or hand painting, which effectively coat many thin layers to achieve the desired catalyst layer thickness. This creates catalyst layers with uniform distributions of materials throughout the thickness of the full catalyst layer. In contrast, roll-to-roll methods, like slot die and gravure, coat the catalyst layer as a single wet film. As the film dries, ink constituents are able to segregate, leading to a heterogeneous distribution of materials through the thickness of the layer. It is also known that solvent and drying rate can affect the morphology of the catalyst layer. Thus, there is a need to understand how ink formulation and the drying process influence the distribution of materials and catalyst layer morphology. Here, we have explored the influence of solvent and drying rate on morphology of roll-to-roll coated catalyst layers. We show the development of the Kelvin probe method as a rapid screening technique for qualitative analysis of ionomer distribution. We also utilize nano X-ray computed tomography to visualize electrode structure and to quantify particle-size and pore-size distributions, thickness-dependent ionomer distribution, tortuosity, and effective transport properties. We find that solvent has a strong influence on ionomer distribution, with less of an effect on porosity. Conversely, drying temperature has a strong influence on porosity, but less influence on ionomer distribution. Finally, we utilize in situ fuel cell performance testing and other advanced diagnostics to quantify the impact of catalyst layer properties on fuel cell performance and demonstrate that roll-to-roll coating is capable of coating high performance catalyst layers in multi-meter lengths.

30 DIRECT ENERGY CONVERSION↗