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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ensuring Safe, Effective, and Reliable Use of Artificial Intelligence-Based Applications for Nuclear Electricity Generation through a Systems Approach

There is a significant potential to reduce operating and maintenance cost at nuclear power plants using artificial intelligence (AI) and machine learning (ML). For instance, AI/ML has the potential to significant improve work management processes, condition reporting, and plant surveillance activities. However, the nuclear industry has been slow in adopting AI/ML due to several multifaceted barriers discussed in this paper. This work presents such multidisciplinary approach intended to 1) accelerate industry adoption of AI/ML-based applications at nuclear power plants and 2) ensure their safe, reliable, and effective use. This approach is discussed further in this work and will be used to address overarching challenges associated with AI deployment at scale to provide industry guidance that support accelerated adoption of AI/ML technologies throughout the industry.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Agnostic capture of pathogens for the detection and diagnostics of emerging threats

The continued emergence of pathogens, whether novel, re-emerging, or engineered, poses a persistent global biosecurity and public health challenge. Recent outbreaks, including COVID-19, Lassa fever, Marburg virus, mpox, and avian influenza, underscore the urgent need for robust systems that enable rapid surveillance, early diagnosis, and timely countermeasures before widespread human transmission occurs. In this article, we focus on early detection technologies and systematically evaluate current diagnostic and sensing modalities. We highlight sequencing and spectroscopy as two complementary approaches capable of providing broad, agnostic detection and rich biological insight. Our analysis emphasizes that scientific innovation alone is insufficient: effective preparedness also requires improved data curation, integration, and sharing to build AI-ready resources that accelerate future responses. We argue for coordinated advances in both technological capabilities and supporting infrastructure to enable the rapid identification and characterization of emerging pathogens and to fully leverage modern science against evolving infectious threats.

Environmental health↗
DOE OSTI2021

Lithium, a path to make fusion energy affordable

In this tutorial article, we review the technological, physics, and economic basis for a magnetic fusion device utilizing a flowing liquid lithium divertor (molten metal velocity in the range of cm/s) and operating in a low-recycling plasma regime. When extrapolated to magnetic fusion reactor scale, the observed effects of a liquid lithium boundary on recycling reduction, confinement increase, and anomalous heat transport mitigation may offer a fundamentally distinct and promising alternative route to fusion energy production. In addition, this lithium-driven low recycling regime could accelerate fusion’s commercial viability since such a device would be smaller, dramatically decreasing plant and electricity costs if all technological complexities are solved. First, the theoretical basis of the energy confinement and fusion performance as well as the related possibilities of low recycling regimes driven by flowing lithium plasma-facing components are reviewed. Then the paper emphasizes the technological obstacles that need to be overcome for developing the necessary systems for such a flowing liquid lithium solution at reactor scale and details how many of these have been overcome at laboratory and/or proof-of-concept scale. Finally, the current and planned scientific and engineering endeavors being performed at the University of Illinois at Urbana-Champaign regarding this alternative reactor option are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Verification of diesel spray ignition phenomenon in dual-fuel diesel-piloted premixed natural gas engine

Dual-fuel (DF) engines, in which premixed natural gas and air in an open-type combustion chamber is ignited by diesel-fuel pilot sprays, have been more popular for marine use than pre-chamber spark ignition (PCSI) engines because of their superior durability. However, control of ignition and combustion in DF engines is more difficult than in PCSI engines. In this context, here we focus on the ignition stability of n-heptane pilot-fuel jets injected into a compressed premixed charge of natural gas and air at low-load conditions. To aid understanding of the experimental data, chemical-kinetics simulations were carried out in a simplified engine-environment that provided insight into the chemical effects of methane (CH4) on pilot-fuel ignition. The simulations reveal that CH4 has an effect on both stages of n-heptane autoignition: the small, first-stage, cool-flame-type, low-temperature ignition (LTI) and the larger, second-stage, high-temperature ignition (HTI). As the ratio of pilot-fuel to CH4 entrained into the spray decreases, the initial oxidization of CH4 consumes the OH radicals produced by pilot-fuel decomposition during LTI, thereby inhibiting its progression to HTI. Using imaging diagnostics, the spatial and temporal progression of LTI and HTI in DF combustion are measured in a heavy-duty optical engine, and the imaging data are analyzed to understand the cause of severe fluctuations in ignition timing and combustion completeness at low-load conditions. Images of cool-flame and hydroxyl radical (OH*) chemiluminescence serve as indicators of LTI and HTI, respectively. The cycle-to-cycle and spatial variation in ignition extracted from the imaging data are used as key metrics of comparison. The imaging data indicate that the local concentration of the pilot-fuel and the richness of the surrounding natural-gas air mixture are important for LTI and HTI, but in different ways. In particular, higher injection pressures and shorter injection durations increase the mixing rate, leading to lower concentrations of pilot-fuel more quickly, which can inhibit HTI even as LTI remains relatively robust. Decreasing the injection pressure from 80 MPa to 40 MPa and increasing the injection duration from 500 µs to 760 µs maintained constant pilot-fuel mass, while promoting robust transition from LTI to HTI by effectively slowing the mixing rate. This allows enough residence time for the OH radicals, produced by the two-stage ignition chemistry of the pilot-fuel, to accelerate the transition from LTI to HTI before being consumed by CH4 oxidation. Thus from a practical perspective, for a premixed natural gas fuel–air equivalence-ratio, it is possible to improve the “stability” of the combustion process by solely manipulating the pilot-fuel injection parameters while maintaining constant mass of injected pilot-fuel. This allows for tailoring mixing trajectories to offset changes in fuel ignition chemistry, so as to promote a robust transition from LTI to HTI by changing the balance between the local concentration of the pilot-fuel and richness of the premixed natural gas and air. This could prove to be a valuable tool for combustion design to improve fuel efficiency or reduce noise or perhaps even reduce heat-transfer losses by locating early combustion away from in-cylinder walls.

42 ENGINEERING↗
DOE OSTI2024

Mechanisms of electromagnetic field control on mineral scaling in brackish water reverse osmosis: Combined homogenous and heterogeneous nucleation

Electromagnetic field (EMF) treatment has emerged as a promising approach for scaling control due to its cost-effectiveness, simplicity, and low energy consumption. However, there is a limited understanding of the mechanisms by which applied EMF impacts mineral scaling in reverse osmosis (RO) systems. This has led to inconclusive and varied results and uncertainties regarding its effectiveness. This study elucidates the impacts of EMF on homogenous and heterogeneous nucleation and membrane performance during RO desalination of different feedwaters. Our results reveal that EMF exhibits greater efficacy in treating near-saturated water (SI∼0), especially when coupled with extended hydraulic flushing (HF). For saturated brackish water desalination, heterogeneous scaling predominantly occurs on membrane surfaces, with the effectiveness of EMF in inhibiting scaling primarily attributed to the hydration effect. In supersaturated solutions, EMF promotes bulk precipitation due to the magnetohydrodynamic effect, quickly blocking membrane pores. Thus, when the saturation reaches a certain high level during RO desalination, magnetohydrodynamic EMF effects can accelerate flux decline caused by homogeneous scaling. In conclusion, this work provides an efficient method for predicting EMF efficiency, emphasizing the importance of saturation conditions and HF cleaning duration in determining membrane performance, suggesting these show promise for improving undersaturated or near-saturated feedwater desalination via RO.

42 ENGINEERING↗
DOE OSTI2026

Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning

Machine learning (ML) is transforming materials research, yet potential for biopolymer discovery remains constrained by fragmented data and nonstandardized reporting. Biopolymers differ significantly from synthetic polymers, requiring specialized approaches to represent their biosynthetic origins, hierarchical structures, and application-specific metrics. In this Perspective, we identify three core challenges limiting biopolymer representation: information encoding, data quality, and data sharing. We describe the most pressing issues and propose commensurate approaches to address each key challenge. Recommendations include the design and adoption of biopolymer-specific fingerprinting and representation frameworks, development of hybrid human-large language model (LLM) data extraction strategies, and expanding Findable, Accessible, Interoperable, Reusable (FAIR)-compliant repositories. We propose a robust foundation to define interoperable, high-quality data sets that capture the full context of biopolymer materials. Standardized metadata, shared ontologies, and community-driven infrastructure would enable scalable, reproducible workflows and accelerate the ML-driven development of biopolymers.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Geochemical and Hydromechanical Stimulation for Reaction Acceleration (GeoHydRA) of Serpentinization for In Situ Hydrogen Production

The project team aims to develop an innovative approach to stimulate geological hydrogen generation by enhancing in-situ serpentinization of ultramafic rocks. This method can maximize hydrogen production by promoting reactive surface areas through controlled fracturing and optimizing fluid chemistry and flow rates. Key objectives include establishing optimal conditions for hydrogen generation, investigating subsurface limitations, and sustaining production via hierarchical crack systems.

08 HYDROGEN↗
DOE OSTI2022

Hypothesis testing via AI: Generating physically interpretable models of scientific data with machine learning (Full Technical Report)

Deep learning has demonstrated an exceptional ability to solve complex tasks (an engineering success); however, it has done so at the expense of the ability to generate new knowledge (a scientific failure). We propose an alternative framework—entitled Deep Symbolic Regression (DSR)—in which artificial neural networks (NNs) rapidly generate hypotheses about physical relationships among inputs. This framework bypasses the need to interpret an NN altogether, while still leveraging the representational power of deep learning. The resulting models are tractable mathematical expressions, which are inherently and readily human interpretable and can provide insights into underlying physical phenomena. Further, we fold this methodology into the scientific process by allowing the scientist to directly integrate a priori knowledge and beliefs to accelerate learning. We demonstrate this methodology on symbolic regression—the problem of rediscovering underlying expressions describing a dataset—and achieve state-of-the-art performance across a wide variety of symbolic regression problems. Further, we generalize our DSR framework to apply to the more general class of symbolic optimization problems, in which one seeks to optimize a sequence of symbols or “tokens” under a black-box reward function. Examples of other symbolic optimization problems include neural architecture search and computational antibody design. Our generalized tool, Deep Symbolic Optimization (DSO), has been demonstrated on the task of learning symbolic control policies for reinforcement learning environments, and has been adopted as an enabling capability for computational antibody design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A modular approach for high-flux lactic acid production from methane in an industrial medium using engineered Methylomicrobium buryatense 5GB1

Abstract Convergence of market drivers such as abundant availability of inexpensive natural gas and increasing awareness of its global warming effects have created new opportunities for the development of small-scale gas-to-liquid (GTL) conversion technologies that can efficiently utilize methane, the primary component of natural gas. Leveraging the unique ability of methanotrophs that use methane as carbon and energy source, biological GTL platforms can be envisioned that are readily deployable at remote petroleum drilling sites where large chemical GTL infrastructure is uneconomical to set-up. Methylomicrobium buryatense, an obligate methanotroph, has gained traction as a potential industrial methanotrophic host because of availability of genetic tools and recent advances in its metabolic engineering. However, progress is impeded by low strain performance and lack of an industrial medium. In this study, we first established a small-scale cultivation platform using Hungate tubes for growth of M. buryatense at medium-to-high-throughput that also enabled 2X faster growth compared to that obtained in traditional glass serum bottles. Then, employing a synthetic biology approach we engineered M. buryatense with varying promoter (inducible and constitutive) and ribosome-binding site combinations, and obtained a strain capable of producing l-lactate from methane at a flux 14-fold higher than previously reported. Finally, we demonstrated l-lactate production in an industrial medium by replacing nitrate with less-expensive ammonium as the nitrogen source. Under these conditions, l-lactate was synthesized at a flux approximately 50-fold higher than that reported previously in a bioreactor system while achieving a titer of 0.6 g/L. These findings position M. buryatense closer to becoming an industrial host strain of choice, and pave new avenues for accelerating methane-to-chemical conversion using synthetic biology.

Garg, Shivani↗
DOE OSTI2022

A Framework for Closed-Loop Optimization of an Automated Mechanical Serial-Sectioning System via Run-to-Run Control as Applied to a Robo-Met.3D

Optimization of automated data collection is gaining increased interest for the purposes of enabling closed-loop self-correcting systems that inherently maximize operational efficiencies and reduce waste. Many data collection systems have several variables which influence data accuracy or consistency and which can require frequent user interaction to be monitored and maintained. Operating upon a Robo-MET.3D™ automated mechanical serial-sectioning system, a run-to-run control algorithm has been developed to accelerate data collection and reduce data inconsistency. Here, using historical data amassed over a decade of experiments, a linear regression model of the deterministic system dynamics is created and used to employ a run-to-run control algorithm that optimizes selected system inputs to reduce operator intervention and increase efficacy while reducing variance of system output.

42 ENGINEERING↗
DOE OSTI2025

ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers

We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mechanical tunneling based threshold-annealing process. The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing dynamics onto a network of integrate-and-fire neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN annealer and the resulting spiking dynamics replicates the optimal escape mechanism and convergence of SA, particularly at low-temperatures. To validate the effectiveness of our neuromorphic Ising machine, we systematically solved benchmark combinatorial optimization problems such as MAX-CUT and Max Independent Set. Across multiple runs, NeuroSA consistently generates distribution of solutions that are concentrated around the state-of-the-art results (within 99%) or surpass the current state-of-the-art solutions for Max Independent Set benchmarks. Furthermore, NeuroSA is able to achieve these superior distributions without any graph-specific hyperparameter tuning. For practical illustration, we present results from an implementation of NeuroSA on the SpiNNaker2 platform, highlighting the feasibility of mapping our proposed architecture onto a standard neuromorphic accelerator platform.

42 ENGINEERING↗
DOE OSTI2017

Renewable Firming EnergyFarm Final Report

The American Recovery and Reinvestment Act (ARRA) of 2009 (Recovery Act) provided the U.S. Department of Energy (DOE) with funds to modernize the electric power grid. One program under this initiative is the Smart Grid Demonstration program (SGDP). The SGDP mandate is to demonstrate how a suite of existing and emerging smart grid technologies can be innovatively applied and integrated to prove technical, operational, and business-model feasibility. Primus Power is a provider of low cost, long life and long duration energy storage systems. The Company’s flow batteries are shipping to US and international microgrid, utility, military, commercial and industrial customers. Primus Power’s EnergyPod® is a modular battery system for grid scale applications available in configurations ranging from 25 kW to more than 25 MW. The EnergyPod provides nameplate power for 5 hours. This long duration unlocks economic benefits on both sides of the electric meter. It allows commercial and industrial customers to shift low cost electricity purchased at night to offset afternoon electrical peaks to reduce utility demand charges. It also allows utilities to economically reduce power peaks and defer costly upgrades to distribution infrastructure. An EnergyPod contains one or more EnergyCells-a highly engineered flow battery core made from low cost, readily available materials. An EnergyCell includes a membrane-free stack of titanium electrodes located above a novel liquid electrolyte management system. This patented design enables reliable, low maintenance operation for decades. It is safe and robust, featuring non-flammable aqueous electrolyte, sophisticated fault detection and built-in secondary containment. Unlike Li Ion batteries, the EnergyCell is not susceptible to thermal runaway. This cooperative agreement project was started in Feb 2010. The objectives of the project are: 1. Trigger rapid adoption of grid storage systems in the US by demonstrating a low cost, robust and flexible EnergyFarm®. 2. Accelerate adoption of renewable energy and enhance grid stability by firming the output of wind & solar farms. 3. Demonstrate improved grid asset utilization by storing energy during off-peak periods for dispatch during local load peaks. 4. Establish an advanced battery manufacturing industry in the U.S. 5. Reduce CO2 emissions from utilities. This report summarizes the key milestones, data, results and lessons learned from the project. The desired goals and benefits of the cooperative agreement with the DOE have all been achieved. The project has contributed to reducing power costs, accelerating adoption of renewable energy resources, reducing greenhouse gas emissions and establishing advanced battery manufacturing in the U.S. The Recovery Act funds provided thru the DOE have been leveraged multiple times by additional private equity investment. Primus Power continues to ship low cost, long life and long duration EnergyPod® flow battery systems to utilities, commercial/industrial, microgrid and data center customers. After the conclusion of this project, Primus Power has modified the EnergyPod® design to optimize around energy performance. Primus Power has moved to a prefabricated enclosure instead of multiple EnergyCells in a container. This lowers capital and maintenance costs and can optimize site design. Utilities are starting to adopt energy storage for a variety of functions. The market will grow as the technology is proven and profitable applications expand.

25 ENERGY STORAGE↗
DOE OSTI2015

Development of Modified Pag (Polyalkylene Glycol) High VI High Fuel Efficient Lubricant for LDV Applications

Engine oils play a critical role in friction reduction. Improvements in engine oil technology steadily improved fuel economy as the industry moved through ILSAC GF-1 to GF-5 specifications. These improvements were influenced by changes in base oil chemistry, development of new friction modifiers and their treat levels, and the total additive package consisting of various other components. However, the improvements are incremental and further fuel consumption reduction opportunities are becoming more challenging. Polyalkylene glycol (PAG) based engine oils are being explored as a step forward for significant fuel consumption reduction. Although PAG fluids are used in many industrial applications, its application as an engine oil has been explored in a limited way. The objective of this project is to deep dive in exploring the applicability of PAG technology in engine oil, understanding the benefits, and limitations, elucidating the mechanism(s) for friction benefits, if any, and finally recommending how to address any limitations. The project was designed in four steps, starting with selection of lubricant technology, followed by friction and wear evaluations in laboratory bench tests which are relatively simple and inexpensive and also served as a screener for further evaluation. Selected formulations were chosen for more complex engine component level tests i.e., motored valvetrain friction and wear, piston ring friction using a motored single cylinder, and motored engine tests. A couple of formulations were further selected based on component level tests for engine dyno tests i.e., Sequence VID (ASTM D6709) for fuel economy, Sequence IVA (ASTM D6891) for valvetrain wear, and Sequence VG (ASTM D6593) for sludge and varnish protection. These are some of the industry standard tests required for qualifying engine oils. Out of these tests, a single PAG oil was selected for chassis roll dynamometer tests for fuel economy and emission measurements using FTP (Federal Test Procedure) metro/highway cycles. Five different PAG chemistries were selected by varying the starting alcohol, the oxide monomers (ethylene oxide, propylene oxide, or butylene oxide), capped or uncapped, homopolymer or random copolymer. All formulations contained a proprietary additive package and one which contained additional antiwear and friction modifier additives. Laboratory bench tests (Pin-on-Disk, High Frequency Reciprocating Rig (HFRR), Block-on-Ring, Mini-Traction Machine (MTM) identified formulations having friction, wear, and load carrying characteristics similar to or better than baseline GF-5 SAE 5W-20 oil. Motored valvetrain and motored piston ring friction tests showed nearly 50% friction reduction for some of the PAG formulations compared to GF-5 SAE 5W-20 oil. Motored engine tests showed up to 15% friction benefit over GF-5 SAE 5W-20 oil. It was observed that friction benefits are more related to PAG base oil chemistry than their lower viscosity compared to GF-5 SAE 5W-20 oil. Analysis of wear surfaces from laboratory bench tests and bucket tappets from motored valvetrain tests confirmed the presence of PAG molecules. The adsorption of these polar molecules is believed to be reason for friction reduction. However, the wear surfaces also had thin tribo-film derived from additive components. The tribo-film consisting of phosphates, sulfides, and molybdenum disulfide (when molybdenum additive was present) were observed for both GF-5 SAE 5W-20 and PAG fluids. However, when using PAG fluids, motored valvetrain tests showed high initial wear, which is believed to be due to delay in protective tribo-film formation. After the initial wear, the wear rate of PAG fluids was comparable to GF-5 SAE 5W-20 oil. The PAG oil containing additional antiwear and friction reducing additives showed low initial wear as expected. However, when this oil was evaluated in Sequence IVA test, it showed initially low wear comparable to GF-5 oil but wear accelerated with oil aging indicating rapid deterioration of additive components. ASTM Sequence VG test showed good sludge protection capability but failed to meet varnish rating for GF-5 requirement. Chassis roll dynamometer tests with PAG oil 15-1 showed about 1% fuel economy benefit over GF-5 SAE 5W-20 oil in EPA city cycles only and when the oil was slightly aged (500 miles). No fuel economy benefits could be observed in combined EPA metro/highway cycles. Also, no fuel economy benefit could be observed with continued (500- 10000 miles) oil aging. However, the emission level was comparable to the reference oil and was within EPA limits. Analysis of the PAG oil following tests showed low iron content although additive components were significantly degraded. The results indicate that PAG fluids have significant friction reduction potential but there are challenges with wear and varnish protection capabilities. These limitations are primarily because the selected additive components were chosen to provide a fluid with no metal content that forms little or no sulphated ash. Significant development work is needed to identify additive components compatible with PAG chemistry including their solubility in PAG oil. Miscibility of PAG fluids with mineral base oil is another challenge for oil change service. There is PAG chemistry (oil soluble PAG, OSP) which is soluble in mineral oils but the formulation explored in this investigation did not show significant friction reduction in motored engine tests. Again, highlighting the need for additive development for specific PAG chemistry. The thermal oxidation behavior of these oils has not been explored in this investigation and needs attention.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Designing and prototyping extensions to the Message Passing Interface in MPICH

As HPC system architectures and the applications running on them continue to evolve, the MPI standard itself must evolve. The trend in current and future HPC systems toward powerful nodes with multiple CPU cores and multiple GPU accelerators makes efficient support for hybrid programming critical for applications to achieve high performance. However, the support for hybrid programming in the MPI standard has not kept up with recent trends. The MPICH implementation of MPI provides a platform for implementing and experimenting with new proposals and extensions to fill this gap and to gain valuable experience and feedback before the MPI Forum can consider them for standardization. Here, in this work, we detail six extensions implemented in MPICH to increase MPI interoperability with other runtimes, with a specific focus on heterogeneous architectures. First, the extension to MPI generalized requests lets applications integrate asynchronous tasks into MPI’s progress engine. Second, the iovec extension to datatypes lets applications use MPI datatypes as a general-purpose data layout API beyond just MPI communications. Third, a new MPI object, MPIX_Stream, can be used by applications to identify execution contexts beyond MPI processes, including threads and GPU streams. MPIX stream communicators can be created to make existing MPI functions thread-aware and GPU-aware, thus providing applications with explicit ways to achieve higher performance. Fourth, MPIX Streams are extended to support the enqueue semantics for offloading MPI communications onto a GPU stream context. Fifth, thread communicators allow MPI communicators to be constructed with individual threads, thus providing a new level of interoperability between MPI and on-node runtimes such as OpenMP. Lastly, we present an extension to invoke MPI progress, which lets users spawn progress threads with fine-grained control to adapt the communication performance to their application designs. We describe the design and implementation of these extensions, provide usage examples, and highlight their expected benefits with performance results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

LDRD-DR: Accelerating Nuclear Fuel Qualification through Integrated Multiscale and Multiphysics Models [Slides]

Nuclear fuel qualification implies ensuring that a new fuel can safely operate in a reactor under both normal and accident conditions. Historically, extensive irradiation campaigns probe fuel performance relationships and interconnections in an integral sense, which is very time-consuming and costly. The presentation focus is on introducing advanced modeling and simulation into the qualification process in order to reduce the qualification timeline by targeting fewer integral tests

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Ionomer-free and recyclable porous-transport electrode for high-performing proton-exchange-membrane water electrolysis

Abstract Clean hydrogen production requires large-scale deployment of water-electrolysis technologies, particularly proton-exchange-membrane water electrolyzers (PEMWEs). However, as iridium-based electrocatalysts remain the only practical option for PEMWEs, their low abundance will become a bottleneck for a sustainable hydrogen economy. Herein, we propose high-performing and durable ionomer-free porous transport electrodes (PTEs) with facile recycling features enabling Ir thrifting and reclamation. The ionomer-free porous transport electrodes offer a practical pathway to investigate the role of ionomer in the catalyst layer and, from microelectrode measurements, point to an ionomer poisoning effect for the oxygen evolution reaction. The ionomer-free porous transport electrodes demonstrate a voltage reduction of > 600 mV compared to conventional ionomer-coated porous transport electrodes at 1.8 A cm −2 and <0.1 mg Ir cm −2 , and a voltage degradation of 29 mV at average rate of 0.58 mV per 1000-cycles after 50k cycles of accelerated-stress tests at 4 A cm −2 . Moreover, the ionomer-free feature enables facile recycling of multiple components of PEMWEs, which is critical to a circular clean hydrogen economy.

08 HYDROGEN↗
DOE OSTI2018

Geometric Analysis Based Double Closed-loop Iterative Learning Control of Output PDF Shaping of Fiber Length Distribution in Refining Process

In order to improve the pulp quality and to reduce the energy consumption, the fiber length distribution (FLD) is generally employed as one of the important technological indexes in the refining process. Considering that the traditional mean and variance of fiber length are unable to adequately characterize the non-Gaussian distribution properties, this paper proposes a novel geometric analysis based double closed-loop iterative learning control (ILC) method for probability density function (PDF) shaping of output FLD in the refining process. Primarily, a RBF neural network (NN) with Gaussian-type is utilized to approximate the square root PDF in the inner loop, where the RBF basis function parameters (center and width) are tuned between any two adjacent batches by using an ILC law, and the subspace identification method can be applied to establish the state-space model of weight vector. Then, for the sake of accelerating the convergence rate of the closed-loop sysyem, a geometric analysis based ILC method is adopted in the outer loop. Lastly, both simulation and experiments demonstrate the effectiveness and practicability of the proposed approach.

42 ENGINEERING↗
DOE OSTI2016

Machining of Two-Dimensional Sinusoidal Defects on Ignition-Type Capsules to Study Hydrodynamic Instability at the National Ignition Facility

Hydrodynamic instability growth and its effects on capsule implosion performance are being studied at the National Ignition Facility (NIF). Experimental results have shown that low-mode instabilities are the primary culprit for yield degradation. Ignition type capsules with machined 2D sinusoidal defects were used to measure low-mode hydrodynamic instability growth in the acceleration phase of the capsule implosion. The capsules were imploded using ignition-relevant laser pulses and the ablation-front modulation growth was measured using x-ray radiography. The experimentally measured growth was in good agreement with simulations.

42 ENGINEERING↗