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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

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

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

biojet↗
DOE OSTI2024

Accelerating Multivariate Functional Approximation Computation with Domain Decomposition Techniques⋆

Modeling large datasets through Multivariate Functional Approximations (MFA) provide an elegant way to handle many visualization and scientific analysis workflows. The process necessitates scalable data partitioning methods to compute MFA representations efficiently without compromising the accuracy or continuity of the reconstructed solution. We propose a domain -decomposed method for computing the MFA with B -spline bases, which reduces the total work per task and uses a restricted Additive Schwarz (RAS) method to converge the control point data degrees -of -freedom along subdomain boundaries. We provide an in-depth analysis of the parallel approach with domain decomposition solvers, aiming to minimize local subdomain error residuals and recover high -order continuity at subdomain interfaces with appropriate choices of knot overlaps. The communication cost, determined by the overlap regions in the RAS implementation, is optimized to recover the numerical error profile of the single subdomain case. Our proposed method stands in contrast to previous methods, which typically only recover either C 0 or at best C 1 continuity for arbitrary B -spline degree expansions, or those that require post -processing to blend discontinuities in the reconstructed data. We demonstrate the effectiveness of our approach using analytical and real -world datasets in 1D, 2D, and 3D through both strong and weak scaling studies. The performance results indicate that the overall cost of computing the approximation is directly proportional to the underlying nearest -neighbor communication implementation, and is only weakly dependent on the overlap region size that determines the size of the messages. This finding underscores the efficiency and scalability of our proposed method, making it a promising solution for handling large datasets in scientific workflows.

additive Schwarz solvers↗
DOE OSTI2024

NIOLD Run 4 Report

In this report we discuss the motivation, methods, and analysis of the IOTA Run 4 studies of the Nonlinear Integrable Optics, Landau Damping (NIOLD) experiment. We introduce Landau Damping, an effect that damps collective instabilities in particle accelerators. We also introduce and discuss a new method to measure stability diagrams which quantify the strength of Landau Damping, that employs an antidamper. We also present the data collection process, the data analysis procedure, and the preliminary results. The first results qualitatively agree with the analytical predictions and the simulations. Next steps for the current data and goals for future data are also discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Are light curve classification metrics good proxies for SN Ia cosmological constraining power?

Context. When selecting a light curve classifier for use as part of a photometric supernova Ia (SN Ia) cosmological analysis, it is common to make decisions based on metrics of classification performance, such as the contamination within the photometrically classified SN Ia sample, rather than a measure of cosmological constraining power. If the former is an appropriate proxy for the latter, this practice would eliminate the computational expense of a full cosmology forecast in the analysis pipeline design process. Aims. This study tests the assumption that light curve classification metrics are an appropriate proxy for cosmology metrics. Methods. We emulated photometric SN Ia cosmology light curve samples with controlled contamination rates of individual contaminant classes and evaluated each of them under a set of classification metrics. We then derived cosmological parameter constraints from all samples under two common analysis approaches and quantified the impact of contamination by each contaminant class on the resulting cosmological parameter estimates. Results. We observe that cosmology metrics are sensitive to both the contamination rate and the class of the contaminating population, whereas the classification metrics are shown to be insensitive to the latter. Conclusions. Based on these findings, we discourage any exclusive reliance on light curve classification-based metrics for analysis design decisions, which (counterintuitively) include but are not limited to the classifier choice. Instead, we recommend optimising science analysis pipeline design choices using a metric of the information gained about the physical parameters of interest.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Digital Twin Approach to Study Additive Manufacturing Processing Using Embedded Optical Fiber Sensors and Numerical Modeling

One of the major challenges for metal-powder-based additive manufacturing is measuring and mitigating residual strain induced during the manufacturing processes. This article reports distributed fiber optic sensors embedded in Inconel alloy components as experimental means to validate numerical models of additive manufacturing process. Electroplating was used to deposit a metal protective jacket onto standard telecom single-mode fibers for strain measurements, Fiber sensors were embedded in an Inconel alloy substrate using the laser engineered net shaping (LENS) process. Here by using a Rayleigh-scattering optical frequency domain reflectometer (OFDR), temperature changes, and residual strain in the metal substrate were monitored with 5 mm spatial resolution during the LENS process. Using finite element analysis, temperature and strain profiles induced by the LENS deposition processes were also numerically studied. Discrepancies between the simulated temperature and strain profiles and those measured directly were less than 10%. Results presented in this article demonstrates a digital twin approach to fuse modeling results with distributed fiber sensor measurement data to study additive manufacturing process toward design and fabrication process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Framework for the Analysis of Compiler Optimizations

Compilers transform program source code to machine executable code. During this transformation, they perform a number of compiler optimizations to improve the performance of the generated executable code. Importantly, applying those optimizations depends on the source code structure, such as the parallel programming model used to parallelize an algorithm. Often, implementations of the same algorithm with different programming models have vastly different performance because the compiler optimized them differently. We create FAROS, a framework to structure and automate the analysis of compiler optimizations on programs. FAROS automates the building process, execution profiling, and analysis of compiler optimization of programs, through a configuration interface. It outputs compiler optimization reports to show which optimizations applied to which line of source code, leveraging compilation remarks output by the compiler. Also, FAROS supports benchmarking performance of different program versions by collecting execution time results. In this first release of FAROS, we provide a configuration file to analyze compiler optimization differences for sequential vs OpenMP compilation, including 38 programs consisting of HPC proxy/mini/large applications, and NAS and Rodina kernels for analysis.

Georgakoudis, Giorgis↗
DOE OSTI2024

Electrochemical leaching of spent LIBs: Kinetics, novel reactor, and modeling

The use of electrons as main reagent for the recovery and recycling of critical metals from spent lithium-ion batteries (LIBs) is a process electrification strategy that can be used to close the life-cycle loop of LIBs through more sustainable methods. Electrochemical leaching, a process that uses a reductant that is constantly regenerated electrochemically for the leaching of lithium-ion battery black mass (LIBBM), has shown high extraction efficiencies and sustainable scores. However, slow kinetics, reactor design challenges and lack of deeper understanding of the underlying processes are barriers to the optimization, scale-up, and market adoption of this technology. In this paper, a kinetic study and mathematical model for dissolving LIBBM is presented to better understand the underlying mechanisms aiming to reduce the processing time and make predictions for future design and scale-up. The effect of acid and electrochemically mediated reductant concentrations, LIBBM loading, and cathode/reactor designs were explored. As a result, the leaching time was reduced from 7h to under 1h at a pulp density of 73 g/L, without external heating. A novel reactor with parallel baffle electrodes (PBE) was developed, which significantly reduced the leaching time by improving convection in a stirred slurry electrochemical reactor. Dimensionless numbers were deduced from an unsteady state model, which can be used in dimensional analysis for future process design and scale-up.

25 ENERGY STORAGE↗
DOE OSTI2020

Impacts of advanced diesel combustion operation and fuel formulation on soot nanostructure and reactivity

Advanced diesel combustion, accomplished via a single pulse fuel injection and high levels of exhaust gas recirculation (referred to as “PCCI”, partially-premixed charge compression ignition), is shown to be a path to reduce oxides of nitrogen and particulate matter simultaneously. This is well established in the literature. Less established is the extent to which such dilute combustion processes influence soot formation and affect soot that is emitted from diesel engines under such combustion modes. This work focuses on characterization of the nanostructure and oxidative reactivity of soot generated by a light-duty turbodiesel engine operating under a PCCI combustion mode, a dilute, low-temperature combustion process. Previous work on a type of PCCI combustion, referred to as high-efficiency clean combustion (HECC), showed soot samples having a fullerenic nanostructure, characterized by high levels of tortuosity of the fringe layers as seen in transmission electron micrograph images and as quantified using an image processing algorithm. Thermogravimetric analysis of the HECC mode soot samples showed that they displayed higher rates of oxidation than soot samples from a conventional diesel combustion mode. The present work returns to PCCI combustion, considering the timing of the main fuel injection, and the effects of operating on fuels rich in n-alkanes, particularly a fuel produced from a low temperature Fischer-Tropsch process (LTFT) and a renewable diesel fuel (RD) produced via hydrodeoxygenation of a plant oil. PCCI combustion conditions yield soot that shows higher reactivity compared to soot from the conventional combustion mode regardless of fuel type. LTFT and RD fuels produce soots with lower reactivity compared to ULSD. Soots produced from PCCI combustion have higher surface oxygen concentration and higher proportion of amorphous carbon. In addition, TEM images show that PCCI soots from all three fuels have smaller primary particle and particle aggregate sizes, and smaller graphene layers. These properties explain the higher reactivity of soot from PCCI combustion. The less reactive soots, which are produced from LTFT and RD fuel under conventional combustion, show internal burning during oxidation. However, soots with higher reactivity which are produced from late injection PCCI combustion and ULSD show shrinking core oxidation, most likely because of their overall amorphous structure.

42 ENGINEERING↗
DOE OSTI2022

pnnl/memgaze

MemGaze is a memory analysis toolset that combines high-resolution trace analysis and low overhead measurement, both with respect to time and space. It provides high-resolution by collecting world-level memory access traces, where the highest resolution supported is back-to-back sequences. It achieves low-overhead in space and time by leveraging sampling and various methods of hardware support for collecting traces. Its post-mortem trace processing provides multiresolution analysis for locations vs. operations; accesses vs. spatio-temporal reuse, and reuse (distance, rate, volume) vs. access patterns.

Central, PNNL Developer↗
DOE OSTI2022

An ontology-based fault generation and fault propagation analysis approach for safety-critical computer systems at the design stage

Abstract Fault propagation analysis is a process used to determine the consequences of faults residing in a computer system. A typical computer system consists of diverse components (e.g., electronic and software components), thus, the faults contained in these components tend to possess diverse characteristics. How to describe and model such diverse faults, and further determine fault propagation through different components are challenging problems to be addressed in the fault propagation analysis. This paper proposes an ontology-based approach, which is an integrated method allowing for the generation, injection, and propagation through inference of diverse faults at an early stage of the design of a computer system. The results generated by the proposed framework can verify system robustness and identify safety and reliability risks with limited design level information. In this paper, we propose an ontological framework and its application to analyze an example safety-critical computer system. The analysis result shows that the proposed framework is capable of inferring fault propagation paths through software and hardware components and is effective in predicting the impact of faults.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Air Pollutant Emissions and Regulatory Implications of Co-Processing Raw Bio-Oil in U.S. Petroleum Refineries

Pyrolysis oil, often referred as bio-oil or biocrude, has attracted considerable attention because it has higher energy density and carbon content compared to the raw biomass. Using pyrolysis oil derived from low cost and underutilized biomass to produce renewable hydrocarbon fuels provides a viable link between the agriculture/forestry and (petro-) chemical industry. Co-processing bio-oil with vacuum gas oil (VGO) in petroleum refinery is appealing due to the relatively low capital requirement to retrofit refinery and the compatibility of fuel products with existing infrastructure. In order to implement co-processing, a petroleum refinery would need to consider several process modifications and therefore could be subject to varying levels of air permitting requirements based on bio-oil co-processing ratio, co-processing capacity in existing units, the amount of regulated pollutants expected to be emitted, among other factors. This analysis considers co-processing 5% (by weight) of raw bio-oil with VGO in the fluidized catalytic cracking (FCC) unit(s) in a petroleum refinery. We estimate the potential increase in emissions of regulated air pollutants resulting from the modifications required to implement bio-oil co-processing and evaluate the regulatory and permitting implications for three FCC size categories in the U.S. refineries, which could co-processes raw bio-oil to produce partially renewable transportation fuels. We also assess technically feasible emission control options petroleum refineries could adopt to avoid being subject to major source permitting requirements if the estimated increase in regulated air pollutant emission exceeds the applicable threshold for major modifications. Insights from our analysis can help overcome challenges and risks associated with timely obtaining air permits required to implement bio-oil co-processing in existing refineries. Our analysis also provides needed information for decision makers (e.g., local air quality agencies) to understand the potential air quality implications for retrofitting refineries to co-process bio-oil, particularly in areas with high concentration of petroleum refineries.

09 BIOMASS FUELS↗
DOE OSTI2023

Speciation Underpinned by Unexpected Molecular Diversity in the Mycorrhizal Fungal Genus Pisolithus

The mutualistic ectomycorrhizal (ECM) fungal genus Pisolithus comprises 19 species defined to date which colonize the roots of >50 hosts worldwide suggesting that substantial genomic and functional evolution occurred during speciation. To better understand this intra-genus variation, we undertook a comparative multi-omic study of nine Pisolithus species sampled from North America, South America, Asia, and Australasia. We found that there was a small core set of genes common to all species (13%), and that these genes were more likely to be significantly regulated during symbiosis with a host than accessory or species-specific genes. Thus, the genetic “toolbox” foundational to the symbiotic lifestyle in this genus is small. Transposable elements were located significantly closer to gene classes including effector-like small secreted proteins (SSPs). Poorly conserved SSPs were more likely to be induced by symbiosis, suggesting that they may be a class of protein that tune host specificity. The Pisolithus gene repertoire is characterized by divergent CAZyme profiles when compared with other fungi, both symbiotic and saprotrophic. This was driven by differences in enzymes associated with symbiotic sugar processing, although metabolomic analysis suggest that neither copy number nor expression of these genes is sufficient to predict sugar capture from a host plant or its metabolism in fungal hyphae. Our results demonstrate that intra-genus genomic and functional diversity within ECM fungi is greater than previously thought, underlining the importance of continued comparative studies within the fungal tree of life to refine our focus on pathways and evolutionary processes foundational to this symbiotic lifestyle.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Direct Air Reactive Capture and Conversion for Utility-Scale Energy Storage (Final Report)

This final report for FEW0277 summarizes the work performed over the project performance period of October 2021 – March 2025. This project was funded under the “Reactive Capture and Conversion R&D” lab call released in FY2021. The goal of the project was to develop dual-function materials and process for capturing CO 2 from the atmosphere and converting it into CH 4 . The work was organized into four parallel tracks in 1) direct air capture materials synthesis and characterization, 2) catalysts for CO 2 conversion, 3) mechanistic investigations via ab initio simulations, and 4) process modeling, technoeconomic analysis, and lifecycle assessment. The project was split into two budget periods. The first budget period focused on development of amine-based materials, due to their known performance for CO 2 direct air capture and their potential to act synergistically with metal catalysts to enable a low-temperature methanation pathway. The second budget period focused on development of alkali-based materials and a simulated-moving-bed process for high conversion catalytic reduction of captured CO 2 to CH 4 . All project milestones were completed during the project performance period and are summarized in this report. Our work resulted in publication of eight peer-reviewed manuscripts, one patent application, and numerous presentations given at domestic and international conferences and invited academic department seminars.

03 NATURAL GAS↗
DOE OSTI2022

Understanding slurry formulations to guide solution-processing of solid electrolytes

Scalable processing of thin solid electrolytes is crucial for engineering solid-state batteries with practical energy densities. To leverage existing battery production infrastructure, pathways for solution processing of solid electrolyte films must be investigated. Here, roll-to-roll compatible film preparation of aluminum-doped lithium lanthanum zirconate oxide was studied. Four slurry configurations accounting for different solvent properties and solid loadings were evaluated to investigate the ternary interactions within the dispersions. Rheological and coating stability analyses were carried out on the processed slurries. The analysis and characterization indicated that improved component interactions within the ethanol/toluene system result in homogenized distribution of the particles, binder, and plasticizer, and enable the extraction of free-standing thin films with thicknesses of 20 μm and large area (>15 in. 2 ). The drawbacks and opportunities of several protocols for high temperature sintering of the dried green films were investigated. This study highlights the importance of engineering dispersions during the development of processing protocols for solid electrolytes and provides guidelines for the best practices that can be leveraged for solution processing–based fabrications for a wide range of solid electrolyte materials.

25 ENERGY STORAGE↗
DOE OSTI2021

Modelling the complex evaporated gas flow and its impact on particle spattering during laser powder bed fusion

The additive manufacturing (AM) of metals is becoming an increasingly important production process with the potential to replace traditional techniques such as casting. Laser Powder Bed Fusion (LPBF) is used in many applications to print metal parts from powder. The metal powder is heated locally with sufficient laser radiation that the liquid melt easily reaches its boiling temperature, which leads to a metallic vapour jet that can entrain both powder bed particles and molten droplets. The small size of laser-matter interaction site makes a detailed experimental analysis of the process challenging. Synchrotron X-ray imaging experiments are one of the few methods which can capture the dynamic melting and solidification processes. Comparing such experiments with computer simulations of the process is an important approach in order to better understand the manufacturing process and to analyse the influence of process parameters on the evaporated gas jet and the subsequent impact on particle ejection, leading to potentially reduced AM component quality. The melting and solidification of the metal powder is simulated using an Eulerian multiphase approach based on a control volume discretization of powder bed and substrate and a volume of liquid separation from melt and gas phase. The gas phase modelled as an ideal gas reaches velocities up to 100 m/s. Lagrangian particle tracking in the simulation demonstrates that the velocity fields calculated by the Eulerian multi-phase approach in combination with a standard drag-force model lead to particle accelerations in good agreement with those measured experimentally. In order to avoid numerical laborious Lagrangian calculations, a direct method to compare an Eulerian multiphase simulation with synchrotron X-ray experiments was introduced and validated. This approach is used to analyse the influence of process parameters including laser power and laser speed on the maximal acceleration of particles from the melt pool area. While the particle acceleration increases linearly with line energy in the conduction mode, a linear decrease of the acceleration with increasing line energy can be found in the transition mode before the accel-eration increases again with line energy in the keyhole mode.

36 MATERIALS SCIENCE↗
DOE OSTI2023

PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis

Abstract. This paper describes the new open-source framework PyFLEXTRKR (Python FLEXible object TRacKeR), a flexible atmospheric feature tracking software package with specific capabilities to track convective clouds from a variety of observations and model simulations. This software can track any atmospheric 2D objects and handle merging and splitting explicitly. The package has a collection of multi-object identification algorithms, scalable parallelization options, and has been optimized for large datasets including global high-resolution data. We demonstrate applications of PyFLEXTRKR on tracking individual deep convective cells and mesoscale convective systems from observations and model simulations ranging from large-eddy resolving (∼100s m) to mesoscale (∼10s km) resolutions. Visualization, post-processing, and statistical analysis tools are included in the package. New Lagrangian analyses of convective clouds produced by PyFLEXTRKR applicable to a wide range of datasets and scales facilitate advanced model evaluation and development efforts as well as scientific discovery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

CO 2 Conversion to Butene via a Tandem Photovoltaic–Electrochemical/Photothermocatalytic Process: A Co-design Approach to Coupled Microenvironments

Here, we developed a tandem, unassisted, solar-driven electrochemical and photothermocatalytic process for the single-pass conversion of CO 2 to butene using only simulated solar irradiation as the energetic input. The two-step process involves electrochemical CO 2 reduction (CO 2 R) to ethylene followed by ethylene dimerization to butene. We assessed two unassisted electrochemical setups to concentrate ethylene in the CO 2 R reactor, achieving concentrations up to 5.4 vol.% with 1.8% average solar-to-ethylene conversion and 5.6% average CO 2 -to-ethylene single-pass conversion under 1-sun illumination. When passed through the photothermocatalytic ethylene oligomerization reactor, we generated 600 ppm of butene under 3-sun illumination. Through analysis of this process, we identified that the presence of H 2 , CO, and H 2 O leads to rapid deactivation of the Ni-based ethylene oligomerization catalyst.

14 SOLAR ENERGY↗
DOE OSTI2022

ThunderSecure: deploying real-time intrusion detection for 100G research networks by leveraging stream-based features and one-class classification network

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks (40/100G) across distributed sites for processing, storage, and analysis. Like general purpose networks, research networks experience intrusions. However, monitoring anomalies in such high-speed network traffics is challenging given current cyber-infrastructure. Moreover, traditional network intrusion detection systems (NIDS) are signature based. However, anomaly patterns are difficult to define and that rulesets are often not updated frequently enough to reflect the changes of attack behaviors. We present ThunderSecure, a high-throughput, unsupervised learning-based intrusions detection system for 100G research networks. ThunderSecure implements an efficient packet processing and detection pipeline using multi-cores and GPUs. It extracts statistical and temporal features from real-time network data streams and feeds them to a one-class anomaly detection network. A baseline of normal distribution will be created based on the training observation. Testing traffic deviated from the learned profile will be marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets mirrored from two 100G network connections at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. Results show that ThunderSecure can recognize science data traffic captured long after the training and made nearly certain detection on the segment of the streams where anomalous flows were injected.

100G research network↗