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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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This viewgraph presentation reviews operational reliability and its role in achieving high reliability through design and process reliability. The topics include: 1) Reliability Engineering Major Areas and interfaces; 2) Design Reliability; 3) Process Reliability; and 4) Reliability Applications.
Biogas derived from anaerobic digestion of waste streams such as biorefinery wastewater, animal, agricultural, and municipal solid waste, offers a versatile renewable energy source. Total domestic methane potential from landfill material, animal manure, wastewater, and organic waste, combined with biogas generated from AD of lignocellulosic biomass, is estimated to offer >4 quadrillion Btu potential energy. This energy could displace nearly half of current domestic natural gas consumption in the electric power sector and all consumption in the transportation sector. However, despite this promise of this feedstock, its gaseous state prevents facile integration with extant transportation and industrial infrastructure. Microbial conversion of biogas to liquid fuel and chemical intermediates offers valorization potential. However, biogas biocatalysis is currently limited by poor substrate gas-to-liquid mass transfer, low conversion efficiencies, and incomplete biogas utilization. To this end, the Biogas Biocatalysis AOP aims to develop a carbon- and energy-efficient biogas bioconversion process via techno-economic-informed strain and fermentation engineering strategies. Efforts here will improve both process economics and sustainability via process-intensified, carbon-efficient biogas bioconversion to value-added platform molecules, enabling bolt-on deployment for valorization of biogas derived from standalone AD infrastructure.
Boiling is an effective mode of heat transfer since high heat flux levels are possible driven by relatively small temperature differences. The high heat transfer coefficients associated with boiling have made the use of these processes increasingly attractive to aerospace engineering. Applications of this type include compact evaporators in the thermal control of aircraft avionics and spacecraft environments, heat pipes, and use of boiling to cool electronic equipment. In spite of its efficiency, cooling based on liquid-vapor phase change processes has not yet found wide application in aerospace engineering due to specific problems associated with the low gravity environment. After a heated surface has reached the superheat required for the initiation of nucleate boiling, bubbles will start forming at nucleation sites along the solid interface by evaporation of the liquid. Bubbles in contact with the wall will continue growing by this mechanism until they detach. In terrestrial conditions, bubble detachment is determined by the competition between body forces (e.g. buoyancy) and surface tension forces that act to anchor the bubble along the three phase contact line. For a given body force potential and a balance of tensions along the three phase contact line, bubbles must reach a critical size before the body force can cause them to detach from the wall. In a low gravity environment the critical bubble size for detachment is much larger than under terrestrial conditions, since buoyancy is a less effective means of bubble removal. Active techniques of heat transfer enhancement in single phase and phase change processes by utilizing electric fields have been the subject of intensive research during recent years. The field of electrohydrodynamics (EHD) deals with the interactions between electric fields, flow fields and temperature fields. Previous studies indicate that in terrestrial applications nucleate boiling heat transfer can be increased by a factor of 50 as compared to values obtained for the same system without electric fields. Imposing an external electric field holds the promise to improve pool boiling heat transfer in low gravity, since a phase separation force other than gravity is introduced. The goal of our research is to experimentally investigate the potential of EHD and the mechanisms responsible for EHD heat transfer enhancement in boiling in low gravity conditions.
Diffractive deep neural networks (D 2 NNs), comprised of spatially engineered passive surfaces, collectively process optical input information at the speed of light propagation through a thin diffractive volume, without any external computing power. Diffractive networks were demonstrated to achieve all‐optical object classification and perform universal linear transformations. Herein, a “time‐lapse” image classification scheme using a diffractive network is demonstrated for the first time, significantly advancing its classification accuracy and generalization performance on complex input objects by using the lateral movements of the input objects and/or the diffractive network, relative to each other. In a different context, such relative movements of the objects and/or the camera are routinely being used for image super‐resolution applications; inspired by their success, a time‐lapse diffractive network is designed to benefit from the complementary information content created by controlled or random lateral shifts. The design space and performance limits of time‐lapse diffractive networks are numerically explored, revealing a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR‐10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive network on the CIFAR‐10 dataset. Time‐lapse diffractive networks will be broadly useful for the spatiotemporal analysis of input signals using all‐optical processors.
Almost every engineering course involves mathematics in one form or another. The analytical process of developing mathematical models is very important for engineering students. However, the computational process involved in the solution of some mathematical problems may be very tedious and time consuming. There is a significant amount of mathematical software such as Mathematica, Mathcad, and Maple designed to aid in the solution of these instructional problems. The use of these packages in classroom teaching can greatly enhance understanding, and save time. Integration of computer technology in mathematics classes, without de-emphasizing the traditional analytical aspects of teaching, has proven very successful and is becoming almost essential. Sample computer laboratory modules are developed for presentation in the classroom setting. This is accomplished through the use of overhead projectors linked to graphing calculators and computers. Model problems are carefully selected from different areas.
Ga2O3 is being actively explored for power electronics, and other applications due to its ultra- wide bandgap and low projected fabrication cost of high-quality crystals. N-type doping of Ga2O3 can be achieved and tuned, but p-type doping faces fundamental obstacles. Successful engineering of Ga2O3 based devices requires critical control of doping density, Fermi level position, providing opportunities for predictive process simulation. We use first-principles defect equilibrium calculations to simulate a 3-step growth- annealing-quench protocol for hydrogen assisted Mg doping in Ga2O3, taking into account the hydrogen- oxygen-water gas phase equilibrium. We predict type conversion to a net p-type regime following O-rich annealing after growth under reducing conditions in the presence of H2. We show that there is an optimal temperature that maximizes the net acceptor density during the equilibrium annealing step for a given Mg doping level. Quenching of non-equilibrium annealed samples then results in a Fermi level EF below mid-gap down to about EV +1.5 eV, creating a significant number of uncompensated neutral MgGa0 acceptors. This type converted Ga2O3 can create significant built-in field in a p-n junction with an adjoining n-type material and could enable vertical MOSFETs.
Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of ICP and exploits the iterative aspect of ICP by caching previous correspondences to expedite each iteration. An algorithmic analysis and comparison is conducted showing an order of magnitude speedup for both serial and vector processor implementation.
This study explores additive manufacturing of carbon fiber-reinforced thermoplastic composites using the Composite-Based Additive Manufacturing (CBAM) process. Carbon/Nylon 12 and Carbon/PEEK composites were fabricated and evaluated through mechanical (compression, tensile, flexural, and impact) and thermal (DSC and TGA) tests. Carbon/PEEK exhibited superior mechanical performance, with 97.5% higher tensile strength, 79.8% higher elastic modulus, and 59.6% higher flexural strength compared to Carbon/Nylon 12. Thermal testing showed that Carbon/PEEK had higher thermal stability, beginning degradation at 350 °C versus 298 °C for Carbon/Nylon. These results indicate that CBAM-fabricated Carbon/PEEK composites are suitable for applications requiring high strength and temperature resistance.
The variability in multi-pulse gamma-ray bursts (GRBs) may help to reveal the mechanism of underlying processes from the central engine. To investigate whether the self-organized criticality (SOC) phenomena exist in the prompt phase of GRBs, we statistically study the properties of GRBs with more than 3 pulses in each burst by fitting the distributions of several observed physical variables with a Markov Chain Monte Carlo approach, including the isotropic energy E iso , the duration time T, and the peak count rate P of each pulse. Our sample consists of 454 pulses in 93 GRBs observed by the CGRO/BATSE satellite. The best-fitting values and uncertainties for these power-law indices of the differential frequency distributions are: α$^{d}_{E}$ = 1.54±0.09, α$^{d}_{T}$ = 1.82±$^{+0.14}_{0.15}$ and α$^{d}_{P}$ = 2.09$^{+0.18}_{–0.19}$, while the power-law indices in the cumulative frequency distributions are: α$^{c}_{E}$ = 1.44$^{+0.08}_{–0.10}$, α$^{c}_{T}$ = 1.75$^{+0.141}_{–0.13}$ and α$^{c}_{P}$ = 1.99$^{+0.16}_{–0.19}$. We find that these distributions are roughly consistent with the physical framework of a Fractal-Diffusive, Self-Organized Criticality (FD-SOC) system with the spatial dimension S = 3 and the classical diffusion β=1. The results of this study support that the jet responsible for the GRBs should be magnetically dominated and magnetic instabilities (e.g., kink model, or tearing-model instability) lead the GRB emission region into the SOC state.
Austenitic stainless steel overlay claddings are often used to protect nuclear reactor pressure vessels from corrosion. Arc welding processes are traditionally employed, which require a high heat input to bond the dissimilar metals, creating a large heat-affected zone and high residual stress that demands a lengthy stress-relieving post-weld heat treatment, up to 48 hours. High energy beam (e.g., laser, electron beam) directed energy deposition-based overlay cladding processes show promise for fabricating claddings with superior performance compared to arc welded ones because of their localized heat input; however, their necessity and time of post-weld heat treatment are currently unclear. In this research, we investigate the time dependence of a 600 C stress-relieving post-weld heat treatment of austenitic stainless steel clad on low carbon steel by laser-wire-directed energy deposition, comparing the microstructure and mechanical properties of the as-fabricated condition to 2-, 10-, and 48-hour heat treatment times. Diffusion simulations and microscopy show a gradual carburization of the stainless steel cladding and decarburization of the heat-affected zone/base metal. Nanoindentation testing shows up to a 1.8–2.69 embrittlement in the cladding and a 0.33 to 0.69 softening in the heat-affected zone after the 48-hour post-weld heat treatment. Tensile testing of the cladding composites reveals that increasing post-weld heat treatment time raises clad cracking susceptibility due to intergranular carbide formation and shows that laser-wire-directed energy deposition claddings may not require post-weld heat treatment.
Shear assisted processing and extrusion has been used to fabricate multi-metallic tubing that incorporates regions with clad and butt-joined structures. In this work, aluminum alloys 6061, 7075, and 1100 are combined to create a monolithic extruded tubing with weldable ends (6061), a high strength center (7075), and a corrosion-resistant cladding (1100). Optical microscopy shows a gradual, defect-free, transition between alloys at the butt-joint interfaces and a sharp transition between alloys at the clad interface. Tensile testing proved the mechanical coherency of the joint, with failure occurring outside the tri-metallic structure. In conclusion, smoothed particle hydrodynamic simulations identified a spiral pattern of the cladding alloy that leads to inconsistent cladding thickness, and identified a simplified 2-alloy combination of aluminum alloys 6082 and 2014 that accomplished the design objectives of the tri-metallic structure.
Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO 2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. Further, we present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of 7 × 10 6 because our framework is not bound by the Courant–Friedrichs–Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model’s accuracy still lies within a relative error of 7% in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.
Millions of tons of waste polyester plastics, including polyethylene terephthalate (PET), polytrimethylene terephthalate (PTT), and polybutylene terephthalate (PBT), end up in the environment as soil and water contaminants. Recent advances in enzymatic polyester degradation have motivated researchers toward biorecycling of plastic wastes. Leaf-branch compost cutinase (LCC) enzymes have been proven to be effective in the biodegradation of PET. This study focuses on enzymatic depolymerization of waste PET, PTT, and PBT materials by using an ICCG variant of LCC (LCC ICCG ) produced from Escherichia coli BL21(DE3). The degradation efficiency of the polyesters was determined by the monomer terephthalic acid (TPA) released from the depolymerization reaction. It was found that the most efficient depolymerization was achieved for PET, followed by PTT and PBT. A kinetic model based on Langmuir adsorption and the Michaelis-Menten equation was developed to describe the enzymatic depolymerization of PET, PTT, and PBT with various enzyme and substrate loadings. The model simulation results revealed that the LCCICCG enzyme loading should be linearly increased as the work capacity of the polyester substrate increases. A specific enzyme loading of 0.91 mg/g PET is suggested to achieve 90% depolymerization of PET within three days. Furthermore, the experimental data and model simulation results can be used to help further engineer the enzyme and process to achieve a complete biodegradation of polyester wastes at large scale.
Roll-to-roll powder-to-film dry processing (DP) and single-crystal (SC) active materials (AMs) with many advantages are two hot topics of lithium-ion batteries (LIBs). However, DP of SC AMs for LIBs is rarely reported. Consequently, the impact of SC AMs on dry-processed LIBs is not well understood. Herein, for the first time, via a set of experimental and theoretical studies of the conventional polycrystalline-AM- and SC-AM-based DPed electrodes (DPEs), this work not only reports a high-performance dry SC-AM cathode for LIB manufacturing, but also establishes some fundamental understanding of SC-based dry-processed electrodes, including their morphology, structure, mechanical strength, electronic conductivity and LIB electrochemical behavior. Further, the results suggest that DP of SC AMs is promising, which can dramatically improve the electrochemical kinetics at electrode level and particle level. Specifically, for the rate capability and long-term cyclability in full cells, SC DPEs exhibit a discharge specific capacity of 152.1 mAh g -1 at 1C and a capacity retention rate of 79.9 % at C/3 over 500 cycles, which are superior to those of PC DPEs (135.6 mAh g -1 and 68.3 %) at the same conditions and are further confirmed by the simulation data from the theoretical modelling study. Therefore, this comprehensive work marks a significant milestone for DP strategy and SC AMs, enlightening future research and development of LIB manufacturing.
Objective: To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency. Methods: We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis. Results: The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1. Conclusion: Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.
Dry processing of lithium-ion battery electrodes facilely realizes the powder-to-film manner, which is thus regarded as a highly promising strategy for lithium-ion battery manufacturing. However, a fundamental understanding of the impact of the involved dry mixing is still rarely reported. In this work, the degree of dry mixing is monitored by the dry mixing time, and a set of dry-processed electrodes with different degree of dry mixing is accordingly prepared and comprehensively studied. This work novelly reveals that the degree of dry mixing exhibits pronounced impact on the morphology, the homogeneity of electrode components and the degree of PTFE fiberization, which leads to difference in the mechanical strength and electrochemical performance of dry-processed electrodes. Accordingly, it is suggested that a moderate degree of dry mixing is preferred for high-performance dry-processed lithium-ion battery electrodes.
We report a novel approach to economically fabricate dual-layer composite nanoparticle-containing carbon molecular sieve (CMS) hollow fiber membranes having excellent gas separation performance. The economically favored process comprises dip coating engineered support layer fibers to form a dense skin layer in a CMS precursor. The coated fibers are then pyrolyzed to form a high-performance CMS hollow fiber membrane. The nano-particle containing composite carbon molecular sieve hollow fiber membranes showed very attractive selectivities and productivities. As a result, this work shows how to produce high performance CMS hollow fiber membrane by coating, which exceeds that achieved with the standard sol-gel support stabilization technique.
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