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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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Triphilic organochalcogen compounds are added to solid-state sulfur cathodes to establish intimate and durable interfacial contact among cathode components, enhancing ion and electron transport, active material utilization, and cycling stability.
Transient, near-field mixing of a supercritical fluid injected into supercritical and subcritical environments is investigated experimentally. Fuel (propane) from a 100 μm diameter Bosch diesel injector is injected into a high-pressure test rig with a steady flow of heated, low-velocity nitrogen to control the ambient pressure and temperature. The fuel supply pressure is approximately 190 bar, allowing short injections lasting 3–4 ms to be performed in quick succession to acquire statistically significant datasets to represent the unsteady fuel–air mixing. Two test cases with the same ambient density and injector inlet pressure and temperature were chosen in an attempt to isolate effects of mixture phase behavior. Microscopic rainbow Schlieren deflectometry (RSD) is used for visualization and quantitative data acquisition. In the case of the supercritical ambient, the injector inlet and exit and ambient conditions are all supercritical. The RSD analysis reveals a mixing process similar to the single phase fluid mixing, though condensation is also observed locally, and can be explained by the adiabatic mixing theory presented in the paper. For the subcritical ambient case, the injector inlet is supercritical, but the injector exit and ambient conditions are both subcritical. The results show a rapid expansion in the near-field promoting mixing between propane and nitrogen. At downstream locations, significant condensation is also observed as predicted by the adiabatic mixing theory. As a result, the supercritical ambient case exhibited longer and narrower jet penetration compared to the subcritical ambient case.
This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs (I-PINNs) framework (Sarma et al., Comput. Methods Appl. Mech. Eng. 429: 117135, 2024), allowing different activation functions across material interfaces. Further, this ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.
For the first time, we report the whole genome sequence of a hydrocarbonoclastic Chryseobacterium oranimense strain isolated from Trinidad and Tobago (COTT) and its genes involved in the biotransformation of hydrocarbons and xenobiotics through functional annotation. The assembly consisted of 11 contigs with 2,794 predicted protein-coding genes which included a diverse group of gene families involved in aliphatic and polycyclic hydrocarbon degradation. Comparative genomic analyses with 18 crude-oil degrading bacteria in addition to two C. oranimense strains not associated with oil were carried out. The data revealed important differences in terms of annotated genes involved in the hydrocarbon degradation process that may explain the molecular mechanisms of hydrocarbon and xenobiotic biotransformation. Notably, many gene families were expanded to explain COTT’s competitive ability to manage habitat-specific stressors. Gene-based evidence of the metabolic potential of COTT supports the application of indigenous microbes for the remediation of polluted terrestrial environments and provides a genomic resource for improving our understanding of how to optimize these characteristics for more effective bioremediation.
Methane hydrate deposits are one of the largest fractions of hydrocarbons in the Earth's crust. They are found mainly in ocean sediments, and the configuration of the deposits—at the largest (kilometer) and smallest (micrometer) scales—determines how and when gaseous methane is released. Here, in this work, using thermodynamic arguments, we show how the confined spaces of the sediment allow methane hydrate to coexist with both water and gas to create a three-phase region. We find that the hydrate pore occupancy changes depending on the depth of the three-phase region, due to changes in methane density of the gas phase. We estimate the thickness of three-phase regions (hydrate, gas, and water) within ocean sediments. We further show how this directly predicts how the presence of hydrate affects the flow properties of gaseous methane and water through porous media.
Here we investigate the development of mobility inversion and fingering when a granular suspension is injected radially between horizontal parallel plates of a cell filled with a miscible fluid. While the suspension spreads uniformly when the suspension and the displaced fluid densities are exactly matched, even a small density difference is found to result in a dense granular front which develops fingers with angular spacing that increase with granular volume fraction and decrease with injection rate. We show that the timescale over which the instability develops is given by the volume fraction dependent settling timescale of the grains in the cell. We then show that the mobility inversion and the nonequilibrium Korteweg surface tension due to granular volume fraction gradients determine the number of fingers at the onset of the instability in these miscible suspensions.
We present a systematic ab initio study of the low-lying states in beryllium isotopes from 7 Be to 12 Be using nuclear lattice effective field theory with the N 3 LO interaction. Our calculations achieve good agreement with experimental data for energies, radii, and electromagnetic properties. We introduce a novel, model-independent method to quantify nuclear shapes, uncovering a distinct pattern in the interplay between positive and negative parity states across the isotopic chain. By combining Monte Carlo sampling of the many-body density operator with a novel nucleon-grouping algorithm, the prominent two-center cluster structures, the emergence of one-neutron halo, complex nuclear molecular dynamics such as 𝜋 orbital and 𝜎 orbital, emerge naturally.
Many complex intermetallic structures possess cagelike environments that can host additional guest atoms. In Al 10 V, these atoms give rise to low-frequency, localized vibrations (Einstein modes) that dominate the thermodynamic response at low temperature. They become imaginary under volume expansion as temperature rises, invalidating the harmonic approximation. Here, we develop a framework to incorporate these strongly anharmonic vibrational modes into first-principles thermodynamic calculations. By explicitly modeling the cage potential and solving the associated Schrödinger equation numerically, we compute the full anharmonic free energy contribution and demonstrate its impact on the thermodynamic behavior of Al 10 V. This allows us to examine structures with different cage fillings and construct the Al-V phase diagram in the relevant composition range. Our results reproduce key experimental signatures, including the anomalous rise in the thermal expansion coefficient and heat capacity at low temperatures, and reveal that the presence and the extent of cage filling by guest atoms is essential to stabilizing the Al 10 V phase at elevated temperatures.
The atmospheric concentration of methane has more than doubled since the start of the Industrial Revolution. Methane is the second-most-abundant greenhouse gas created by human activities and a major driver of climate change. This APS-Optica report provides a technical assessment of the current state of monitoring U.S. methane emissions from oil and gas operations, which accounts for roughly 30% of U.S. anthropogenic methane emissions. The report identifies current technological and policy gaps and makes recommendations for the federal government in three key areas: methane emissions detection, reliable and systematized data and models to support mitigation measures, and effective regulation.
Real-time monitoring of water volume fraction in multiphase flows is an important problem for a number of industrial applications. The water phase in the multiphase flows may correspond to either the dispersed phase or the continuous phase. In the past, several lowcost and nonintrusive techniques based on the electrical capacitance tomography (ECT) has been developed to image and monitor in real-time multiphase flows containing water. Furthermore, such monitoring becomes increasingly challenging for high salinity levels, and no reliable ECTbased method is presently available which could work for obtaining water volume fraction in multiphase flows for all water salinity levels. In this paper, we propose a new approach based on the Hanai’s formula for complex dielectric constant and taking advantage of the Maxwell- Wagner-Sillars effect to obtain, to a good approximation, water volume fractions in multiphase flows containing water as either dispersed or continuous phase.
This article presents a fiber optic rotary encoder-based pressure sensor for downhole applications. The rotary encoder, which is composed of an encoding pad and an all-glass optical fiber sensing head, converts the rotation angles of a pressure transducer to digital codes. The optical fiber only serves as the telemetry channel to directly transmit the data in digital format such that the environmental temperature influence and the long-time drift are minimized. The glass additive and subtractive manufacturing (ASM) techniques are used to embed the multichannel optical fibers into a bulk-fused silica glass substrate. Because the fused silica glass shares a similar material property to the optical fiber (e.g., thermal expansion coefficient), the optical fiber sensing head is well sealed and fixed even at an elevated temperature. The picosecond laser blackening technique is used to fabricate the high reflectivity contrast encoding pad with high accuracy. Here, the proposed pressure sensor was manufactured and experimentally verified to have a signal-to-noise ratio (SNR) of 23 dB and a linear pressure response with a root-mean-square error (RMSE) of 0.37. During the 400-h 250 °C long-term stability test, there was no digital code variation. In addition, a mathematical model to study the relationships between the sensor’s performances and design parameters was established. According to the model, a 12-bit encoder (0.02% pressure sensitivity) can be achieved with a radius of 20 mm.
Deep transfer learning has a great success story in computer vision (CV), natural language processing, and many other fields. In this communication, we are going to push forward the deep transfer learning to the hydraulic fracture imaging problem by proposing a two-step approach: 1) train a convolutional neural network (CNN) to reconstruct target geometries by a relatively large amount of approximated field patterns generated from a simplified model and 2) fine-tune the top layers of transferred CNN by a small amount of true field patterns generated through a full model. The advantages include the rapid generation of large amount of data through the simplified model and the high reconstruction accuracy through a careful design of the deep transfer learning. Here, the CNN trained through the deep transfer learning can accurately reconstruct the lateral extent and direction of fractures with unseen conductivity and white Gaussian noise, showing a notable acceleration/accuracy enhancement over the previous CNN trained by mixing a small number of true data into the approximated data as data augmentation.
Measurement of phase volume fractions in water-containing multiphase flows is necessary for the optimization of a host of industrial flow processes. Many water-containing multiphase flows can be classified as either water-dispersed or water-continuous mixtures. A recently developed approach based on Hanai’s mixture formula and utilizing electrical capacitance tomography (ECT) sensors have shown good potential for obtaining water volume fraction estimates in two-phase water-containing flows with different water salinity levels. However, the proposed approach was investigated via controlled experiments restricted to static configurations while, in practice, multiphase flows can be dynamic and unpredictable. In this work, we perform a flow loop study of the proposed ECT-based method for volume fraction estimation in oil–water two-phase flows. Here, we evaluate the performance of the proposed method in both water-dispersed and water-continuous flow regimes by employing different types of capacitive sensors in cylindrical arrangements and parallel-plate rectangular arrangements.
Drilling systems that use downhole rotation must react torque either through the drill-string or near the motor to achieve effective drilling performance. Problems with drill-string loading such as buckling, friction, and twist become more severe as hole diameter decreases. Therefore, for small holes, reacting torque downhole without interfering with the application of weight-on-bit, is preferred. In this paper, we present a novel mechanism that enables effective and controllable downhole weight on bit transmission and torque reaction. This scalable design achieves its unique performance through four key features: (1) mechanical advantage based on geometry, (2) direction dependent behavior using rolling and sliding contact, (3) modular scalability by combining modules in series, and (4) torque reaction and weight on bit that are proportional to applied axial force. As a result, simple mechanical devices can be used to react large torques while allowing controlled force to be transmitted to the drill bit. We outline our design, provide theoretical predictions of performance, and validate the results using full-scale testing. The experimental results include laboratory studies as well as limited field testing using a percussive hammer. These results demonstrate effective torque reaction, axial force transmission, favorable scaling with multiple modules, and predictable performance that is proportional to applied force.
Advanced engine design and alternative fuels present the possibility of fuel injection at purely supercritical conditions in diesel engines and gas turbines. The complex interactions that govern this phenomenon still need significant research, particularly the boundary conditions for fuel injection are critical for accurate simulation. However, the flow inside the injector itself is often omitted to reduce the computational efforts, and thus, velocity, mass flux, or total pressure is specified at the injector exit (or domain inlet), often with simplified velocity profiles and turbulence levels. This simplified inlet boundary treatment has minimal effects on results for conventional fuel injection conditions, however, the validity of this approach at supercritical conditions has not been assessed. Comprehensive real-gas and binary fluid mixing models have been implemented for computational fluid dynamics (CFD) analysis of fuel-air mixing at supercritical conditions. The model is verified using prior CFD results from the literature. The model is used to investigate the effects of the shape of axial velocity and mass fraction profiles at the inlet boundary with the goal to improve the comparison of predictions to experimental data. Results show that the boundary conditions have a significant effect on the predictions, and none of the cases match precisely with experimental data. Furthermore, the study reveals that the physical location of the inlet boundary might be difficult to infer correctly from the experiments and highlights the need for high-quality, repeatable measurements at supercritical conditions to support the development of relevant high-fidelity models for fuel-air mixing.
Understanding and optimizing the key mechanisms used in the synthesis of pitch-based carbon fibers (CFs) are challenging, because unlike polyacrylonitrile-based CFs, the feedstock for pitch-based CFs is chemically hetero- geneous, resulting in complex fabrication leading to inconsistency in the final properties. In this work, we use molecular dynamics simulations to explore the processing and chemical phase space through a framework of CF models to identify their effects on elastic performance. The results are in excellent agreement with experiments. We find that density, followed by alignment, and functionality of the molecular constituents dictate the CF mechanical properties more strongly than their size and shape. Last, we propose a previously unexplored fabrication route for high-modulus CFs. Unlike graphitization, this results in increased sp 3 fraction, achieved via generating high-density CFs. In addition, the high sp 3 fraction leads to the fabrication of CFs with isometric compressive and tensile moduli, enabling their potential applications for compressive loading.
Critical temperature for localized corrosion can be a good design parameter because localized corrosion is not likely to occur below that temperature. The critical temperature depends on alloy composition, microstructure, and environment chemistry (including its redox potential). This paper reviews the literature on critical temperature for localized corrosion, expressed either as Critical Pitting Temperature (CPT) or Critical Crevice Temperature (CCT). A history of various testing methods is presented. Different approaches for modeling the temperature of transition to active pit growth are reviewed, including probabilistic aspects of critical temperature. A semi-empirical, electrolyte-based, model is described that can be useful in predicting CCT in service environments that differ from standard laboratory test environments. The model predictions are compared to experimental data for various alloys. The effect of solvent on CCT/CPT is described briefly and future avenues of research are recommended.
Advanced combustion strategies are necessary for the use of more environmentally sustainable fuels than traditional diesel. Alcohol fuels and alcohol/gasoline blends are of particular interest as they are readily available in the marketplace. Heavy-duty engines typically use compression ignited, conventional diesel mixing controlled combustion. Mixing controlled combustion features a non-premixed diffusion flame with a wide range of local equivalence ratios, leading to potentially high rates of soot formation. This work studies the sooting behavior of iso-octane and ethanol as a function of equivalence ratio. Measurements are carried out in a rapid compression machine (RCM) and are reported for pre-ignition conditions of 10–30 bar and temperatures of 650–800 K. Theoretical equilibrium and bulk gas temperatures are calculated for both fuels. These data are used to identify the critical equivalence ratio, the lowest equivalence ratio where soot is detected with a single-pass laser extinction diagnostic. The critical equivalence ratio for iso-octane varies between 1.82 and 1.77 for compressed pressures of 10 and 20 bar, respectively. Ethanol, sometimes considered sootless, had a critical equivalence ratio between 2.37 and 2.12 for compressed pressures of 20 and 30 bar, respectively. When characterizing soot formation by oxygenated equivalence ratio, the critical equivalence ratios for ethanol approach those of iso-octane. This suggests the oxygenated nature of alcohol fuels reduces sooting tendency, but other factors such as fuel molecular structure and morphology may play a role. It was observed that ethanol will form soot at equivalence ratios only slightly higher than iso-octane, which could have implications in mixing controlled combustion. It was seen for both fuels that soot formation is pressure sensitive, with the critical equivalence ratio being inversely proportional to compressed pressure and the rate of soot formation. Future work will investigate the sooting behavior of gasoline/ethanol blends.