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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Biosensors for the detection of chorismate and cis,cis -muconic acid in Corynebacterium glutamicum

Abstract Corynebacterium glutamicum ATCC 13032 is a promising microbial chassis for industrial production of valuable compounds, including aromatic amino acids derived from the shikimate pathway. In this work, we developed two whole-cell, transcription factor based fluorescent biosensors to track cis,cis-muconic acid (ccMA) and chorismate in C. glutamicum. Chorismate is a key intermediate in the shikimate pathway from which value-added chemicals can be produced, and a shunt from the shikimate pathway can divert carbon to ccMA, a high value chemical. We transferred a ccMA-inducible transcription factor, CatM, from Acinetobacter baylyi ADP1 into C. glutamicum and screened a promoter library to isolate variants with high sensitivity and dynamic range to ccMA by providing benzoate, which is converted to ccMA intracellularly. The biosensor also detected exogenously supplied ccMA, suggesting the presence of a putative ccMA transporter in C. glutamicum, though the external ccMA concentration threshold to elicit a response was 100-fold higher than the concentration of benzoate required to do so through intracellular ccMA production. We then developed a chorismate biosensor, in which a chorismate inducible promoter regulated by natively expressed QsuR was optimized to exhibit a dose-dependent response to exogenously supplemented quinate (a chorismate precursor). A chorismate–pyruvate lyase encoding gene, ubiC, was introduced into C. glutamicum to lower the intracellular chorismate pool, which resulted in loss of dose dependence to quinate. Further, a knockout strain that blocked the conversion of quinate to chorismate also resulted in absence of dose dependence to quinate, validating that the chorismate biosensor is specific to intracellular chorismate pool. The ccMA and chorismate biosensors were dually inserted into C. glutamicum to simultaneously detect intracellularly produced chorismate and ccMA. Biosensors, such as those developed in this study, can be applied in C. glutamicum for multiplex sensing to expedite pathway design and optimization through metabolic engineering in this promising chassis organism. One-Sentence Summary High-throughput screening of promoter libraries in Corynebacterium glutamicum to establish transcription factor based biosensors for key metabolic intermediates in shikimate and β-ketoadipate pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Automated Design and Analysis of a Variable Displacement Linkage Motor

Hydrostatic drives consisting of a variable displacement pump and a low speed high torque (LSHT) motor are frequently used in off-highway vehicles. A variable displacement traction motor is desirable because of the ability to downsize the pump and thereby run the hydrostat at higher efficiency, as well as the possibility of hybridization of the drivetrain. Currently on the market are fixed and discrete speed LSHT radial piston motors and high speed low torque variable displacement axial piston motors. The radial piston motors are displacement dense but are not continuously variable, whereas the axial piston motors are continuously variable but require gearboxes, introducing packaging and robustness concerns. The Variable Displacement Linkage Motor (VDLM) is a LSHT motor that is continuously variable. It offers several benefits over current LSHT motors in that it is highly efficient over its operating range, it has low torque ripple, and it is displacement dense due to its multi-lobed cam and radial packaging. As with the design of any motor, the process is iterative and must be performed whenever performance objectives change. This paper describes an automated method for rapid exploration of the solution space for a variable displacement motor with specific application to the VDLM. This method leads to a motor design that theoretically can achieve 97% efficiency with a torque ripple below 5% at full displacement.

42 ENGINEERING↗
DOE OSTI2018

High Brightness Superconducting RF Gun Cavity

The Phase I goals were to numerically optimize a TM020/TM010 SRF gun with a demountable back wall, using SLAC’s state-of-the-art massive parallel ACE3P suite of 3D Finite Element Analysis codes available at the National Energy Research Scientific Computing Center (NERSC) , which includes: a) TM020 cell shape, including impact of RF focusing on beam dynamics; b) optimum number of TM010-cells to achieve low emittances; c) multipacting simulations back wall opening; d) dark current investigations; e) studies on LOM and HOM damping with waveguides/port on TM020-cells using wakefield solver can be conducted as well but are not required stringently; f) conceptual design of a power input coupler; g) multiphysics simulations to optimize the gun thermal management and cooling requirements for a superconducting joint; h) multivariate beam dynamical optimization in conjunction with the gun optimization; and i) finalize an engineering design for vertical high power tests at JLab.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

MODELING THE INFLUENCE OF NOZZLE-GENERATED TURBULENCE ON DIESEL SPRAYS

Predictive engine simulations are key for rapidly exploring and optimizing the design of cleaner burning and more fuel efficient engines. Injection strategies in advanced engine concepts are resulting in the injection and atomization of fuel under a wide range of operating conditions in order to meet stringent emission regulations. However, the physics governing the breakup of an injected liquid fuel jet into droplets under these conditions have not been well studied or experimentally characterized to date. In the sprays literature, three agents have been proposed as the likely mechanisms contributing to primary atomization in diesel sprays, namely the aerodynamic growth of waves on the fuel jet surface, turbulence generated in the injector nozzle, and cavitation. If computational design tools are to be used to guide the use of direct injection strategies for cleaner and more fuel efficient engines, the physics underpinning the role of these primary atomization mechanisms must be better understood to ensure the development of predictive simulations of fuel-air mixing and vaporization within the engine. Thus, the central aim of this thesis is to improve the physical representation of spray breakup physics within today’s engine simulation packages. The work presented in this thesis investigates the role of the proposed physical mechanisms on the primary atomization process in diesel sprays. In order to advance current understanding of spray breakup, the dynamic and geometric factors contributing to cavitation were suppressed so that primary atomization due to aerodynamics and nozzle-generated turbulence could be studied in isolation. In the absence of sufficiently resolved images to visualize the primary atomization process under dieselrelevant conditions, droplet sizing spray measurements are needed to characterize the outcomes of the spray breakup process. Therefore, a new experimental methodology was developed and applied in a high-pressure spray chamber to characterize the average size of droplets formed from the spray breakup process. This experimental data, in conjunction with x-ray measurements from the Advanced Photon Source at Argonne National Laboratory, have been used to assess predictions from existing spray breakup models. Comparison between predicted and measured drop size distributions revealed that a spray atomization model, premised on droplet formation from the growth of aerodynamically induced instabilities, could capture experimentally observed sensitivities and features in the measured droplet size distributions under conventional diesel engine conditions. However, for injection into relatively lower ambient density environments, aerodynamic breakup models could not accurately predict the initial rate of droplet size decrease in the near-nozzle region, suggesting that other mechanisms, such as turbulence generated inside the nozzle, likely augment and enhance the primary breakup process. Evaluation of newly available droplet sizing measurements under low ambient density conditions allowed for the turbulence-induced breakup process to be studied, while minimizing the influence of aerodynamic inertial forces on the spray. Although several turbulence-induced breakup models have been proposed in the literature, the scaling of droplet sizes with the integral length scale, assumed in the majority of turbulent breakup models, was found to be inconsistent with the experimentally observed trends in droplet size along the spray centerline. However, empirical correlations describing droplets formed from eddies within the inertial sub-range of the turbulence spectrum were better able to capture the measured sensitivities in droplet size to changes in ambient and injection conditions. These findings informed recommendations for an improved hybrid spray breakup model, capable of representing both aerodynamic and turbulent breakup mechanisms in the atomization of non-cavitating diesel sprays.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network

As a label‐free imaging technique, quantitative phase imaging (QPI) provides optical path length information of transparent specimens for various applications in biology, materials science, and engineering. Multispectral QPI measures quantitative phase information across multiple spectral bands, permitting the examination of wavelength‐specific phase and dispersion characteristics of samples. Herein, the design of a diffractive processor is presented that can all‐optically perform multispectral quantitative phase imaging of transparent phase‐only objects within a snapshot. The design utilizes spatially engineered diffractive layers, optimized through deep learning, to encode the phase profile of the input object at a predetermined set of wavelengths into spatial intensity variations at the output plane, allowing multispectral QPI using a monochrome focal plane array. Through numerical simulations, diffractive multispectral processors are demonstrated to simultaneously perform quantitative phase imaging at 9 and 16 target spectral bands in the visible spectrum. The generalization of these diffractive processor designs is validated through numerical tests on unseen objects, including thin Pap smear images. Due to its all‐optical processing capability using passive dielectric diffractive materials, this diffractive multispectral QPI processor offers a compact and power‐efficient solution for high‐throughput quantitative phase microscopy and spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Gradient-Based Optimization of Wind Farms with Different Turbine Heights: Preprint

Turbine wakes reduce power production in a wind farm. Current wind farms are generally built with turbines that are all the same height, but if wind farms included turbines with different tower heights, the cost of energy (COE) may be reduced. We used gradient-based optimization to demonstrate a method to optimize wind farms with varied hub heights. Our study includes a modified version of the FLORIS wake model that accommodates three-dimensional wakes integrated with a tower structural model. Our purpose was to design a process to minimize the COE of a wind farm through layout optimization and varying turbine hub heights. Results indicate that when a farm is optimized for layout and height with two separate height groups, COE can be lowered by as much as 5%-9%, compared to a similar layout and height optimization where all the towers are the same. The COE has the best improvement in farms with high turbine density and a low wind shear exponent.

17 WIND ENERGY↗
DOE OSTI2020

The Model System Saccharomyces cerevisiae Versus Emerging Non-Model Yeasts for the Production of Biofuels

Microorganisms are effective platforms for the production of a variety of chemicals including biofuels, commodity chemicals, polymers and other natural products. However, deep cellular understanding is required for improvement of current biofuel cell factories to truly transform the Bioeconomy. Modifications in microbial metabolic pathways and increased resistance to various types of stress caused by the production of these chemicals are crucial in the generation of robust and efficient production hosts. Recent advances in systems and synthetic biology provide new tools for metabolic engineering to design strategies and construct optimal biocatalysts for the sustainable production of desired chemicals, especially in the case of ethanol and fatty acid production. Yeast is an efficient producer of bioethanol and most of the available synthetic biology tools have been developed for the industrial yeast Saccharomyces cerevisiae. Non-conventional yeast systems have several advantageous characteristics that are not easily engineered such as ethanol tolerance, low pH tolerance, thermotolerance, inhibitor tolerance, genetic diversity and so forth. Currently, synthetic biology is still in its initial steps for studies in non-conventional yeasts such as Yarrowia lipolytica, Kluyveromyces marxianus, Issatchenkia orientalis and Pichia pastoris. Therefore, the development and application of advanced synthetic engineering tools must also focus on these underexploited, non-conventional yeast species. Herein, we review the basic synthetic biology tools that can be applied to the standard S. cerevisiae model strain, as well as those that have been developed for non-conventional yeasts. In addition, we will discuss the recent advances employed to develop non-conventional yeast strains that are efficient for the production of a variety of chemicals through the use of metabolic engineering and synthetic biology.

09 BIOMASS FUELS↗
DOE OSTI2021

Liquid-Cooled Heat Sink Optimization for Thermal Imbalance Mitigation in Wide-Bandgap Power Modules

Power semiconductor die placement on substrates used in high-power modules is generally optimized to minimize electrical parasitic (e.g., stray inductance, common-mode capacitance), taking into account the minimum spacing between semiconductor dies for thermal decoupling. The layout assumes sufficient heat spreading and transfer from dies to the cooling structure. Insulated metal substrate-based power module designs may lead to asymmetrical thermal resistance across the dies, which may cause significant temperature differences among the devices. Such unintentional thermal asymmetries can lead to oversizing the cooling system design or underusing the semiconductor power processing capability. This article proposes a thermal imbalance mitigation method that uses evolutionary optimized liquid-cooled heat sinks to improve the thermal loading among devices.

42 ENGINEERING↗
DOE OSTI2021

AN AUTOMATED MACHINE LEARNING-GENETIC ALGORITHM FRAMEWORK WITH ACTIVE LEARNING FOR DESIGN OPTIMIZATION

The use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

Owoyele, Opeoluwa↗
DOE OSTI2022

Design and optimization of supersonic turbines for detonation combustors

Detonation-based engines offer a potential surge in efficiency for compact thermal power systems. However, these cycles require ad-hoc components adapted to the high outlet velocity from the detonation combustors. This paper presents the design methodology of turbine stages suitable for supersonic inlet conditions and provides a detailed analysis of optimized turbine geometries. A reduced-order solver examines the supersonic blade rows’ functional design space, quantifies the turbine’s non-isentropic performance, and budgets the turbine loss for different optimized leading-edge designs and chord to pitch ratios. The shock-wave interactions were identified as the predominant contributor to turbine losses, and optimal pitch-chord ratios were determined for various inlet Mach numbers. Finally, with this tool, the specific-power output for a wide range of design configurations was computed; and the metal angle that ensures flow starting and maximizes power extraction was calculated. The detailed numerical study describes the flow interactions in a supersonic turbine and offers new correlations to guide the design of future supersonic turbines.

42 ENGINEERING↗
DOE OSTI2022

Interleukin-2 superkines by computational design

Significance While computational engineering of therapeutic proteins is a desirable goal, in practice the optimization of protein–protein interactions requires substantial experimental intervention. We present here a computational approach that focuses on stabilizing core protein structures rather than engineering the protein–protein interface. Using this approach, we designed thermostabilized interleukin-2 (IL-2) variants that bind tightly to their receptor without experimental optimization, mimicking the properties of the yeast-display engineered IL-2 variant “super-2.” Our results suggest that structure-guided stabilization may be a general method for in silico affinity maturation of protein–protein interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Modular Engineering of Biomass Degradation Pathways

Production of fuels and chemicals from renewable lignocellulosic feedstocks is a promising alternative to petroleum-derived compounds. Due to the complexity of lignocellulosic feedstocks, microbial conversion of all potential substrates will require substantial metabolic engineering. Non-model microbes offer desirable physiological traits, but also increase the difficulty of heterologous pathway engineering and optimization. The development of modular design principles that allow metabolic pathways to be used in a variety of novel microbes with minimal strain-specific optimization will enable the rapid construction of microbes for commercial production of biofuels and bioproducts. In this review, we discuss variability of lignocellulosic feedstocks, pathways for catabolism of lignocellulose-derived compounds, challenges to heterologous engineering of catabolic pathways, and opportunities to apply modular pathway design. Implementation of these approaches will simplify the process of modifying non-model microbes to convert diverse lignocellulosic feedstocks.

09 BIOMASS FUELS↗
DOE OSTI2024

Durable and High-Performance SOECs Based on Proton Conductors for Hydrogen Production

Proton-conducting solid oxide electrolysis cells (P-SOECs) are a promising technology for cost-effective and efficient production of green hydrogen. Breakthroughs in materials development, optimization of cell structure, and achievement of high performance and durability are essential to significantly increase the commercial competitiveness of these technologies. The main objective of this project is to gain scientific knowledge for the rational design, fabrication, and demonstration of a robust, highly efficient, and low-cost SOEC technology based on a proton-conducting electrolyte membrane for hydrogen production. We focused on better understanding the degradation mechanisms of proton-conducting electrolytes, air electrodes, and catalyst materials under electrolysis mode to develop an effective strategy for rationalizing new materials that are vital for enhancing cell performance and durability. The scope includes enhancing the performance and durability of the electrolyte and electrode materials under realistic operating conditions, developing highly active and robust catalysts to minimize electrode losses while improving tolerance to contaminant poisoning, revealing the mechanism of enhanced activity and stability of the catalyst, and understanding the underlying degradation mechanisms. In addition, various characterization techniques were employed to gain a fundamental understanding of the materials’ behavior and their impact on cell performance, providing vital information to guide materials discovery and cell design. After defect chemistry engineering, the optimized donor and acceptor co-doped electrolytes BaMo/W 0.03 Ce 0.71 Yb 0.26 O 3-δ (BM/W03) showed substantially improved chemical stability against high concentrations of CO 2 and H 2 O compared to the state-of-the-art electrolyte (BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3-δ , BZCYYb1711) while maintaining comparable ionic conductivity and ionic transference number. To bypass the inherent trade-off between conductivity and chemical stability, we fabricated a bi-layer electrolyte composed of BZCYYb1711 coated with a highly-stable thin layer of BaHf 0.83 Yb 0.17 O 3-δ (BHYb). This bi-layer electrolyte displayed excellent chemical stability against high concentration CO 2 ; there was no detectable formation of BaCO 3 after exposure to 97% CO 2 (with 3% H 2 O) at 500 °C for 1000 hours and the rate of degradation in resistance was about 0.4% per 1,000 hours (kh). In contrast, the same BZCYYb1711 electrolyte without a BHYb coating degraded significantly under the same testing conditions; the degradation rate was increased to 5.1%/kh. In addition, a triple conducting air electrode Ba 0.9 Pr 0.1 Hf 0.1 Y0.1Co 0.8 O 3-δ (BPHYC) was developed by heavily doping transition metal ions into a proton-conducting material. This air electrode material, composed of 3 distinct phases, exhibits superior electrocatalytic activity due to the synergistic effect from the three component phases. Moreover, an active and durable catalyst, La 2 Ni 0.5 Fe 0.5 O 4+δ (LNF), was developed, showing excellent catalytic activity and contaminant tolerance, with a degradation rate of only 0.49%/kh when exposed to high concentrations of steam and Cr. Finally, single cells were constructed from the best electrolytes, electrodes, and catalyst coatings developed in this project. These cells demonstrated superior high current density at a given cell voltage, high roundtrip efficiency, and remarkable durability (up to 1000 hours of operation).

08 HYDROGEN↗
DOE OSTI2022

Sensitivity Analyses for Monte Carlo Sampling-Based Particle Simulations

Computational design-based optimization is a well-used tool in science and engineering. Our report documents the successful use of a particle sensitivity analysis for design-based optimization within Monte Carlo sampling-based particle simulation—a currently unavailable capability. Such a capability enables the particle simulation communities to go beyond forward simulation and promises to reduce the burden on overworked analysts by getting more done with less computation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Phase Transformation and Water Adsorption Behavior of As‐Deposited and Annealed Ru Metal Thin Films Prepared by Atomic Layer Deposition

ABSTRACT Surfaces play a central role in catalytic processes, and understanding the transformation of ruthenium metal into ruthenium oxide during annealing is essential for tailoring functional catalytic interfaces. In this study, we systematically investigate ≈22 nm thick Ru metal films deposited by atomic layer deposition (ALD) at 300°C, focusing on their chemical composition, structural evolution, and surface hydration behavior following post‐deposition annealing in air from 400 to 600°C. Lab‐based and synchrotron X‐ray photoelectron spectroscopy (XPS) reveal a gradual conversion from metallic Ru to fully oxidized Ru 4+ with increasing annealing temperature, accompanied by a corresponding increase in lattice oxygen. X‐ray diffraction (XRD) shows amorphous Ru oxide phases at 400°C and 500°C that evolve into crystalline RuO 2 at 600°C, while atomic force microscopy (AFM) indicates enhanced grain growth and surface roughening upon annealing. Ambient‐pressure XPS (AP‐XPS) under controlled H 2 O vapor environments (1–17 Torr) demonstrates that samples annealed at 400°C and 500°C exhibit initially high hydroxyl coverage that decreases with increasing water vapor pressure, concurrent with a rise in molecular H 2 O adsorption. In contrast, the crystalline RuO 2 surface formed at 600°C maintains stable hydroxylation and supports increased water uptake. Overall, this work provides fundamental insight into Ru oxide–H 2 O interactions and establishes design principles for engineering oxide surfaces optimized for electrocatalytic applications.

APXPS↗
DOE OSTI2025

CFD modeling of near-wall combustion and unburned methane prediction in natural gas spark ignition engines

Natural gas-powered engines play a critical role in gas drilling, compression, and transmission sectors, but methane (CH 4 ) from engine combustion slip can be significant over their lifespan, contributing to atmospheric pollution and signaling reduced engine efficiency. Here, to address this challenge, computational fluid dynamics (CFD) simulations offer valuable insights into the in-cylinder combustion process, enabling the optimization of combustion strategies and engine designs to minimize unburned CH 4 slip. This study aims to evaluate and improve combustion models for simulating the combustion process and predicting unburned CH 4 concentrations in natural gas spark-ignition (SI) engines, including engines that are part of combined reformer-engine systems. Specifically, the performance of two flamelet-based combustion models—the Extended Coherent Flame Model (ECFM) and the G-equation model—was assessed using experimental engine data collected under varying excess-air ratio (λ) conditions and fuel compositions, including natural gas and syngas blends. In addition, to enhance the predictive capabilities of the G-equation model, a flame-wall interaction (FWI) sub-model was integrated into its framework. The effects of its model parameters, such as quenching and influence distance, on combustion behavior and unburned methane predictions were analyzed in detail. The ECFM tended to predict delayed combustion phasing under diluted mixture conditions, resulting in overprediction of unburned CH 4 concentrations. In contrast, the G-equation model provided reasonable predictions of combustion pressure, while representing higher the CH 4 reduction rate across the operating condition compared to experimental data. Incorporating the FWI sub-model—with the quenching distance calculated based on a pressure-dependent relation (P -0.48 ) and a fixed influence distance of 1.5 mm—further improved the G-equation model’s accuracy in predicting CH 4 reduction rates without compromising its ability to simulate the combustion process.

Combustion model↗
DOE OSTI2021

Molecular Origin of Wettability Alteration of Subsurface Porous Media upon Gas Pressure Variations

Upon extraction/injection of a large quantity of gas from/into a subsurface system in shale gas production or carbon sequestration, the gas pressure varies remarkably, which may significantly change the wettability of porous media involved. Mechanistic understanding of such changes is critical for designing and optimizing a related subsurface engineering process. Using molecular dynamics simulations, in this work we have calculated the contact angle of a water droplet on various solid surfaces (kerogen, pyrophyllite, calcite, gibbsite, and montmorillonite) as a function of CO 2 or CH 4 gas pressure up to 200 atm at a temperature of 300 K. The calculation reveals a complex behavior of surface wettability alteration by gas pressure variation depending on surface chemistry and structure, and molecular interactions of fluid molecules with surfaces. As the CO 2 gas pressure increases, a partially hydrophilic kerogen surface becomes highly hydrophobic, while a calcite surface becomes more hydrophilic. Considering kerogen and calcite being the major components of a shale formation, we postulate that the wettability alteration of a solid surface induced by a gas pressure change may play an important role in fluid flows in shale gas production and geological carbon sequestration.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗