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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Identification of genetic interactions with priB links the PriA/PriB DNA replication restart pathway to double-strand DNA break repair in Escherichia coli

Collisions between DNA replication complexes (replisomes) and impediments such as damaged DNA or proteins tightly bound to the chromosome lead to premature dissociation of replisomes at least once per cell cycle in Escherichia coli. Left unrepaired, these events produce incompletely replicated chromosomes that cannot be properly partitioned into daughter cells. DNA replication restart, the process that reloads replisomes at prematurely terminated sites, is therefore essential in E. coli and other bacteria. Three replication restart pathways have been identified in E. coli: PriA/PriB, PriA/PriC, and PriC/Rep. A limited number of genetic interactions between replication restart and other genome maintenance pathways have been defined, but a systematic study placing replication restart reactions in a broader cellular context has not been performed. We have utilized transposon-insertion sequencing to identify new genetic interactions between DNA replication restart pathways and other cellular systems. Known genetic interactors with the priB replication restart gene (uniquely involved in the PriA/PriB pathway) were confirmed and several novel priB interactions were discovered. Targeted genetic and imaging-based experiments with priB and its genetic partners revealed significant double-strand DNA break accumulation in strains with mutations in dam, rep, rdgC, lexA, or polA. Modulating the activity of the RecA recombinase partially suppressed the detrimental effects of rdgC or lexA mutations in ΔpriB cells. Taken together, our results highlight roles for several genes in double-strand DNA break homeostasis and define a genetic network that facilitates DNA repair/processing upstream of PriA/PriB-mediated DNA replication restart in E. coli.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

MERS-CoV Accessory ORFs Play Key Role for Infection and Pathogenesis

While dispensable for viral replication, coronavirus (CoV) accessory open reading frame (ORF) proteins often play critical roles during infection and pathogenesis. Utilizing a previously generated mutant, we demonstrate that the absence of all four Middle East respiratory syndrome CoV (MERS-CoV) accessory ORFs (deletion of ORF3, -4a, -4b, and -5 [dORF3-5]) has major implications for viral replication and pathogenesis. Importantly, attenuation of the dORF3-5 mutant is primarily driven by dysregulated host responses, including disrupted cell processes, augmented interferon (IFN) pathway activation, and robust inflammation. In vitro replication attenuation also extends to in vivo models, allowing use of dORF3-5 as a live attenuated vaccine platform. Finally, examination of ORF5 implicates a partial role in modulation of NF-κB-mediated inflammation. Together, the results demonstrate the importance of MERS-CoV accessory ORFs for pathogenesis and highlight them as potential targets for surveillance and therapeutic treatments moving forward. Here, the initial emergence and periodic outbreaks of MERS-CoV highlight a continuing threat posed by zoonotic pathogens to global public health. In these studies, mutant virus generation demonstrates the necessity of accessory ORFs in regard to MERS-CoV infection and pathogenesis. With this in mind, accessory ORF functions can be targeted for both therapeutic and vaccine treatments in response to MERS-CoV and related group 2C coronaviruses. In addition, disruption of accessory ORFs in parallel may offer a rapid response platform to attenuation of future emergent strains based on both SARS- and MERS-CoV accessory ORF mutants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Fourier Neural Networks as Function Approximators and Differential Equation Solvers

We present a Fourier neural network (FNN) that can be mapped directly to the Fourier decomposition. The choice of activation and loss function yields results that replicate a Fourier series expansion closely while preserving a straightforward architecture with a single hidden layer. The simplicity of this network architecture facilitates the integration with any other higher-complexity networks, at a data pre- or postprocessing stage. We validate this FNN on naturally periodic smooth functions and on piecewise continuous periodic functions. We showcase the use of this FNN for modeling or solving partial differential equations with periodic boundary conditions. The main advantages of the current approach are the validity of the solution outside the training region, interpretability of the trained model, and simplicity of use.

Fourier decomposition↗
DOE OSTI2025

Estimating Switchgrass Biomass Yield and Lignocellulose Composition from UAV-Based Indices

Innovative methods for estimating commercial-scale switchgrass yields and feedstock quality are essential to optimize harvest logistics and biorefinery efficiency for sustainable aviation fuel production. This study utilized vegetation indices (VIs) derived from multispectral images to predict biomass yield and lignocellulose concentrations of advanced bioenergy-type switchgrass cultivars (“Liberty” and “Independence”) under two N rates (28 and 56 kg N ha –1 ). Field-scale plots were arranged in a randomized complete block design (RCBD) and replicated three times at Urbana, IL. Multispectral images captured during the 2021–2023 growing seasons were used to extract VIs. The results show that linear and exponential models outperformed partial least square and random forest models, with mid-August imagery providing the best predictions for biomass, cellulose, and hemicellulose. The green normalized difference vegetation index (GNDVI) was the best univariate predictor for biomass yield (R 2 = 0.86), while a multivariate combination of the GNDVI and normalized difference red-edge index (NDRE) enhanced prediction accuracy (R 2 = 0.88). Cellulose was best predicted using the NDRE (R 2 = 0.53), whereas hemicellulose prediction was most effective with a multivariate model combining the GNDVI, NDRE, NDVI, and green ratio vegetation index (GRVI) (R 2 = 0.44). These findings demonstrate the potential of UAV-based VIs for the in-season estimation of biomass yield and cellulose concentration.

09 BIOMASS FUELS↗
DOE OSTI2024

TNet: A Model-Constrained Tikhonov Network Approach for Inverse Problems

Deep learning (DL), in particular deep neural networks, by default is purely data-driven and in general does not require physics. This is the strength of DL but also one of its key limitations when applied to science and engineering problems in which underlying physical properties—such as stability, conservation, and positivity—and accuracy are required. DL methods in their original forms are often not capable of respecting the underlying mathematical models or achieving desired accuracy even in big-data regimes. On the other hand, many data-driven science and engineering problems, such as inverse problems, typically have limited experimental or observational data, and DL would overfit the data in this case. Leveraging information encoded in the underlying mathematical models, we argue, not only compensates for missing information in low data regimes but also provides opportunities to equip DL methods with the underlying physics, hence promoting better generalization. This paper develops a model-constrained DL approach and its variant TNet—a Tikhonov neural network—which are capable of learning not only information hidden in the training data but also in the underlying mathematical models to solve inverse problems governed by partial differential equations in low data regimes. We provide the constructions and some theoretical results for the proposed approaches for both linear and nonlinear inverse problems. Since TNet is designed to learn inverse solutions with Tikhonov regularization, it is interpretable: in fact it recovers Tikhonov solutions for linear cases while potentially approximating Tikhonov solutions for nonlinear inverse problems. We also prove that data randomization can enhance not only the smoothness of the networks but also their generalizations. Comprehensive numerical results confirm the theoretical findings and show that with even as little as 1 training data sample for one-dimensional (1D) deconvolution, 5 for an inverse 2D heat conductivity problem, 100 for inverse initial conditions for a time-dependent 2D Burgers’s equation, and 50 for inverse initial conditions for 2D Navier–Stokes equations, TNet solutions can be as accurate as Tikhonov solutions while being several orders of magnitude faster. Furthermore, this is possible owing to the model-constrained term, replications, and randomization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hanford low-activity waste glass composition-temperature-melt viscosity relationships

This study developed a model for predicting viscosity of alkali-alumino-borosilicate glass melts as functions of composition and temperature. The model is based on a total of 3935 viscosity-temperature data from 574 glasses with viscosity values ranging from 2.53 to 7260 Poise (P) in the temperature range of 900–1260°C. Several different model forms were surveyed, including those based on Arrhenius, Vogel-Fulcher-Tammann, Avramov-Milchev, and Mauro-Yue-Ellison-Gupta-Allen. For each of these models, combinations of the temperature-independent parameters were fitted to composition. It was found that generally fitting more than one temperature-independent parameter as functions of composition resulted in overfitting. The Avramov-Milchev-based model was found to best represent the Hanford low-activity waste glass melt viscosity data based on model fit and validation statistics. A 21-term partial quadratic mixture model was recommended for use. This model predicts melt viscosity with a root-mean square error of .1736 ln(P), which is similar to the error in viscosity measurements from replicate glass analyses of .1383 ln(P). Viscosity was found to be most increased by SiO 2 > Al 2 O 3 > ZrO 2 > SnO 2 and most decreased by Li 2 O > Na 2 O > B 2 O 3 > CaO > K 2 O > MgO, at temperatures from 900 to 1260°C.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

Lunar Explorer Instrument for space biology Applications (LEIA): An overview of planned science concept of operations

Radiation and reduced gravity pose biological risks to crewed deep space exploration. To better understand deep space radiation biology, the LEIA mission will measure charged particles and fast neutrons as well as yeast growth, metabolic rate, and bioengineered production of carotenoids at the lunar surface. LEIA will be delivered to the south polar region of the Moon by the Commercial Lunar Payload Services (CLPS) program. The LEIA science concept of operations was developed to isolate radiation and partial gravity from other environmental conditions experienced by the payload. Due to CLPS integration requirements, there will be at least eight months from loading yeast into the payload until activation on the lunar surface. Replicate yeast strains will be loaded in a randomized complete block design to minimize batch differences in desiccation tolerance and positional effects in the microfluidics culture system. Temperature and relative humidity will be controlled throughout integration and flight to maintain yeast viability and to enable measurement of yeast growth parameters within the lunar surface operations timeframe, with the ground control matching environmental conditions where possible. LEIA is co-manifested with the European Space Agency’s PROSPECT mission, which will be operating a drill during yeast growth. Vibration translated through the lunar lander will be mitigated and quantified to account for potential impacts on LEIA optical measurements and yeast growth rate. Total space radiation dose, including the transit exposure from Earth to the Moon, will be measured to obtain more accurate charged particle and fast neutron dose rates on the lunar surface. These radiation data will also yield ground truth radiation dose experienced by the yeast during the mission. Quantifying these environmental factors impacting the LEIA payload will allow more accurate ground control experiments to better isolate the biological responses to radiation and reduced gravity at the lunar surface.

Lunar Surface Mission↗
NASA NTRS2010

Simulation and Optimization of Vacuum Swing Adsorption Units for Spacesuit Carbon Dioxide and Humidity Control

Controlling carbon dioxide (CO2) and humidity levels in a spacesuit is critical to ensuring both the safety and comfort of an astronaut during extra-vehicular activity (EVA). Traditionally, this has been accomplished utilizing non-regenerative lithium hydroxide (LiOH) or regenerative metal oxide (MetOx) canisters which pose a significant weight burden. Although such technology enables air revitalization, the volume requirements to store the waste canisters as well as the mass to transport multiple units become prohibitive as mission durations increase. Consequently, motivation exists toward developing a fully regenerative technology for environmental control. The application of solid amine materials with vacuum swing adsorption technology has shown the capacity to control CO2 and concomitantly manage humidity levels through a fully regenerative cycle eliminating mission constraints imposed with non-regenerative technologies. Experimental results for full-size and sub-scale test articles have been collected and are described herein. In order to accelerate the developmental efforts, an axially-dispersed plug ow model with an accompanying energy balance has been established and correlated with the experimental data. The experimental and simulation results display good agreement for a variety of ow rates (110-170 SLM), replicated metabolic challenges (100-590 Watts), and atmosphere pressures under consideration for the spacesuit (248 and 760 mm Hg). The relationship between swing adsorption cycles for an outlet criterion of 6.0 mm Hg of CO2 partial pressure has been established for each metabolic challenge. In addition, variable metabolic profiles were imposed on the test articles in order to assess the ability of the technology to transition to new operational constraints. The advent of the model provides the capacity to apply computer-aided engineering practices to support the ongoing efforts to optimize and mature this technology for future application to space exploration.

Swickrath, Michael J.↗
DOE OSTI2023

Structural basis for cloverleaf RNA-initiated viral genome replication

The genomes of positive-strand RNA viruses serve as a template for both protein translation and genome replication. In enteroviruses, a cloverleaf RNA structure at the 5' end of the genome functions as a switch to transition from viral translation to replication by interacting with host poly(C)-binding protein 2 (PCBP2) and the viral 3CD pro protein. We determined the structures of cloverleaf RNA from coxsackievirus and poliovirus. Cloverleaf RNA folds into an H-type four-way junction and is stabilized by a unique adenosine-cytidine-uridine (A•C-U) base triple involving the conserved pyrimidine mismatch region. The two PCBP2 binding sites are spatially proximal and are located on the opposite end from the 3CD pro binding site on cloverleaf. We determined that the A•C-U base triple restricts the flexibility of the cloverleaf stem–loops resulting in partial occlusion of the PCBP2 binding site, and elimination of the A•C-U base triple increases the binding affinity of PCBP2 to the cloverleaf RNA. Based on the cloverleaf structures and biophysical assays, we propose a new mechanistic model by which enteroviruses use the cloverleaf structure as a molecular switch to transition from viral protein translation to genome replication.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

SPUS-Small-PDE-U-net-Solver

Small PDE U-Net Solver (SPUS) is a compact and efficient foundation model (FM) designed as a unified neural operator for solving a wide range of partial differentialequations (PDEs). SPUS leverages a lightweight residual U-Net-based architecture as a foundation model architecture. To enable effective learning in this minimalist framework, SPUS utilizes a simple yet powerful auto-regressive pretraining strategy which closely replicates the behavior of numerical solvers to learn the underlying physics. SPUS is designed to be pretrained on a diverse set of fluid dynamics PDEs from public benchmark datasets.

Siddik, Abu↗
NASA NTRS1984

Simulating certain aspects of hypogravity: Effects on the mandibular incisors of suspended rats (PULEH model)

The effect of a hypogravity simulating model on the rate of mandibular incisor formation, dentinogenesis and, amelogenesis in laboratory rats was studied. The model is the partial unloading by elevating the hindquarters. In this system, rat hindquarters are elevated 30 to 40 deg from the cage floors to completely unload the hindlimbs, but the animals are free to move about using their forelimbs. This model replicates the fluid sift changes which occur during the weightlessness of spaceflight and produces an osteopenia in the weight bearing skeletons. The histogenesis and/or mineralization rates of the mandibular incisor during the first 19d of PULEH in young growing rats are recorded.

Simmons, D. J.↗
NASA NTRSDate not supplied

Mars Space Suit Materials Testing Using SHERLOC Calibration Target Data: The Max-CF Project

The Mars 2020/“Perseverance” rover carries a suite of space suit materials as part of the SHERLOC* calibration target [1]. The materials are periodically analyzed by SHERLOC as part of a regular calibration routine and are generating a rich data set regarding their degradation in the martian surface environment. The Maximization of Calibration Fabrics (Max-CF) project will effectively turn SHERLOC data into a measure of space suit material service lifetimes by exposing a second set of materials in a Mars chamber, replicating SHERLOC measurements using the analogous ACRONM** instrument at JSC, and then performing materials testing to include tensile testing. These data can be used to inform space suit design and/or materials development, improving crew safety for future Mars missions. This will partially address NASA’s Strategic Knowledge Gap 8 (Mars Surface Technology) which identifies a need to develop technologies to “sustain humans on the surface of Mars [and] enable human mobility and exploration” [2]. This abstract describes the overall Max-CF project and progress on the laboratory-based study to date.

M Fries↗
NASA NTRSDate not supplied

Human Thermal Assessment of Traverse and Geology Task Iterations During Simulated Lunar Extravehicular Activity

Spacewalks or extravehicular activities (EVA) in microgravity are mentally and physically demanding. Current microgravity EVAs are predominantly focused on upper body tasks on engineered surfaces; however, the introduction of gravity means that crew members during future lunar EVAs will be required to perform tasks that generate full body workloads such as navigating natural lunar terrain while conducting geological sampling. To date, only 12 people have walked on the surface of the Moon resulting in limited knowledge of suited thermal regulation under lunar-relevant physical workloads. To address this gap, a study is underway to focus on spacesuit operations with simulated lunar EVA workloads. This study presents methodology for collecting standard thermal measures during suit testing. In this pilot study, two suited subjects underwent simulated lunar EVAs using the NASA Active Response Gravity Offload System (ARGOS) to simulate the effects of partial gravity. Each lunar EVA, lasting three to five hours, included various metabolically demanding EVA tasks. Subjects donned the NASA Mark III (MK III) space suit and were offloaded to 1/6th G (lunar gravity). A subset task circuit from one of these simulations replicated an EVA traverse to a lunar crater, taking a geological sample, and returning to base. The suited subject walked one 1500 m (0% grade) and three 500 m traverses at three different grades (10, 20, 30%). Between each traverse, subjects performed geology sampling tasks (0 and 10% grade). Thermal measurements included core and skin temperature, liquid cooling garment (LCG) inlet and outlet temperature, and spacesuit gas inlet and outlet temperature and humidity. Compared to baseline core temperature (S1 = 37.07±0.32 °C, S2 = 37.27±0.01 °C) and mean skin temperature (S1 = 32.38±0.34 °C, S2 = 33.62±0.03 °C), after a 1500 m traverse, each suited subject showed an increased core temperature (S1 = 37.23±0.12 °C, S2 = 37.41±0.11°C) and decreased mean skin temperature (S1 = 32.16±0.17 °C, S2 = 31.53±1.15 °C) at a delta LCG temperature (S1 = 1.86±0.24 °C, S2 = 2.20±0.76 °C) and delta suit humidity (S1 = 9.66±1.49 %, S2 = 11.50±1.58 %). Core temperature continued to increase from compounding traverse and geology tasks (S1 = 38.04±0.03 °C, S2 = 38.1±0.02 °C) accompanied by an increase in delta LCG temperature (S1 = 2.24±0.13 °C, S2 = 2.67±0.12 °C) and delta suit humidity (S1 = 28±2.42 %, S2 = 31±2.13 %). Conversely, as the circuit progressed, mean skin temperature continued to decrease due to sustained LCG heat rejection (S1 = 30.08±0.15 °C, S2 = 30.28±0.07 °C). During the simulated EVA circuit core temperature increased to elevated values and remained elevated as heat was retained while mean skin temperature decreased due to peripheral heat offloaded to the LCG. The thermal measures collected during this study provided critical heat loading dynamics during lunar EVA tasks. Including core and skin temperatures along with suited thermal measures provides a standard data collection scheme for human thermal metrics during EVA task management. Data collected in this configuration can be used to build future lunar EVA task circuits and human thermal predictions.

Bradley Hoffmann↗
DOE OSTI2017

Middle East Respiratory Syndrome Coronavirus Nonstructural Protein 16 Is Necessary for Interferon Resistance and Viral Pathogenesis

ABSTRACT Coronaviruses (CoVs) encode a mixture of highly conserved and novel genes, as well as genetic elements necessary for infection and pathogenesis, raising the possibility of common targets for attenuation and therapeutic design. In this study, we focused on highly conserved nonstructural protein 16 (NSP16), a viral 2' O -methyltransferase (2' O -MTase) that encodes critical functions in immune modulation and infection. Using reverse genetics, we disrupted a key motif in the conserved KDKE motif of Middle East respiratory syndrome CoV (MERS-CoV) NSP16 (D130A) and evaluated the effect on viral infection and pathogenesis. While the absence of 2' O -MTase activity had only a marginal impact on propagation and replication in Vero cells, dNSP16 mutant MERS-CoV demonstrated significant attenuation relative to the control both in primary human airway cell cultures and in vivo . Further examination indicated that dNSP16 mutant MERS-CoV had a type I interferon (IFN)-based attenuation and was partially restored in the absence of molecules of IFN-induced proteins with tetratricopeptide repeats. Importantly, the robust attenuation permitted the use of dNSP16 mutant MERS-CoV as a live attenuated vaccine platform protecting from a challenge with a mouse-adapted MERS-CoV strain. These studies demonstrate the importance of the conserved 2' O -MTase activity for CoV pathogenesis and highlight NSP16 as a conserved universal target for rapid live attenuated vaccine design in an expanding CoV outbreak setting. IMPORTANCE Coronavirus (CoV) emergence in both humans and livestock represents a significant threat to global public health, as evidenced by the sudden emergence of severe acute respiratory syndrome CoV (SARS-CoV), MERS-CoV, porcine epidemic diarrhea virus, and swine delta CoV in the 21st century. These studies describe an approach that effectively targets the highly conserved 2' O -MTase activity of CoVs for attenuation. With clear understanding of the IFN/IFIT (IFN-induced proteins with tetratricopeptide repeats)-based mechanism, NSP16 mutants provide a suitable target for a live attenuated vaccine platform, as well as therapeutic development for both current and future emergent CoV strains. Importantly, other approaches targeting other conserved pan-CoV functions have not yet proven effective against MERS-CoV, illustrating the broad applicability of targeting viral 2' O -MTase function across CoVs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Using Artificial Soiling to Rank Anti-Soiling Coatings for Arid Climates

A simple method to evaluate (e.g., screen and rank) the performance of coatings that are fully or partially anti-soiling (AS) is lacking within the PV industry. Artificial soiling may be used as a rapid and economical assessment approach, offering an efficient alternative to time-intensive, site-specific field testing. In this study, we present an artificial soiling method to replicate the anti-soiling performance rankings of two groups of coated glass samples supplied by two manufacturers (group C with CA, CP, and CS coatings; group A with AC coating). These are compared to field aging in two climates: semi-arid Lemoore, California over 4 months, and the hot desert in Mesa, Arizona over 7 months. Both field and indoor performance ranking utilize the transmittance ratio, defined as the optical transmittance between a coated sample and an uncoated reference in each group, as an evaluation metric to assess the effectiveness of the artificial soiling approach in replicating field soiling. It is critical to mimic the dominant field meteorological conditions associated with soiling-prone days and vulnerable times of day during the soiling season. Our results reveal that the use of artificial soiling for field performance ranking among coatings strongly depends on the soil type (composition and particle distribution), dust surface density, and prevalent environmental factors (wetting saturation by humidity, dew condensation extent, and prior weathering history of the coating). The similar rank order relative to the field suggests that the artificial soiling approach presented may be used for down-selecting anti-soiling coatings prior to prolonged field validation.

14 SOLAR ENERGY↗
NASA NTRS2016

Electromagnetic Basis of Metabolism and Heredity

Living organisms control their cellular biological clocks to maintain functional oscillation of the redox cycle, also called the "metabolic cycle" or "respiratory cycle". Organization of cellular processes requires parallel processing on a synchronized time-base. These clocks coordinate the timing of all biochemical processes in the cell, including energy production, DNA replication, and RNA transcription. When this universal time keeping function is perturbed by exogenous induction of reactive oxygen species (ROS), the rate of metabolism changes. This causes oxidative stress, aging and mutations. Therefore, good temporal coordination of the redox cycle not only actively prevents chemical conflict between the reductive and oxidative partial reactions; it also maintains genome integrity and lifespan. Moreover, this universal biochemical rhythm can be disrupted by ROS induction in vivo. This in turn can be achieved by blocking the electron transport chain either endogenously or exogenously by various metabolites, e.g. hydrogen sulfide (H2S), highly diffusible drugs, and carbon monoxide (CO). Alternatively, the electron transport in vivo can be attenuated via a coherent or interfering transfer of energy from exogenous ultralow frequency (ULF) and extremely low frequency (ELF) electromagnetic (EM) fields, suggesting that-on Earth-such ambient fields are an omnipresent (and probably crucially important) factor for the time-setting basis of universal biochemical reactions in living cells. Our work demonstrated previously un-described evidence for quantum effects in biology by electromagnetic coupling below thermal noise at the universal electron transport chain (ETC) in vivo.

Electromagnetic↗
DOE OSTI2023

Direct Numerical Simulation of Partial Fuel Stratification Assisted Lean Premixed Combustion for Assessment of Hybrid G-Equation/Well-Stirred Reactor Model

Partial fuel stratification (PFS) is a promising fuel injection strategy to stabilize lean premixed combustion in spark-ignition (SI) engines. PFS creates a locally stratified mixture by injecting a fraction of the fuel, just before spark timing, into the engine cylinder containing homogeneous lean fuel/air mixture. Further, this locally stratified mixture, when ignited, results in complex flame structure and propagation modes similar to partially premixed flames and allows for faster and more stable flame propagation than a homogeneous lean mixture. This study focuses on understanding the detailed flame structures associated with PFS-assisted lean premixed combustion. First, a two-dimensional direct numerical simulation (DNS) is performed using detailed fuel chemistry, experimental pressure trace, and realistic initial conditions mapped from a prior engine large-eddy simulation (LES), replicating practical lean SI operating conditions. DNS results suggest that the conventional triple flame structure is prevalent during the initial stage of flame kernel growth. Both premixed and nonpremixed combustion modes are present with the premixed mode contributing dominantly to the total heat release. Detailed analysis further reveals the effects of flame stretch and fuel pyrolysis on flame displacement speed. Based on the DNS findings, the accuracy of a hybrid G-equation/well-stirred reactor (WSR) combustion model is assessed for the PFS-assisted lean operation in the LES context. It is found that the G-equation model qualitatively captures the premixed branches of the triple flame, while the WSR model predicts the nonpremixed branch of the triple flame. Finally, potential needs for improvements to the hybrid G-equation/WSR modeling approach are discussed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Investigating the Effect of the Closure in Partially-Averaged Navier–Stokes Equations

For this study, the importance of the turbulence closure to the modeling accuracy of the partially-averaged Navier–Stokes equations (PANS) is investigated in prediction of the flow around a circular cylinder at Reynolds number of 3900. A series of PANS calculations at various degrees of physical resolution is conducted using three Reynolds-averaged Navier–Stokes equations (RANS)-based closures: the standard, shear-stress transport (SST), and turbulent/nonturbulent (TNT) k–ω models. The latter is proposed in this work. The results illustrate the dependence of PANS on the closure. At coarse physical resolutions, a narrower range of scales is resolved so that the influence of the closure on the simulations accuracy increases significantly. Among all closures, PANS–TNT achieves the lowest comparison errors. The reduced sensitivity of this closure to freestream turbulence quantities and the absence of auxiliary functions from its governing equations are certainly contributing to this result. It is demonstrated that the use of partial turbulence quantities in such auxiliary functions calibrated for total turbulent (RANS) quantities affects their behavior. On the other hand, the successive increase of physical resolution reduces the relevance of the closure, causing the convergence of the three models toward the same solution. Finally, this outcome is achieved once the physical resolution and closure guarantee the precise replication of the spatial development of the key coherent structures of the flow.

42 ENGINEERING↗