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At least 19 records

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NASA NTRS2003

An Arabidopsis Ran-binding protein, AtRanBP1c, is a co-activator of Ran GTPase-activating protein and requires the C-terminus for its cytoplasmic localization

Ran-binding proteins (RanBPs) are a group of proteins that bind to Ran (Ras-related nuclear small GTP-binding protein), and thus either control the GTP/GDP-bound states of Ran or help couple the Ran GTPase cycle to a cellular process. AtRanBP1c is a Ran-binding protein from Arabidopsis thaliana (L.) Heynh. that was recently shown to be critically involved in the regulation of auxin-induced mitotic progression [S.-H. Kim et al. (2001) Plant Cell 13:2619-2630]. Here we report that AtRanBP1c inhibits the EDTA-induced release of GTP from Ran and serves as a co-activator of Ran-GTPase-activating protein (RanGAP) in vitro. Transient expression of AtRanBP1c fused to a beta-glucuronidase (GUS) reporter reveals that the protein localizes primarily to the cytosol. Neither the N- nor C-terminus of AtRanBP1c, which flank the Ran-binding domain (RanBD), is necessary for the binding of PsRan1-GTP to the protein, but both are needed for the cytosolic localization of GUS-fused AtRanBP1c. These findings, together with a previous report that AtRanBP1c is critically involved in root growth and development, imply that the promotion of GTP hydrolysis by the Ran/RanGAP/AtRanBP1c complex in the cytoplasm, and the resulting concentration gradient of Ran-GDP to Ran-GTP across the nuclear membrane could be important in the regulation of auxin-induced mitotic progression in root tips of A. thaliana.

NASA Discipline Plant Biology↗

DOE OSTI2022

Hybrid RANS-LES of the Atmospheric Boundary Layer for Wind Farm Simulations: Preprint

Wind farm simulations often do not accurately represent wake-atmospheric boundary layer (ABL) interactions, blade boundary layer (BL) dynamics, and turbine-turbine interactions. In this work, we use Active Model Split (AMS), a new hybrid Reynolds-Averaged Navier Stokes (RANS)-large eddy simulation (LES) model, which is well suited to capture these effects because the model can (i) accurately simulate the ABL with the Coriolis effect, (ii) is accurate in adverse pressure gradients such as those near wind turbine blades, and (iii) has sufficiently low computational cost to simulate multiple turbines while resolving the blade BL. For simplicity and consistency we develop AMS to be used throughout the domain rather than in a zonal method. We implement our work in the massively parallel flow solver, Nalu-Wind, so that our model can access the compute resources needed for blade-resolved simulations of multiple wind turbines. To accomplish these aims, we modify the baseline AMS by changing the RANS contribution to SST k - omega with a length scale limiter, adding the Coriolis effect, and developing an appropriate wall treatment. We show that AMS of the ABL with the Coriolis effect matches LES reference results better than those obtained with RANS. We describe our plans to add buoyancy effects and wind turbines to our AMS simulations.

atmospheric boundary layer↗

DOE OSTI2023

Predicting wind farm operations with machine learning and the P2D‐RANS model: A case study for an AWAKEN site

Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.

17 WIND ENERGY↗

NASA NTRS2015

Unsteady Three-Dimensional Simulation of a Shear Coaxial GO2/GH2 Rocket Injector with RANS and Hybrid-RAN-LES/DES Using Flamelet Models

Historically, the analysis and design of liquid rocket engines (LREs) has relied on full‐scale testing and one‐dimensional empirical tools. The testing is extremely expensive and the one‐dimensional tools are not designed to capture the highly complex, and multi‐dimensional features that are inherent to LREs. Recent advances in computational fluid dynamics (CFD) tools have made it possible to predict liquid rocket engine performance, stability, to assess the effect of complex flow features, and to evaluate injector‐driven thermal environments, to mitigate the cost of testing. Extensive efforts to verify and validate these CFD tools have been conducted, to provide confidence for using them during the design cycle. Previous validation efforts have documented comparisons of predicted heat flux thermal environments with test data for a single element gaseous oxygen (GO2) and gaseous hydrogen (GH2) injector. The most notable validation effort was a comprehensive validation effort conducted by Tucker et al. [1], in which a number of different groups modeled a GO2/GH2 single element configuration by Pal et al [2]. The tools used for this validation comparison employed a range of algorithms, from both steady and unsteady Reynolds Averaged Navier‐Stokes (U/RANS) calculations, large‐eddy simulations (LES), detached eddy simulations (DES), and various combinations. A more recent effort by Thakur et al. [3] focused on using a state‐of‐the‐art CFD simulation tool, Loci/STREAM, on a two‐dimensional grid. Loci/STREAM was chosen because it has a unique, very efficient flamelet parameterization of combustion reactions that are too computationally expensive to simulate with conventional finite‐rate chemistry calculations. The current effort focuses on further advancement of validation efforts, again using the Loci/STREAM tool with the flamelet parameterization, but this time with a three‐dimensional grid. Comparisons to the Pal et al. heat flux data will be made for both RANS and Hybrid RANSLES/ Detached Eddy simulations (DES). Computation costs will be reported, along with comparison of accuracy and cost to much less expensive two‐dimensional RANS simulations of the same geometry.

Westra, Doug G.↗

DOE OSTI2021

RAN and YBX1 are required for cell proliferation and IL-4 expression and linked to poor prognosis in oral squamous cell carcinoma

Highlights: • RAN and YBX1 are co-expressed and required for cell proliferation in OSCC cells. • RAN and YBX1 are positively associated with the immune infiltrates of CD4{sup +} Th2 cells. • RAN and YBX1 can promote IL-4 expression. • RAN is a direct target of YBX1. • RAN and YBX1 can serve as potential co-biomarkers for poor prognosis in OSCC. Oral squamous cell carcinoma (OSCC) is one of the most common malignancies in the world, with a high mortality rate. RAN is a member of the Ras GTPase family and is overexpressed in a range of cancers, however, the relationship between RAN and OSCC is rarely reported. In this study, we found that RAN is overexpressed in OSCC tissues. RAN inhibition retarded OSCC cell proliferation and led to apoptosis and cell cycle arrest. Knockdown of RAN inhibited tumor growth in vivo. Strikingly, we found that RAN and oncogene Y-box binding protein-1 (YBX1) are positively associated with the immune infiltrates of CD4{sup +} Th2 cells in multiple types of cancer, and can promote IL-4 expression. IL-4 treatment can partially rescue RAN knockdown-induced cell apoptosis in OSCC cells. Moreover, overexpression of RAN could rescue cell growth inhibition caused by knockdown of YBX1. Furthermore, patients with low expression of both RAN and YBX1 had better overall survival than others. Collectively, these findings indicate that RAN is a target of YBX1. RAN and YBX1 are required for cell proliferation and IL-4 expression. RAN and YBX1 are co-expressed and can serve as potential co-biomarkers for poor prognosis in OSCC.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Evaluation of RANS vs. LES simulation of fluid flow through 3 × 3 rod bundle with a simple spacer grid as a precursor to coupled fluid–structure interaction simulations

The research literature on Computational Fluid Dynamics (CFD) of coolant flow through rod bundles with spacer-grids and mixing vanes is replete, ranging from high fidelity Large Eddy Simulation (LES)/Direct Numerical Simulation (DNS) simulations to Reynolds-Averaged Navier–Stokes (RANS) modeled studies. The mixing of flow between subchannels and the pressure drop through the bundle are fundamental quantities useful for comparing and evaluating CFD methods. Less commonly observed and compared are the forces exerted onto the structure by the fluid. The present study seeks to evaluate the use of RANS simulations for predicting the structural response to fluid flow. Wall resolved RANS simulations are benchmarked against LES simulations of fluid flow at a Reynolds number of 15,000 through a 3 × 3 fuel rod bundle with a simple spacer grid. Velocity line-plots are compared showing good agreement between RANS and LES results, ascertaining that the former is capable of capturing the essential time-averaged velocity profile. Additionally, the distribution of forces on the spacer grid and fuel rods are collected as a function of time and space. The RANS methods are evaluated using the frequency and magnitude of the fluctuating forces on various portions of the structure as compared to LES. In conclusion, the power spectral density evaluation of the models reveal underprediction of force amplitude on the rod walls by RANS and also discrepancy in the prediction of high frequency spectra, especially in the immediate vicinity of spacer-grid structure, which may be attributed to the lack of random turbulence fluctuation or insufficient modeling of small-scale eddies in RANS simulation.

FIV↗

DOE OSTI2025

Risk-Aware Reinforcement Learning Framework for User-Centric O-RAN

The evolution of Open Radio Access Networks (O-RAN) presents an opportunity to enhance network performance by enabling dynamic orchestration of configuration and optimization parameters (COPs) through online learning methods. However, leveraging this potential requires overcoming the limitations of traditional cell-centric RAN architectures, which lack the necessary flexibility. On the other hand, despite their recent popularity, the practical deployment of online learning frameworks, such as Deep Reinforcement Learning (DRL)-based COP optimization solutions, remains limited due to their risk of deteriorating network performance during the exploration phase. In this article, we propose and analyze a novel risk-aware DRL framework for user-centric RAN (UC-RAN), which offers both the architectural flexibility and COP optimization to exploit this flexibility. We investigate and identify UC-RAN COPs that can be optimized via a soft actor-critic algorithm implementable as an O-RAN application (rApp) to jointly maximize latency satisfaction, reliability satisfaction, area spectral efficiency, and energy efficiency. We use the offline learning on UC-RAN to reliably accelerate DRL training, thus minimizing the risk of DRL deteriorating cellular network performance. Results show that our proposed solution approaches near-optimal performance in just a few hundred iterations with a decrease in risk score by a factor of ten.

6G and beyond↗

NASA NTRS2004

Numerical Study of Flow Past a Circular Cylinder Using RANS, Hybrid RANS/LES and PANS Formulations

Two multiscale type turbulence models are implemented in the PAB3D solver. The models are based on modifying the Reynolds Averaged Navier-Stokes (RANS) equations. The first scheme is a hybrid RANS/LES model utilizing the two-equation (k(sub epsilon)) model with a RANS/LES transition function dependent on grid spacing and the computed turbulence length scale. The second scheme is a modified version of the partially averaged Navier-Stokes (PANS) model, where the unresolved kinetic energy parameter (f(sub k)) is allowed to vary as a function of grid spacing and the turbulence length scale. Solutions from these models are compared to RANS results and experimental data for a stationary and rotating cylinder. The parameter f(sub k) varies between zero and one and has the characteristic to be equal to one in the viscous sub layer, and when the RANS turbulent viscosity becomes smaller than the LES viscosity. The formulation, usage methodology, and validation example are presented to demonstrate the enhancement of PAB3D's time-accurate and turbulence modeling capabilities. The models are compared to RANS results and experimental data for turbulent separated flows (TS) and laminar separated flows (LS) around stationary and rotating cylinders. For a stationary cylinder, the TS case is accurately simulated using the general two-equation k(sub epsilon) turbulence model (eddy-viscosity model). PAB3D accurately predicts the drag coefficient (CD), lift coefficient (CL) and the Strouhal number (St). The LS case was a challenge for the RANS computation with an eddy-viscosity turbulence model. The RANS/LES and PANS performed well and showed marked improvements over the RANS solution. The modified PANS model was the most accurate. For the rotating cylinder, the spin ratio varied from zero to one, and the PANS results were in good agreement with published experimental data. RANS/LES and PANS capture both temporal and spatial fluctuations and produce large-scale structures that do not occur in the RANS simulation. The current results show promise for the capability of PANS in simulating unsteady and complex flow phenomena.

Elmiligui, Alaa A.↗

NASA NTRS2013

A New Formulation for Hybrid LES-RANS Computations

Ideally, a hybrid LES-RANS computation would employ LES only where necessary to make up for the failure of the RANS model to provide sufficient accuracy or to provide time-dependent information. Current approaches are fairly restrictive in the placement of LES and RANS regions; an LES-RANS transition in a boundary layer, for example, yields an unphysical log-layer shift. A hybrid computation is formulated here to allow greater control over the placement of LES and RANS regions and the transitions between them. The concept of model invariance is introduced, which provides a basis for interpreting hybrid results within an LES-RANS transition zone. Consequences of imposing model invariance include the addition of terms to the governing equations that compensate for unphysical gradients created as the model changes between RANS and LES. Computational results illustrate the increased accuracy of the approach and its insensitivity to the location of the transition and to the blending function employed.

Woodruff, Stephen L.↗

NASA NTRS2018

Model-Invariant Hybrid RANS-LES Computations on Unstructured Meshes

Hybrid RANS-LES computations combine the bene ts of RANS and LES so that LES is used in regions where the accuracy of RANS deteriorates. The numerous hybrid approaches are limited by the speci cation of the LES-RANS interface, which can cause nonphysical results such as log-layer mismatch and low shear stress. The hybrid RANS-LES approach based on the concept of model invariance, mitigates these problems, enabling seamless blending of the RANS and LES regions while forming the basis for interpreting the results in the interface region. This hybrid formulation was implemented in the NASA FUN3D unstructured code and computations for ow in a channel at Reynolds number of 3300 (based on the channel half width h and the bulk in ow velocity u(infinity) were carried out. An isotropic stochastic turbulence generator was implemented to generate in ow turbulence. The present approach was able to eliminate the log-layer mismatch and predict the shear stress fairly well. Thus, the model-invariant hybrid formulation coupled with the isotropic turbulence in ow generation serves as a physically meaningful way of performing hybrid RANS-LES computations.

Sreekanth, R.↗

DOE OSTI2025

Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds

Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.

5G↗

DOE OSTI2025

Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds

5G↗

NASA NTRSDate not supplied

A Hybrid RANS-LES Perspective for the High Lift Common Research Model Using LAVA

An assessment of a Hybrid RANS/LES (HRLES) approach for𝐶𝐿,max prediction is presented for the NASA High-Lift Common Research Model (CRM-HL). Both the free air and the wind tunnel configuration of the CRM-HL are investigated and the results are compared to the QinetiQ wind tunnel experiments and to two other numerical approaches: Reynolds Averaged Navier-Stokes (RANS) and Wall-Modeled Large Eddy Simulations (WMLES). For the free-air configuration, HRLES was shown to address some of the known shortcomings in RANS methods and prevent inboard and outboard flow separation particularly in the region of𝐶𝐿,max and post-stall. To achieve these improvements over RANS, LES-appropriate grids and numerical discretizations are required. HRLES predicts a weak pitch break at the highest angle-of-attack due to onset of wing-root corner flow separation whereas the free-air corrected experiment values indicate an occurrence of a much stronger pitch break. The improvements of HRLES over a URANS approach has been objectively shown by computing a set of solutions with the same grid, same numerics and time-step size and comparing the solutions. It was also found that when applying HRLES to a RANS best practice grid and numerics that the HRLES method significantly under performed RANS. For the in tunnel configuration, HRLES showed good agreement with the loads, surface pressure and oil-flow photographs obtained in the experiment. HRLES was able to improve upon the RANS simulations, which showed a sharp loss of lift at the two highest angles-of-attack due to large scale inboard and outboard separation on the wing, by correctly predicting the corner flow separation and showing remarkably close agreement in the flow topologies with the experiment.

TTT↗

DOE OSTI2021

Toward Predictive RANS and SRS Computations of Turbulent External Flows of Practical Interest

In this work, we investigate the main challenges to prediction of turbulent external flows of practical interest with Reynolds-Averaged Navier–Stokes equations (RANS) and Scale-Resolving Simulation (SRS) models. This represents a crucial step toward further developing and establishing these formulations so they can be confidently utilized in engineering problems without reference data. The study initiates by identifying the major challenges to prediction. A literature review is performed to illustrate their effects in RANS and SRS computations. Afterward, we evaluate the impact of the challenges to prediction by analyzing representative statistically steady and unsteady flows with prominent RANS and SRS methods. These include multiple turbulent viscosity and second-moment RANS closures, and hybrid and bridging SRS models. The results demonstrate the potential of the selected SRS models to predict engineering flows. Yet, they also show the importance of considering the challenges to prediction during the setup and conduction of numerical experiments. These can suppress the advantages of using SRS formulations. The data also indicate that only SRS models can confidently predict statistically unsteady flows. In contrast, the results demonstrate that mean-flow quantities of statistically steady flows can be efficiently calculated with RANS closures, especially second-moment closures. Among the selected SRS methods, bridging models reveal better suited for prediction due to their ability to prevent commutation errors and enable the robust evaluation of numerical and modeling errors. This last property allows the use of a new validation technique that does not require reference data.

42 ENGINEERING↗

DOE OSTI2024

Numerical simulation of involute-plate research reactor flow behavior using RANS, LES and DNS

This paper investigates the flow behavior of involute-plate research reactors by performing Reynolds-Averaged Navier Stokes simulation (RANS), Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) of the channel flow between fuel plates. By modeling turbulence with different numerical approaches, this study provides data with three levels of fidelity. For the RANS simulation, three widely used turbulence models, i.e., k-ε, k-ω, Reynolds Stress Turbulence model (RST) are applied by using the commercial CFD code STAR-CCM +. For LES and DNS, the open-source CFD code, Nek5000, is used given its outstanding scalability on High Performance Computer (HPC) and high-order technique. The results from RANS simulations are compared with that from LES and DNS for benchmarking. Both macroscale parameters and turbulence statistics, such as velocity magnitude, lateral velocity and turbulence kinetic energy, are presented and analyzed. The results from RANS simulation achieve good agreement with LES and DNS on velocity and turbulence kinetic energy prediction. The RST turbulence model predicts the most similar flow pattern of lateral velocity as compared to LES and DNS. The Lambda-2 (λ2) criterion with a reasonable threshold is used to demonstrate the instantaneous vortices distribution in the involute channel from both LES and DNS calculation. The DNS simulation captures more detailed turbulence especially near the corner, which explains the discrepancy between LES and DNS results near the corner. The normalized RMS error are defined and calculated to assess the performance of those turbulence models. The RST model captures the anisotropic feature of turbulence, which enable it to outperform other turbulence models for predicting the flow behavior in an involute channel. Although some discrepancies are found between LES and DNS results in the corner, the overall deviations between LES and DNS are found to be small. In conclusion, given that the computational cost of DNS calculation is an order of magnitude higher, using LES data for benchmarking RANS model is a cost-effective approach.

DNS↗

NASA NTRSDate not supplied

High-Lift Common Research Model: RANS, HRLES and WMLES Perspectives for CLmax Prediction Using LAVA

A unified assessment of three turbulence treatments: Reynolds Averaged Navier-Stokes(RANS), Hybrid RANS/LES (HRLES) and Equilibrium Wall-Modelled Large Eddy Simulation(WMLES) is presented for the High-Lift Common Research Model (CRM-HL). For the free-air configuration, steady-state RANS simulations show very accurate drag polar predictions in the low-𝛼linear regime. However, strong grid sensitivity is reported near the maximum lift-state(𝐶𝐿max), with finer-grids showing larger errors and predicting erroneous flow topologies on the wing. Our RANS simulations show that several corrections for the Spalart-Allmaras (SA)turbulence model widely used in the community lead to more erroneous results compared to the baseline closure, without exception. Both scale-resolving methods (HRLES and WMLES)address these drawbacks and predict an outboard separation pattern on the main element that is in good agreement with the oil flow photographs taken from the QinetiQ wind tunnel experiments, when LES-appropriate grids and numerical discretizations are used. While RANS simulations with the baseline SA closure do not show any wing-root separation post𝐶𝐿max, both HRLES and WMLES show onset of corner flow separation with varying degrees of progression, along with a weak pitch break in the wing-contribution of the overall pitching moment. This post-𝐶𝐿max pitch break seen in the free-air simulations is weaker than the break observed in experiments, with a weaker break reported in WMLES for each iteration of grid-refinement. In-tunnel simulations using both SA-baseline RANS and WMLES show a much stronger post-𝐶𝐿max break with the WMLES predictions showing excellent agreement with the experiment in terms of both the flow-topology observed and the pressure-coefficients at various spanwise stations. Sensitivity to the tunnel wall boundary layer is characterized via comparisons between viscous and inviscid treatments for the tunnel walls. WMLES predictions show moderate sensitivity at the predicted inboard flow-state at 𝐶𝐿max along with the progression towards a post-𝐶𝐿max stall; however, this stalled state at 𝛼≈20◦(inside the tunnel) obtained with both tunnel wall treatments appears to be largely identical.

TTT↗

DOE OSTI2023

Machine learning from RANS and LES to inform coarse grid simulations

Nuclear system thermal hydraulic analysis has historically relied on computationally inexpensive 1D codes. However, such tools are unable to capture multiscale multidimensional effects in large nuclear reactor enclosures. On the other hand, simulations with higher fidelity can be too expensive for such purposes. One of the ways to reduce computational cost is to perform simulations on a coarse grid, which, unfortunately, introduces large discretization errors. In this paper, two high-to-low data-driven approaches are investigated: (1) a coarse grid turbulence model to predict eddy viscosity and (2) correction of errors in coarse grid velocity fields. The approaches aim to reduce grid- and turbulence model-induced errors in coarse grid Reynolds-averaged Navier–Stokes (RANS) simulations. Two sources of high-fidelity data, RANS and large eddy simulations (LES), are explored. To extract the eddy viscosity from the LES data, an inverse optimization problem is solved. However, the LES eddy viscosity is shown to be comparable to the RANS eddy viscosity in terms of error reduction. Therefore, the directly available RANS eddy viscosity was used to develop a coarse grid data-driven turbulence model. Additionally, error correction in velocity is used to reduce the remaining uncertainties and bring the results closer to reality. In conclusion, the performance of the frameworks is demonstrated for a scaled upper plenum of a gas-cooled reactor facility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

LES and RANS Spray Combustion Analysis of OME 3-5 and n-Dodecane

Clean-burning oxygenated and synthetic fuels derived from renewable power, so-called e-fuels, are a promising pathway to decarbonize compression–ignition engines. Polyoxymethylene dimethyl ethers (PODEs or OMEs) are one candidate of such fuels with good prospects. Their lack of carbon-to-carbon bonds and high concentration of chemically bound oxygen effectively negate the emergence of polycyclic aromatic hydrocarbons (PAHs) and even their precursors like acetylene (C 2 H 2 ), enabling soot-free combustion without the soot-NO x trade-off common for diesel engines. The differences in the spray combustion process for OMEs and diesel-like reference fuels like n-dodecane and their potential implications on engine applications include discrepancies in the observed ignition delay, the stabilized flame lift-off location, and significant deviations in high-temperature flame morphology. For CFD simulations, the accurate modeling and prediction of these differences between OMEs and n-dodecane proved challenging. This study investigates the spray combustion process of an OME 3 − 5 mixture and n-dodecane with advanced optical diagnostics, Reynolds-Averaged Navier–Stokes (RANS), and Large-Eddy Simulations (LESs) within a constant-volume vessel. Cool-flame and high-temperature combustion were measured simultaneously via high-speed (50 kHz) imaging with formaldehyde (CH 2 O) planar laser-induced fluorescence (PLIF) representing the former and line-of-sight OH* chemiluminescence the latter. Both RANS and LES simulations accurately describe the cool-flame development process with the formation of CH 2 O. However, CH 2 O consumption and the onset of high-temperature reactions, signaled by the rise of OH* levels, show significant deviations between RANS, LES, and experiments as well as between n-dodecane and OME. A focus is set on the quality of the simulated results compared to the experimentally observed spatial distribution of OH*, especially in OME fuel-rich regions. The influence of the turbulence modeling is investigated for the two distinct ambient temperatures of 900 K and 1200 K within the Engine Combustion Network Spray A setup. The capabilities and limitations of the RANS simulations are demonstrated with the initial cool-flame propagation and periodic oscillations of CH 2 O formation/consumption during the quasi-steady combustion period captured by the LES.

CFD↗