Guilhem Lacaze

Effects of Real-Fluid Thermodynamics on High-Pressure Fuel Injection Processes

This paper first summarizes a new theoretical description that quantifies the ef fects of real-fluid thermodynamics on liquid fuel injection processes as a function of pressure at typical engine operating conditions. It then focuses on the implications this has on modeling such flows with emphasis on application of the Large Eddy Simulation (LES) technique. The theory explains and quantifies the major differences that occur in the jet dynamics compared to that described by classical spray theory in a manner consistent with experimental observations. In particular, the classical view of spray atomization as an appropriate model at some engine operating conditions is questionable. Instead, non-ideal real-fluid behavior must be taken into account using a multicomponent formulation that applies to hydrocarbon mixtures at high-pressure supercritical conditions. To highlight the implications and needs related to modeling, we present a series of studies using LES that focus on experiments being conducted in the high-pressure combustion vessel at Sandia National Laboratories. We extend LES studies performed previously to the Engine Combustion Network (www.sandia.gov/ECN) Spray-H and Spray-A injectors using n-heptane and n-dodecane as the respective fuels. The accompanying analysis reveals the structural characteristics associated with the inherent scalar mixing processes at conditions directly relevant to advanced Diesel engines.

Numerical Benchmark for High-Reynolds-Number Supercritical Flows with Large Density Gradients

Because of the extreme complexity of physical phenomena at high pressure, only limited data are available for solver validation at device-relevant conditions such as liquid rocket engines, gas turbines, or diesel engines. In the present study, a two-dimensional direct numerical simulation is used to establish a benchmark for supercritical flow at a high Reynolds number and high-density ratio at conditions typically encountered in liquid rocket engines. Emphasis has been placed on maintaining the flow characteristics of actual systems with simple boundary conditions, grid spacing, and geometry. Results from two different state-of-the-art codes, with markedly different numerical formalisms, are compared using this benchmark. The strong similarity between the two numerical predictions lends confidence to the physical accuracy of the results. The established database can be used for solver benchmarking and model development at conditions relevant to many propulsion and power systems.

A Priori Analysis of a Compressible Flamelet Model using RANS Data for a Dual-Mode Scramjet Combustor

In an effort to make large eddy simulation of hydrocarbon-fueled scramjet combustors more computationally accessible using realistic chemical reaction mechanisms, a compressible flamelet/progress variable (FPV) model was proposed that extends current FPV model formulations to high-speed, compressible flows. Development of this model relied on observations garnered from an a priori analysis of the Reynolds-Averaged Navier-Stokes (RANS) data obtained for the Hypersonic International Flight Research and Experimentation (HI-FiRE) dual-mode scramjet combustor. The RANS data were obtained using a reduced chemical mechanism for the combustion of a JP-7 surrogate and were validated using avail- able experimental data. These RANS data were then post-processed to obtain, in an a priori fashion, the scalar fields corresponding to an FPV-based modeling approach. In the current work, in addition to the proposed compressible flamelet model, a standard incompressible FPV model was also considered. Several candidate progress variables were investigated for their ability to recover static temperature and major and minor product species. The effects of pressure and temperature on the tabulated progress variable source term were characterized, and model coupling terms embedded in the Reynolds- averaged Navier-Stokes equations were studied. Finally, results for the novel compressible flamelet/progress variable model were presented to demonstrate the improvement attained by modeling the effects of pressure and flamelet boundary conditions on the combustion.

A Priori Analysis of Flamelet-based Modeling for a Dual-Mode Scramjet Combustor

An a priori investigation of the applicability of flamelet-based combustion models to dual-mode scramjet combustion was performed utilizing Reynolds-averaged simulations (RAS). For this purpose, the HIFiRE Direct Connect Rig (HDCR) flowpath, fueled with a JP-7 fuel surrogate and operating in dual- and scram-mode was considered. The chemistry of the JP-7 fuel surrogate was modeled using a 22 species, 18-step chemical reaction mechanism. Simulation results were compared to experimentally-obtained, time-averaged, wall pressure measurements to validate the RAS solutions. The analysis of the dual-mode operation of this flowpath showed regions of predominately non-premixed, high-Damkohler number, combustion. Regions of premixed combustion were also present but associated with only a small fraction of the total heat-release in the flow. This is in contrast to the scram-mode operation, where a comparable amount of heat is released from non-premixed and premixed combustion modes. Representative flamelet boundary conditions were estimated by analyzing probability density functions for temperature and pressure for pure fuel and oxidizer conditions. The results of the present study reveal the potential for a flamelet model to accurately model the combustion processes in the HDCR and likely other high-speed flowpaths of engineering interest.

Global Sensitivity Analysis and Quantification of Model Error for Large Eddy Simulation in Scramjet Design

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress towards optimal engine designs requires both accurate flow simulations as well as uncertainty quantification (UQ). However, performing UQ for scramjet simulations is challenging due to the large number of uncertain parameters involved and the high computational cost of flow simulations. We address these difficulties by combining UQ algorithms and numerical methods to the large eddy simulation of the HIFiRE scramjet configuration. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, helping reduce the stochastic dimension of the problem and discover sparse representations. Second, as models of different fidelity are available and inevitably used in the overall UQ assessment, a framework for quantifying and propagating the uncertainty due to model error is introduced. These methods are demonstrated on a non-reacting scramjet unit problem with parameter space up to 24 dimensions, using 2D and 3D geometries with static and dynamic treatments of the turbulence subgrid model.

Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions

Compressive sensing is a powerful technique for recovering sparse solutions of underdetermined linear systems, which is often encountered in uncertainty quantification analysis of expensive and hig...

Probabilistic parameter estimation in a 2-step chemical kinetics model for n-dodecane jet autoignition

This paper demonstrates the development of a simple chemical kinetics model designed for autoignition of n-dodecane in air using Bayesian inference with a model-error representation. The model error, i.e. intrinsic discrepancy from a high-fidelity benchmark model, is represented by allowing additional variability in selected parameters. Subsequently, we quantify predictive uncertainties in the results of autoignition simulations of homogeneous reactors at realistic diesel engine conditions. We demonstrate that these predictive error bars capture model error as well. The uncertainty propagation is performed using non-intrusive spectral projection that can also be used in principle with larger scale computations, such as large eddy simulation. While the present calibration is performed to match a skeletal mechanism, it can be done with equal success using experimental data only (e.g. shock-tube measurements). Since our method captures the error associated with structural model simplifications, we believe that the optimised model could then lead to better qualified predictions of autoignition delay time in high-fidelity large eddy simulations than the existing detailed mechanisms. This methodology provides a way to reduce the cost of reaction kinetics in simulations systematically, while quantifying the accuracy of predictions of important target quantities.

Computational Modeling of Supercritical and Transcritical Flows

We examine the use of a single-fluid model with the Peng Robinson equation of state to model supercritical and transcritical flows with combustion. Both non-reacting and reacting flows are considered to understand the modeling challenges. Several modifications of the equations of state and mixture rules are tested and shown to have varying strengths and weaknesses. No method works uniformly well for both non reacting and reacting flows, although the use of the Peng-Robinson model with Amagat’s mixture rule and modified compressibility factors appears to be robust and reasonably accurate for supercritical and transcritical combustion.

Modeling Auto-Ignition Transients in Reacting Diesel Jets

The objective of the present work is to establish a framework to design simple Arrhenius mechanisms for simulation of Diesel engine combustion. The goal is to predict auto-ignition and flame propagation over a selected range of temperature and equivalence ratio, at a significantly reduced computational cost, and to quantify the accuracy of the optimized mechanisms for a selected set of characteristics. The methodology is demonstrated for n-dodecane oxidation by fitting the auto-ignition delay time predicted by a detailed reference mechanism to a two-step model mechanism. The pre-exponential factor and activation energy of the first reaction are modeled as functions of equivalence ratio and temperature and calibrated using Bayesian inference. This provides both the optimal parameter values and the related uncertainties over a defined envelope of temperatures, pressures, and equivalence ratios. Non-intrusive spectral projection is then used to propagate the uncertainty through homogeneous auto-ignitions. A benefit of the method is that parametric uncertainties can be propagated in the same way through coupled reacting flow calculations using techniques such as Large Eddy Simulation to quantify the impact of the chemical parameter uncertainty on simulation results.© 2015 ASME