Multiple mapping conditioning in homogeneous reacting flows

Multiple mapping conditioning (MMC) is used to model local extinction and reignition phenomena in homogeneous, isotropic decaying turbulence. It is recognized that mixture fraction alone is not sufficient to account for turbulent scalar fluctuations and that more than one reference variable needs to be introduced. We introduce a second reference variable with a dual character: the second variable is a dissipation-like variable that emulates the intermittent behaviour of scalar dissipation and it is therefore the cause for local extinction in our modelling. However, the second variable is also used to match the scalar variance of a reaction progress variable to ensure consistency in temperature flucutations of the MMC model and Direct Numerical Simulations. The resulting model provides a (fully) closed formulation for the modelling of local extinction and re-ignition events and predictions of the joint probability distribution of mixture fraction and sensible enthalpy, of reactive species and of the global conversion rates are good and clear improvments over conventional mixture fraction based methods that use mixture fraction as the only conditioning paramenter

Mixing modelling framework based on Multiple Mapping Conditioning for the prediction of turbulent flame extinction

A stochastic implementation of the Multiple Mapping Conditioning (MMC) approach has been applied to a turbulent piloted jet diffusion flame (Sandia flame F) that is close to extinction. Two classic mixing models (Curl’s and IEM) are introduced in the MMC context to model the turbulent mixing. The suggested model involves the use of a reference space (that is mapped to mixture fraction space) in order to define particle proximity. The addition of the MMC ideas to the IEM and Curl’s models, that is suggested in the current work, aspires to combine the simplicity of these two models with the enforced compositional locality without violating the linearity and independence principles. The formulation of the approach is discussed in detail and results are presented for the mixing field and reactive species. The predictions are compared with joint-scalar PDF simulations using the same mixing models and experimental data. Moreover, the sensitivity of the model to the particle number is examined. It is shown that MMC is less sensitive to the number of particles and can generally produce improved predictions of major and minor chemically reacting species with a lower number of particles

Gradient boosted decision trees for combustion chemistry integration

This study introduces the gradient boosted decision tree (GBDT) as a machine learning approach to circumvent the need for a direct integration of the typically stiff system of ordinary differential equations that govern the temporal evolution of chemically reacting species. Stiffness primarily relates to the chemistry integration and here, hydrogen/air systems are taken to train and test the ensemble learning approach. We use the LightGBM (Light Gradient Boosting Machine) algorithm to train GBDTs on the time series of various self-igniting mixtures from the time of ignition to equilibrium composition. The GBDT model provides reasonable predictions of the species compositions and thermodynamic states at the next time step in an a priori study. A much more challenging a posteriori study shows that the model can reproduce a full time–history profile of the igniting H/air mixtures, as the results agree very well with those obtained from a direct integration of the ODEs. The GBDT model can be deployed as standalone C++ codes and a speed-up by one order of magnitude has been demonstrated. The GBDT approach can thus be considered as an efficient method to represent the chemical kinetics in the simulation of reactive flows. It provides an alternative to deep artificial neural networks (ANNs) that is comparable in accuracy but easier to couple with existing CFD codes

Multiple mapping conditioning of turbulent jet diffusion flames

The multiple mapping conditioning (MMC) approach is applied to two non-piloted CH4/H2/N2CH4/H2/N2 turbulent jet diffusion flames with Reynolds numbers of Re = 15,200 and 22,800. The work presented here examines primarily the suitability of MMC to simulate CH4/H2CH4/H2 flames with varying Re numbers. The equations are solved in a prescribed Gaussian reference space with only one stochastic reference variable emulating the fluctuations of mixture fraction. The mixture fraction is considered as the only major species on which the remaining minor species are conditioned. Fluctuations around the conditional means are ignored. It is shown that the statistics of the mapped reference field are an accurate model for the statistics of the physical field for both flames. A transformation of the Gaussian reference space introduced in previous work on MMC is used to express the MMC model in the same form as CMC. The most important advantage of this transformation is that the conditionally averaged scalar dissipation term is in a closed form. The corresponding temperature and reactive species predictions are generally in good agreement with experimental data. The application to real laboratory flames and the assessment of the new conditional scalar dissipation model for the closure of the singly conditioned CMC equation is the major novelty of this paper. The results are therefore primarily examined with respect to changes of the conditionally averaged quantities in mixture fraction space

Towards a Free-form Transformable Structure: A critical review for the attempts of developing reconfigurable structures that can deliver variable free-form geometries

In continuation of our previous research (Hussein, et al., 2017), this paper examines the kinetic transformable spatial-bar structures that can alter their forms from any free-form geometry to another, which can be named as Free-form transformable structures (FFTS). Since 1994, some precedents have been proposed FFTS for many applications such as controlling solar gain, providing interactive kinetic forms, and control the users' movement within architectural/urban spaces. This research includes a comparative analysis and a critical review of eight FFTS precedents, which revealed some design and technical considerations, issues, and design and evaluation challenges due to the FFTS ability to deliver infinite unpredictable form variations. Additionally, this research presents our novel algorithmic framework to design and evaluate the infinite form variations of FFTS and an actuated prototype that achieved the required movement. The findings of this study revealed some significant design and technical challenges and limitations that require further research work

Mixing Time Scale Models for Multiple Mapping Conditioning with Two Reference Variables

A novel multiple mapping conditioning (MMC) approach has been developed for the modelling of turbulent premixed flames including mixture inhomogeneities due to mixture stratification or mixing with the cold surroundings. MMC requires conditioning of a mixing operator on characteristic quantities (reference variables) to ensure localness of mixing in composition space. Previous MMC used the LES-filtered reaction progress variable as reference field. Here, the reference variable space is extended by adding the LES-filtered mixture fraction effectively leading to a double conditioning of the mixing operator. The model is used to predict a turbulent stratified flame and is validated by comparison with experimental data. The introduction of the second reference variable also requires modification of the mixing time scale. Two different mixing time scale models are compared in this work. A novel anisotropic model for stratified combustion leads to somewhat higher levels of fluctuations for the passive scalar when compared with the original model but differences remain small within the flame front. The results show that both models predict flame position and flame structure with good accuracy

Stochastic multiple mapping conditioning for a piloted, turbulent jet diffusion flame

A stochastic implementation of the multiple mapping conditioning (MMC) approach has been applied to a turbulent jet diffusion flame (Sandia Flame D). This implementation combines the advantages of the basic concepts of a mapping closure methodology with a probability density approach. A single reference variable has been chosen. Its evolution is described by a Markov process and then mapped to the mixture fraction space. Scalar micro-mixing is modelled by a modified “interaction by exchange with the mean” (IEM) mixing model where the particles mix with their -in reference space- conditionally averaged means. The formulation of the closure leads to localness of mixing in mixture fraction space and consequently improved localness in composition space. Results for mixture fraction and reactive species are in good agreement with the experimental data. The MMC methodology allows for the introduction of an additional “minor dissipation time scale” that controls the fluctuations around the conditional mean. A sensitivity analysis based on the conditional temperature fluctuations as a function of this time scale does not endorse earlier estimates for its modelling, but only relatively large dissipation time scales of the order of the integral turbulence time scale yield acceptable levels of conditional fluctuations that agree with experiments. With the choice of a suitable dissipation time scale, MMC-IEM thus provides a simple mixing model that is capable of capturing extinction phenomena, and it gives improved predictions over conventional PDF predictions using simple IEM mixing models

Fully-resolved simulations of coal particle combustion using a detailed multi-step approach for heterogeneous kinetics

Fully-resolved simulations of the heating, ignition, volatile flame combustion and char conversion of single coal particles in convective gas environments are conducted and compared to experimental data (Molina and Shaddix, 2007). This work extends a previous computational study (Tufano et al., 2016) by adding a significant level of model fidelity and generality, in particular with regard to the particle interior description and hetero- geneous kinetics. The model considers the elemental analysis of the given coal and interpolates its properties by linear superposition of a set of reference coals. The improved model description alleviates previously made assumptions of single-step pyrolysis, fixed volatile composition and simplified particle interior properties, and it allows for the consideration of char conversion. The results show that the burning behavior is affected by the oxygen concentration, i.e. for enhanced oxygen levels ignition occurs in a single step, whereas decreasing the oxygen content leads to a two-stage ignition process. Char conversion becomes dominant once the volatiles have been depleted, but also causes noticeable deviations of temperature, released mass, and overall particle con- version during devolatilization already, indicating an overlap of the two stages of coal conversion which are usually considered to be consecutive. The complex pyrolysis model leads to non-monotonous profiles of the combustion quantities which introduce a minor dependency of the ignition delay time $τ_{ign}$ on its definition. Regardless of the chosen extraction method, the simulations capture the measured values of $τ_{ign}$ very well

Sparse-Lagrangian PDF Modelling of Silica Synthesis from Silane Jets in Vitiated Co-flows with Varying Inflow Conditions

This paper presents a comparison of experimental and numerical results for a series of turbulent reacting jets where silica nanoparticles are formed and grow due to surface growth and agglomeration. We use large-eddy simulation coupled with a multiple mapping conditioning approach for the solution of the transport equation for the joint probability density function of scalar composition and particulate size distribution. The model considers inception based on finite-rate chemistry, volumetric surface growth and agglomeration. The sub-models adopted for these particulate processes are the standard ones used by the community. Validation follows the “paradigm shift” approach where elastic light scattering signals (that depend on particulate number and size), OH- and SiO-LIF signals are computed from the simulation results and compared with “raw signals” from laser diagnostics. The sensitivity towards variable boundary conditions such as co-flow temperature, Reynolds number and precursor doping of the jet is investigated. Agreement between simulation and experiments is very good for a reference case which is used to calibrate the signals. While keeping the model parameters constant, the sensitivity of the particulate size distribution on co-flow temperature is predicted satisfactorily upstream although quantitative differences with the data exist downstream for the lowest coflow temperature case that is considered. When the precursor concentration is varied, the model predicts the correct direction of the change in signal but notable qualitative and quantitative differences with the data are observed. In particular, the measured signals show a highly non-linear variation while the predictions exhibit a square dependence on precursor doping at best. So, while the results for the reference case appear to be very good, shortcomings in the standard submodels are revealed through variation of the boundary conditions. This demonstrates the importance of testing complex nanoparticle synthesis models on a flame series to ensure that the physical trends are correctly accounted for

Anesthesia depresses cerebrovascular reactivity to acetazolamide in pediatric moyamoya vasculopathy

Measurements of cerebrovascular reactivity (CVR) are essential for treatment decisions in moyamoya vasculopathy (MMV). Since MMV patients are often young or cognitively impaired, anesthesia is commonly used to limit motion artifacts. Our aim was to investigate the effect of anesthesia on the CVR in pediatric MMV. We compared the CVR with multidelay-ASL and BOLD MRI, using acetazolamide as a vascular stimulus, in all awake and anesthesia pediatric MMV scans at our institution. Since a heterogeneity in disease and treatment influences the CVR, we focused on the (unaffected) cerebellum. Ten awake and nine anesthetized patients were included. The post-acetazolamide CBF and ASL-CVR were significantly lower in anesthesia patients (47.1 ± 15.4 vs. 61.4 ± 12.1, p = 0.04; 12.3 ± 8.4 vs. 23.7 ± 12.2 mL/100 g/min, p = 0.03, respectively). The final BOLD-CVR increase (0.39 ± 0.58 vs. 3.6 ± 1.2% BOLD-change (mean/SD), p Scientific Assessment and Innovation in Neurosurgical Treatment Strategie