Jacqueline Chen: "DNS of Turbulent Combustion in Complex Flows"

– [Presenter] Good afternoon everyone My name is Mung Chiang, I’m the John A Edwardson Dean of the College of Engineering A distinct pleasure to introduce our distinguished speaker today but first of all, to those who are watching later in archive or right now streaming, I want to highlight that we’ve got truly standing room only here in the theater and walk I can see there are about, what, 50 people or so standing in the back I hope they will not become turbulent over the course of this distinguished lecture Now this is a new exciting program that the College of Engineering introduced last semester, so every academic year, we’ll invite six to eight distinguished lecturer to the college across different schools in the college That is part of our effort as we aspire to attain the pinnacle of excellence at scale And today we are so excited to welcome Dr. Jacqueline Chen, or Jackie Chen, as the distinguished lecturer Jackie is a distinguished member of the technical staff at Sandia National Labs And she has contributed broadly to research in direct numerical simulations, so DNS of turbulent combustion and elucidating turbulence chemistry interactions in turbulent flames and ignition processes And these interactions govern the overall combustion rate, emissions, the degree of local extinction, and ignition timing And she and her collaborators have discovered new physical insights related to the turbulent pre-mixed and stratified flame propagation, preferential diffusion, intrinsic flame instability, lifted flame stabilization and heated flows, reactive scalar mixing, compression ignition, and flashback in boundary layers Now these benchmark simulation data have also been used by the modeling community to validate turbulent combustion models Now, we’ll have to keep the introduction short so that we have more time to listen to Jackie Chen I do want to highlight that within the past year alone she was elected a member of the National Academy of Engineering and received the Combustion Institutes Bernard Lewis Gold Medal Award and the Society of Women Engineers Achievement Award in 2018 What a pleasure to welcome you here to Purdue Engineering Jackie, thank you so much – Thank you (faintly speaking) for the introduction (audience applauds) Okay, can everybody hear me? I guess my, I’m wired It’s a pleasure to be here at Purdue Thank you Dean Chiang And would like to also thank Bob Luck for inviting me He’s been trying to get me to come out here to give a seminar for a while now So it’s a pleasure to be here I’m gonna talk today about direct numerical simulations of turbulent combustion in complex flows For those of you who aren’t familiar with DNS, as it’s so-called, it’s where we resolve all of the turbulent scales from the largest link scales that correspond to a device, for example, internal combustion engine all the way down to the Kolmogorov scales where heat and kinetic energy are dissipated We saw those exactly without any models, with very accurate numerical methods But then we do have to incorporate chemical kinetics models, spray models, radiation models, and et cetera, if you want to look at the coupling of turbulence within reacting flows So, what we’ve been chasing for the last 20 years, I call ourselves the tornado chasers in some sense, is high performance computing, because to do DNS and perform it DNS in parameter ranges of interest, you have to have really large computers and a lot of computing cycles And so, like tornado chasers who chase after the next storm and risk their lives, we don’t do that, we’re all also chasing and watching very closely what the high performance computing industry and research communities do, because we depend upon their research and their advances in order for us to do our science And so we formed a very tight collaboration with high performance computing folks And the next frontier, right now we’re at petascale 10 and 15 floating point operations per second,

and in the very near future, maybe in less than five years, we’re pushing towards exascale which is another thousandfold increase in computing power And so what’s driving us, is to use these machines to gain fundamental insights into multi-scale multi-physics problems associated with turbulent flames or ignition And to use that fundamental knowledge, not only for scientific, for the leading understanding, but also to generate high-fidelity DNS database benchmarks for model development, both for Rans as well as for large Eddy simulation And so, the regimes that we’re now pushing towards are more higher Reynolds numbers, simulations, more representative of what are conditions in actual devices At high pressures, gas turbines and IC engines operate at high pressures, 20 to 100 atmospheres, with large intense turbulent velocity fluctuations in some cases at very high speeds with compressibility effects A lot of what we know in our community is inherited from what we know from non-reacting flows But when you add combustion and reacting flows, you have variable density of heat release dilatational effects that may render some of those closures invalid And so we wanna explore, using these high performance tools, what closures might work in these more complex reactive flow settings We’re also interested in what happens if we inject energy at small scales Usually you think of energy coming in from the large, driven by large scale phenomena, like mean sheer or compression or something like that and then energy just goes down the turbulence cascade in the forward direction, but what if you generate sources of energy at the dissipation range in the small scales, does that energy move back up into larger scales or does it stay localized and then just dissipate? And so there’s some fundamental questions with multi-scale energy transfer processes And then what we’re, the direction, I’ll say a little bit about where we’re moving forward into in the next couple of years is doing these high-fidelity DNS but also considering hybrid DNS and LES methodologies in combination So we’re trying to throw everything that we know how to do into the picture, including adaptive mesh refinement and hybrid schemes so that we can get into realistic device level regimes And we also are paying close attention to what our chemists friends are doing in terms of providing adequate chemical fidelity enough to differentiate affects, fuel affects when there are very strong turbulence-chemistry interactions So the trend these days with gas turbines and IC engines is to burn more overall more fuel lean, more dilute mixing with EGR, exhaust gas recirculation and so on in order to still get high efficiencies but kind of reduce the emissions of both soot as well as nitric oxide PCL And so, burning at those ragged limits or leaner limits presents challenges and greater coupling between finite rate chemistry and turbulent mixing So, a lot of my research in my group has been motivated by auto-ignition processes in engines, both engines for power generation, for the airplanes we fly, as well as an internal combustion engines And for example, if you look at this local equivalence ratio plot where fuel rich conditions are up here at the top, stoichiometric is this horizontal dashed line and lean is below that Plotted versus temperature, you see that a lot of the gasoline engines or spark ignited engines that we drive are burning more or less stoichiometric conditions and they sit in this nox island for emissions If we look at diesel engines and the trucks on the road, they end up straddling richer conditions that also introduce soot particulates which is harmful to the environment and human health And so the trend in the I c engine world is been to get higher compression ratios, to use compression ignition, greater efficiencies to use compression ignition technologies, and to burn at lower temperatures and leaner conditions, what’s known as l t c types of conditions And similarly, in the gas turbine world, people are looking at introducing more hydrogen and syngas types of fuels to reduce for carbon capture storage

types of technologies And there are companies like Alston Power and Ansaldo Energia, who are looking at staged, axially staged types of sequential combustors where the products of combustion from the first stage, which are vitiated at higher temperatures, you would inject additional fuel like hydrogen into the second stage, which might burn because of the hot vitiated gases through different modes of combustion, including auto ignition perhaps in combination with pre-mixed flames And so we’d like to dive in to some of the details of these types of technologies As I said already, we’re motivated by using DNS to understand mixed combustion regimes where we’re overall fuel lean, under partially pre-mixed conditions We’re interested in exploring multistage auto ignition types of problems, where you have low temperature ignition followed by high temperature ignition and looking at the sensitivity of fuel chemistry and it’s coupling with turbulent mixing So over the years, I’ve developed a DNS code in my group called s three d, it’s a compressible reactive flow solver It solves the compressible reacting Navier-Stokes, total energy, species continuity equations, and it uses high-order finite-difference methods, eighth order in space, do you have a different, this one died Okay, thanks And it has detailed reaction kinetics treatment models for both detailed skeletal or reduced chemical models and molecular transport models We also have a Lagrangian particle tracking method embedded in that software technology to either allow us to track tracer particles or to treat polydisperse dilute spray, sprays as well as soot More recently, I’ve been working with computer science groups to allow us to easily incorporate in-situ analytics, as well as visualization methods while the codes running And so we can start to look at chemical analysis on the fly or various machine learning methods coupled with our calculations as it’s running or volume vis or particle vis on the fly This code has been refactored numerous times from m p I only code to m p I plus x, where x can be open m p, it can be open a c c if I wanna run it with pragmazon directives on graphics processing units, or in more research oriented environments that use dynamic task based run times in systems in order to orchestrate the mapping of the code onto compute resources with heterogeneous machines with GPUs and CPUs Over the years we’ve kind of got into DNS early on in the 80’s, and what I’d like to just show is that the computation of intensity of DNS has kept up with Moore’s law So, we’ve had exponential growth in our problem sizes from about 1995 through present, so this is not a semi-log plot In the early days we were only able to perform DNS either on very low Reynold’s numbers with one single global step or in two dimensions so you can’t represent turbulence in two d but maybe with a little bit more fidelity in the chemistry And so it’s only been recently with the advent of super computings at the PetaScale and TeraScale that we’ve been able to bring both real turbulence together with detailed chemistry And so we’re at the point now where we can, at least for small flames, smallish flames, do direct comparisons with laboratory experiments for example, high Karolwitz turbulent pre-mixed flames or looking at multi-injection diesel types of problems So, what I’d like to do in my remaining time is to give you a taste of the kinds of things we’ve been studying with this tool, so I’ll give you a couple vignettes and then I’d like to discuss what the path is to actually moving to Exascale, given the changes and advances in hardware architecture, software stack, and computing capability So the first vignette I’d like to describe is

our work in looking at turbulent autoignition of a fuel like n-Dodecane, which is a diesel surrogate fuel, at 25 Bar, and so this is at relatively high pressures And what’s visualized here is one of the important low temperature intermediate species called Ketohydroperoxide or Kete for short So the interest here is to, to really try to understand what low temperature combustion, it’s understand the coupling between low temperature combustion and turbulent mixing And the idea is to burn, as I said earlier, at lower temperatures to reduce the emissions as well as keeping the efficiency high And in these LTC conditions combustion occurs in both pre-mixed as well as spontaneous autoignition modes but also occurs kind of sequentially through low temperature ignition followed by intermediate stage ignition all the way to hot ignition And so there’s very, very strong sensitivity of mixing and transport affects coupled with low temperature chemistry So what’s known about ignition has largely been known for about high temperature ignition and if you look at the plot on the right here, what we see is the ignition delay time on a semilog plot versus mixture fraction, this is the degree of mixing between fuel and oxidizer And you see that there’s a minimum point for various mechanisms, and so these homogeneous reactor calculations, that is, that is there’s no transport here at all, it’s zero d, show that there is a minimum ignition delay time and that occurs at a preferred mixture fraction in each of these instances We also know from strained one d flamelet simulations that the ignition delay time in this bottom plot increases, or is pretty much, the ignition delay time increases with the mixing rate until a critical value is reached at which point it’s gonna take forever to ignite And so, mixing or strain rate impedes the progress of ignition If left alone, the thought is that that’s the fastest you’ll ever ignite a mixture, in the absence of any kind of transport And so the question then becomes, which of these high temperature ignition features carries over when you have low temperature ignition? And so there’s been some experimentation done in engines, and these are some experiments done by Scott Skeen at the Combustion Research Facility in an optical engine, and the kinds of measurements they can make as a function of increasing time or crank angle degree, these are after start of injection, 140 microseconds all the way to about half a millisecond, are things like formaldehyde imaging and time resolved Schlieren imaging, measures the density gradient And so you can kind of see that okay, well a 190 microseconds after the fuel’s injected into a diesel engine you start to see formaldehyde, which is a nice marker of low temperature ignition, happening on the sides, near the head of the jet And then that rose into the head of the jet starts to ignite and then, the entire volume inside of the leading edge of this jet ignites I should say the fuel’s injected from left here and then into a heated ambient, typically about 900 kelvin or so at very high pressures, 40 60 bar And if we then look at Schlieren images, you see sort of a similar pattern where, if you focus your attention at point a, at one time and slightly little bit later time, point a, which is like an eddy that’s been ejected sideways outside of the leading edge of this jet, you see that it starts to disappear and vanish because it’s undergone low temperature ignition which generates a tiny bit of heat release and raises it’s temperature, maybe a hundred to 200 degrees, which causes a decrease in the Schlieren image And likewise, you see points b, present here hasn’t ignited, at low temperature ignition’s happened here So this is a consistent picture with

what the formaldehyde image is also showing that, ignition seems to happen under these less fuel rich, or more lean mixtures first, and then it propagates into the center where now, at this time, a hundred microseconds, you see that the leading edge of that jet has, the Schlieren image is almost completely gone where you have volume metric ignition occurring And so that’s kind of about all you can surmise from these types of measurements So, if we want to understand what’s really going on, the only way at present, is to do this computationally and drill down into the details And so we set up a DNS configuration which is a temporally evolving ignitive jet at 25 bar, this is reduced oxygen, I’m sorry reduced air, so it’s 15% oxygen, 85% nitrogen, in the ambient heated to 960 kelvin and then the fuel stream is n-Dodecane at equivalence ratio of point three, this is a pre-mixture that people have measured and the engine is slightly richer but after the fuel’s evaporated, and before it’s ignited, that’s about the condition at 450 kelvin And we’ve used a chemical kinetics mechanism for n-Dodecane involving 35 species that’s been reduced by Tianfeng Lu, that includes both the high temperature oxidization as well as the low temperature oxidization And the types of turbulent Reynolds numbers that we can achieve at present are about a thousand for turbulent Reynolds number, jet Reynolds number about 7000 So this computation, because of the high pressure and the kinds of resolution we needed to resolve these ignition fronts, we needed three micron grids And so that’s the really tiny mesh, in order to resolve the internal structures of these pre-mix flames and of the spontaneous ignition fronts And so it required three billion mesh points and 40 perimeter variables including 35 species that we transported plus, density, momenta, and total energy And to give you an idea, this is quite a small domain, it was only about several millimeters on each side, and we were able to go one millisecond in physical time, taking very, very small time steps to observe the dynamics of ignition through to the full burning happening in that domain So just to give you a sense of what to expect, first we look at multi-stage ignition when it’s zero d, no transport, and so you would plot the ignition delay versus mixture fraction and when you have both, two stage ignition you have the low temperature ignition stage, shown in the red dash line, and the high temperature ignition, shown in the solid black line, the stoichiometric mixture fraction is sitting here at kind of lean conditions at about point oh five And you see that consistent with what Mastorakos had found for high temperature ignition, you’d have a minimum ignition delay time at a preferred mixture fraction that’s slightly rich of stoichiometric for low temperature ignition and much richer at point one two for the high temperature ignition So they’re kind of separated in mixture fractions space We also find that there’s about a three to four fold difference in ignition delay times between the low and high temperature ignition And so you might expect, because of this large separation in time scales, that the low temperature ignition might occur first and then sequentially the high temperature ignition However, when you get to richer mixtures, that gap shrinks and so there may be some significant overlap in the ignition processes for low and high temperature ignition for rich mixtures Now, just to give you a sense of the dynamics of what’s going on, I’m going to show you a video of a low temperature intermediate species marker called Ketohydroperoxide on the left, and on the right, I’ll show you what hydrogen peroxide looks like These are things that you’d like to measure in the laboratory if you were gonna demarcate low temperature and intermediate temperature ignition processes Then, on top of that, I’m going to show you on the left, the hot ignition, when the hot ignition kernels form and these will be demarcated by temperature threshold of 1150 kelvin, and those will be shown in red So the Kete, I think, is shown in blue, I’ll go ahead and play it, so you can see what happens So this is in a sheer layer, so there’s a slab of this pre-mixture in the middle,

and then the ambient on either side of that And so you can see the Kete forming on the left, this is it’s low temperature marker, and now all of the sudden you see these little red spots which are the hot ignition kernels forming sequentially And likewise, on the right, you see the formation and then the disappearance of the Kete and also the disappearance and consumption of the h two o two as it thermally disassociates to form o h when you undergo hot ignition Okay, so then eventually the ignition kernels propagate out towards the stoichiometric mixture and then you end up with edge flames on the edges of the sheer layer So if we look at this phenomenon of sequential ignition in the conditional statistics of it, is plotted as a mixture fraction, we see that temperature evolution if you follow the first top row, the keto-hydroperoxide evolution is the bottom row, and the conditional means and standard deviations of h two oh two are shown in the middle row And so, initially, you just have a frozen mixing line for temperature then you see the low temperature ignition occurs near the preferred mixture fraction which is pretty lean for low temperature ignition and then it propagates into richer mixtures and then the richer mixtures auto-ignite first here at slightly richer conditions then predicted from the homogeneous scenario, which I’ll explain in a minute, and then eventually it marches back to stoichiometric conditions where you’ll have a high temperature flame And likewise for keto-hydroperoxide, the low temperature builds up here under very lean conditions it forms cool flames, the cool flames propagate towards richer mixtures, consumed when it’s undergoes low temperature ignition and moves into intermediate ignition types of chemistry So the standard deviations are indicated by the vertical bars, the red solid lines are the conditional means averages So what’s happening here, and why is this quite different than what engine designers have relied on homogeneous reactor simulations for a long time to make their predictions? So, what we’re finding is that if you plot ignition delay time versus mixture fraction, as I showed you earlier, for homogeneous systems with no transport We have the high temperature ignition, denoted by the black solid line, the low temperature ignition delay curve, denoted by the red dash line But what you see here is the turbulent parcels of fluid for low temperature ignition are shown by these blue isocontour values between where the parcels are between five and 50% ignited So, if you follow this, initially you see that indeed, low temperature ignitions happens where you have the preferred mixture fraction, consistent with what Mastorakos found for hot ignition And it’s delayed a little bit because of mixing effects, right? I told you earlier that strain rate impedes ignition But then what you see in time is that the low temperature ignition as it moves out to richer mixtures, as we saw in the conditional statistics, ends up actually igniting at a shorter time than the corresponding homogeneous scenario So, that kind of leads to a question in our heads as to how did that happen? And then more interesting is, what happens with the hot, if you look at the fluid parcels that undergo hot ignition shown by the red isocontours? We see that between 1% and 50% is in the red Is that the first hot ignition kernel happens out here at rich conditions quite a bit richer than what homogeneous ignition, high temperature ignition would predict and at earlier times So at fuel richer conditions and at smaller times So these are both kind of contradictory to what the theoretical, our knowledge of ignition is So, as I said, the low temperature ignition happens at the preferred mixture fraction The ignition wave propagates into richer mixtures, high temperature ignition happens, richer and at smaller times And so we were very curious as to what was happening, and so what we found is that if you analyze this data in detail, you see that the propagation mechanism, once you have low temperature ignition it propagates

into richer mixtures either as a pre-mixed flame where we find a nice balance between reaction and the diffusion for a species like Kete or h two oh two, or it propagates as just a spontaneous ignition front with lots of reaction, with very little diffusion So basically it’s propagating down in an ignition delay gradient in temperature or composition And so if we analyze this we see that the percentage, what we did was then take an h two oh two isocontour and using a marching cube segmentation method figured out the local ignition front normals and identified the, going along each normal of that ignition front identified whether it was a flame or whether it was a auto-ignition type of front propagation And if we plot the percentage of fronts that are propagating as a flame we find that under very lean conditions and under very rich conditions, it’s propagating almost exclusively as a flame but for mixture fractions in between you have both auto-ignition as well as low temperature cool flame propagation that’s responsible for the propagation mechanism So these are diffusively supported cool flames and so, what’s happening is these cool flames that first ignite, propagate towards richer mixtures in many instances much faster than those rich mixtures could ignite on their own So, they’re basically delivering the goods The enthalpy and the low temperature intermediate radicals that bootstrap those harder to ignite rich conditions and leads to much faster ignition And so the experimental engine guys have kind of observed this in the laboratory but we’re not able to explain the mechanism which we have uncovered from these kinds of calculations And so I won’t show more of these details but we can start to look at issues like how the Kete or the h two oh two low temperature markers are correlated or how well they’re correlated with the turbulent mixing rates, the scalar dissipation rate, in mixture fraction space and the essence of this, without going into the statistical details, is that ignition kernels like to form initially where they’re sheltered from losses And so they like to form where the scalar dissipation rate and the mixing rates are low But once they’re, and for those regions that have very high mixing rates, they’re not able to ignite and what happens is whatever buildup of radicals or enthalpy are removed from those sheltered environments and through turbulent diffusion, are brought into much richer mixtures And so for the very rich mixtures and the very lean mixtures that have very long ignition delay times, that would take a long time to self-ignite, it depends on turbulent diffusion and Laminar cool flame propagation to bring the heat and the radicals to those locations So, then I just want to say, that if we sum up the different combustion modes that happen during the course of this auto-ignition, we find that low temperature induction, that is the build up of the low temperature radical soup, the transition of low temperature ignition to high temperature induction processes, these blue and green and yellow regions, occupies about, contributes about 30% of the overall heat release rate And the rest of the heat release rate is predominantly due to pre-mixed flame propagation once the high temperature kernels have ignited And very small percentages due to the high temperature diffusion flame shown in purple But the low temperature ignition does contribute a non-negligible fraction of overall heat release rate in the pressurized rate and so, it’s worth getting the physics right and the models right for the low temperature region We also see, as kind of hinted at in the ignition delay curves, that there’s really largely a separation in the low temperature ignition occurring sequentially before high temperature ignition processes occur, they don’t really overlap much in time So from this vignette we kind of learn

that low temperature reactions create the conditions for hot ignition to occur faster than under homogeneous conditions These low temperature ignition fronts propagate through a diffusively supported cool flame in much of the region and that high scalar dissipation rate delays the low temperature ignition, however it leads to faster ignition under very rich mixture conditions And that the high temperature ignitions starts at conditions richer than homogeneous conditions and eventually form edge flames at stoichiometric conditions And so, what we’re doing now is to look at multi-injection processes, so we inject a pilot fuel followed by a primary injection with a dwell time in between and look at the effect of ambient temperature and the presence of these low temperature ignition intermediates generated by the pilot and what it’s affect is on the primary injection auto-ignition development We’re also layering in on top of that, the spray aspects to look at the enthalpy and momentum exchange between the phases and, we’re starting to look at adding soot into this problem, as well So, let’s see. What time is it? – Five minutes – Okay so I want to skip the next talk Why don’t I just quickly say that this next talk is motivated by how to stabilize a flame for example, in a scramjet And so we have an ongoing project together with Harsha Chelliah sponsored by the Air Force and NSF, where we’re doing the DNS and he’s doing the experiments in the wind tunnel And so we really want to understand how do you stabilize a flame under these high speed compressibility conditions So, what we’ve done to our DNS code is to extend it to be multi-blocks, so basically think of it as Lego blocks that you can piece together to get some geometry, very simple geometry into it So we can do flows that look like cavities or using immersed boundary methods, include a close-up linear ramp cavity on the right And then what we have done is to generate a separate turbulence in flow feed data by running a turbulent periodic channel flow and the conditions here we’re looking at are, it’s a scaled down cavity that Harsha’s built and we’re looking at ethylene air at lean equivalence ratios of about point four, flow velocities of 200 meters a second, RMS velocity’s about 10% of the bulk flow velocity, and pre-heated conditions of 1125 kelvin And so this just gives you a snapshot of what the instantaneous turbulence field looks like through the enstrophy on the left and the heat release rate on the right It’s not very clear but there is a rectangular cavity in there but the outline of it’s not projecting very well If we take a slice of it through the center plane, we see the enstropy and we also see the boundaries of the flame as shown by progress-variable isocontours in black So you can see the flame is this corrugated black line, two lines of progress-variable between point oh five and point nine five, and enstrophy coming from the boundary layer, here’s the step, I should say this is a case that is a littler simpler than the cavity So this is just a backward facing step, but it still looks at the dynamics of how you anchor that flame The interesting thing that caught our eye was that turbulence, we always think of turbulence happening on the reactant side, so the ethylene air is flowing in here, from left to right, and products are on the other side of the flame And the odd thing that we saw here was that the enstrophy switches or moves from being on the reactant side to being predominantly on the product side As we move downstream from the in the cavity, and so we thought that was a little odd and so we’re trying to understand what happened So if you look at the conditional enstrophy plotted versus progress variable, at three different axial locations downstream from the step, indeed it’s peaked on the reactant side right immediately behind the step, and then it migrates, the peak migrates over to the product side So if we look at the stabilization here, what we see is there is this nice recirculation zone

if you do range averages of the streamline So that’s a recirculating radicals like o h and so on And so if you look at the flux of o h in the axial direction there’s definitely a flux going to the right and then coming back to the left due to this large scale recirculation region behind the step And likewise for the transverse o h flux The other interesting thing we found is if you look at the reaction rate for o h at these different axial positions, red, green, blue, and purple as you go down stream, is that there’s no production of o h immediately behind the step and it’s not until it gets considerable distance downstream of the step that you see that production starts to kick in Whereas you do see consumption of o h throughout And so what I think is happening, we see the similar picture for c o and so what I think’s happening is that the strain rates and the residence times are so short here, due to this high speed strained flame, that you basically have quenched your oxidation layer So you’re no longer producing c o 2 and water but in fact, the flame sits out closer to the product stream in the near field behind the corner and those oxidation reactions are actually operating in the reverse direction And so you’re not producing radicals and this flame is staying alive solely by the advective transport of o h generated farther downstream and bring brought back up through the recirculation bubble Okay Let me skip some of the rest of this And for those of you who are interested I can talk about the re-heat combustion problem Anyway, just to show one quick slide of that Okay, so what we found is, when we looked at re-heat combustion with hydrogen air systems, and we are doing this together with Ansaldo Energia and Syntef, they’re interested in the flame stabilization mechanism when you have vitiated gases coming in in the mixing section at high temperatures in excess of a thousand And what I’m showing here is the enstrophy in the boundary layers colored by temperature and then I’m showing you the combustion rate, or heat release rate, by this triangular red region highly wrinkled in the combustor section So, basically you have a mixing section followed by a sudden expansion into a combustor It’s a duct in a duct And what we found from these calculations is that there are two combustion states that are observed, there’s the design state which is mainly due to auto-ignition in the combustion chamber with a little bit of pre-mixed flame propagation at the corners, again this is a backward facing step, with recirculation So, you have pre-mixed flames here and then auto-ignition in the middle but, more interestingly, we found that intermittently we see auto-ignition happening in the mixing tube, in the mixing section which is an off design state – [Audience Member] So this is for exhaust gas recirculation type application for the gas turbine? – [Dr. Jacqueline Chen] This is for stationary, for power – [Audience Member] (Indistinct speaking) – [Dr. Jacqueline Chen] Right, it’s a sequential burner for the g t 36, what was it And so what we’ve found is, for the design state we find, here’s a heat release in a slice of this and this part is auto-ignition near the center line, these weaker heat release thin regions, or flames that are anchored just like the backward facing step problem I showed before, due to recirculation behind there, right? From the sudden expansion And we see the temperature burns more brightly for the auto-ignition near the center, less brightly as you’d expect near the flames and we also see evidence of auto-ignition due to the presence of h o two ahead of the auto-ignition in the, of the heat release Because for hydrogen you get chain branching without thermal explosion first And then we confirmed that through various methods that transport budget analysis that it is a flame in cross section b where we see a nice balance between diffusion and reaction, when you take a cut along the normal, whereas it’s just auto-ignition in the middle When you take a cut along a you see balance between reaction and advection And then we’ve applied, I don’t have time to talk about it but more, detailed chemical explosive mode analysis, which is an iget analysis of the reaction rate to cobean and coupling that with the diffusive flux you can actually quantitatively distinguish between the different combustion modes

so, when this ratio of the non-chemical source or diffusion term relative to the chemical source term is greater than one, you have assisted ignition propagation and when it’s between minus one and one, this parameter from the chemical explosive analysis shows you that it’s primarily auto-ignition And when this parameter’s less than minus one, it’s quenching And so if we plot these parameters it’s consistent with the transport analysis I showed previously, this is just more quantitative, it shows the propensity to auto-ignite towards the center line, the pre-mix flame propagation is shown in the red regions, propensity here, and then near the wall where there heat loss is it’s propensity for diffusion to dominate chemistry And then if we wait, the fuel consumption based on the different types of modes, we find that overwhelmingly the fuel consumption is due to auto-ignition So, now just briefly, what happens in the off design state So, when we have intermittent ignition, we occasionally see these blobs of kernels that extend from one wall to the other in the mixing section which you don’t want to have happen, ’cause of combustion dynamics, and so on And so it shows up in temperature it also shows up in h oh two And so the source of this, we believe, is due to the fact that you have auto-ignition happening in the combustor section and this generates lots of pressure fluctuations Both longitudinal and transverse And those pressure waves, or compression waves, emanate from these ignition kernels and they propagate to the right out of the domain but they also propagated upstream back into the mixing tube And these waves can ricochet back off the wall but occasionally you get constructive interference patterns that lead to a slight pressure rise in the mixing tube and then through isotropic compression you also get temperature rise, maybe of 20 to 30 degrees kelvin And hydrogen being as reactive a fuel as it is, that will modify your ignition delay times as much as 30% And so therefore we occasionally do see evidence of flashback in ignition happening in the mixing tube which is not desirable So, last, can I have one more minute? Okay so, last thing, just to change the subject completely, is what are we gonna do to get on to these more difficult platforms that exascale is bringing? And the constraints that we face are that you don’t want to have a power plant just dedicated to running a super computing facility So power is the major design constraint And what consumes a lot of power is not just the computing itself but rather moving data a tiny faction across a chip Five microns across a chip will cost picajewels per operation compared to only 10 picajewels to compute a floating point operation So what that implies is you want to reuse your data and keep it local as much as you can, because if you move it a little bit even within a node, it’s gonna cost a lot and or across the interconnect, it’s gonna cost even more Furthermore, since the processor speeds have kind of stalled, the only way we’re gonna get from petascale to exascale is through large concurrencies, you’re gonna have around millions of these processes concurrently to get the parallelism And so the big challenge, then, for a software developers for science applications is, how do you express data locality, independence, and express massive parallelism, minimize data movement, and reduce any need for synchronization, and to detect and address faults, ’cause having millions of processors all up and running 24 seven isn’t gonna happen And so there has been this program at DOE, it’s called Exascale computing project And it’s adopted a kind of a holistic approach that use the principles of co-design and integration to achieve capable exascale machine in the next few years And this combines application development software technology, hardware, and systems integration involving collaborations between computer scientists, computation scientists, and applied mathematicians And so, we have a piece of this project in developing combustion application and the extensions of our application include things like adaptive mesh refinement,

so we can take care of the disparate scales, when you move up in pressure As well as multi-phase spray types of multi-physics and thermal radiation And so this is a multi laboratory project between Argon, Berkeley labs, Oakridge, Sandia, NREL, and several university partners, funded by ECP And so I’ll just stop here by saying that we’re excited with the prospects of doing DNS at these engine conditions We’ve started in some preliminary aim r calculations to look at multi-injection, multi-injection diesel fuel with n-Dodecane So on the left movies, I’m showing the mixture fraction of a pilot and a primary jet, followed by the temperature Some of the low temperature species markers and o h on the right And we can start to get really, really detailed knowledge if take a cut at the, again mixture fraction of the pilot on the left, followed by mixture fraction of the main on the right after dwell time Temperature h two oh two and oh h on the right So we get just an incredible amount of information and we’re trying to push up into much more practically relevant regimes So, I’ll stop here – Thank you Dr. Chen (Audience applause) – As you saw Dr. Chen has a lot of material to share, is the mic on? So, she has a tremendous amount of material to share Those of us in the combustion community know that Dr. Chen answers questions very well, as well Even by e-mail, so I’ll take that privilege to ask her questions by e-mail and if you mail those to us we can forward those to Dr. Chen In the hurry to get started I handed the mic directly the Dr. Chiang, our Dean, Mung Chiang, if I may say a couple of sentences Our Dean Mung Chiang is the John A. Edwardson Dean of The College of Engineering – There’s no need to talk about me – Oh, only two more sentences His research received 2013 Alan T. Waterman award His online courses have been taken by 250,000 students and he has founded several start up companies, we are delighted to have Dean Chiang join us Would you take one question– – Sure Two questions, ma’am? – Sure – We have two minutes to take two questions so, who would like to ask those two questions? One from a student, yeah – Hang on for the mic please Any questions? – Thank you for the wonderful speech So my question is I’m a graduate student who works on that computational method So, sometimes for us the computational method lacks the way of how to develop the code might be a problem for us to do the research while the most important thing for us to do is to find some break through on the physics so, what do you think of the inconsistency between what we need to learn for study and what we really want to do for study? – Well, so I would’ve said, 20 years ago, you can do it all Given the complexity of where computing is headed today, I don’t think the one man shop, or one woman shop, is not really feasible to develop algorithms, code them up on a large heterogeneous machine, and then do the physical sciences after that And so I think other communities like the climate community and there’s many others, have kind of formed community codes and community ways to share date and software and so I think we’re kind of evolving in that direction in our community in turbulent combustion And I think there’s a lot of software, maybe not your specific set-up for you flow configuration, which you’re gonna have to come up with, but maybe sharing some of the tools and the infrastructure underneath that Both codes, I mean there are codes like open foam and others that are open source codes, the new code we’re developing for exascale called pele is also open source So pretty soon you should be able to download that

And then having some ways to, gateways so that our communities can share, not only software but also databases that were previously generated from computation, making those available for modelers to investigate other physics or model assumptions that the person generating the data may not have interest or had not thought about So these calculations are very expensive, they’re not something that everyone can just hop on a computer and run, they take some expertise and so I think that is a very valid point is, what do we share and what parts do various people need to contribute or build on top of – Let me take a moment to say that tomorrow at 9:30 am unleashing the power of computing and data at scale panel session will be held in this building, in room 3122 So your question was very valid and the panel will continue discussing it tomorrow One last question or comment Professor Myers – Yeah, so just a curiosity from someone who’s not specializing in computations So you had a four nanosecond time step, – Yeah – Which is way shorter than to resolve anything in the flow, – Right – So is that based on the stability of the code, how did you arrive at that sort of– – Well, there’s the c f l condition for compressible flow, we have to resolve, we need the fine mesh in order to resolve the flame front or the ignition front scales And so that dictates the time steps, it’s a compressible solver that we can take And also because the, when the chemical mechanism was reduced, it was reduced such that any chemical radical time scales, you still want to preserve all of the relevant chemical time scales, and some of those are quite short So we didn’t put all of the, some of the species are not put into steady state because it would make the chemistry less accurate So it’s a combination of the c f l condition as well as trying to resolve accurately the chemistry – Okay, and so you have multi-resolution meshing for the spatial domain, is there also multi-resolution on the temporal domain as well? – No, but there is what’s called a low mach limit formulation where we filter out the acoustic waves, sounds waves, therefore then we can take much bigger time steps that aren’t restricted by the c f l conditions And so both of those, doing adaptive mesh refinement which would allow us to put the mesh where the high gradient regions are, like the flames, as well as filtering out sound waves for conditions that are not so compressible Certainly right near the injector it is highly compressible so you need to resolve that but, as you move further downstream where it becomes highly subsonic you can maybe get away with the low mach formulation So, that’s the direction we’re headed – Let’s thank Professor Chen for a great seminar (Audience applause)