Chapter 1Introduction

The internal combustion (IC) engine is one the greatest inventions since theindustrial revolution. The computer marked the advent of the informational rev-olution. The use of computer models in IC engine design and optimization hassignificantly improved engine efficiency and reduced engine pollutant emissionsover the past decades. In the foreseeable future, computer-aided engine optimi-zation will continue to strengthen the vitality and the role of IC engines in moderntransportation. The present chapter reviews the important role of IC engines andthe challenges that IC engines are facing in terms of sustainability, and theirimpact on the environment is emphasized. We also briefly summarize the currentstatus of engine modeling and review recent progress on computational optimi-zation of IC engines in this chapter.

1.1 Roles of Internal Combustion Engines

Internal combustion (IC) engines have dominated the transportation sector for acentury. The high thermal efficiency and high power output-to-volume ratio aretwo major features that maintain the viability of IC engines as the primary powersource in vehicles. But increasing fuel prices and depleting petroleum reserveshave endangered this viability. Emerging technologies, such as the use of elec-tromotors with high energy density batteries or fuel cells, are expected to playincreasing roles in the transportation sector. Moreover, the US EnvironmentalProtection Agency (EPA) ranks transportation as the second major greenhouse gascontributing sector after power generation (EPA 2010). And IC engines are blamedfor contributing approximately one fourth of the total greenhouse gases that areemitted annually in the US. IC engines are also well-known contributors of nitricoxide and particulate matter emissions. However, the primary role of IC engines isnot expected to be completely replaced by any of these technologies in the nextfew decades. In other words, means have to be sought to improve current IC

Y. Shi et al., Computational Optimization of Internal Combustion Engines,DOI: 10.1007/978-0-85729-619-1_1, � Springer-Verlag London Limited 2011

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engine designs in order to alleviate ever-increasing energy demands and to reduceharmful pollutant emissions.

Traditionally, spark-ignition (SI) gasoline engines and compression-ignition(CI) diesel engines are employed for light-duty and heavy-duty applications,respectively. The design of an SI gasoline engine is usually lighter and morecompact than that of a CI diesel engine and they also operate quieter, which is ademanding feature of passenger cars. In contrast, diesel engines are more powerfuland consume less fuel per power output than that of gasoline engines, which isdesirable for trucks and off-highway engineering applications. Recent progress indiesel engine downsizing has made diesel engines potential power plants forpassenger cars with better fuel economy and lower pollutant emissions. Dieselengines now share more than 50% of the passenger car market in Europe, and thispercentage is expected to further increase. Recently Gasoline Direct-Injection(GDI) engines have also shown much improved fuel economy and emissionscompared to conventional intake charge SI gasoline engines. This drives the trendthat more new passenger car models are being equipped with GDI engines in theUS market. On the other hand, emerging engine combustion techniques, such asHomogeneous Charge Compression Ignition (HCCI) and Partially PremixedCombustion (PPC), enable more flexible choices of fuels in IC engines. Forexample, Kalghatgi et al. (2007) conducted an experimental study of a heavy-dutycompression-ignition engine fueled with gasoline and diesel and operated at PPCmode. They showed that the gasoline CI engine has better fuel economy and loweremissions than the diesel CI engine. Due to the limited reserve of petroleum fuels,sustainable fuels, such as bio-fuels, are gradually becoming alternative energysources for IC engines. Gasoline with 10% blended ethanol is now a standardpump fuel in many states of the US. It is anticipated that the amount of alternativefuel usage in transportation sector will keep increasing, which will require mod-ifications of current engine designs. In the foreseeable future, new generation ICengines will directly benefit from better engine downsizing approaches, improveddirect-injection systems, advanced in-cylinder combustion techniques, and alter-native fuels. As a result, the future IC engine combustion system will become morecomplicated. Therefore, this book particularly focuses on describing enginecombustion system optimization using state-of-the-art modeling tools with sys-tematic optimization and regression methods.

1.2 Modeling of Internal Combustion Engines

The advent of computers has created a new branch of scientific and engineeringresearch, namely, numerical simulation. The gas exchange and combustion pro-cesses of IC engines are characterized by complex heat transfer, gas dynamics,multi-phase flows, and turbulence-chemistry interactions. IC engine combustionspans multiple regimes that include premixed flame propagation, mixing-con-trolled burning, and chemical-kinetics-controlled processes, which may occur

2 1 Introduction

simultaneously within a single device (Haworth 2005). The task of modeling ICengines is to completely or partly describe these physical and chemical processesusing mathematical models with stable and accurate numerical schemes so that theoutput of the modeling can reveal desirable information about engine cycles.

Early IC modeling studies can be traced back to 1950s when the computingcapability of computers only allowed for efficient calculation of simple mathe-matical formulae. For example, the best known empirical engine model is theWiebe function (Wiebe 1956, 1962), which is used to predict the burn fraction andburn rate. The Wiebe function and its derivatives, such as double Wiebe functions,have since been widely applied in zero-dimensional engine modeling tools. Thehistoric aspects of the Wiebe function were recently reviewed by Ghojel (2010).Progress in engine heat transfer modeling was also made by Woschni (1967) whoproposed the famous Woschni model for engine convective heat transfer calcu-lation. The model formulae and constants were empirically based on many engineexperiments and fundamental heat transfer physics. Such empirical heat transfermodels were reviewed by Finol and Robinson (2006). Studies of that age showedthat the combination of these empirically-based combustion and wall heat releasemodels with well tuned model variables was able to match the pressure traces ofengine experimental measurements satisfactorily.

The infancy of Computational Fluid Dynamics (CFD) in-cylinder enginemodeling started from the 1970s. However, until the 1980s, engine CFD modelingwas not generally applied in engine development due to two facts: first, thecomputer capacity was still a limiting factor; second, general engine CFD code orsoftware was not available. Instead, engine modeling with phenomenologicalmodels was the main stream in this period. For instance, coupling of phenome-nological quasi-steady spray models (Hiroyasu et al. 1978) and soot and NOformation models (Heywood 1976; Hiroyasu and Kodota 1976) largely extendedthe capability of engine modeling tools compared to zero-dimensional simulations.Details of engine phenomenological models of different physical processes havebeen reviewed by Lakshminarayanan and Aghav (2010).

In 1985, a group at the Los Alamos National Laboratory developed an open-source code called KIVA (Amsden et al. 1985) that integrated different compo-nents of engine CFD modeling, including moving meshes, compressible flows,spray and droplet evaporation, and fuel combustion chemistry. KIVA provides anopen source CFD modeling tool for engine reactive flow simulations, which hassignificantly stimulated the development of engine physical and chemical modelssince then. Reitz and Rutland (1995) reviewed various advanced diesel engine sub-models within the framework of KIVA 3 (Amsden 1993) and concluded that theCFD modeling tool was able to match experimental engine pressure traces andheat release well over investigated conditions and good quantitative agreements inNOx and soot emissions were also attainable. With the rapid increase of compu-tational power of personal computers and demands for better simulating advancedengine combustion techniques, detailed fuel chemistry solvers have also become astandard part of many engine CFD tools since 2001 (Kong et al. 2001). Also,flexible mesh generation techniques are found in many commercial engine CFD

1.2 Modeling of Internal Combustion Engines 3

software nowadays, which significantly expedites the complex mesh generationprocess and thus speeds-up the overall engine simulation cycle.

Despite the fact that even the state-of-the-art engine modeling tools normallyhave larger quantitative uncertainties than engine experiments, engine simulationshave some significant advantages over experimental measurements in enginedevelopment and optimization. These advantages include low cost, the ability tostudy a wide range of parametric space, separated physical and chemical pro-cesses, and detailed in-cylinder information, which is normally not available or isinaccessible in experiments. Continuous efforts in the research fields of meshgeneration techniques, numerical methods, heat transfer, turbulence, chemicalkinetics, and multi-phase flows will further improve the predictability of IC enginemodeling tools. Hence, the quantitative prediction capability of the next generationof IC modeling tools should be even better. Chapter 2 reviews current physical andchemical IC engine models in more detail.

1.3 Computational Optimization of InternalCombustion Engines

Engine CFD simulations provide insights about the engine working cycle andpollutant formation. The ultimate goal of engine modeling is to directly guidedesigners to improve engine performance and to reduce pollutant emissions.Computational optimization of IC engines has become more accepted in assistingpractical engine designs. The task of computational optimization of IC engines isto identify optimal combinations of design variables that can achieve minimum ormaximum objective functions of interest. This section reviews recent progress incomputational optimization of IC engines. Representative research from severalrelevant research areas are reviewed, and salient features of these studies aredescribed in three categories as follows.

1.3.1 Engine Optimization with Parametric Studies

Systematic optimization methods are not required for computational optimizationof IC engines. Indeed, optimal solutions can be found through parametric studiesthat extend over the practical range of design variables using modeling tools. Inparametric studies, the number of evaluations needed to achieve the optimalsolutions significantly increases with the number of design variables, which limitstheir applications in complex design problems. The experience and intuition ofengine designers are critically important to efficiently perform such parametricstudies for engine optimization. The interaction of data analysis and experimentalmeasurements can also expedite the exploration of the parametric space in order tolocate optimal designs.

4 1 Introduction

In the study of Sher and Bar-Kohany (2002), a computer program MICE(Modeling Internal Combustion Engines), which featured a semi-empirical gasexchange model, was employed to study the effects of variable valve timings(VVT) on the torque and fuel consumption of a gasoline SI engine. Three designvariables, including the exhaust valve opening, intake valve opening, and intakevalve closing times, were parameterized. Because the evaluation of engine per-formance used a simple modeling tool which was fast, the parametric study wasable to find the optimal combination of valve timings for different engine operatingconditions. They concluded that the optimal timing of each valve depends linearlyon the engine load and speed. Also, when the VVT strategy was applied, themaximum torque at any engine load was shifted towards a lower engine speed. COand NOx phenomenological models were also used in this study, but the con-clusion about the effect of VVT on the emissions was less reliable than the engineperformance because the predictability of semi-empirical modeling tools for pol-lutant formation is normally poor, especially over a wide range of operatingconditions.

Ibrahim and Bari (2008) adopted a similar approach to optimize a natural gas SIengine using a two-zone combustion model. The EGR strategy in a high pressureinlet condition, the compression ratio, and the start of combustion timing wereoptimized in order to obtain the lowest fuel consumption, accompanied with highpower and low NO emissions. They found that the use of 20–30% EGR effectivelysuppressed engine knock and allowed use of high inlet pressure for compressionratios up to 13 and the optimal EGR rate depended on engine speed.

Parametric studies over a full range of three or more design variables normallycreate a large parametric space, which prohibits practical engine optimizationusing computationally expensive CFD modeling tools. In this case, the enginedesigners’ experience and reliable experimental data are very important to narrowdown the parametric space so that parametric studies can still effectively andefficiently seek optimal solutions of interest. This interactive method that involvesboth computational and experimental efforts and human intelligence is usuallyused in production engine development and optimization. For example, in aseries of optimization works, Lippert et al. (2004a, b) and Szekely et al. (2004) atGeneral Motors and Suzuki Motor, demonstrated that parametric studies usingCFD modeling and well-designed experiments significantly enhanced the under-standing of charge stratification, combustion chamber shape, and spray impinge-ment in a small displacement spark-ignition direct injection (SIDI) gasoline engineand thus expedited the overall engine development and optimization process.Through detailed CFD analysis for the SIDI gasoline engine, Lippert et al. (2004a)found that the reverse tumble that accompanies elevated swirl levels, is pivotal inlifting the mixture towards the spark gap; the piston depth strongly affected theengine performance and emissions; and an adequate bowl volume was key tosufficient mixing at higher loads in the part-load operation regime. Based on thesefindings, Szekely et al. (2004) further optimized the combustion chamber for thisreverse-tumble, wall-controlled gasoline direct-injection engine. This was con-ducted by systematically optimizing each design element of the combustion

1.3 Computational Optimization of Internal Combustion Engines 5

system, including piston-bowl depth, piston-bowl opening width, piston-bowl-volume ratio, exhaust-side squish height, bowl-lip draft angle, distance betweenspark-plug electrode and piston-bowl lip, spark plug-electrode length, and injectorspray-cone angle. They varied each design variable independently to investigate itssensitivity to combustion stability, fuel consumption, and emissions. Finally, a fewoptimal piston designs were recommended by interpreting the simulation resultsusing human intelligence and data analysis tools. On the same engine, Lippertet al. (2004b) also identified several key factors that affect the high-load operatingcondition, which can be grouped as those pertaining to volumetric efficiency, tomixing and stratification, and to system issues. The corresponding design vari-ables, such as the injection timings and strategy, the piston and port designs, andthe intake flow structure and swirl level, were studied separately. Consequently, asignificant improvement in fuel consumption and emissions was obtained relativeto the initial baseline engine configuration, and the expected gains in torque andpower over an equivalent port fuel injection (PFI) engine were also achieved.

Similar parametric studies were also applied in CFD upfront optimization of thein-cylinder flow, spray pattern, and piston shape for a Ford 3.5L V6 EcoBoost GDIengine (Iyer and Yi 2009a, b; Xu et al. 2009). In the first phase, Iyer and Yi(2009a) assessed the effects of intake port design and spray injection timings onthe tumble intensity using the MESIM 3D CFD code. By quantification andvisualization of engine tumble flows they concluded that the effect of intake valvemasking was beneficial for improving the air–fuel mixing, especially at part load.Delaying the start of injection timing allowed for the generation of higher tumbleflow that, in turn, generated higher turbulence intensity at TDC. But a too lateinjection timing had a detrimental effect on air–fuel mixing. The study indicatedthat further optimization of the spray pattern and piston geometry was necessary.Thus, the companion study of Iyer and Yi (2009b) concentrated on optimization ofthe spray pattern. The main target of the second phase was to reduce soot emis-sions and to improve engine cold-start stability, which directly correlates withspray mixing and surface wetting.

Three optimal spray patterns were selected from many parametric studies forfurther experimental assessment on a single-cylinder engine. Finally, a singleoptimal spray pattern with a wide spray angle was tested on a multi-cylinderengine with promising results. Xu et al. (2009) focused on the piston geometry ofthe same Ford GDI engine, particularly under engine cold-start conditions. In theirstudy a multi-component spray model was found to be critical to the accuracy ofthe model prediction of the fuel air preparation process under cold start conditions.The CFD modeling methodology with the multi-component spray model wasapplied to optimize the piston top designs. It was found that robust fuel–airmixture formation was the key for stable combustion under the cold start condi-tion. Effects of piston design parameters on fuel air mixture preparation wereinvestigated and a wide bowl design was developed to generate improved mixtureformation. In addition, they showed that smoothing the dome design of the widebowl achieved further improvement of the turbulence intensity at the boostedcondition while maintaining the same cold start performance.

6 1 Introduction

1.3.2 Engine Optimization with Non-Evolutionary Methods

In computational engine optimization with parametric studies, the designers’knowledge and experience are profoundly important in guiding the simulations tosearch for better design variables. This process is inefficient if a large number ofdesign variables needs to be optimized; objective functions are contradicting; andglobal optimization is desirable. Systematic optimization methodologies canovercome these difficulties by replacing human intelligence with automaticsearching methods. The section reviews a few computational optimization workswith non-evolutionary methods.

The performance of non-evolutionary methods relies heavily on spatial infor-mation, such as the gradient of response surfaces of objective functions to designvariables. In real world optimization problems, such response surfaces can be verycomplicated and non-differentiable, which limits the use of non-evolutionaryoptimization methods. This explains why the application of non-evolutionaryoptimization methods is less popular than evolutionary methods in engine researchcommunity. But with some special algorithm treatments, a few studies haverevealed that non-evolutionary methods can also be efficient and effective for somespecific engine optimization problems. For example, Naik and Ramadan (2004)studied the effects of equivalence ratio (mass of injected fuel), injection timing,ignition timing, engine speed, spray cone angle, and velocity of fuel injection onGDI engine performance and HC emissions. Their optimization work onlyinvolved three parameters, i.e., fuel mass injected, ignition timing, and injectiontiming. Optimal combinations of these parameters were obtained in an automatedoptimization process by linking the engine CFD software KIVA and the optimi-zation software VisualDOC with the Sequential Quadratic Programming (SQP)method. The entire optimization was done in two steps. The first step was to seekfor optimal solutions of fuel mass injected and ignition timing for maximum workoutput. The subsequent step was to further optimize the injection timing of theoptimal solutions obtained in the first step to minimize HC emissions. The twoseparated procedures ensure the effectiveness and efficiency of the SQP method inthe engine design problem. Also, that fact that minimization of HC emissions isusually highly correlated with maximization of engine work, reduces the searchingload of the optimization method for multi-objective functions, so that the use ofSQP method was successful in this study.

Tanner and Srinivasan (2005) explored the conjugate gradient optimizationmethod for a non-road direct injection diesel engine optimization. In their con-jugate gradient method, a line search is performed with a backtracking algorithmand the initial backtracking step employs an adaptive step size mechanism whichdepends on the steepness of the search direction (i.e., based on the gradient of theresponse surfaces). The optimization parameters included the start of injection, theinjection duration and the number of nozzle orifices. The objective was to lowerthe engine soot and NOx emissions with simultaneously reduced fuel consump-tion. Because the conjugate gradient method is only capable of optimizing for a

1.3 Computational Optimization of Internal Combustion Engines 7

single objective function, a cost function that includes all objective functions hadto be defined in their study. Consequently, three different optimizations werecarried out using different weights and exponents in the cost function. Theydemonstrated that the final optimal solutions and the convergence of the optimi-zation algorithm were sensitive to the choice of the cost function. In all testedcases, less than twenty-five engine simulations were required for an optimum to bereached. This is much more efficient than other engine optimization problems thathave been reported in the open literature. But such high efficiency came with thefacts that the investigated range of the design parameters was relatively narrow,the response surface of the engine performance to the injection parameters was notcomplicated and good initial values were guessed.

In light of their successful optimization study with the conjugate gradientmethod, Tanner and Srinivasan (2009) pointed out that the development of anadaptive cost function strategy for the gradient-based method is necessary. Theadaptive cost function is based on a penalty method such that the penalty term isstiffened after every line search. In this way, the cost function is adaptively cor-related with the searching direction. The optimization method was used toinvestigate an asynchronous split injection scheme, in which the first and thesecond injection were carried out via two orifices that allow for independentparameter optimization, such as the orifice diameter and injection timing. Theyshowed that this asynchronous split injection scheme outperformed the conven-tional split injection method in terms of engine performance and emissions. It wasshown that only about 30 simulations were needed to achieve the optimal solu-tions. They concluded that the adaptive steepest decent method applied to engineoptimization is a computationally very effective tool to explore new optimalinjection strategies, but is only efficient when good enough initial values areavailable.

Jeong et al. (2006) directly adopted a response surface method, i.e., the Krigingmodel to optimize the combustion chamber for a passenger car diesel engine. TheKriging estimator was used to predict the search direction during the optimizationprocess. However, in order to obtain an unknown model variable for the estimator,they reformulated the problem into a sub-optimization process, in which a geneticalgorithm was used. Technically, they developed a hybrid optimization methodthat involves both non-evolutionary and evolutionary methods. In the optimiza-tion, initial sample points were simulated using engine CFD modeling tools basedon piston geometrical parameters that were generated through Latin HypercubeSampling (LHS). Then the points which had a large probability of being optimumwere estimated using the Kriging model, and used as additional sample points toupdate the Kriging model. The method successfully identified two optimal com-bustion chambers out of a total of 48 initial simulations and 43 additional samples.The CO, soot, and NOx emissions, as well as the engine thermal efficiency of thetwo optimal designs were improved compared to the baseline engine configuration.In terms of the total simulations, the method is more efficient than the previouslyemployed evolutionary method, as claimed by the authors. But it should beemphasized that their method only found two optimal solutions, and the use of the

8 1 Introduction

k-means method to search for additional sample points most likely weakens itscapability of reaching the global optimum.

Aittokoski and Miettinen (2008) used a non-differentiable interactive multi-objective bundle-based optimization system (NIMBUS, Miettinen and Mäkelä1995) to optimize the exhaust pipe dimensions for a two-stroke engine. Within theframework of NIMBUS, the optimality of the solutions is not directly evaluatedbased on objective functions. Instead, there is an interaction phase that requires thedesigners’ wishes to classify optimal solutions into five classes at each optimi-zation iteration. Each class reflects the priority of the optimal solutions, the weightof the objective functions, and the target of the designers’ wishes. Any optimi-zation method can fit in this framework, and particularly in the study of Aittokoskiand Miettinen (2008), an extended Controlled Random Search (CRS, Price 1977;Ali and Storey 1994) method was employed. They found that the classification-based interactive method is a convenient way to express designers’ wishes so thatthe designer can guide the solution process within a limited number of objectivefunction evaluations. Therefore, interactive methods may be a good way to reducethe number of objective function evaluations required, and also enable control ofthe solution process.

1.3.3 Engine Optimization with Evolutionary Methods

Compared to non-evolutionary methods, evolutionary methods, such as geneticalgorithms (GA) and particle swarm optimization (PSO) methods, have been morewidely used in computational engine optimization, because these methods aremore generally applicable for optimizing complex non-linear real world problems.For example, Wickman et al. (2001) integrated a single-objective genetic algo-rithm with the engine CFD code KIVA to optimize nine design variables,including piston geometrical parameters, injection patterns, swirl ratio, and EGRrate, for a high-speed direct injection (HSDI) diesel engine and a heavy-duty dieselengine. Although each task took 2–4 weeks for 400 individual simulations, theoptimization method was still deemed to be efficient considering the large searchspace and the complexity of the problem. They found that the small-bore andheavy-duty diesel engines both favored relatively large diameter shallow pistonbowls, long injection durations at high pressure through small holes, and moderateswirl, at medium speed and high load. The optimal start of injection timing andEGR level were very sensitive to the NOx target value chosen. In addition, precisecontrol over the global air/fuel ratio was very important for achieving simulta-neous emissions and fuel consumption reductions.

A similar approach was adopted by Shrivastava et al. (2002) to investigate theperformance and emissions of a diesel engine using variable intake valve actuationwith boost pressure, EGR and multiple injections. Again, the CFD code KIVA wasextended to interface with a 1-D gas dynamic code in order to accurately predictthe engine intake flow. In their study, a total of eight parameters, including SOI,

1.3 Computational Optimization of Internal Combustion Engines 9

injection duration, EGR, percentage of total fuel mass injected in first pulse of asplit injection rate shape, dwell in between the pulses of the split injection, boostpressure, and the gas swirl and tumble ratios at Intake Valve Closing (IVC) weresimultaneously optimized to locate solutions with reduced NOx, soot, and UHCemissions, as well as low fuel consumption for two engine speeds and loads. In allcases, the engine emissions and fuel consumption were considerably reduced forthe optimal designs as compared to their baseline values. They also observed thatthe optimal boost pressure was considerably higher compared to the baselinevalue. The increase in boost pressure, in combination with the other variables suchas the multiple injection parameters, led to a considerable reduction in soot for-mation. The high optimal EGR rate led to a drastic reduction in engine-out NOx.The effect of swirl and tumble ratios on emissions reduction was found to be mostprominent at high speed and low load. Finally, longer intake valve open durationsand a higher value of maximum valve lift led to better flow development at IVC.

Chen et al. (2003) also used genetic algorithms to optimize an HCCI engine,specifically for a power generator. In their study, optimal sets of equivalence ratio,EGR rate, intake temperature, and pressure were sought to achieve maximumengine thermal efficiency and torque and minimum NO emissions. The GA opti-mization revealed that a mixture of high equivalence ratio with a large amount ofEGR can be used to achieve high thermal efficiency and low NOx emission. GA-searched results also suggested that variable power demand can be convenientlymet by only adjusting the intake pressure while keeping other conditionsunchanged.

Many studies have also shown that genetic algorithms are helpful in deter-mining proper injection strategies for diesel engines under various operatingconditions. For example, Kim et al. (2005) applied a micro-genetic algorithm tostudy the injection parameters and intake conditions for a heavy-duty dieselengine. They found that the GA optimization efficiently located optimal engineoperating parameters that demonstrated low emissions and improved fuel con-sumption capabilities of a diesel engine. The predicted optimal injection timingwas very advanced, which suggests that HCCI-like combustion is useful for lowemissions diesel engines at the considered mid-load condition. The optimizationshowed that the resulting long ignition delay allowed enough time for mixing andreduced the extent of fuel rich regions. This indicates that high levels of EGR canbe used to control NOx and prevent soot formation. Not surprisingly, the optimalcombustion system recommended by the GA is exactly the premixed chargecompression ignition (PCCI) engine strategy, which has been well accepted by theengine community recently. The powerful capability of GA is thus proven.

Similarly, Bergin et al. (2005) identified a novel spin spray combustionapproach for a heavy-duty diesel engine. The study demonstrated that 2006 non-road emissions targets were met by optimizing the spray events with an injectorthat featured two rows of nozzle holes with asynchronous injection for each nozzlerow. No other means of emission reduction were needed. The spin-spray com-bustion that is realized by injecting two neighbor sprays with different cone anglesat different times creates large recirculation structures that greatly enhance mixing.

10 1 Introduction

The optimal configuration of spray cone angles, injection timing, and split injec-tion amount leads to an optimal combustion event that favors soot oxidation,because the formation and decay of the spin spray combustion recirculationstructure allows a more efficient transfer of energy from the injected liquid spray tothe bulk fluid. In other words, this novel injection approach stores the kineticenergy within the flow field and leads to greater late cycle turbulence with sig-nificantly reduced soot emissions due to the resultant improved mixing. Theenhanced mixing also results in more homogenous combustion, which directlybenefits NOx reduction and thermal efficiency increase.

It should be pointed out that all these studies were based on single objectivegenetic algorithms. For engine optimization with multiple objective functions, asingle merit (cost) function has to be defined to include the multiple real objectivefunctions. However, similar to the problem that was discussed by Tanner andSrinivasan (2005), the formula of such single merit function influences the finaloptimal solutions and algorithm convergence. Unfortunately, definition of anappropriate merit function is usually unclear to designers in the real world opti-mization process. This motivates interest in studies and application of multi-objective evolutionary methods (Deb 2001). These methods are becoming thepredominant approach for computational engine optimization and design. Forexample, Tibaut and Marohni (2006), Kurniawan et al. (2007), and Genzale et al.(2007) are among the pioneers who coupled multi-objective genetic algorithmswith engine CFD simulations for automatic engine design optimization. Thesestudies used different optimization algorithms which were integrated in the com-mercial optimization software iSIGHT, modeFRONTIER, and from a multi-objective micro-genetic algorithm source code, respectively. None of theseresearchers compared the performance of the different multi-objective geneticalgorithms, so information about which method suits computational engine opti-mization best was lacking.

To address this problem, Shi and Reitz (2008a) assessed three widely usedmulti-objective genetic algorithms, namely, l-GA (Coello Coello and Pulido2001), NSGA II (Deb et al. 2002), ARMOGA (Sasaki and Obayashi 2005). Theyapplied the three methods to optimize the piston geometry, spray targeting, andswirl ratio for a heavy-duty diesel engine at high-load with CFD simulations. Theyalso defined four quantities that quantify the performance of the optimizationmethods in terms of the optimality and diversity of the optimal solutions. NSGA IIwith a large population size was found to perform the best in their study. Chapter 4describes this study in more detail.

Jeong et al. (2008) developed a hybrid evolutionary method that includes agenetic algorithm and a particle swarm optimization method. The basic idea camefrom the fact that GAs maintain diverse solutions, while PSO shows fast con-vergence to the optimal solution in multi-objective optimization problems. Theytested the hybrid algorithm using two sets of mathematical functions and showedthat the hybrid algorithm had better performance than either a pure GA or a purePSO. However, due to the high computational cost, the performance of the hybridmethod was not compared with other methods for engine optimization problems.

1.3 Computational Optimization of Internal Combustion Engines 11

Shi and Reitz (2008b) extended their previous optimization work (Shi and Reitz2008a) to low-load operating conditions of the same heavy-duty diesel engineusing NSGA II and the engine CFD code KIVA 3v release 2 (KIVA3v2). Bycomparing the optimal solutions of the high-load condition to those of the low-load, they discovered that the high-load operating condition is more sensitive tothe combustion chamber geometrical design compared with the low-load condi-tion. By choosing an optimal combustion chamber design from the high-loadoptimization study and varying swirl ratio, and injection timing and pressure,excellently performing designs were also found using the high-load optimalchamber geometry for the low-load condition. Thus, they suggested that engineoptimization studies for all operating loads should start with an optimization studyof piston geometry and spray targeting for the high-load condition. Further opti-mization on the spray injection event and swirl ratio should then be conducted forthe low-load condition.

In practice, engine optimization over all operating conditions is of moreinterest, but it is also more challenging due to two facts. First, the optimal sets ofdesign variables achieved from an optimization study of a specific operatingcondition are usually not applicable to other conditions. Second, many enginedesign variables are not adjustable under different operating conditions, such as thepiston geometry. To tackle this difficulty, Ge et al. (2010a) proposed a method-ology for engine development using multi-dimensional CFD and computer opti-mization. A multi-objective genetic algorithm NSGA II and the KIVA3v2 codewere used to optimize a light-duty diesel engine. Design parameters of the dieselengine were divided into two categories: hardware design (piston geometry,number of nozzle orifices, injection angle) and controllable design (SOI, swirlratio, boost pressure, and injection pressure). Hardware design parameters wereoptimized first under the full (high)-load condition, as suggested by Shi and Reitz(2008b). Then, the optimal hardware design was fixed for subsequent optimiza-tions of the controllable parameters under different operating conditions. Theyillustrated that with fixed optimal hardware design and optimal sets of controllableparameters for each case, optimal designs which simultaneously reduce fuelconsumption and pollutant emissions were obtained in all cases except for a verylow load case. In addition, strong correlations among the controllable designparameters were not observed, which implies that these controllable parameterscan be optimized separately.

Different from single objective optimization methods, which always lead to asingle global optimal objective function, multi-objective optimization methodsnormally produce many optimal solutions in engine design problems. It is atedious work to analyze such large volume of data using human intelligence.Therefore, the use of regression methods in computational engine optimization isalso desirable. The data-mining process is sometimes equally as important as theoptimization process. In the studies of Shi and Reitz (2008a, b) and Ge et al.(2010a, b), a non-parametric regression analysis method, the COmponent Selec-tion and Smoothing Operator (COSSO) method (Lin and Zhang 2006) was used toestablish the response surfaces of design variables to objective functions. Jeong

12 1 Introduction

et al. (2008) used the Self-Organising Map (SOM), which is a data mining tech-nique using an advanced variant of unsupervised neural networks and clusteringanalysis. Ge et al. (2009a) employed a K-nearest method to analyze a large amountof optimal solutions in an optimization study with a heavy-duty diesel engine. Shiand Reitz (2010a) assessed four regression methods, including K-nearest neigh-bors (KN), Kriging (KR), Neural Networks (NN), and Radial Basis Functions(RBF), for an engine optimization study. They trained these methods using resultsfrom engine CFD simulations and showed that by dynamically training theregression methods during the course of GA optimization, the predicted resultsfrom trained response surfaces agree well with the real CFD simulations. Theperformance of KN and KR methods was better than that of the NN and RBFmethods in their comparative study. This study is also a subject of Chap. 4.

Many studies have proven that engine CFD modeling tools with simplifiedignition and combustion models, such as the Shell/CTC (Characteristic TimeCombustion) model (Kong and Reitz 1993), can be reliable simulators for dieselengine optimization within conventional operating regimes where fuel/air mixingand diffusion flames dominate the combustion and pollutant formation processes(Bergin et al. 2005; Shi and Reitz 2008a, b). The individual simulation using suchapproaches only requires a few hours on the latest personal computers, so thewhole optimization process can be completed within a week or two with multi-objective evolutionary methods, which is highly attractive for industrial optimi-zation designs. But the advanced combustion techniques in modern diesel engines,such as HCCI, PCCI, and Modulated Kinetics (MK), are primarily controlled byfuel chemistry. In this case, accurate engine CFD simulations require a detaileddescription of the chemical kinetics of the fuels. It is not uncommon to find one totwo orders of magnitude increase in the required computer time when solvingdetailed reaction mechanisms in engine CFD simulations compared to usingsimplified combustion models. Therefore, engine optimization using CFD simu-lation with detailed chemistry is generally not practically feasible, given theexcessively long optimization cycle.

Significant efforts have been made recently to accelerate engine CFD simulationswith detailed chemistry, which can be categorized into four major approaches. First,the development of mesh-independent spray models (Munnannur 2007; Abani et al.2008a; Abani and Reitz 2010) enables engine CFD simulations using coarser mesheswithout losing accuracy compared to those of fine meshes (Abani et al. 2008b).Second, multi-zone or multi-grid methods (Babajimopoulos et al. 2005; Shi et al.2009a; Goldin et al. 2009; Liang et al. 2009a) divide computational domains into sub-domains by grouping thermodynamically-similar cells, which largely reduces thecalling frequency to the chemistry solver in engine CFD simulations. Third, efficientparallelization schemes (Shi et al. 2009b) take advantage of the multi-core archi-tecture of latest central processing units. Finally, reaction mechanism reductiontechniques (Lu and Law 2005; Pepiot-Desjardins and Pitsch 2008a; Sun et al. 2010)and the on-the-fly model reduction schemes (Liang et al. 2009b, c; Shi et al. 2010b)greatly decrease the reaction mechanism size needed to describe the chemical kineticsof fuel oxidation and combustion. These methods are described in Chap. 3 in detail.

1.3 Computational Optimization of Internal Combustion Engines 13

Cumulative benefits are attainable by combining these methods for enhancingcombustion modeling efficiency, which makes possible computational engineoptimization. It has been shown that by using one or more such chemistry solveracceleration techniques, the optimization cycle using engine CFD simulationwith detailed chemistry can be reduced to approximately one month (Ge et al.2010a, b; Shi and Reitz 2010c). Ge et al. (2010b) studied a HSDI diesel engineoperated at a low-load condition in the MK combustion mode. They optimizedthe engine piston geometry, spray targeting, and swirl ratio with NSGA II andCFD simulations using the full chemistry solver and with the accelerated solverwith the adaptive multi-grid chemistry (AMC) model (Shi et al. 2009a).Although for individual cases, the accelerated chemistry solver introducesapproximation to the full chemistry solver, they found that the optimizationusing the AMC model produced consistent optimal solutions to those of the fullchemistry model, but only cost half the computer time. In an extended study, Geet al. (2010b) used the engine CFD simulations with the AMC model to optimizethe same HSDI engine for a full range of operating conditions. Shi et al. (2010)integrated a on-the-fly mechanism reduction scheme with the AMC model intothe engine CFD simulation software KIVA3v2, which further improved thecomputational efficiency for their optimization study of a heavy-duty compres-sion-ignition engine fueled with diesel and gasoline-like fuels (Shi et al. 2010c).The entire optimization cycle for six tasks was completed within six weeks,which would be six months if the accelerated chemistry solver were not used.This engine optimization work showed that gasoline-like fuels exhibit greatpotential for cleaner combustion than with conventional diesel fuel. Different in-cylinder flow patterns were identified in the optimal engine designs with thedifferent fuels. Due to the diffusion-type combustion, diesel fuel exhibits stag-nation-point dominated flow fields in many optimal cases, while gasoline-likefuels show more volumetric-heat-release-driven flows due to their premixed-typecombustion. The results of the optimization study also indicate that lower octanenumber gasoline-like fuels may be more helpful to improve the controllability ofcompression-ignition engines in the Partially Premixed Combustion (PPC) modeand to reduce engine noise.

To conclude, high-fidelity CFD modeling tools with detailed fuel chemistryenable engine designers to obtain reliable simulation results. Efficient opti-mization methods and accelerated CFD solvers significantly shorten thecomputer time of optimization cycles, which makes the computational opti-mization approach more competitive than experiments. In the rest of the book,we will revisit several of the aforementioned optimization works in moredetail to show that computational optimization of internal combustion enginesis becoming an indispensable part of practical engine designs, and to providean up-to-date reference to developments and future directions in the field ofengine modeling.

14 1 Introduction