<p>The main accomplishments of this research are </p> <p>(1) developed a high fidelity computational methodology based on large eddy simulation to capture lean blowout (LBO) behaviors of different fuels; </p> <p>(2) developed fundamental insights into the combustion processes leading to the flame blowout and fuel composition effects on the lean blowout limits; </p> <p>(3) developed artificial intelligence-based models for early detection of the onset of the lean blowout in a realistic complex combustor. </p> <p>The methodologies are demonstrated by performing the lean blowout (LBO) calculations and statistical analysis for a conventional (A-2) and an alternative bio-jet fuel (C-1).</p> <p>High-performance computing methodology is developed based on the large eddy simulation (LES) turbulence models, detailed chemistry and flamelet based combustion models. This methodology is employed for predicting the combustion characteristics of the conventional fuels and bio-derived alternative jet fuels in a realistic gas turbine engine. The uniqueness of this methodology is the inclusion of as-it-is combustor hardware details such as complex hybrid-airblast fuel injector, thousands of tiny effusion holes, primary and secondary dilution holes on the liners, and the use of highly automated on the fly meshing with adaptive mesh refinement. The flow split and mesh sensitivity study are performed under non-reacting conditions. The reacting LES simulations are performed with two combustion models (finite rate chemistry and flamelet generated manifold models) and four different chemical kinetic mechanisms. The reacting spray characteristics and flame shape are compared with the experiment at the near lean blowout stable condition for both the combustion models. The LES simulations are performed by a gradual reduction in the fuel flow rate in a stepwise manner until a lean blowout is reached. The computational methodology has predicted the fuel sensitivity to lean blowout accurately with correct trends between the conventional and alternative bio-jet fuels. The flamelet generated manifold (FGM) model showed 60% reduction in the computational time compared to the finite rate chemistry model. </p> <p>The statistical analyses of the results from the high fidelity LES simulations are performed to gain fundamental insights into the LBO process and identify the key markers to predict the incipient LBO condition in swirl-stabilized spray combustion. The bio-jet fuel (C-1) exhibits significantly larger CH₂O concentrations in the fuel-rich regions compared to the conventional petroleum fuel (A-2) at the same equivalence ratio. It is observed from the analysis that the concentration of formaldehyde increases significantly in the primary zone indicating partial oxidation as we approach the LBO limit. The analysis also showed that the temperature of the recirculating hot gases is also an important parameter for maintaining a stable flame. If this temperature falls below a certain threshold value for a given fuel, the evaporation rates and heat release rated decreases significantly and consequently leading to the global extinction phenomena called lean blowout. The present study established the minimum recirculating gas temperature needed to maintain a stable flame for the A-2 and C-1 fuels. </p> The artificial intelligence (AI) models are developed based on high fidelity LES data for early identification of the incipient LBO condition in a realistic gas turbine combustor under engine relevant conditions. The first approach is based on the sensor-based monitoring at the optimal probe locations within a realistic gas turbine engine combustor for quantities of interest using the Support Vector Machine (SVM). Optimal sensor locations are found to be in the flame root region and were effective in detecting the onset of LBO ~20ms ahead of the event. The second approach is based on the spatiotemporal features in the primary zone of the combustor. A convolutional autoencoder is trained for feature extraction from the mass fraction of the OH ( data for all time-steps resulting in significant dimensionality reduction. The extracted features along with the ground truth labels are used to train the support vector machine (SVM) model for binary classification. The LBO indicator is defined as the output of the SVM model, 1 for unstable and 0 for stable. The LBO indicator stabilized to the value of 1 approximately 30 ms before complete blowout.

<p> One primary focus on internal combustion engines is that these engines create multiple harmful exhaust gases that can cause damage to the environment. There are a number of advanced strategies that are currently being investigated to help reduce the amount of these harmful emissions that are emitted from IC engines. One such method of reducing harmful emission gases focuses on the three-way-catalyst. A three-way-catalyst (TWC) is an exhaust emission control device that is designed in such a way to take harmful exhaust gases and convert them into less harmful gases through various chemical reactions within the TWC. To help further the reduction of these harmful gases in the TWC, a novel two-loop control and estimation strategy is used. This control and estimation strategy involves the use of two loops with an inner-loop controller, outer-loop robust controller, and an estimator in the outer-loop. The estimator consists of a TWC model and an extended Kalman filter which is used to estimate the fractional oxidation state (FOS) of the TWC. This estimated FOS is then used by the robust controller, along with other parameters, to produce a desired engine lambda reference signal, λup. This desired lambda signal is then used by the inner-loop controller to control the engine lambda. Accurate control of lambda is important because the air-fuel-ratio range for a TWC to effectively achieve oxidation and reduction simultaneously is extremely narrow. Another primary focus in the field of internal combustion engines is designing and tuning advanced models within GT-Power that can accurately predict what will happen when running an actual engine. Designing, troubleshooting, and testing a GT-Power model is an extensive but rewarding process. Creating an accurate engine model can not only provide one with primary engine data that is also measurable in a test cell, but can also provide insight into some of the intricate processes and nature of the engine that are difficult or impossible to physically measure. Cummins has an extensive process of tuning GT-Power engine models. This process include items such as initial model calibrations, model discretizations, turbocharger tunings, and other items. Some of these processes are used to calibrate both Cummins Power Systems Business Unit engines as well as a Purdue B6.7N natural gas engine. </p>

<div>Stoichiometric air-fuel ratio (AFR) and air/EGR flow control are essential control problems in today’s advanced spark-ignited (SI) engines to enable effective application of the three-way-catalyst (TWC) and generation of required torque. External exhaust gas recirculation (EGR) can be used in SI engines to help mitigate knock, reduce enrichment and improve efficiency[1 ]. However, the introduction of the EGR system increases the complexity of stoichiometric engine-out lambda and torque management, particularly for high BMEP commercial vehicle applications. This thesis develops advanced frameworks for sensing and control architecture designs to enable robust air handling system management, stoichiometric cylinder air-fuel ratio (AFR) control and three-way-catalyst emission control.</div><div><br></div><div><div>The first work in this thesis derives a physically-based, control-oriented model for turbocharged SI engines utilizing cooled EGR and flexible VVA systems. The model includes the impacts of modulation to any combination of 11 actuators, including the throttle valve, bypass valve, fuel injection rate, waste-gate, high-pressure (HP) EGR, low-pressure (LP) EGR, number of firing cylinders, intake and exhaust valve opening and closing timings. A new cylinder-out gas composition estimation method, based on the inputs’ information of cylinder charge flow, injected fuel amount, residual gas mass and intake gas compositions, is proposed in this model. This method can be implemented in the control-oriented model as a critical input for estimating the exhaust manifold gas compositions. A new flow-based turbine-out pressure modeling strategy is also proposed in this thesis as a necessary input to estimate the LP EGR flow rate. Incorporated with these two sub-models, the control-oriented model is capable to capture the dynamics of pressure, temperature and gas compositions in manifolds and the cylinder. Thirteen physical parameters, including intake, boost and exhaust manifolds’ pressures, temperatures, unburnt and burnt mass fractions as well as the turbocharger speed, are defined as state variables. The outputs such as flow rates and AFR are modeled as functions of selected states and inputs. The control-oriented model is validated with a high fidelity SI engine GT-Power model for different operating conditions. The novelty in this physical modeling work includes the development and incorporation of the cylinder-out gas composition estimation method and the turbine-out pressure model in the control-oriented model.</div></div><div><br></div><div><div>The second part of the work outlines a novel sensor selection and observer design algorithm for linear time-invariant systems with both process and measurement noise based on <i>H</i>2 optimization to optimize the tradeoff between the observer error and the number of required sensors. The optimization problem is relaxed to a sequence of convex optimization problems that minimize the cost function consisting of the <i>H</i>2 norm of the observer error and the weighted <i>l</i>1 norm of the observer gain. An LMI formulation allows for efficient solution via semi-definite programing. The approach is applied here, for the first time, to a turbo-charged spark-ignited (SI) engine using exhaust gas recirculation to determine the optimal sensor sets for real-time intake manifold burnt gas mass fraction estimation. Simulation with the candidate estimator embedded in a high fidelity engine GT-Power model demonstrates that the optimal sensor sets selected using this algorithm have the best <i>H</i>2 estimation performance. Sensor redundancy is also analyzed based on the algorithm results. This algorithm is applicable for any type of modern internal combustion engines to reduce system design time and experimental efforts typically required for selecting optimal sensor sets.</div></div><div><br></div><div><div>The third study develops a model-based sensor selection and controller design framework for robust control of air-fuel-ratio (AFR), air flow and EGR flow for turbocharged stoichiometric engines using low pressure EGR, waste-gate turbo-charging, intake throttling and variable valve timing. Model uncertainties, disturbances, transport delays, sensor and actuator characteristics are considered in this framework. Based on the required control performance and candidate sensor sets, the framework synthesizes an H1 feedback controller and evaluates the viability of the candidate sensor set through analysis of the structured</div><div>singular value μ of the closed-loop system in the frequency domain. The framework can also be used to understand if relaxing the controller performance requirements enables the use of a simpler (less costly) sensor set. The sensor selection and controller co-design approach is applied here, for the first time, to turbo-charged engines using exhaust gas circulation. High fidelity GT-Power simulations are used to validate the approach. The novelty of the work in this part can be summarized as follows: (1) A novel control strategy is proposed for the stoichiometric SI engines using low pressure EGR to simultaneously satisfy both the AFR and air/EGR-path control performance requirements; (2) A parametrical method to simultaneously select the sensors and design the controller is first proposed for the internal combustion engines.</div></div><div><br></div><div><div>In the fourth part of the work, a novel two-loop estimation and control strategy is proposed to reduce the emission of the three-way-catalyst (TWC). In the outer loop, an FOS estimator consisting of a TWC model and an extended Kalman-filter is used to estimate the current TWC fractional oxygen state (FOS) and a robust controller is used to control the TWC FOS by manipulating the desired engine λ. The outer loop estimator and controller are combined with an existing inner loop controller. The inner loop controller controls the engine λ based on the desired λ value and the control inaccuracies are considered and compensated by the outer loop robust controller. This control strategy achieves good emission reduction performance and has advantages over the constant λ control strategy and the conventional two-loop switch-type control strategy.</div></div>

<div> <div> <div> <p>Commercial vehicles in the United States account for a significant fraction of greenhouse gas emissions and NOx emissions. The objectives of this work are reduction in commercial vehicle NOx emissions through enhanced aftertreatment thermal management via diesel engine variable valve actuation and the reduction of commercial vehicle fuel consumption/GHG emissions by enabling more effective class 8 truck platooning. </p> <p><br></p><p>First, a novel diesel engine aftertreatment thermal management strategy is proposed which utilizes a 2-stroke breathing variable value actuation strategy to increase the mass flow rate of exhaust gas. Experiments showed that when allowed to operate with modestly higher engine-out emissions, temperatures comparable to baseline could be achieved with a 1.75x exhaust mass flow rate, which could be beneficial for heating the SCR catalyst in a cold-start scenario. </p> <p><br></p><p>Second, a methodology is presented for characterizing aerodynamic drag coefficients of platooning trucks using experimental track-test data, which allowed for the development of high-fidelity platoon simulations and thereby enabled rapid development of advanced platoon controllers. Single truck and platoon drag coefficients were calculated for late model year Peterbilt 579’s based on experimental data collected during J1321 fuel economy tests for a two-truck platoon at 65 mph with a 55’ truck gap. Results show drag coefficients of 0.53, 0.50, and 0.45 for a single truck, a platoon front truck, and a platoon rear truck, respectively. </p> <p><br></p><p>Finally, a PID-based platoon controller is presented for maximizing fuel savings and gap control on hilly terrain using a dynamically-variable platoon gap. The controller was vetted in simulation and demonstrated on a vehicle in closed-course functionality testing. Simulations show that the controller is capable of 6-9% rear truck fuel savings on a heavily-graded route compared to a production-intent platoon controller, while increasing control over the truck gap to discourage other vehicles from cutting in. </p></div></div></div>

<p>Stringent regulatory requirements and modern diesel engine technologies have engaged automotive manufacturers and researchers in accurately predicting and controlling diesel engine-out emissions. As a result, engine control systems have become more complex and opaquer, increasing the development time and costs. To address this challenge, Model-based control methods are an effective way to deal with the criticality of the system study and controls. And physics-based combustion engine modeling is a key to achieve it. This thesis focuses on development and validation of a physics-based model for both engine and emissions using model-based design tools from MATLAB & Simulink. Engine model equipped with exhaust gas circulation and variable geometry turbine is adopted from the previously done work which was then integrated with the combustion and emission model that predicts the heat release rates and NO_xemission from engine. Combustion model is designed based on the mass fraction burnt from CA10 to CA90 and then NO_xpredicted using the extended Zeldovich mechanism. The engine models are tuned for both steady state and dynamics test points to account for engine operating range from the performance data. Various engine and combustion parameters are estimated using parameter estimation toolbox from MATLAB and Simulink by applying least squared solver to minimize the error between measured and estimated variables. This model is validated against the virtual engine model developed in GT-power for Cummins 6.7L turbo diesel engine. To account the harmonization of the testing cycles to save engine development time globally, a world harmonized stationary cycle (WHSC) is used for the validation. Sub-systems are validated individually as well as in loop with a complete model for WHSC. Engine model validation showed promising accuracy of more than 88.4 percent in average for the desired parameters required for the NO_xprediction. NO_x estimation is accurate for the cycle except warm up and cool down phase. However, NO_xprediction during these phases is limited due to actual NO_xmeasured data for tuning the model for real time NO_xestimation. Results are summarized at the end to compare the trend of NO_xestimation from the developed combustion and emission model to show the accuracy of in-cylinder parameters and required for the NO_x estimation. </p>

<p>One of the major challenges in the field of internal combustion engines is keeping up with the advancements in electrification and hybridization. Automakers are striving to design environment – friendly and highly efficient engines to meet stringent emission standards worldwide. Improving engine efficiency and reducing heat losses are critical aspects of this development. Therefore, accurate heat transfer prediction capabilities play a vital role in engine design process. Current methods rely on computationally intensive 3D numerical analyses, there is a growing interest in reliable simplified models. </p> <p>In this study, a 1D diesel engine model featuring predictive combustion was integrated with a detailed finite element thermal primitive based on the 3D meshing feature available in GT Suite. Coolant and oil hydraulic circuits were incorporated in the model. The model proves to be an effective means to assess the impact on heat rejection and engine heat distribution given by an engine calibration and operating conditions. </p> <p>This work also contributes to the advancement of virtual IC engine development methods by focusing on the design and tuning of complex engine system models using GT Power for accurate prediction of engine performance. The current processes in engine simulations are assessed to identify sources of errors and opportunities for improvements. The methods discussed in this work include isolated sub system level calibration and model evolution specifically address the issue of identifying noise factors and issues in smaller parts. Additionally, the study aims on improving the model’s trustworthiness by computing 1st law sanity checks, replicating real-life compressor map calculations and refining GT’s existing global convergence criteria. </p>