Mayank Mittal

In-cylinder fluid motion has a substantial impact on air-fuel mixture formation, combustion process and emission formation. In the present paper, a simulation study of the in-cylinder fluid flow is performed using a computational fluid dynamic (CFD) model of a port-fuel-injection (PFI) engine (volume: 110 cc). First, a 1-D model is developed, and validated with the cylinder pressure traces acquired in an optical engine experimentally. The model provided the boundary conditions for multi-dimensional numerical simulations. The predicted velocity fields from CFD are then compared with the measured data obtained using particle image velocimetry (PIV) at various crank angle positions with low throttle opening condition. A good relevance is observed on comparing numerically estimated results with experimental results.

Cycle-to-cycle combustion variations not only degrade the performance and combustion characteristics of an engine, it also has a considerable influence on engine durability. Thus, a deeper understanding of cycle-to-cycle combustion variations, particularly at low loads, is required. In the present study, both statistical and wavelet transform methods have been used to investigate the cycle-to-cycle combustion variations at different operating loads. A single-cylinder spark-ignited engine was used, and cylinder pressure data was recorded for 1200 consecutive engine cycles at each operating point for the analysis of cycle-to-cycle variations. As expected, it was found from the statistical analysis that cycle-to-cycle variations in indicated mean effective pressure (IMEP) were decreased with the increase in load. Wavelet analysis revealed that cycle-to-cycle variations occur at multiple time scales for both loads. However, wavelet power spectrum at the lower load was characterized by intermittent high frequency oscillations, and with the increase in load, these high frequency oscillations were shifted to lower frequency range.

Laser-based diagnostic techniques like particle image velocimetry (PIV) and molecular tagging velocimetry (MTV) are used to measure flow fields at a high spatial resolution. Post-processing of the obtained flow fields is essential for outlier correction as the datasets may be skewed by local flow vectors with a disproportionate disparity in magnitude or directions from neighborhood vectors. The rationale behind this work is to propose an algorithm using proper orthogonal decomposition (POD), namely, POD-OROC (POD-based outlier removal and outlier correction), which can correct outliers in an ensemble of flow fields. The proposed algorithm is first validated on synthetic flows with a known percentage of outlier rate and then applied to engine in-cylinder flow fields. The algorithm ran for a few iterations for both flow datasets and rejected frames with high outlier rates (above 15%) and then post-processed the remaining ones to detect and correct local spurious vectors. It was found that outlier vectors with larger deviation from neighboring vectors are detected in earlier iterations. An error analysis was performed to quantify the total error in an ensemble and, in using it, two types of errors - over-detection and under-detection - were identified. With this insight, several parameters of the model for synthetic flows were optimized for best performance, and then the model was modified for application to in-cylinder flows. The impact of POD-OROC was studied through changes in the POD energy spectra where the energy share of the first mode increased to 99.9% for synthetic flows and to 82.5% and 68.9% for the two in-cylinder flow sets. Finally, POD-OROC is now matured enough to be applied to in-cylinder flow datasets and can detect and correct both single and cluster outliers.

Internal combustion engines play a major role in biogas-based stationary power generation applications in rural areas, and serious progress on effective utilization of bio-resources by considering engine stability is not achieved yet. In the present study, combustion characteristics and cycle-to-cycle variations (CCVs) of a spark-ignition (SI) engine fueled with gasoline, biogas, and surrogate of bio-methane are analyzed. A single-cylinder, four-stroke SI engine (with a flexible gaseous fuel system) was operated at a couple of load points (8 Nm and 11.5 Nm) with a rotational speed of 1500 rpm. CCVs are analyzed using a statistical approach considering 1000 consecutive engine cycles for each operating condition. Results at 8 Nm showed relatively higher CCVs of indicated mean effective pressure (IMEP), peak in-cylinder pressure (Pmax), and flame initiation duration (FID) for biogas compared to methane. It is also found that a linear relationship existed between Pmax and its corresponding location (θPmax) for methane, whereas the hook-back phenomenon was observed for biogas, which is because of the successive slow and fast burning cycles as noted in the return map of the IMEP.

In the present work, the possibility of engine knock detection is investigated based on in-cycle speed data, which is readily available to the ECU. Experiments were conducted at 3000 rpm with wide-open throttle condition in a single-cylinder, air-cooled, port-fuel-injection spark-ignition engine at different levels of knocking. It was found that amplitude of speed oscillations increased with the knock intensity for considered window with the size of 100 crank angle degree, starting from the top dead center of compression. The proposed knock indicators based on in-cycle speed oscillations were found to be able to identify the knock-limited spark timings at different operating conditions. Results showed that the amplitude of speed oscillations, derived from in-cycle speed data with resolution of six crank angle degree, could also be used to quantify the knock. The knock frequency based on speed oscillations also showed a sharp increase at the onset of knock. Cycle by cycle knock estimation was also done using the speed oscillations. Thus, methods based on in-cycle oscillations of speed have the potential for detecting the knock in small spark-ignition engines.

Improving internal combustion engine performance is a significant concern over the past few decades for engine researchers and automobile manufacturers. One of the promising methods for improving the engine performance is variable valve actuation system with camless technology. In the camless system, the conventional spring-operated valve actuation mechanism is removed, and an actuator is used to independently control the valve events (lift, timing, and duration). Among different camless systems, electromagnetic variable valve actuation (EMVA) becomes more viable because of its faster valve operation. However, the major challenge is to control the valve seating velocity (velocity at which valve comes to rest during seating on the cylinder head) due to the absence of the cam mechanism. A sophisticated control system must be developed to achieve an acceptable valve seating velocity. In this study, a proportional-integral-derivative (PID) controller was used to control the EMVA system. A machine learning tool, i.e., genetic algorithm, and an iterative method, i.e., Ziegler-Nichols, were used to optimize the PID controller's gain values. The valve lift profiles obtained using the Ziegler-Nichols method and the genetic algorithm were compared. It was found that the developed algorithm for the EMVA system can achieve faster rise time compared to the experimental results [25] utilized inverse square method. A parametric investigation was performed to verify the robustness of the PID controller with a change in temperature. It is concluded that the temperature rise may increase the resistance and inductance, but the controller with the updated gain values can control the EMVA system without affecting the performance parameter. The simulation was performed for both forward and backward strokes to investigate the valve seating velocity. It was found that the controller can achieve an acceptable valve seating velocity. Hence, the machine learning tool helps in optimizing the PID controller's gain values to achieve faster valve operation with an acceptable valve seating velocity.

Investigation of the combustion process in engines for improved fuel economy and emissions is best done by combining experiments and simulations. In the present work both experiments and simulations are conducted considering a naturally aspirated common-rail direct-injection (CRDI) diesel engine. The CFD model is developed based on experiments conducted at two operating points, representing to a 0.9 l, two-cylinder, diesel engine. The developed CFD model is then used to study the effects of different in-cylinder strategies attempted at reducing emissions, without compromising on performance. Previous researches conducted on diesel engine CFD simulations are generally based on large-bore large-capacity single-cylinder engines, and mostly investigated a single operating point. The present study investigates the effects of combustion chamber geometry, injection timing, multiple injections as well as EGR, individually as well as in combination, on NOx and soot emissions, at two different operating points in a small naturally aspirated CRDI diesel engine.

A multi-zone phenomenological model is developed based on mass and energy conservations for predicting cylinder pressure and temperature in a spark ignition (SI) engine. During the combustion process, it is assumed that cylinder charge consists of unburned and burned gas zones separated by a thin flame front. The pressure and temperature data, along with equilibrium composition of species, are then used to predict emissions. These predicted values are compared with available data obtained through experiments. A good agreement between predicted and measured cylinder pressure traces and emissions is obtained, indicating that the presented model is reliable for studying SI engine combustion and emission characteristics at different operating conditions.

The electro-magnetic valve actuation improves the performance of an internal combustion engine. In this work, the control of electro-magnetic valve actuation (EMVA) is considered. The control of valve seating velocity, rise and settling time of valve with minimum peak overshoot for a certain valve lift position are the main challenges. The proposed work is based on proportional-integral-derivative (PID) controller with particle swarm optimization (PSO) to overcome the issues. The PID controller's parameters were first obtained using Ziegler-Nichols method and then further optimized with multi-objective PSO technique. The simulation results indicate that the proposed multi-objective PSO-based PID controller can achieve justifiable seating velocity with faster valve operation for both forward and return strokes. The weighted sum approach was applied on the multi-objective PSO, and effect of varying weights on performance was also studied. The valve list profiles of particle swarm optimization were compared with Ziegler-Nichols based PID controller and a better performance was achieved using the proposed control technique.

Spark advance is an important operating variable in a spark-ignition engine that affects nearly all engine outputs. Therefore, an accurate prediction of optimum spark advance (OSA) at a given operating condition is important for optimal engine performance. In the present work, a single-cylinder gasoline-fueled spark-ignition engine is operated over a wide range of loads with different equivalence ratios. The measured data is then used to predict the optimum spark advance for a given operating condition using artificial neural network (ANN). The ANN model is developed based on 29 operating points. Randomly, 80% of data was used to train the ANN model using Levenberg-Marquardt algorithm. In order to obtain best performance, number of neurons and transfer function of the hidden layer were changed. The ANN model, incorporated logarithmic sigmoid function in the hidden layer with 34 neurons, showed the best performance - with mean square error and correlation coefficient of 0 and 1, respectively. The OSA of remaining 20% of data was determined using the ANN model. It was found that the ANN model compared well with the measured data at different operating conditions.