Simulation of a Diesel Engine with Variable Geometry Turbocharger and Parametric Study of Variable Vane Position on Engine Performance

Modelling of a turbocharger is of interest to the engine designer as the work developed by the turbine can be used to drive a compressor coupled to it. This positively influences charge air density and engine power to weight ratio. Variable geometry turbocharger (VGT) additionally has a controllable nozzle ring which is normally electro-pneumatically actuated. This additional degree of freedom offers efficient matching of the effective turbine area for a wide range of engine mass flow rates. Closing of the nozzle ring (vanes tangential to rotor) result in more turbine work and deliver higher boost pressure but it also increases the back pressure on the engine induced by reduced turbine effective area. This adversely affects the net engine torque as the pumping work required increases. Hence, the optimum vane position for a given engine operating point is to be found through simulations or experimentation. A thermodynamic simulation model of a 2.2l 4 cylinder diesel engine was developed for investigation of different control strategies. Model features map based performance prediction of the VGT. Performance of the engine was simulated for steady state operation and validated with experimentation. The results of the parametric study of VGT’s vane position on the engine performance are discussed

Development of a Fuel Quantity based Engine Control Unit Software Architecture

   Conventionally diesel engines are controlled in open loop with maps based on engine speed and throttle position wherein fuel quantity is indirectly fixed using the rail pressure and injection duration maps with engine speed and throttle position as the independent variables which are measured by the respective sensors. In this work an engine control unit (ECU) software architecture where fuel quantity is directly specified in relation to the driver demand was implemented by modifying the control logic of a throttle position based framework. A desired fuel quantity for a given engine speed and throttle position was mapped from base line experiments on the reference engine. Injection durations and rail pressure required for this quantity was mapped on a fuel injector calibration test bench. The final calculation of injection duration in the new architecture is calculated using the fuel injector model. This enables determination of fuel quantity injected at any moment which directly indicates the torque produced by the engine at a given speed enabling smoke limited fuelling calculations and easing the implementation of control functions like all-speed governing

Development and Demonstration of Control Strategies for a Common Rail Direct Injection Armoured Fighting Vehicle Engine

The development of a controller which can be used for engines used in armoured fighting vehicles is discussed. This involved choosing a state of the art reference common rail automotive Diesel engine and setting-up of a transient engine testing facility. The dynamometer through special real-time software was controlled to vary the engine speed and throttle position. The reference engine was first tested with its stock ECU and its bounds of operation were identified. Several software modules were developed in-house in stages and evaluated on special test benches before being integrated and tested on the reference engine. Complete engine control software was thus developed in Simulink and flashed on to an open engine controller which was then interfaced with the engine. The developed control software includes strategies for closed loop control of fuel rail pressure, boost pressure, idle speed, coolant temperature based engine de-rating, control of fuel injection timing, duration and number of injections per cycle based on engine speed and driver input. The developed control algorithms also facilitated online calibration of engine maps and manual over-ride and control of engine parameters whenever required. The software was further tuned under transient conditions on the actual engine for close control of various parameters including rail pressure, idling speed and boost pressure. Finally, the developed control strategies were successfully demonstrated and validated on the reference engine being loaded on customised transient cycles on the transient engine testing facility with inputs based on military driving conditions. The developed controller can be scaled up for armoured fighting vehicle engines

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset