In this article, a novel drive mode, “intelligent vehicle drive mode” (IVDM), was proposed, which augments the vehicle engine performance in real-time. This drive mode predicts the driver behavior vector (DBV), which optimizes the vehicle engine performance, and the metric of optimal vehicle engine performance was defined using the elements of engine operating point (EOP) and heating ventilation and air conditioning system (HVAC). Deep learning (DL) models were developed by mapping the vehicle level vectors (VLV) with EOP and HVAC parameters, and the trained functions were utilized to predict the future states of DBV reflecting augmented vehicle engine performance. The iterative analysis was performed by empirically estimating the future states of VLV in the allowable range of DBV and was fed into the DL model to predict the performance vectors. The defined vehicle engine performance metric was applied to the predicted vectors, and thus optimal DBV is the instantaneous output of the IVDM. The analytical and validation techniques were developed using field data obtained from General Motors Inc., Warren, Michigan. Finally, the proposed concept was quantified by analyzing the instantaneous engine efficiency (IEE) and smoothness measure of the instantaneous engine map (IEM).

Exergy efficiency can be used as an objective function in order to improve systems efficiency. Thus, the most efficient regions for the operation parameters can be searched easily. Exergy efficiency data of a turboprop engine’s components that have been calculated using basic engine parameters in the previous studies are modeled using cubic spline curve fitting methodology. Spline curves are on the two dimensional plane, where x axis is the input parameter and y axis is the exergy efficiency of the component. A spline curve is defined by the points subject to arbitrary selection of number and position. Initially positions of the points are located with two different methods and then in order to obtain better accuracy point positions are improved by ‘Ant colony’ and ‘Goldsection’ optimisation methods. Sum of Squares of the errors between the fitted value and data value was used as the fitness function. Least square error of 5 × 10−9 is assumed as acceptable accuracy which yields to a minimum R = 0.9998 linear correlation coefficient. In the optimisation step, independent engine variable versus calculated engine performance parameters were checked against spline fitted values. Improvement of the fitness function is observed as the number of fitting points is increased. Ant colony optimisation in engine exergy efficiency parametric modeling is a new approach in authors’ point of view.

This paper deals with the study of the power matching of the propulsion system and on-board systems changing the on-board systems’ electrification level. In particular, four system architectures have been studied, each one with a different level of electrification starting from the More Electric Aircraft (MEA) to the All Electric Aircraft (AEA) systems. The mass and the power requirement of each system architectures have been analysed together with the change in engine specific fuel consumption. Then, these results have been used to quantify the influences of engine and systems power matching to the entire aircraft. In particular, the beneficial effect of system electrification has been evaluated as an increment of aircraft range. Moreover, two reference aircraft – a regional jet and a short/medium range liner – have been selected to understand the variance of the power matching changing aircraft dimensions and mission range. The study is carried out using a distributed and collaborative Multi-Disciplinary Design Analysis and Optimization (MDAO) environment. The results show a beneficial effect of systems electrification on systems mass and engine specific fuel consumption. At aircraft level, the results point out an increment of aircraft range up to 7.7% with a different trend for the two studied cases.

Infrared signal measurements from a micro-turbojet engine are conducted to understand the characteristics of the engine performance and the infrared signal by varying the exhaust nozzle configuration. A cone type nozzle and five rectangle type nozzles whose aspect ratios vary from one to five are used for this experimental work. As a result, it is confirmed that the thrust and the fuel consumption rate of the engine do not change greatly by varying the exhaust nozzle shape. In the case of the aspect ratio of 5, the specific fuel consumption of the engine is increased by about 3% compared to the reference cone nozzle, but the infrared signal can be reduced by up to 14%. As a result of measuring the temperature distribution of the plume gas, the correlation of infrared signal with plume gas temperature distribution can be understood. In the case of a cone shape, the distribution of plume gas formed to circular shape, and the high-temperature core region of plume gas continued to develop farther to the downstream. However, the temperature distribution was maintained in the rectangular shape as the aspect ratio increased, and the average temperature decreased sharply. As the aspect ratio increases, the plume spreads more widely.