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Usage of gas turbine engines has increased by day due to rising demand for military and civil applications. This case results in investigating diverse topics related to energy efficiency and irreversibility of these systems. The purpose of this paper is to perform a detailed entropy assessment of turbojet engines for different flight conditions.

In this study, for small turbojet engines used in unmanned aerial vehicles, parametric cycle analysis is carried out at (sea level-zero Mach (hereinafter phase-I)) and (altitude of 9,000 m- Mach of 0.7 (hereinafter phase-II)). Based on this analysis, variation of performance and thermodynamic parameters with respect to change in isentropic efficiency of the compressor (CIE) and turbine (TIE) is examined at both phases. In this context, the examined ranges for CIE is between 0.78 and 0.88 whereas TIE is between 0.85 and 0.95.

Increasing isentropic efficiency decreases entropy production of the small turbojet engine. Moreover, the highest entropy production occurs in the combustor in the comparison of other components. Namely, it decreases from 2.81 to 2.69 kW/K at phase-I and decreases from 1.44 to 1.39 kW/K at phase-II owing to rising CIE.

It is thought that this study helps in understanding the relationship between entropy production and the efficiency of components. Namely, the approach used in the current analysis could help decision-makers or designers to determine the optimum value of design variables.

Due to rising isentropic efficiencies of both components, it is observed that specific fuel consumption (SFC) decreases whereas specific thrust (ST) increases. Also, the isentropic efficiency of a compressor affects relatively SFC and ST higher than that of the turbine.

This paper aims to present the results of a design optimization study for the impeller of a small mixed‐flow compressor. The objective of the optimization is to obtain an impeller geometry that could minimize a cost function based on the specific thrust and the thrust specific fuel consumption of a small turbojet engine.

The design methodology is based on an optimization process that uses a configurational database for various compressor geometries. The database is constructed using design of experiments and the compressor configurations are generated using one‐dimensional in‐house design codes, as well as various tools and programs of the Agile Engineering Design System^®, which is a commercially available turbomachinery design system developed at Concepts NREC. The cost function variations within the design space are represented through a neural network. The optimum configuration that minimizes the cost function is obtained using a direct search optimization procedure.

The optimization study generated a small 86 mm diameter mixed‐flow impeller with a 50° meridional exit angle. The optimized compressor, as well as the engine that it is designed for, were shown to have improved performance characteristics.

Preliminary performance and flow analysis of the optimized impeller show shock structures and possible shock‐boundary layer interactions within the blade passages indicating further geometrical fine tuning may be required based on more detailed computational studies or experimental tests.

A further study including the effect of diffuser is required to carry the results to a more practical level.

The originality and the value of the paper comes mainly from two different aspects: combining various in‐house and commercial turbomachinery design codes in one robust methodology to obtain an optimum mixed‐flow compressor impeller that will maximize the performance requirements of a small unmanned air vehicle (UAV) turbojet engine under restricted size and power conditions; and investigation of the design optimization and analysis of a mixed‐flow compressor that could have potential applications in small jet engines to be used in high‐performance UAV applications. Design optimization studies on this type of compressor are very limited in the open literature. For many years, these compressors have been disregarded because of their bulky design in large‐scale engines. However, as mentioned above, they present a great potential for small‐scale jet engines by supplying enough pressure rise, as well as high mass flow rate compared to their centrifugal counterparts.

This study seeks the effect on static thrust, thrust specific energy consumption (TSEC) and exhaust emissions of euro diesel-hydrogen dual-fuel combustion in a small turbojet engine.

Experimental studies are performed in a JetCat P80-SE type small turbojet engine. Euro diesel and hydrogen is fed through two different inlets in a common rail distributing fuel to the nozzles. Euro diesel fuel is fed by a liquid fuel pump to the engine, while hydrogen is fed by a fuel-line with a pressure of 5 bars from a gas cylinder with a pressure of approximately 200 bars.

At different engine speeds, it is found that there is a decrease at the TSEC between a range of 1% and 4.8% by different hydrogen energy fractions (HEF).

The amount of hydrogen is adjusted corresponding to a range of 0–20% of the total heat energy of the euro diesel and hydrogen fuels. The small turbojet engine is operated between a range of 35,000 and 95,000 rpm engine speeds.

On the other hand, remarkable improvements in exhaust emissions (i.e. CO, CO₂, HC and NO_x) are observed with HEFs.

This is through providing improvements in performance and exhaust emissions using hydrogen as an alternative to conventional jet fuel in gas turbine engines.

Determining the performance parameters of the propulsion systems of the aircraft, which is the key product of the aviation industry, plays a critical role in reducing adverse environmental impacts. Therefore, the purpose of this paper is to present a temperature performance template for turbojet engines at the design stage using a neural network model that defines the relationship between the performance parameters obtained from ground tests of a turbojet engine used in unmanned aerial vehicles (UAV).

The main parameters of the flow passing through the engine of the UAV propulsion system, where ground tests were performed, were obtained through the data acquisition system and injected into a neural network model created. Fifteen sensors were mounted on the engine – six temperature sensors, six pressure sensors, two flow meters and one load cell were connected to the data acquisition system to make sense of this physical environment. Subsequently, the artificial neural network (ANN) model as a complement to the approach was used. Thus, the predicted model relationship with the experimental data was created.

Fuel flow and thrust parameters were estimated using these components as inputs in the feed-forward neural network. In the network experiments to estimate fuel flow parameter, r-square and mean absolute error were calculated as 0.994 and 0.02, respectively. Similarly, for thrust parameter, these metrics were calculated as 0.994 and 1.42, respectively. In addition, the correlation between fuel flow, thrust parameters and each input parameters was examined. According to this, air compressor inlet (AC_inlet,temp) and outlet (AC_outlet,temp) temperatures and combustion chamber (CC_inlet,temp, CC_outlet,temp) temperature parameters were determined to affect the output the most. The proposed ANN model is applicable to any turbojet engines to model its behavior.

Today, deep neural networks are the driving force of artificial intelligence studies. In this study, the behavior of a UAV is modeled with neural networks. Neural networks are used here as a regressor. A neural network model has been developed that predicts fuel flow and thrust parameters using the real parameters of a UAV turbojet engine. As a result, satisfactory findings were obtained. In this regard, fuel flow and thrust values of any turbojet engine can be estimated using the neural network hyperparameters proposed in this study. Python codes of the study can be accessed from https://github.com/tekinonlayn/turbojet.

The originality of the study is that it reports the relationships between turbojet engine performance parameters obtained from ground tests using the neural network application with open source Python code. Thus, small-scale unmanned aerial propulsion system provides designers with a template showing the relationship between engine performance parameters.

This paper aims to present a nonlinear mathematical model of a small-scale turbojet aeroengine and also a speed controller design that is conducted for the constructed nonlinear mathematical model.

In the nonlinear mathematical model of the turbojet engine, temperature, rotational speed, mass flow, pressure and other parameters are generated using thermodynamic equations (e.g. mass, energy and momentum conservation laws) and some algebraic equations. In calculation of the performance parameters, adaptive neuro fuzzy inference system (ANFIS) method is preferred in related components. All calculated values from the mathematical model are then compared with the cycle data of the turbojet engine. Because of the single variable control need and effect of noise factor, modified proportional–integral–derivative (PID) controller is treated for speed control. For whole operation envelope, various PID structures are designed individually, according to the operating points. These controller structures are then combined via gain-scheduling approach and integrated to the nonlinear engine model. Simulations are performed on MATLAB/Simulink environment for design and off-design operating points between idle to maximum thrust levels.

The cascade structure (proposed nonlinear engine aero-thermal model and speed controller) is simulated and tested at various operating points of the engine and for different transient conditions. Simulation results show that the transitions between the operating points are found successfully. Furthermore, the controller is effective for steady-state load changes. It is suggested to be used in real-time engine applications.

Because of limited data, only speed control is treated and simulated.

It can be used as an application in the industry easily.

First point of novelty in the paper is in calculation of the performance parameters of compressor and turbine components. ANFIS method is preferred to predict performance parameters in related components. Second novelty in the paper can be seen in speed controller design part. Because of the single variable control need and effect of noise factor, modified PID is treated.

The purpose of the paper is to present component matching and off-design calculations using generic components maps.

Multi objective hybrid optimization code is integrated with turbojet function code. Both codes are developed for the research study. Initially, methodology is applied on a numerical propulsion system simulation (NPSS) example engine cycle calculations. Effect of matching constants are shown. Later, component matching and application is done on JetCat engine. Calculations are compared with measured test data. And additional operating conditions are calculated using the matched component constants.

Obtained matching constants provided very good results with NPSS example and also JetCat test measurements. Optimization algorithm is practical for turbojet engine component matching and off-design calculations. Off-design matching provides information about the turbine and exhaust areas of an unknown turbine engine. Thus it is possible to perform off design calculations at various operating conditions. Finding detailed turbine maps is difficult than finding compressor maps. In that case characteristic turbine curve may be a good alternative.

Selected component maps and the target engine components should be similar characteristics. For a one/two stage turbine, characteristic curves can be applied. Validation should be extended on different type of compressor and turbines.

Operators and researchers usually need more information about the available turbojet engines for increasing the effective usage. Generally, manufacturers do not provide such detailed information to public. This study introduces an alternative methodology for engine modeling by using generic component maps and thus obtaining information for off-design calculations. User is flexible for selecting/scaling the compressor and turbine maps.

A hybrid optimization code is used as a new approach. It can be used with other engine functions; for instance functions corresponding to turboshaft or turbofan engines, by modifying the engine function. Number of input parameters and objective functions can be modified accordingly.

This paper aims to investigate the cycle performance of a small size turbojet engine used in unmanned aerial vehicles at 0–5,000 m altitude and 0–0.8 Mach flight speeds with real component maps.

The engine performance calculations were performed for both on-design and off-design conditions through an in-house code generated for simulating the performance of turbojet engines at different flight regimes. These calculations rely on input parameters in which fuel composition are obtained through laboratory elemental analysis.

Exemplarily, according to comparative results between in-house developed performance code and commercially available software, there is 0.25% of the difference in thrust value at on-design conditions.

Once the on-design performance parameters and fluid properties were determined, the off-design operation calculations were performed based on the compressor and turbine maps and scaling methodology. This method enables predicting component maps and fitting them to real conditions.

A method to be used easily by researchers on turbojet engine performance calculations which best fits to real conditions.

THE Bristol Siddeley Viper 520 turbojet engine which powers the de Havilland DH 125 jet executivc aircraft, employs an eight‐stage axial compressor which is driven by a single‐stage turbine. The combustion chamber is of the annular vaporizing type. Two Viper 520 engines are mounted in nacelles attached to the rear fuselage of the DH 125 such that the intakes are clear of the fuselage boundary layer at this position. Maximum take‐off thrust of the engines is 3,000 lb. for a specific fuel consumption of 0·985 lb./lb. thrust/hr.

THIS paper is not intended to provide any startling revelations of Soviet technology but is a detailed survey and analysis of contemporary developments in Soviet turbine powered transport aircraft. The major portion of the work is based on Soviet sources of information in an attempt to assure authenticity and accuracy.

This paper aims to discuss engine health monitoring for unmanned aerial vehicles. It is intended to make consistent predictions about the future status of the engine performance parameters by using their current states.

The aim is to minimize risks before they turn into problems. In accordance with these objectives, temporal and financial savings are planned to be achieved by contributing processes such as extending the engine life, preventing early disassembly-reassembly and mechanical wears and reducing the maintenance costs. Based on this point of view, a data-based software is developed in MATLAB (Matrix Laboratory) program for the so-called process.

The software is operated for the performance parameters of the turbojet engine that is used in a small unmanned aerial vehicle of Tusas Engine Industry. The obtained results are compared with the real data of the engine. As a result of this comparison, a fault that may occur in the engine can be detected before being determined.

It is clearly demonstrated that the engine operation in adverse conditions can be prevented. This situation means that the software developed operates successfully.