Helicopter Turboshaft Engine Research Articles

Robust Health Condition Prediction of Helicopter Turboshaft Engines Using Ensemble Machine Learning Models

This paper presents a novel ensemble approach that combines multiple machine-learning algorithms to deliver robust predictions of helicopter turboshaft engine health status (nominal or faulty) using operational data. Engine health is evaluated through the torque margin, defined as the percentage difference between the measured and target torque values. A Gaussian process model is used to estimate the torque margin as a probability distribution function (PDF), and these predictions are incorporated as features into various machine-learning models. These models are then employed to perform binary classification, determining the engine's health state. To enhance performance, a reference set is defined for each unseen data point, allowing a comparison of the relative performances of the models, with the best performer selected for the final prediction. Our ensemble method achieves high accuracy in health classification while providing precise torque margin estimates. The results demonstrate that ensemble models offer superior generalization and reliability compared to individual machine-learning algorithms, especially when applied to complex, multivariate datasets like those from helicopter turboshaft engines.

Intelligent Method of Identifying the Nonlinear Dynamic Model for Helicopter Turboshaft Engines

This research focused on the helicopter turboshaft engine dynamic model, identifying task solving in unsteady and transient modes (engine starting and acceleration) based on sensor data. It is known that about 85% of helicopter turboshaft engines operate in steady-state modes, while only around 15% operate in unsteady and transient modes. Therefore, developing dynamic multi-mode models that account for engine behavior during these modes is a critical scientific and practical task. The dynamic model for starting and acceleration modes has been further developed using on-board parameters recorded by sensors (gas-generator rotor r.p.m., free turbine rotor speed, gas temperature in front of the compressor turbine, fuel consumption) to achieve a 99.88% accuracy in identifying the dynamics of these parameters. An improved Elman recurrent neural network with dynamic stack memory was introduced, enhancing the robustness and increasing the performance by 2.7 times compared to traditional Elman networks. A theorem was proposed and proven, demonstrating that the total execution time for N Push and Pop operations in the dynamic stack memory does not exceed a certain value O(N). The training algorithm for the Elman network was improved using time delay considerations and Butterworth filter preprocessing, reducing the loss function from 2.5 to 0.12% over 120 epochs. The gradient diagram showed a decrease over time, indicating the model’s approach to the minimum loss function, with optimal settings ensuring the stable training.

Analytical Neural Network System for the Helicopter Turboshaft Engines Operating Modes Classification

A novel neural network-based analytical system has been developed for classifying helicopter turboshaft engine operating modes during flight operations. This system utilizes a neural network architecture comprising an input layer with three neurons, two fully connected hidden layers, and an output layer with two neurons. The proposed approach demonstrates an exceptional recognition accuracy of 0.997 (99.7%) across steady-state, unsteady, and transient operating modes of helicopter turboshaft engines. A new method for training this neural network has been introduced, employing forward propagation, loss calculation, backpropagation, and weight updates, enhanced by an adaptive learning rate and the cross-entropy function as the loss criterion. The method also incorporates a novel modified Smooth ReLU activation function for hidden layer neurons. This innovation led to a near-perfect accuracy in network training and reduced the loss to 0.025 (2.5%), highlighting the high quality and reliability of the neural network in classifying engine operating modes during flight. Furthermore, it has been empirically shown that the application of this neural network significantly reduces type I errors by a factor of 2.09 to 2.14 and type II errors by 2.05 to 2.21 times compared to traditional classifiers based on ART-1 and BAM networks. This advancement marks a substantial improvement in classification accuracy and error minimization for helicopter turboshaft engine operating modes.

Analysing temperature distributions in turbine first-stage rotor blades of a helicopter turboshaft engine

In helicopter turboshaft engines, turbine blades operate under extreme conditions. With increasing engine power, the gas temperature following the combustion chamber can reach approximately 1300 K. The turbine rotors endure significant centrifugal forces due to their high rotational speeds. Additionally, they experience thermal and aerodynamic loads from the flow of combustible gases, which non-uniformly impact the turbine blades at high temperatures. Furthermore, the mechanical properties of turbine blade materials are limited and strongly influenced by operating temperatures. This article presents a numerical investigation focusing on the temperature distribution of first-stage turbine rotor blades that do not feature internal cooling channels. The results indicate the regions of peak temperatures and evaluate rotor blade strength. Comparative analysis between theoretical and numerical calculations of blade temperature distribution reveals minor disparities: approximately 30 degrees at the blade shroud, 8 degrees at the mid-span section, and 15 degrees at the hub. These variations amount to less than 3% at the shroud, 1% at the mid-span section, and 1.5% at the hub.

The Helicopter Turboshaft Engine’s Reconfigured Dynamic Model for Functional Safety Estimation

This research substantiates the necessity for developing and implementing structural reconfiguration methods for automatic control systems in the event of a parametric sensor failure to enhance the helicopter turboshaft engine’s overall reliability and safety. The research aim is the substantiation of the helicopter turboshaft engine’s mathematically reconfigured automatic control system in the event of the failure of a standard sensor, which will ensure the helicopter turboshaft engine’s stable operation under failure conditions, minimizing the impact on engine control and performance. A theorem was developed and proven concerning the reconfiguration of the helicopter turboshaft engine’s automatic control system structure, defining the system’s new mathematical form using nonlinear thermogas-dynamic parameters. A method was proposed to determine the values of these parameters that keep the reconfigured control system stable. This method uses numerical optimization to find the best thermogas-dynamic parameters to ensure system stability. Experimental results showed that for slow changes, using parameters from the previous step works best, while for fast changes, restarting is more effective due to significant differences in the system states. The accuracy of the proposed mathematical model for the reconfigured control system was confirmed through mean square error analysis (within 0.4% and 0.77% under white noise), regression analysis (with a determination coefficient of 0.986), and cross-validation (with a metric deviation from the maximum mean square error of 3.88%).

Thermodynamic Optimization and Energy-Exergy Analyses of the Turboshaft Helicopter Engine

Energy demand is a critical contemporary concern, with significant implications for the future. While exploring renewable or sustainable energy sources offers potential solutions, optimizing energy consumption in existing power generation systems is also key. Aviation accounts for a substantial portion of energy demand, underscoring the importance of energy efficiency in this sector. Conventional energy analyses may be misleading; hence, employing exergy-based analyses provides a clearer understanding of energy consumption. Also, most of these analyses do not include the effect of the turbine blade’s cooling in calculations. In the present study, exergy analyses have been conducted on a helicopter turboshaft engine with turbine-blades cooling, focusing on design parameters such as ambient temperature, compressor pressure ratio, and turbine inlet temperature. Thermodynamic optimizations are conducted using a genetic algorithm. Results show that increasing pressure ratio and turbine inlet temperature boost performance, yet technical restrictions on compressor and turbine size, and metallurgical constraints on turbine blades’ material limit these gains. Sea level scenario prioritizes ambient temperature-drop for enhancing net-work and efficiency, while altitude-gain boosts turboshaft performance. Combustion chambers incur the highest exergy destruction of 74-80%, followed by 16-20% and 4-6% exergy destructions in the turbine and compressor, respectively. Lower air temperatures and higher flight altitudes demand larger fuel consumption for equivalent turbine inlet temperature, albeit enhancing cooling capacity and reducing required cooling air fraction for turbine blades.

Helicopter Turboshaft Engine Residual Life Determination by Neural Network Method

Helicopter Turboshaft Engine Residual Life Determination by Neural Network Method

Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions.

The article's main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies.

The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks

The research aims to develop a neural network-based lost information restoration method when the complex nonlinear technical object (using the example of helicopter turboshaft engines) sensors fail during operation. The basis of the research is an auto-associative neural network (autoencoder), which makes it possible to restore lost information due to the sensor failure with an accuracy of more than 99%. An auto-associative neural network (autoencoder)-modified training method is proposed. It uses regularization coefficients that consist of the loss function to create a more stable and common model. It works well on the training sample of data and can produce good results on new data. Also, it reduces its overtraining risk when it adapts too much to the training data sample and loses its ability to generalize new data. This is especially important for small amounts of data or complex models. It has been determined based on the computational experiment results (the example of the TV3-117 turboshaft engine) that lost information restoration based on an auto-associative neural network provides a data restoring error of no more than 0.45% in the case of single failures and no more than 0.6% in case of double failures of the engine parameter registration sensor event.

Neural Network Approximation of Helicopter Turboshaft Engine Parameters for Improved Efficiency

The work is devoted to the development of a method for neural network approximation of helicopter turboshaft engine parameters, which is the basis for researching engine energy characteristics to improve efficiency, reliability, and flight safety. It is proposed to use a three-layer direct propagation neural network with linear neurons in the output layer for training in which the scale conjugate gradient algorithm is modified by introducing a moment coefficient into the analytical expression. This modification helps in calculating new model parameters to avoid falling into a local minimum. The dependence of the energy released during helicopter turboshaft engine compressor rotation on the gas-generator rotor r.p.m. was obtained. This enables the determination of the optimal gas-generator rotor r.p.m. region for a specific type of helicopter turboshaft engine. The optimal ratio of energy consumption and compressor operating efficiency is achieved, thereby ensuring helicopter turboshaft engines’ optimal performance and reliability. Experimental data support the high efficiency of using a three-layer feed-forward neural network with linear neurons in the output layer, trained using a modified scale conjugate gradient algorithm, for approximating parameters of helicopter turboshaft engines compared to the analogues. Specifically, this method better predicts the relations between the energy release during compressor rotation and gas-generator rotor r.p.m. The efficiency coefficient of the proposed method was 0.994, which exceeded that of the closest analogue (0.914) by 1.09 times.

A computational method to study heat transfer in a helicopter turboshaft engine compartment for waste energy recovery purposes

It is important to take action to make aviation more energy-efficient and less pollutant. One of the approaches currently under development is waste energy recovery. This would be specially advantageous in turboshaft engines, which present an exhaust flow that is not being used for propulsive purposes and with high thermal residual energy that increases infrared signature of helicopters.To evaluate where to locate waste energy recovery devices such as thermoelectric generators or organic Rankine cycles the following is needed: temperatures of exhaust gas flow temperature and exhaust ducts wall temperature, i.e. hot source temperature, and engine compartment temperature, i.e. heat sink temperature. This variables are usually not known beforehand. The present paper develops a method that, with minimal data input, translates flight conditions and engine operation into conditions inside the engine compartment that are later applied to obtain heat transfer in the exhaust ducts, where the waste energy recovery should take place to minimize the influence on the engine. The procedure is based on an algorithm that uses Computational Fluid Dynamics (CFD) simulations validated with experiments.The main findings of this work are: (i) cruise flight velocity has minimal impact on engine compartment temperatures, but hover operation increases it by 5–16 K, (ii) cruise altitude significantly affects engine compartment temperature, with a 12 K difference between 2500 ft and 10000 ft, (iii) considering only the axial component of velocity in exhaust ducts may underestimate wall temperature, (iv) heat transfer in exhaust ducts can be estimated using empirical correlations for natural convection, multiplied by a specific factor, (v) non-invasive methods can recover 23.6 to 31.2 kW of heat from exhaust duct walls, potentially converting up to half of it into useful work.

Off-Design Analysis Method for Compressor Fouling Fault Diagnosis of Helicopter Turboshaft Engine

Fouling, caused by the adhesion of fine materials to the blades of the compressor’s last stages, changes the airfoil’s shape and function and the inlet flow angle on the blades. As the fouling increases, the range of influence increases, and the mass flow rate and overall engine efficiency reduce. Therefore, the compressor is choked at lower speeds. This study aims to simulate compressor performance during off-design conditions due to fouling and to present an approach for modeling faults in diagnostic and health monitoring systems. A computational fluid dynamics analysis is carried out to evaluate the proposed method on General Electric’s T700-GE turboshaft engine, and the performance is evaluated at different flight conditions. The results show promising outcomes with an average accuracy of 88% that would help future turboshaft health monitoring systems.

Impact of dust erosion on the reduction of axial compressor efficiency of a turboshaft engine and on the stability of its operation

This paper aimed to solve the impact of operational abrasive wear on the rotor blades of the axial compressor of the turboshaft engine on the decrease in its total compression efficiency ηCt and its transition into unstable work mode (surge). This process is analyzed based on the obtained data by operating TV3-117 helicopter turboshaft engines in high dust atmosphere conditions. Abrasive wear of the rotor blades of axial compressors causes mechanical damage to the blades, reducing their strength, changing their geometry, and aerodynamic properties, reducing the life of the whole compressor and thus the entire engine. Destruction of the compressor of a turboshaft engine may occur suddenly as a result of unstable compressor operation caused by damaged axial compressor blades due to their damage by the abrasive effect of dust.

Calculation of Air Velocity on the Helicopter Turboshaft Engines Inlet

Calculation of Air Velocity on the Helicopter Turboshaft Engines Inlet

Helicopter Turboshaft Engine Database as a Conceptual Design Tool

Helicopter Turboshaft Engine Database as a Conceptual Design Tool

Helicopter Turboshaft Engine Emission Estimation Based on Standard Flight Profile

Over the past decades, environmental protection, including regulation of air pollution, has become an important economic and political issue. Although aviation accounts for only 2% of atmospheric pollution, regulation at national, international and global level is still very important. The high-level aim of our current research is to determine the emissions of aircraft engines in the defense sector and to explore ways to reduce them by using alternative fuels, focusing military aviation. This article presents the methodology for developing a standard flight profile, which is the basis for calculating the operational emissions of military helicopters.

Research on dynamic modeling and performance analysis of helicopter turboshaft engine's start-up process

It is a vital precondition that the aeroengine start-up quickly and successfully to ensure a safe flight, but the engine start-up process is a very complex non-equilibrium, non-linear aerothermodynamics process. This paper establishes a start-up process model of helicopter turboshaft engine by combining theoretical modeling with experimental data. Firstly, based on the stage-stacking method, the characteristics of low-speed components of compressor and turbine are approximately estimated with the characteristics of typical components, design point parameters and component structure. Then in full consideration of the change of combustion efficiency, the impact of the combustion chamber and turbine's thermal inertia, a helicopter turboshaft engine's start-up process model is developed on the basis of component method, and the above-idle state modeling method is successfully applied to the start-up process. The simulation shows that the model is in conformity with real-time and accuracy requirements, and simulates the start-up process quite well. Finally, the performance analysis of the start-up process is carried out based on the model, which shows that the model provides a general platform and engineering reference value for the design of the engine start-up control system.

Multi-objective optimization and system evaluation of recuperated helicopter turboshaft engines

Targeting higher efficiency and lower emissions, the employment of recuperators in helicopters is well recognized as an attractive technical strategy to enhance the operational capabilities. Primary surface recuperator (PSR) is proposed for such applications, as its favorable characteristics of good heat transfer performance and compact structure enable a light weight recuperator design in the aeroengine industry. Aimed at designing a PSR potentially applicable to rotorcraft powerplants, initially, promising heat transfer surface geometries are evaluated based on their aerothermal performance. Subsequently, through the execution of multi-objective genetic algorithm optimization, interdependencies between recuperator weight and thermal effectiveness are quantified under specific constraints, with respect to the selected heat transfer surface geometries. Eventually, acquired results of optimal designs are further analyzed within a multidisciplinary simulation framework for performance assessment of complete helicopter operations under given flight conditions. It is found from helicopter mission analysis that the optimum trade-off between fuel saving benefits and associated recuperator weight penalty is attained for the employed effectiveness of 76.1% with recuperator weight of 27.48 kg. The proposed methodology could be adopted as a cost-effective and computationally-efficient tool for the multidisciplinary design and optimization of rotorcraft powerplant systems incorporating high performance recuperators.

Power Sources of Military Helicopters

In the early 1940s the first practically usable helicopters rose into the sky. Their importance was quickly recognised both by the military and civilian decision makers. A good summary of their most important advantage is the next quotation: “If you are in trouble anywhere in theworld, an airplane can fly over and drop flowers, but a helicopter can land and save your life.” (Igor Sikorsky, 1947) Just after their appearance it immediately became an urgent problem to replace the relatively low-power and heavy piston engines, forwhich the much lighter and more powerful turboshaft engines offered a good alternative. Significant improvement of helicopter engines, which has embodied mainly in power to weight ratio, thermal cycle efficiency, specific fuel consumption, together with reliability and maintainability, of course, has influenced the technicaltactical parameters of helicopters. In this paper I introduce the evolution of helicopter turboshaft engines, the most important manufacturers and engine types. Through statistical analysis I display what kind of performance parameters the helicopter turboshaft engines had in the past and have in the present days.

Gas turbine power calculation method of turboshaft based on simulation and performance model

Gas turbine power of turboshaft engine cannot be measured, a total of five typical steady state point test data from the ground slow state to the maximum state were selected according to the factory acceptance test drive of a certain type of carrier-based helicopter turboshaft engine. Combustion chamber three-dimensional simulation model was established to carry on simulation analysis of different typical steady state combustion process. The simulated combustion chamber exit section parameters are input into the established gas turbine isentropic adiabatic aerodynamic calculation model to obtain the gas turbine power and outlet temperature. Select five typical steady state points of five sets of turboshaft engines on the same type to repeat the above calculation process, and compare the calculated value of gas turbine outlet temperature with the acceptance test values, it is found that the error values are all within 5%, and the effectiveness and accuracy of the gas turbine power calculation method are verified.