Prediction of jet engine parameters for control design using genetic programming

The simulation of a jet engine behavior is widely used in many different aspects of the engine development and maintenance. Achieving high quality jet engine control systems requires the iterative use of these simulations to virtually test the performance of the engine avoiding any possible damage on the real engine. Jet engine simulations involve the use of mathematical models which are complex and may not always be available. This paper introduces an approach based on Genetic Programming (GP) to model different parameters of a small engine for control design such as the Exhaust Gas Temperature (EGT). The GP approach has no knowledge of the characteristics of the engine. Instead, the model is found by the evolution of models based on past measurements of parameters such as the pump voltage. Once the model is obtained, it is used to predict the behaviour of the jet engine one step ahead. The proposed approach is successfully applied for the simulation of a Behotec j66 jet engine and the results are presented

Controls and guidance research

The objectives of the control group are concentrated on research and education. The control problem of the hypersonic space vehicle represents an important and challenging issue in aerospace engineering. The work described in this report is part of our effort in developing advanced control strategies for such a system. In order to achieve the objectives stated in the NASA-CORE proposal, the tasks were divided among the group based upon their educational expertise. Within the educational component we are offering a Linear Systems and Control course for students in electrical and mechanical engineering. Also, we are proposing a new course in Digital Control Systems with a corresponding laboratory

Marine Power Systems

Marine power systems have been designed to be a safer alternative to stationary plants in order to adhere to the regulations of classification societies. Marine steam boilers recently achieved 10 MPa pressure, in comparison to stationary plants, where a typical boiler pressure of 17 MPa was the standard for years. The latest land-based, ultra-supercritical steam boilers reach 25 MPa pressure and 620 °C temperatures, which increases plant efficiency and reduces fuel consumption. There is little chance that such a plant concept could be applied to ships. The reliability of marine power systems has to be higher due to the lack of available spare parts and services that are available for shore power systems. Some systems are still very expensive and are not able to be widely utilized for commercial merchant fleets such as COGAS, mainly due to the high cost of gas turbines. Submarine vehicles are also part of marine power systems, which have to be reliable and accurate in their operation due to their distant control centers. Materials that are used in marine environments are prone to faster corrosive wear, so special care also should be taken in this regard. The main aim of this Special Issue is to discuss the options and possibilities of utilizing energy in a more economical way, taking into account the reliability of such a system in operation

Applications and enhancements of aircraft design optimization techniques

The aircraft industry has been at the forefront in developing design optimization strategies ever since the advent of high performance computing. Thanks to the large computational resources now available, many new as well as more mature optimization methods have become well established. However, the same cannot be said for other stages along the optimization process - chiefly, and this is where the present thesis seeks to make its first main contribution, at the geometry parameterization stage.The first major part of the thesis is dedicated to the goal of reducing the size of the search space by reducing the dimensionality of existing parameterization schemes, thus improving the effectiveness of search strategies based upon them. Specifically, a refinement to the Kulfan parameterization method is presented, based on using Genetic Programming and a local search within a Baldwinian learning strategy to evolve a set of analytical expressions to replace the standard 'class function' at the basis of the Kulfan method. The method is shown to significantly reduce the number of parameters and improves optimization performance - this is demonstrated using a simple aerodynamic design case study.The second part describes an industrial level case study, combining sophisticated, high fidelity, as well as fast, low fidelity numerical analysis with a complex physical experiment. The objective is the analysis of a topical design question relating to reducing the environmental impact of aviation: what is the optimum layout of an over-the-wing turbofan engine installation designed to enable the airframe to shield near-airport communities on the ground from fan noise. An experiment in an anechoic chamber reveals that a simple half-barrier noise model can be used as a first order approximation to the change of inlet broadband noise shielding by the airframe with engine position, which can be used within design activities. Moreover, the experimental results are condensed into an acoustic shielding performance metric to be used in a Multidisciplinary Design Optimization study, together with drag and engine performance values acquired through CFD. By using surrogate models of these three performance metrics we are able to find a set of non-dominated engine positions comprising a Pareto Front of these objectives. This may give designers of future aircraft an insight into an appropriate engine position above a wing, as well as a template for blending multiple levels of computational analysis with physical experiments into a multidisciplinary design optimization framework

Data regarding dynamic performance predictions of an aeroengine

none2noThe design of aeroengine real-time control systems needs the implementation of machine learning based techniques. The lack of in-flight aeroengine performance data is a limit for the researchers interested in the development of these prediction algorithms. Dynamic aeroengine models can be used to overcome this lack. This data article presents data regarding the performance of a turbojet that were predicted by the dynamic engine model that was built using the Gas turbine Simulation Program (GSP) software. The data were also used to implement an Artificial Neural Network (ANN) that predicts the in-flight aeroengine performance, such as the Exhaust Gas Temperature (EGT). The Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network was used. The neural network predictions have been also given as dataset of the present article. The data presented here are related to the article entitled “MultiGene Genetic Programming - Artificial Neural Networks approach for dynamic performance prediction of an aeroengine” [1].openDe Giorgi M.G.; Quarta M.De Giorgi, M. G.; Quarta, M

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Multi-Objective structural optimization of repairs of blisk blades

Modern manufacturing technologies offer multiple options to extend the service life of expensive jet engine components through repairs. In this context, the repair processes of blade-integrated disks (blisks) are of particular interest, as the complex design makes replacement of this part very costly. However, currently, repairs of blisks are mainly done manually and repair design decisions still rely on the expertise of maintenance technicians. From a scientific perspective, these subjective, experience-based decisions are a major drawback, as today’s computational methods allow for systematic analysis and evaluation of design alternatives. The present doctoral thesis contributes to the decision-making process related to the repair of blisk blades by blending and patching by providing an engineering optimization framework and simulation routines for structural assessment of different repair designs. First, an object-oriented optimization framework is developed that is ideally suited to address engineering optimization problems such as blisk repair optimization. The design of the software architecture is chosen to achieve a high degree of flexibility and modularity. In particular, the framework provides a unified interface for global and local derivative-free optimization algorithms and custom engineering optimization problems. Thereby, optimization of single- as well as multi-objective problems is supported. The broad applicability of the framework in engineering optimization is demonstrated using examples from wind energy research. Furthermore, the optimization framework forms a suitable environment for structural multi-objective optimization of blend and patch repairs. The second part of this thesis is devoted to the application of the optimization framework to blend repairs of a compressor blisk. The geometry of the removed blade part and the resulting blend is parameterized by three geometric design variables. The two objectives of the optimization correspond to two modal criteria, because especially the vibration behavior of blades is affected by this kind of geometric modification. To check if frequency requirements are harmed by the repair the first objective reflects the deviation of the natural frequencies of the repaired blade to the natural frequencies of the nominal blade. The second objective considers resonance conditions by evaluating the proximity of natural frequencies to excitation frequencies. Pareto optimal repair designs are found by solving the derived optimization problem using appropriate structural mechanics models of a blade sector and employing the developed optimization framework. By analyzing the optimal blend shapes for two different damage patterns, it is shown that the characteristics of Pareto frontiers, like the occurrence of discontinuities, are damage-specific. Therefore, it is concluded that design decisions on blend repairs have to be made on a case-by-case basis. The third part of this thesis is concerned with the multi-objective optimization of patch repairs. While blend repairs change the blade geometry, patch repairs restore the original blade contour. In terms of structural integrity, the most significant modification due to patching is hence associated with the welding process to join patch and blade. The remaining residual stresses, affect the strength of the repaired blade, are therefore the most critical aspect of patch repairs. Utilizing the engineering optimization framework and the parametric simulation model, a multi-objective optimization problem is solved considering the length of the weld and the fatigue strength of the repaired blade. In addition to fatigue strength properties, the weld length is selected as an optimization goal, since the manufacturing effort of the high-tech repair is of practical importance. Pareto optimal repair designs are presented for a damage pattern at the leading edge. The optimization results are further complemented by subsequent thermal and mechanical simulations of the welding and heat treatment process. Different patch geometries are classified from the Pareto optimal solutions. Depending on the preferences in terms of weld length and the High-Cycle Fatigue strength of different load cases, short or long patches are to be used. In addition, the results show that some potential patch designs are not optimal in any case, and therefore can be completely excluded. Finally, the benefits of the unified interface of the engineering optimization framework are emphasized. Different optimization settings of a patch repair optimization are presented and compared utilizing the hypervolume metric. Concluding remarks on the potential of computational methods for improved repair design and an outlook on future maintenance of blisks complete this work.DFG/SFB 871/119 193 472./E

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Micro turbojets are used for propelling radio-controlled aircraft, aerial targets, and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional artificial neural network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and exhaust gas temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions