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An aeronautical gas turbine engine design is a multidisciplinary iterative process requiring an efficient interaction between each discipline tool and process in order to find the best compromise satisfying all the conflicting domains involved. The gas turbine engine design traditionally has two main stages: the pre - detailed design and the detailed design phases. During the first phase of the design, time is the main concern and the fidelity of the results may be impacted. This may compromise the engineers’ ability to thoroughly explore the envelope of potential designs and thus lead to the selection of a sub - optimal system concept. Considering the time - consuming analysis and resources - intensive tools used during the detailed design phase, it is extremely difficult to correct an unsatisfactory concept at that stage of an engine’s design. The use of Multidisciplinary Design Optimization techniques at a preliminary design phase (Preliminary MDO or PMDO) allows correcting this. PMDO system implementation requires bringing as much knowledge as possible in the early phases of the design where the freedom to make modification is at a maximum. This imposes the use of higher fidelity tools that communicate effectively with each other. Considering the impact of the turbine tip clearance on an engine’s efficiency and on preventing blades wear, an accurate tool to predict the tip gap is a mandatory step towards the implementation of a full PMDO system for the turbine design. Tip clearance calculation is a good candidate for PMDO technique implementation considering that it implicates various analyses conducted on both the rotor and stator. As a third and final step to the development of such tip clearance calculator satisfying PMDO principles, the present work integrates the results of the turbine rotor and housing thermal and stress analyses presented in two previous papers. These processes ’ integration leads to the automatic calculation of a turbine stage’s closure during a given mission and the computation of the cold build clearance. Compared to a regular preliminary tip clearance calculation process, the proposed conceptual system offers a considerable increase in the accuracy of results and a time reduction. The system being automated and faster than the one of a regular pre-detailed design phase, it was possible to run optimisation loops in order determine the worst mission in terms of throat closure. The proposed system also allowed running a sensitivity analysis of the tip clearance leading to the identification of parameters that should be focused on when optimizing a turbine’s tip clearance. Finally, by requiring fewer user inputs this system decreases the risk of human errors while entirely leaving the important decisions to the designer., {"references":["[1] Boswell, J., & Tibbott, I. (2013). Tip Clearance Control for Turbine Blades. U.S. Patent US9506369: Rolls - Royce plc.","[2] Hennecke, D. K. (1985). Active and Passive Tip Clearance Control. VKI Lecture Series 1985 - 05: \"Tip Clearance Effects in Axial Turbomachines\" , 34.","[3] Howard, W. D., & Fasching, W. A. (1982). CF6 Jet Engine Diagnostics Programm - High Pressure Turbine Roundness/Clearance Investigation. NASA CR - 165581.","[4] Korte, J. J., Weston, R. P., & Zang, T. A. (1998). Multidisciplinary Optimization Methods for Preliminary Design. Multidisciplinary Optimization Branch, MS 159, NASA Langley Research Center.","[5] Lattime, S. B., & Steinetz, B. M. (2002). Turbine Engine Clearance Control Systems: Current Practices and Future Directions. 38th Joint Propulsion Conference and Exhibit. 20, p. 16. Indianapolis: consoponsored by AIAA, ASME, SAE and ASEE.","[6] Martins, J. R., & Lambe, A. B. (Sept 2013). Multidisciplinary Design Optimization: A Survey of Architectures. AIAA Journal, 51 (9), 2049 - 2075.","[7] Melcher, K. J., & Kypuros, J. (2003). Toward a Fast - Response Active Turbine Tip Clearance Control. XVIth International Symposium on Airbreathing Engines (ISABE) , ( Paper 2003 - 1102). Cleveland.","[8] Moret, M., Delecourt, A., Moustapha, H., Abenhaim, A. - I., & Garnier, F. (2017). Automated Thermal and Stress Preliminary Analyses Applied to a Turbine Rotor. Aerospace Science and Technology , 63, 123 - 131.","[9] Moret, M., Moustapha, H., Phutthavong, P., & Garnier, F. (2018). Automated Thermal and Stress Preliminary Analyses Applied to a Turbine Housing Assembly. Aeronautical Journal (in submition) .","[10] NATO Science and Technology Orga nization. (2006). Integration of Tools and Processes for Affordable Vehicles. Chapter 3: Air Vehicules. NATO RTO Research Task Group AVT 093.","[11] Olsson, W. J., & Martin, R. L. (1982). B747/JT9D Flight Loads and Their Effect on Engine Running Clearances and Pe rformance Deterioration. NASA CR - 165573.","[12] Panchenko, Y., Patel, K., Moustapha, H., Dowhan, M. J., Mah, S., & Hall, D. (2002). Preliminary Multi - Disciplinary Optimization in Turbomachinery Design. Proceedings of RTO/AVT symposium on \"Reduction of Military Vehicle Acquisition Time and Cost through Advanced Modelling and Virtual Simulation\", (p. 22). Paris, France: RTO - MP - 089.","[13] Wieringa, R. (2009). Design Science as Nested Problem Solving. 4th International Conference on Design Science Research in Information Systems and Technology, Article 8, 12 pages. New York City: ACM."]}

One of the steps in the complex design of aircraft propulsion system consists of designing an efficient turbine gas - path . Gas - path geometry affects how the stators, rotors and duct will be designed. For this reason, the optimization of the gas - path needs to be done early in the development process of an engine. This is the concept of Preliminary Multi - Disciplinary Optimization (PMDO). At this stage of the design process, as many configurations as possible should be analysed. Due to the size of the design space to be explore d thousands of configurations have to be considered. Consequently, each of these configurations needs to be simulated rapidly. To meet this requirement , an in - house 1D meanline code based on a correlation loss model is used. The optimization is done with a combination of direct optimization and design exploration. A simple direct optimization is used to generate a first version of the gas - path. Design exploration is achieved with an in - house Framework for Design Exploration (FDE). This framework includes Design of Experiment (DOE) and Surrogate Assisted Optimization (SAO) workflow. SAO is executed to find a global optimum configuration within user defined limitations. These design limits are de fined according to a set of predefined limitations on factors (in put parameters) and constraints on responses (output parameters). Furthermore, any responses can be set as an objective to be minimized, maximized or to be targeted for a specific value. To obtain such an optimized gas - path, a robust parametrization has to be developed. An efficient parametrization will limit the number of nonphysical gas - path configurations in the design space without excluding any optimal configurations. This work focuses on optimizing the turbine gas - path to achieve one of three possible objectives. For most of the cases, the objective is maximizing efficiency. Another possible objective consists of minimizing the total length of the turbine while achieving a specific efficiency. Finally, this optimization tool can be used by turbine aerodynamicists to quickly analyse different stage configurations such as two versus three Power Turbines (PT). Iterating on the number of PT stages becomes much faster when the entire optimization process is automated., {"references":["[1] Asgarshamsi A., Hajilouy Benisi A. and Assempour A. (2015). Multi-Objective Optimum Stator and Rotor Stagger Angle Distributions of an Axial Turbine Stage. Scientia Iranica B 22(2), 448 (458).","[2] Dye C., Staubach J. B., Emmerson D. and Jensen C. G. (2007) CAD - Based Parametric Cross - Section Designer for Gas Turbine Engine MDO Applications, Computer - Aided Design and Applications, 4:1 - 4, 509 - 518, DOI: 10.1080/16864360.2007.10738570","[3] Ghisu T ., Parks G . T., J arrett J. P., and Clarkson P. J. Robust Design Optimization of Gas Turbine Compression Systems (2011), Journal of Propulsion an d Power, 27 ( 2 ), pp. 282 - 295.","[4] Herrera C. M., Yepifanov S. and LobodaI. (2017). Improvement of Turbine Blade Lifetime Assessment by more Accurate Estimation of the Thermal Boundary Conditions. Advances in Mechanical Engineering. 9 (4)","[5] Kacker S. C., Okapuu U. (1982). A Mean Line Prediction Method for Axial Flow Turbine Efficiency. Journal of Engineering for Power. 104, 111 - 119.","[6] Díaz - Manríquez A., Toscano G., Barron - Zambrano J. H. Tello - Leal E. (2016). A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms. Computational Intelligence and Neuroscience. 2016, dx.doi.org/10.1155/2016/9420460","[7] Mohammadi E. and Montazeri - Gh M. (2016) Performance Enhancement of Global Optimization - Based Gas Turbine Fault Diagnosis Systems, Journal of Propulsion and Power, 32(1), 214 - 224.doi.org/10.2514/1.B35710","[8] Moret M., Delecourt A., Moustapha H., Garnier F. and Abenhaim A. I. (2016). Automated Thermal and Stress Preliminary Analysis Applied to a Turbine Rotor. Proceedings of the ASME Turbo Expo 2016 conference. GT 2016, Seoul.","[9] Moradi N., Vlasic E. and Moustapha H. (2015). Rapid Airfoil Design for Uncooled High Pressure Turbine Blade. ASME Turbo Expo 2015: Turbine Technical Conference and Exposition, Montreal.","[10] Pugachev A. O., Sheremetyev A. V., Tykhomirov V. V. and Shpilenko O. I. (2015). Structural Dynamics Optimization of Rotor Systems for a Small - Size Turboprop Engine. Journal of Propulsion and Power 31(4), 1083 - 1093, DOI: 10.2514/1.B35399","[11] Song L., Guo Z., Li J. and Fen g Z. (2018). Optimization and Knowledge Discovery of a Three - Dimensional Parameterized Vane with Nonaxisymmetric Endwall. Journal of Propulsion and Power 34(1), 234 - 246, DOI: 10.2514/1.B36014","[12] Twahir A., Dore S., Moustapha H. and Roy F. (2016). A Frame work for an Integrated Turbine Rotor Design System. Proceedings of the ASME Turbo Expo 2016 conference. GT 2016, Seoul.","[13] Zhang M., Ghou W. and Yao Q. (2016). Reliability - Based Multidisciplinary Design and Optimization for Twin - Web Disk Using Adaptive Kriging Surrogate Model. Advances in Mechanical Engineering 8(9), 1 - 12. DOI: 10.1177/1687814016671448"]}

A streamlined cross-section heat pipe manufactured from 4130 steel, using water as the working fluid and layers of 200 mesh stainless-steel screen for a wick was designed and tested. The experimental effort was directed toward two main objectives. The first objective was to determine the operational details of building a streamlined heat pipe system for experimental use. The second objective was to test the heat pipe at different inclinations to determine the effect of gravity on heat pipe performance. The axial temperature distributions, performance data and parameters for the streamlined heat pipe, at various power input and angles of inclination, were presented, analysed and compared with existing theories and previous investigations. The heat pipe transferred a maximum power of 250 watts while installed horizontally. As the evaporator was raised above the condenser level, the maximum heat decreased, and at the vertical orientation, no thermal equilibrium was attained, at a power input of 25 watts. The performance of the heat pipe was also compared with that of a solid conductor having the same shape. The equivalent thermal conductivity and specific output calculated, were found to be approximately four and thirteen times respectively greater, than that of a similar solid pure copper rod. Master of Engineering (ME)