| 1. |
- Gkoutzamanis, V. G., et al.
(author)
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Thermal Management System Considerations for a Hybrid-Electric Commuter Aircraft
- 2022
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In: Journal of thermophysics and heat transfer. - : AIAA International. - 0887-8722 .- 1533-6808. ; 36:3, s. 650-666
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Journal article (peer-reviewed)abstract
- When it comes to novel aircraft concepts, thermal management system (TMS) design is a ubiquitous task, even at the conceptual design phase. This is owing to its impact on the total weight of the aircraft, cooling drag, and overall performance. The commuter air transportation has recently regained attention and is seen as a solution to employ partial or full electrification in the upcoming decades due to its low power requirement and potential benefit of faster “door-to-door” traveling. This work examines the TMS characteristics to cool a battery-powered aft-fan engine. A literature review is initially performed on other research associated with TMS design. The development and weight evaluation of the baseline TMS for this type of propulsive technology are then presented, including the characterization of system redundancy effects on the overall TMS weight. Results show that the TMS design is a function of the selected propulsive configuration and energy management throughout the mission. Primarily, this relates to the cooling method selected, the heat exchangers as the major mass contributors of the TMS, the positioning of components used for the propulsive configuration, and the imposed certification constraints. Finally, the selected TMS design is calculated to have a combined specific cooling of 0.79 kW∕kg.
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| 2. |
- Iplik, Esin, et al.
(author)
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Parameter estimation and sensitivity analysis for a diesel hydro-processing model
- 2021
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In: Computer Aided Chemical Engineering. - : Elsevier B.V.. ; 50, s. 573-578
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Journal article (peer-reviewed)abstract
- Model-based approaches are essential for the operation, optimization, and control of applications in the process industry. Different structures are often investigated to build representative and robust models, and a set of parameters with the same attributes are required to utilize them effectively. Parameter estimation gets arduous with the increasing complexity of the process, the model, and the size of the parameter space. In this work, a parameter-estimation problem based on a steady-state model of diesel hydrodesulfurization is investigated using gradient-based and gradient-free optimizers. The optimal parameter sets obtained are then assessed in terms of performance and computational time for the different optimizers. Furthermore, the sensitivity of the various parameters is also investigated. Due to the catalytic reactions in this process, some parameters have to be updated depending on the catalyst activity. In addition to the initial estimation, the updated parameters are also studied, and instead of a time-based one, a tolerance-based recalculation schedule is suggested. Finally, the robustness of the final model is analyzed by giving different operating conditions and feed characteristics. The adaptive parameter approach proved better data fitting capabilities by improving the coefficient of determination for temperature predictions.
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| 3. |
- Tsirikoglou, P., et al.
(author)
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Optimization in probabilistic domains : an engineering approach
- 2020
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In: Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications. - : Elsevier. - 9780128226094 - 9780128197141 ; , s. 391-414
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Book chapter (other academic/artistic)abstract
- The uncertain nature of engineering variables and parameters dictates the transition of engineering design from global exploration and deterministic optimization to the uncertainty quantification and probabilistic optimization. Therefore, such optimization processes and algorithmic frameworks emerge as key aspects of engineering design, aiming to derive new solutions to all sorts of products and processes. Nature-inspired computing is one of the main drivers, coupled to the continuously evolving engineering models. In this chapter, several aspects of probabilistic optimization are analyzed from an engineering application perspective to highlight the advances and shortcomings as moving towards the efficient global optimization in probabilistic domains. Moreover, the definition of engineering optimization cases, uncertainty quantification techniques, surrogate modeling, and other common case-related challenges are discussed. Finally, this conceptual analysis focuses mainly on engineering models from the aircraft design field, which can provide different types of engineering cases.
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