Patent
Title: Semi-blended box wing Aircraft
Office: Austrian Patent Office
Applicant: Johannes Kepler University, Linz
Inventor: Azad Khandoer
Numbers of Claims: 18
Status: Patented
Filed Date: 24/03/2022
Received Date: 07/06/2023
Research Articles
e are dedicated to engaging in open-source research, aiming to contribute to the advancement of a knowledgeable society. As part of this commitment, our technological innovations are regularly featured in prestigious international journals. This dissemination aims to encourage sharing, interaction, and the advancement of scientific endeavors. Some of our published articles are as follows:
Title: A requirements elicitation process for a purposeful general aviation (GA) aircraft design based on emerging economies.
Published Year: 2021
Published In: The Aeronautical Journal
Publisher: Cambridge University Press in collaboration with The Royal Aeronautical Society, UK
Abstract: Design success mostly depends on understanding and realizing product requirements, which are nested with different levels of stakeholders and specific market demands. Emerging markets are volatile and non-linear for understanding user-specific needs, particularly related to aircraft design. We attempt herein to elicit specific requirements through a proposed requirements elicitation process based on functional, physical, and behavioral viewpoints. An evaluation of those critical requirements is also performed through Quality Function Deployment (QFD) to reveal the fitness of the customer requirements (raw requirements) in relation to the technical measures. After selecting all projected potential users, a Systems Modelling Language (SysML) use-case diagram is also summarized to illustrate the user’s relation to the system functions. Some raw requirements are verified through constraint analysis and cost examination as an exemplary approach to present the transformation from raw to final requirements. The data are collected and analyzed to construct the aircraft’s key specifications. Most importantly, how to identify good requirements that define the needs, attainability, clarity, and verifiability are demonstrated. In addition, it is demonstrated through this study that there is a need of a specific type of general aviation aircraft that may fulfil the unique demands of emerging economies by satisfying their socio-economic conditions
Title: Predicting the Unpredictable: General Aviation (GA) aircraft cost estimation evaluation
Published Year: 2022
Published In: Journal of Air Transport Management
Publisher: Elsevier
Abstract: Cost estimation is an important part of project planning as well as research endeavors. Since the well-established cost estimation models used in the GA aircraft industry are already several decades old, a re-evaluation of their applicability to current market conditions is essential. Reliable cost estimation may also improve the chances to get external funding – a vital point for start-ups. To tackle this issue, we developed a research method to investigate potential cost models for GA aircraft that can serve as a guideline for, e.g., start-ups and research works. After gathering existing cost estimation models, they are classified and analyzed to find the ones most suitable for small aircraft. For evaluation purposes, the two most promising ones are applied to data from existing aircraft models to compare their accuracy, and finally, the best one is coded as an application in Python to improve usability. With our presented research method, we show a possibility to perform early cost estimation for small GA aircraft and offer a software tool to simplify its application.
Title: Low Fidelity Data Driven Machine Learning Based Optimization Method for Box-Wing Configuration
Published Year: [Under Review]
Submitted In: Journal of Computational Design and Engineering (JCDE)
Publisher: Oxford University Press
Abstract: Wing design optimization traditionally involves computationally expensive high-fidelity simulations, limiting the exploration of design spaces. In this study, we propose a methodology that combines low-fidelity numerical model with machine learning algorithms to efficiently navigate the complex parameters space of box-wing configurations. Through the utilization of surrogate models trained on a limited dataset derived from low-fidelity simulations, our method strives to predict results within an acceptable range, significantly curtailing computational costs and time. The effectiveness of this methodology is demonstrated through a series of case studies involving, Onera M6 and NASA CRM wing as test cases and Bionica box-wing optimization as application case. The initial application of the proposed methodology to the box-wing case successfully achieved a 5% increase in overall aerodynamic efficiency. Its competitive performance compared to conventional optimization methods, alongside its substantial reduction in computational time and resource requirements, is evident. This novel methodology holds promise for enhancing the design optimization process for aviation start-ups by efficiently exploring complex design spaces with reduced computational burden.
