scispace - formally typeset
Search or ask a question
Author

Kristian Amadori

Bio: Kristian Amadori is an academic researcher from Saab Automobile AB. The author has contributed to research in topics: Conceptual design & System of systems. The author has an hindex of 3, co-authored 14 publications receiving 22 citations.

Papers
More filters
Proceedings ArticleDOI
05 Jun 2017
TL;DR: In this article, a multidisciplinary design optimization (MDO) framework is presented for early design stages of UAVs, where the primary focus is on maximizing the performance of the UAV.
Abstract: This paper presents a Multidisciplinary Design Optimization (MDO) framework that is intended to be employed in the early design stages of Unmanned Aerial Vehicles (UAVs) when the primary focus is o ...

5 citations


Cited by
More filters
Journal Article
TL;DR: In this paper, the authors of the paper by Drs. Patricia Sterns and Leslie Tennan stated that private appropriation is prohibited by Article II of the Outer Space Treaty and that this is not an appropriate topic for IISL position papers.
Abstract: Prof./Dr. Frans von der Dunk commented that he had read the paper by Drs. Patricia Sterns and Leslie Tennan. He said that he it was a very good paper and that he agreed with their conclusions. Prof. von der Dunk then said that since we all seem to agree with Sterns and Tennan’s conclusion that private appropriation is prohibited by Article II of the Outer Space Treaty, is this not an appropriate topic for an IISL position paper?

33 citations

Journal ArticleDOI
TL;DR: A comparison between the optimization results and the actual flight performance of the QFHUAV shows that the flight performance is in good agreement with the optimized results, which indicates that the MDO method proposed in this paper is feasible and reasonable.

23 citations

Journal ArticleDOI
TL;DR: In this paper, a holistic approach to aerospace product development that tries spanning from needs to technology assessment is presented and analyzed and key enablers and future research directions are highlighted from an interdisciplinary point of view.
Abstract: Product development, especially in aerospace, has become more and more interconnected with its operational environment. In a constant changing world, the operational environment will be subjected to changes during the life cycle of the product. The operational environment will be affected by not only technical and non-technical perturbations, but also economical, managerial and regulatory decisions, thus requiring a more global product development approach. One way to try tackling such complex and intertwined problem advocates studying the envisioned product or system in the context of system of systems (SoS) engineering. SoSs are all around us, probably in any field of engineering, ranging from integrated transport systems, public infrastructure systems to modern homes equipped with sensors and smart appliances; from cities filling with autonomous vehicle to defence systems.Since also aerospace systems are certainly affected, this work will present a holistic approach to aerospace product development that tries spanning from needs to technology assessment. The proposed approach will be presented and analysed and key enablers and future research directions will be highlighted from an interdisciplinary point of view. Consideration of the surrounding world will require to look beyond classical engineering disciplines.

9 citations

Journal ArticleDOI
20 Apr 2021
TL;DR: The results show that it is possible to break down SoS needs in a consistent way and that ontology with description logic reasoning can be used to process the captured knowledge to both expand and reduce an available design space representation.
Abstract: Aerospace systems are connected with the operational environment and other systems in general. The focus in aerospace product development is consequently shifting from a singular system perspective to a System-of-Systems (SoS) perspective. This increasing complexity gives rise to new levels of uncertainty that must be understood and managed to produce aerospace solutions for an ever-changing future. This paper presents an approach to using architecture frameworks, and ontologies with description logic reasoning capabilities, to break down SoS needs into required capabilities and functions. The intention of this approach is to provide a consistent way of obtaining the functions to be realized in order to meet the overarching capabilities and needs of an SoS. The breakdown with an architecture framework results in an initial design space representation of functions to be performed. The captured knowledge is then represented in an ontology with description logic reasoning capabilities, which provides a more flexible way to expand and process the initial design space representation obtained from the architecture framework. The proposed approach is ultimately tested in a search and rescue case study, partly based on the operations of the Swedish Maritime Administration. The results show that it is possible to break down SoS needs in a consistent way and that ontology with description logic reasoning can be used to process the captured knowledge to both expand and reduce an available design space representation.

8 citations

01 Jan 2018
TL;DR: It is found that probability theory is still the most popular theory for representing uncertainty and Polynomial Chaos Expansions and Stochastic Collocation methods are gaining popularity for propagating uncertainty through a modeling environment, but Monte Carlo Simulations are still widely used.
Abstract: Evaluation and assessment of novel technologies for aerospace applications is essential for business strategy and decision making regarding development efforts. Since technology is evaluated in the conceptual design phase and little is known about the technology, large uncertainty is present. This uncertainty needs to be accurately assessed and managed. To investigate the research efforts that have been performed to perform technology evaluation under uncertainty, a literature review was conducted, focusing on methods and modeling approaches to assign and quantify these uncertainties. It is found that probability theory is still the most popular theory for representing uncertainty. Polynomial Chaos Expansions and Stochastic Collocation methods are gaining popularity for propagating uncertainty through a modeling environment, but Monte Carlo Simulations are still widely used. Commonly, surrogate models are used to reduce computational effort. Other efforts focus on the use of multifidelity approaches to reduce computational effort when high-fidelity methods are required. Four issues that may need to be addressed in future research were identified.

7 citations