The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-based approaches to managing aerospace engineering knowledge. By advancing these principles, research, and industry can achieve more efficient design processes, enhanced collaboration, and a stronger commitment to sustainable aviation.

While Aerospace engineering can benefit greatly from collaborative knowledge management, its infrastructure is still fragmented. Bridging this divide is essential to reduce the current practice of redundant work and to address the challenges posed by the rapidly growing volume of aviation data. This study presents an accessible platform, built on Wikibase, to enable collaborative sharing and curation of aerospace engineering knowledge, initially populated with data from a recent systematic literature review. As a solid foundation, the Aerospace.Wikibase provides over 700 terms related to processes, software and data, openly available for future extension. Linking project-specific concepts to persistent, independent infrastructure enables aerospace engineers to collaborate on universal knowledge without risking the appropriation of project information, thereby promoting sustainable solutions to modern challenges while acknowledging the limitations of the industry.

Actually, the project got started before I got involved. The history is something like this—after it began the Space Shuttle program, NASA began to sell payload space for various flights. One experimenter might spend a million dollars on a flight. NASA felt they wouldn’t sell all the space for major experiments and so decided to offer the leftover space to individuals, to universities, to people in industries who might want to try to do something useful in space but didn’t have that kind of money. So they came up with the Getaway Special Program. We found we could spend $5,000 and get two-and-a-half cubic feet of space and 100 pounds, or $10,000 and get five cubic feet and 200 pounds. The Aerospace Engineering Department chose the cheaper and agreed to pay the money out of department funds. Later, the Pullman Company gave us that sum for a second Getaway Special payload. That’s only part of the cost, of course. The expenses of designing and making the apparatus are additional. I was one of the original group that participated in the first consideration of the project in 1977, but wasn’t the teacher of the class. Professor Leslie Jones taught a course based on our upper-air research using rockets and balloons, which became the Getaway Special Project in 1979. At that time, unbeknownst to the rest of us, he had become ill with leukemia, and he needed the help of Professor Harm Buning in order to help him finish the semester. Soon after that Leslie Jones died. In 1981, I took over the course in the winter semester. I’m working out of a tradition established by Professor Buning, but I think I’ve gone a little bit further. One of the first things I did was to go to a symposium on Getaway Special programs at the Air Force Academy at Colorado Springs. The Academy is very much interested in having students build some of these things. In presentations the students talked about what they were doing, which was essentially what we’re doing now in our course.

Data science, and machine learning in particular, is rapidly transforming the scientific and industrial landscapes. The aerospace industry is poised to capitalize on big data and machine learning, which excels at solving the types of multi-objective, constrained optimization problems that arise in aircraft design and manufacturing. Indeed, emerging methods in machine learning may be thought of as data-driven optimization techniques that are ideal for high-dimensional, non-convex, and constrained, multi-objective optimization problems, and that improve with increasing volumes of data. In this review, we will explore the opportunities and challenges of integrating data-driven science and engineering into the aerospace industry. Importantly, we will focus on the critical need for interpretable, generalizeable, explainable, and certifiable machine learning techniques for safety-critical applications. This review will include a retrospective, an assessment of the current state-of-the-art, and a roadmap looking forward. Recent algorithmic and technological trends will be explored in the context of critical challenges in aerospace design, manufacturing, verification, validation, and services. In addition, we will explore this landscape through several case studies in the aerospace industry. This document is the result of close collaboration between UW and Boeing to summarize past efforts and outline future opportunities.

Explicit quantification of uncertainty in engineering simulations is being increasingly used to inform robust and reliable design practices. In the aerospace industry, computationally-feasible analyses for design optimization purposes often introduce significant uncertainties due to deficiencies in the mathematical models employed. In this paper, we discuss two recent improvements in the quantification and combination of uncertainties from multiple sources that can help generate probabilistic aerodynamic databases for use in aerospace engineering problems. We first discuss the eigenspace perturbation methodology to estimate model-form uncertainties stemming from inadequacies in the turbulence models used in Reynolds-Averaged Navier-Stokes Computational Fluid Dynamics (RANS CFD) simulations. We then present a multi-fidelity Gaussian Process framework that can incorporate noisy observations to generate integrated surrogate models that provide mean as well as variance information for Quantities of Interest (QoIs). The process noise is varied spatially across the domain and across fidelity levels. Both these methodologies are demonstrated through their application to a full configuration aircraft example, the NASA Common Research Model (CRM) in transonic conditions. First, model-form uncertainties associated with RANS CFD simulations are estimated. Then, data from different sources is used to generate multi-fidelity probabilistic aerodynamic databases for the NASA CRM. We discuss the transformative effect that affordable and early treatment of uncertainties can have in traditional aerospace engineering practices. The results are presented and compared to those from a Gaussian Process regression performed on a single data source.

Complex engineering problems can be modelled as optimisation problems. For instance, optimising engines, materials, components, structure, aerodynamics, navigation, control, logistics, and planning is essential in aerospace. Metaheuristics are applied to solve these optimisation problems. The present paper presents a systematic study on applying metaheuristics in aerospace based on the literature. Relevant scientific repositories were consulted, and a structured methodology was used to filter the papers. Articles published until March 2022 associating metaheuristics and aerospace applications were selected. The most used algorithms and the most relevant hybridizations were identified. This work also analyses the main types of problems addressed in the aerospace context and which classes of algorithms are most used in each problem.

Knowledge infrastructures are defined as robust networks of people, artifacts, and institutions that generate, share and maintain specific knowledge. Yet, many domains are fragmented and far from robustly networked, such as science communication or aerospace engineering. While FAIR (Findable, Accessible, Interoperable, Reusable) data management tools exist, their adoption in these domains is limited. Several challenges inhibit this adoption, from complex heterogeneous data formats to lack of structured support to outright incentives against collaboration or legal barriers. This doctoral work outlines how to foster underdeveloped knowledge infrastructures with the use-cases of science communication and aerospace engineering. By analyzing these problems and identifying available solutions, tool-supported workflows towards collaborative infrastructure can be implemented and evaluated. These include human-in-the-loop artificial intelligence (AI)-supported workflows for information extraction and processing, wiki- and knowledge-graph-based digital libraries, and stakeholder-requirement-driven interfaces. While these developed tools for workflow automation and knowledge representation show promise, significant challenges remain. Future work will have to go beyond technical problem-solving and address the societal and legal barriers to unlock the particular domains. Beyond that, advocates of emerging knowledge infrastructures in any domain are welcome to apply the findings of this work to foster the networking of available knowledge.

Recent advances in both Large Language Models (LLMs) and Vision Language Models (VLMs) have seen a step change in their ability to perform visual code completion, but the aerospace industry, which prioritizes safety and explainabilty over rapid LLM adoption, currently has no publicly announced LLM-based geometric design copilot systems in commercial use by aerospace Original Equipment Manufacturers (OEMs). This paper presents a LLM-based visual programming copilot application for aerospace engineering design tasks, using a visual programming variant of the ReAct methodology and GPT 5.4. In addition to the copilot, we describe Wingbuilder, a new Grasshopper plugin library with custom components for aerospace-specific geometry abstraction, and an associated Aerospace Visual Programming Dataset (AVPD) with 18 aerospace expert designed tasks at different levels of difficulty alongside ground truth solutions. We evaluate our copilot application with a user trial involving two experienced aerospace engineers from a large aircraft manufacturing company. We find our copilot visual programming ReAct methodology was successful in generating suggestions that participants found helpful, but slow ReAct inference times limit its usefulness to more complex time-consuming tasks where waiting for good copilot solution suggestion was worthwhile. Participants reported they liked the tool and would be willing to use it in the future.

We present a compositional approach to early modeling and analysis of complex aerospace systems based on assume-guarantee contracts. Components in a system are abstracted into assume-guarantee specifications. Performing algebraic contract operations with Pacti allows us to relate local component specifications to that of the system. Applications to two aerospace case studies (the design of spacecraft to satisfy a rendezvous mission and the design of the thermal management system of a prototypical aircraft) show that this methodology provides engineers with an agile, early analysis and exploration process.

This paper presents the design and fabrication work of adaptive wing using hybrid composite based on their structural response to shape memory alloy (SMA) actuation. The objectives of this study include designing a smart morphing wing, validating its structural response through computational and experimental analysis, and studying actuator placement in the adaptive wing. The computational analysis on the structural and aerodynamic performance is done with MSc Patran/Nasran and ANSYS Fluent respectively. The structural response observed is quite similar with computational analysis. From the obtained results, it can be taken that different actuator placement will produce a different profile of camber due to material stiffness and deflection. The actuator placement for the adaptive wing in this study has been found to produce positive camber and improve lift and drag aerodynamic performance.

AsaPy is a custom-made Python library designed to simplify and optimize the analysis of aerospace simulation data. Instead of introducing new methodologies, it excels in combining various established techniques, creating a unified, specialized platform. It offers a range of features, including the design of experiment methods, statistical analysis techniques, machine learning algorithms, and data visualization tools. AsaPy's flexibility and customizability make it a viable solution for engineers and researchers who need to quickly gain insights into aerospace simulations. AsaPy is built on top of popular scientific computing libraries, ensuring high performance and scalability. In this work, we provide an overview of the key features and capabilities of AsaPy, followed by an exposition of its architecture and demonstrations of its effectiveness through some use cases applied in military operational simulations. We also evaluate how other simulation tools deal with data science, highlighting AsaPy's strengths and advantages. Finally, we discuss potential use cases and applications of AsaPy and outline future directions for the development and improvement of the library.

The main workshop objective was to promote a holistic view and interdisciplinary methods for design, verification and co-ordination of aerospace systems, by combining formal methods with techniques from control engineering and artificial intelligence. The very demanding safety, robustness and performance requirements of these systems require unprecedented integration of heterogeneous techniques and models. The aim of FMA was to bring together active researchers from all the above areas to discuss and present their work.