Innovation has been a key characteristic in the aerospace industry. Launch Vehicles, in comparison, have largely stayed the same over the years; this is changing with the emergence of New Space and the commercialization of space. Competition forces innovation, which leads to companies trying out new materials and propellants that will give them the edge, but such transitions are tied to uncertainty.
This research investigates how material transitions create and amplify uncertainty in megaprojects, focusing on two aerospace test campaigns. The study addresses how uncertainties arise, how they differ between technical and organizational domains, what strategies are used to mitigate them and how a framework can be developed to strengthen procurement and supply chain management in predevelopment.
The study is based on two case studies within ArianeGroup, supported by 12 expert interviews, project documents and a focus group. The Material Transition Map was developed as a tool to visualize interview data and analyze cascading uncertainty across technical and organizational dimensions. The findings show that while technical uncertainty is actively managed through structured modelling and testing processes, organizational uncertainty, especially in supply chains, is more difficult to control. Material transitions trigger cascading effects that link technical changes to procurement delays, supplier challenges and schedule risks. Reliance on heritage plays a dual role in mitigating and amplifying these effects.
Theoretically, the research connects project studies with transition studies, showcasing how material transitions trigger megaproject uncertainty. Empirically, it provides insights into aerospace test campaigns, a sector rarely examined in management literature. Practically, it delivers the Material Transition Map and the Predevelopment Supply Chain Framework, a tailored strategy, validated with practitioners, to support uncertainty management.

Stopping distance is considered to be an important factor for providing a safe roadway network to the road users. Stopping distance is defined as the distance that the vehicle travels before it comes to a complete halt by considering both, the braking distance and the brake reaction/perception distance. With the emergence of Autonomous Vehicles (AVs), new challenges regarding stopping distance are currently posed to the engineering community. Depending on their total computational power, AVs can have a quicker perception as compared to human beings which can be standardized. AVs need a well-defined process to estimate the braking distance as they significantly lack the experience of human drivers. Current AI technology utilized by AVs incorporates a constant coefficient of friction (µ) for estimating braking distance and applies various factors of safety for unfavourable driving conditions. Such practices can overestimate the braking distance or sometimes underestimate it. Either estimates are not good for a smooth travel of the autonomous vehicle. This is because the stopping distance depends on the fundamental friction relationship between tire and pavement. A research study considering input of realistic tire-pavement friction, signified by the variable coefficient of friction into AVs AI toolkits has never been performed. The absence of research with respect to realistic coefficient of friction for AVs can be attributed to the fact that tire-pavement contact is a complex interaction to investigate and model.
The goal of this MSc graduation thesis is to develop a methodology for investigating the variable coefficient of friction between tire-tread rubber blocks and aggregate blocks utilizing a Tribometer. A Finite Element Model (FEM) of the interaction between tire-tread rubber blocks and aggregate blocks is developed to upscale the empirical findings from Tribometer tests to a large range of variables. The data obtained from the scientific methodologies is suggested to be incorporated into intelligent stopping distance algorithms in AVs AI toolkit to improve road safety. Such research involving Tribometer to study the coefficient of friction between tire-tread rubber and aggregates has never being performed. The study also investigated the hysteretic friction in tire-tread rubber which has never been performed previously. Key aspects of the thesis project included studying the effect of varying rubber temperature and frequency on the sliding friction between tire-tread rubber blocks and aggregate blocks. The research studied the varying effect of rubber temperature and frequency on the coefficient of friction for two surface conditions, dry/wet and three types of aggregates: Bestone, Scottish granite, cement concrete. The FEM results offered a preliminary insight on the investigated variable coefficient of friction. The scientific findings from the model and empirical results show a dependence of thermomechanical properties of the rubber material and surface conditions on the coefficient of friction between tire-tread rubber blocks and aggregate blocks. The findings indicate that the coefficient of friction decreases with increase in rubber temperature, interaction frequency and lubrication at the contact.