University research projects often address complex challenges that require systematic methodologies for efficient and transparent problem-solving. This paper proposes a combined bottom-up and top-down approach to reduce technical complexity, improve traceability, and integrate risk management into research planning. Firstly, the paper explores how complex research questions can be broken down efficiently and adapted to university research projects' settings. This leads to hypothesis-driven risk management, which reduces planning efforts and enables a review of objectives. Secondly, an adapted work package structure is developed, incorporating special features such as hypothesis, risk management, rejection, and acceptance criteria to enhance researcher involvement. Thirdly, to validate the approach, a single case study of the 3D simulation of an innovative combustion model, focusing on the challenges of emission reduction, improving the efficiency and the use of alternative fuels, to design the mobility of future sustainable, is conducted. Initial results are demonstrated by a digital risk twin. The developed procedure improves efficient risk management methodology and its integral implementation into the overall research objective. This avoids undesirable developments and enhances the quality of the research results.