The objective of the present proposal is to advance the state of the art of Organic Rankine Cycles (ORC) to assist Europe achieve its goal for energy sustainability. Current ORC design methods rely on numerical models that use inaccurate assumptions and neglect variable operating conditions. Hence, the specific goal, of this proposal, is to develop advance engineering tools to characterize and minimize the impact of aleatory and epistemic uncertainties during the design phase of ORC based machines. The first part of the project targets the integration of uncertainty quantification techniques with: 1) thermodynamic model of an ORC cycle and; 2) aerodynamic model of the turbine. The objective is to provide performance predictions with confidence margins that facilitate reliable design decisions. The numerical results will be validated with data from a new ORC facility at TU Delft. Additionally, innovative Bayesian inference techniques will use experimental data to infer the confidence margins of the parameters used in the model. The second phase aims to quantify the impact of a variable heat sources in the performance of the cycle and its consequent effect on the aerodynamic efficiency of the turbine. With an accurate prediction of the probabilistic density function of the turbine’s boundary conditions, robust optimization method will be developed to maximize the turbine’s performance over the complete operational range. All the tools will be developed in open access to foster academic collaboration and motivate engineers to consider and minimize uncertainties in the early design phase of energy systems.