Transcatheter aortic heart valve replacement (TAVR) is a procedure to replace a failing aortic valve and is becoming the new standard of care for patients that are not candidates for open-heart surgery [2]. However, this minimally invasive technique has shown to cause ischemic brain lesions, or “silent infarcts”, in 90% of TAVR patients, which can increase the patient’s risk for stroke by two to four times in future years [3]. Claret Medical Inc., a medical device company, has developed a cerebral protection system that filters and captures embolic debris released during endovascular procedures, such as TAVR. This thesis utilized CT scans from Claret Medical to create a physical construct of the aortic arch to experimentally validate a theoretical computer model through flow visualization. The hypothesis was that the empirical model can accurately mimic the fluid dynamic properties of the aortic arch in order validate an in silico model using the finite elements program COMSOL MultiPhysics® Modeling Software. The physical model was created from a patient CT scan of the aortic arch using additive manufacturing (3D printing) and polymer casting, resulting in the shape of the aortic arch within a transparent, silicone material. Fluid was pumped through the model to visualize and quantify the velocity of the fluid within the aortic arch. COMSOL MultiPhysics® was used to model the aortic arch and obtain velocity measurements, which were statistically compared to the velocity measurements from the physical model. There was no significant difference between the values of the physical model and the computer model, confirming the hypothesis. Overall, this study successfully used CT scans to create an anatomically accurate physical model that was validated by a computer model using a novel technique of flow visualization. As TAVR and similar procedures continue to develop, the need for experimental evaluation and visualization of devices will continue to grow, making this project relevant to many companies in the medical device industry.

Agent-based simulation models are an important tool to study the effectiveness of policy interventions on the uptake of residential photovoltaic systems by households, a cornerstone of sustainable energy system transition. In order for these models to be trustworthy, they require rigorous validation. However, the canonical approach of validating emulation models through calibration with parameters that minimize the difference of model results and reference data fails when the model is subject to many stochastic influences. The residential photovoltaic diffusion model PVact features numerous stochastic influences that prevent straightforward optimization-driven calibration. From the analysis of the results of a case-study on the cities Dresden and Leipzig (Germany) based on three error metrics (mean average error, root mean square error and cumulative average error), this research identifies a parameter range where stochastic fluctuations exceed differences between results of different parameterization and a minimization-based calibration approach fails. Based on this observation, an approach is developed that aggregates model behavior across multiple simulation runs and parameter combinations to compare results between scenarios representing different future developments or policy interventions of interest.