A Machine Learning based Expert System for Optimizing CFD Solver Parameters

Computational Fluid Dynamics is a viable tool in the field of aerodynamics enabling to reduce time, effort and budget required for experimental testing. Although powerful and established for various years, it remains a complex tool calling for experienced users to ensure consistent high-quality results. This complexity primarily stems from the underlying model, namely the Navier-Stokes equations typically combined with a set of equations resolving the effects of turbulence. Additionally, to obtain accurate high-fidelity result appropriate meshes are required. As a consequence, a substantial number of parameters needs to be selected carefully and the quality of a result often highly depends on individual knowledge and experience of a user. Hence, a strong desire exists to reduce the number of input parameters without causing a loss of accuracy and efficiency. Such reduction of parameters might be viewed as a prerequisite to CFD as a tool in process chains for multidisciplinary applications where typically no user interaction is possible. In this article we propose a machine-learned Expert System for CFD to provide guidance for users in selecting optimal or at least near optimal parameter combinations. The proposed Expert System is divided into two macro steps, the surrogate model and a genetic algorithm to determine from the surrogate model the parameters. Numerical examples are presented to demonstrate the approach

Developing a competency framework for academic physicians

<p><b>Background:</b> There is a mismatch between the requirements of the multifaceted role of academic physicians and their education. Medical institutions use faculty development initiatives to support their junior academic physicians, however, these rarely revolve around academic physician competencies. The aim of this study was to identify these academic physician competencies and develop a competency framework customized to an organizational context.</p> <p><b>Methods:</b> The authors conducted semi-structured interviews and Critical Incident Technique with 25 academic physicians at a teaching medical center in the Middle East region inquiring about the behaviors of academic physicians in teaching, clinical, research, and administrative roles.</p> <p><b>Results:</b> Using content analysis, the authors identified 16 competencies: five “Supporting Competencies”, common to all four roles of academic physicians, and 11 “Function-Specific Competencies”, specific to the role being fulfilled. The developed framework shared similarities with frameworks reported in the literature but also had some distinctions.</p> <p><b>Conclusions:</b> The framework developed represents a step towards closing the gap between the skills medical students are taught and the skills required of academic physicians. The model was customized to the context of the current organization and included a future orientation and addressed the literature calling for increasing focus on the administrative skills of academic physicians.</p