Experimental uncertainty and drag measurements in the national transonic facility

This report documents the results of a study which was conducted in order to establish a framework for the quantitative description of the uncertainty in measurements conducted in the National Transonic Facility (NTF). The importance of uncertainty analysis in both experiment planning and reporting results has grown significantly in the past few years. Various methodologies have been proposed and the engineering community appears to be 'converging' on certain accepted practices. The practical application of these methods to the complex wind tunnel testing environment at the NASA Langley Research Center was based upon terminology and methods established in the American National Standards Institute (ANSI) and the American Society of Mechanical Engineers (ASME) standards. The report overviews this methodology

Design study to simulate the development of a commercial freight transportation system

The Notre Dame Aerospace Engineering senior class was divided into six design teams. A request for proposals (RFP) asking for the design of a remotely piloted vehicle (RPV) was given to the class, and each design team was responsible for designing, developing, producing, and presenting an RPV concept. The RFP called for the design of commercial freight transport RPV. The RFP provided a description of a fictitious world called 'Aeroworld'. Aeroworld's characteristics were scaled to provide the same types of challenges for RPV design that the real world market provides for the design of commercial aircraft. Fuel efficiency, range and payload capabilities, production and maintenance costs, and profitability are a few of the challenges that were addressed in this course. Each design team completed their project over the course of a semester by designing and flight testing a prototype, freight-carrying remotely piloted vehicle

σr Standard Deviation of random variable r

The purpose of this paper was to evaluate a framework for obtaining robust designs of complex, coupled systems. This framework referred to as Iterative Concurrent Subspace Robust Design (ICSRD), is based upon the use of global response surface approximations of the design space. ICSRD incorporates a robust optimization formulation, using a linearization approach. It generates approximate robust designs from artificial Neural Network (NN) approximations in an iterative fashion. Two benchmark problems are presented, one being an analytic problem with two design variables and the other a control-structures problem, which is characterized by complex discipline coupling. Two variations of the latter problem are considered, one with modified bounds for certain design variables and the other with a reduced number of design variables with original bounds. It is observed that the NN training plays a significant role in obtaining a good robust optimum. It is also observed that ICSRD framework yields reasonable robust designs for the test cases implemented