Search NASA⌕ Search

Engineering topics

Nelson, Andrea

Publications and source records attributed to Nelson, Andrea.

4 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

PIV Measurements of the CEV Hot Abort Motor Plume for CFD Validation

NASA s next manned launch platform for missions to the moon and Mars are the Orion and Ares systems. Many critical aspects of the launch system performance are being verified using computational fluid dynamics (CFD) predictions. The Orion Launch Abort Vehicle (LAV) consists of a tower mounted tractor rocket tasked with carrying the Crew Module (CM) safely away from the launch vehicle in the event of a catastrophic failure during the vehicle s ascent. Some of the predictions involving the launch abort system flow fields produced conflicting results, which required further investigation through ground test experiments. Ground tests were performed to acquire data from a hot supersonic jet in cross-flow for the purpose of validating CFD turbulence modeling relevant to the Orion Launch Abort Vehicle (LAV). Both 2-component axial plane Particle Image Velocimetry (PIV) and 3-component cross-stream Stereo Particle Image Velocimetry (SPIV) measurements were obtained on a model of an Abort Motor (AM). Actual flight conditions could not be simulated on the ground, so the highest temperature and pressure conditions that could be safely used in the test facility (nozzle pressure ratio 28.5 and a nozzle temperature ratio of 3) were used for the validation tests. These conditions are significantly different from those of the flight vehicle, but were sufficiently high enough to begin addressing turbulence modeling issues that predicated the need for the validation tests.

Wernet, Mark↗
NASA NTRS2004

Aerodynamic Optimization of Rocket Control Surface Geometry Using Cartesian Methods and CAD Geometry

Aerodynamic design is an iterative process involving geometry manipulation and complex computational analysis subject to physical constraints and aerodynamic objectives. A design cycle consists of first establishing the performance of a baseline design, which is usually created with low-fidelity engineering tools, and then progressively optimizing the design to maximize its performance. Optimization techniques have evolved from relying exclusively on designer intuition and insight in traditional trial and error methods, to sophisticated local and global search methods. Recent attempts at automating the search through a large design space with formal optimization methods include both database driven and direct evaluation schemes. Databases are being used in conjunction with surrogate and neural network models as a basis on which to run optimization algorithms. Optimization algorithms are also being driven by the direct evaluation of objectives and constraints using high-fidelity simulations. Surrogate methods use data points obtained from simulations, and possibly gradients evaluated at the data points, to create mathematical approximations of a database. Neural network models work in a similar fashion, using a number of high-fidelity database calculations as training iterations to create a database model. Optimal designs are obtained by coupling an optimization algorithm to the database model. Evaluation of the current best design then gives either a new local optima and/or increases the fidelity of the approximation model for the next iteration. Surrogate methods have also been developed that iterate on the selection of data points to decrease the uncertainty of the approximation model prior to searching for an optimal design. The database approximation models for each of these cases, however, become computationally expensive with increase in dimensionality. Thus the method of using optimization algorithms to search a database model becomes problematic as the number of design variables is increased.

Nelson, Andrea↗