Performance Prediction Hoftware for IACC Yachts

1993 ◽  
Author(s):  
Eric C. Schlageter ◽  
James R. Teeters
Keyword(s):  
Performance Prediction ◽  
Linear Optimization ◽  
Viscous Drag ◽  
Development Effort ◽  
Added Resistance ◽  
Induced Drag ◽  
Software Development Effort ◽  
Tank Test ◽  
America’S Cup ◽  
Wetted Area

The performance prediction software development effort undertaken by the Partnership for America's Cup Technology (PACT) is reviewed. First, PACT's origin, members, and mandate is covered, interspersed with a historical perspective of prediction software. Next, the new IACC rule with constraints is given. The hydrodynamic model format used in the software is described. Based on PACT tank test data, improved formulations for viscous drag, utilizing dynamic wetted area and length for canoe body drag and a 'stripping' method for appendage drag are presented. Corrections for Froude number and heel effects on induced drag are summarized. A new upwind sail model and added resistance model are discussed. The use of a race modeling program is illustrated with results from three separate design studies: a geosim family, a length scaling family, and an appendage study. Typical upright resistance, drag polar plots, lift plots, sea spectra, and added resistance data are presented. The final section describes current developments including speed enhancements, improved portability, and use of a multi-variable, non-linear optimization scheme to search the design space.

Refinements in the Techniques of Tank Testing Sailing Yachts and the Processing of Test Data

1993 ◽  
Author(s):  
James R. Teeters
Keyword(s):  
Test Data ◽  
Form Factors ◽  
Test Reliability ◽  
Viscous Drag ◽  
Least Squares Regression ◽  
Test Program ◽  
Calm Water ◽  
Tank Test ◽  
America’S Cup ◽  
Wetted Area

The Partnership for America's Cup Technology (PACT) undertook a tank test program in conjunction with the 1992 defense of the America's Cup. The focus of this program was on: - establishing baseline data, in both calm water and waves, for the American defense syndicates; - addressing the "tankery" issues of test reliability and accuracy and expansion to full scale; - developing the test program so that the tank serves as a more capable partner with computational fluid dynamics (CFD); - improving the techniques of processing test data. This paper reviews the results of the PACT program that pertain to the methods of calm water testing. Solutions to specific problems of handling tank data are discussed. Traditional methods used to predict viscous drag are compared with those developed by PACT, which include dynamic wetted area and wetted length, refinement of appendage drag estimates and the use of multiple canoe body form factors. The revised residuary drag that results from these improved methods is directly compared with CFD estimates. Lastly, a mathematical model, employing least squares regression techniques, is discussed as a method for fairing and representing tank test data.

Performance Prediction without Empiricism: A RANS-Based VPP and Design Optimization Capability

2007 ◽  
Author(s):  
Richard Korpus
Keyword(s):  
Design Optimization ◽  
Performance Prediction ◽  
Real World ◽  
Development Effort ◽  
New Approach ◽  
Flow Features ◽  
Velocity Prediction ◽  
Sailing Yacht ◽  
America’S Cup ◽  
Force Data

The use of Velocity Prediction Programs (VPP’s) in sailing yacht design has been standard practice for years. VPP fidelity, however, continues to be limited by the accuracy of aero and hydro force data used to represent a particular yacht. Even the most advanced America’s Cup VPP’s usually derive sail forces from panel or vortexlattice models, and hull forces from potential flow codes or experiment. Real world effects attributed to viscosity are added using simplified theoretical or empirical models that cannot resolve all the complexity of sailboat physics. This paper describes a new approach for performance prediction and design optimization that relies solely on high-resolution Reynolds-Averaged NavierStokes (RANS) computational fluid dynamics. All aero and hydro forces and moments are generated by RANS, and therefore include the real-world flow features of boundary layers, separation, shed vorticity, and turbulence. RANS software and grid model requirements suitable for VPP applications are discussed, and sample aero and hydro solutions included. Examples from America’s Cup design are used to demonstrate the technique’s practicality and accuracy. Finally, since VPP’s require forces from a large number of sailing conditions, the extensive development effort (undertaken through three America’s Cup cycles) to transition state-of-the-art RANS into the practical realm is summarized.

APPLYING EXPERT JUDGMENT TO IMPROVE AN INDIVIDUAL'S ABILITY TO PREDICT SOFTWARE DEVELOPMENT EFFORT

2012 ◽  
Vol 22 (04) ◽  
pp. 467-483 ◽  
Author(s):  
CUAUHTÉMOC LÓPEZ-MARTÍN ◽  
ALAIN ABRAN
Keyword(s):  
Software Development ◽  
Software Process ◽  
Development Effort ◽  
Academic Environment ◽  
Academic Institutions ◽  
Graduate Courses ◽  
Software Projects ◽  
Personal Software Process ◽  
Software Development Effort ◽  
Effort Prediction

Expert-based effort prediction in software projects can be taught, beginning with the practices learned in an academic environment in courses designed to encourage them. However, the length of such courses is a major concern for both industry and academia. Industry has to work without its employees while they are taking such a course, and academic institutions find it hard to fit the course into an already tight schedule. In this research, the set of Personal Software Process (PSP) practices is reordered and the practices are distributed among fewer assignments, in an attempt to address these concerns. This study involved 148 practitioners taking graduate courses who developed 1,036 software course assignments. The hypothesis on which it is based is the following: When the activities in the original PSP set are reordered into fewer assignments, the result is expert-based effort prediction that is statistically significantly better.

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