Unsupervised kernel parameter estimation by constrained nonlinear optimization for clustering nonlinear biological data

Author(s):  
Hyokyeong Lee ◽  
Rahul Singh
2005 ◽  
Author(s):  
Spyros A. Kinnas ◽  
Hanseong Lee ◽  
Hua Gu ◽  
Yumin Deng

Recently developed methods at UT Austin for the analysis of open or ducted propellers are presented, and then coupled with a constrained nonlinear optimization method to design blades of open or ducted propellers for maximum efficiency satisfying the minimum pressure constraint for fully wetted case, or the specified maximum allowable cavity area for cavitating case. A vortex lattice method (named MPUF3A) is applied to analyze the unsteady cavitating performance of open or ducted propellers subject to non-axisymmetric inflows. A finite volume method based Euler solver (named GBFLOW) is applied to predict the flow field around the open or ducted propellers, coupled with MPUF-3A in order to determine the interaction of the propeller with the inflow (i.e. the effective wake) or with the duct. The blade design of open or ducted propeller is performed by using a constrained nonlinear optimization method (named CAVOPT-BASE), which uses a database of computed performance for a set of blade geometries constructed from a base-propeller. The performance is evaluated using MPUF-3A and GBFLOW. CAVOPT-BASE approximates the database using the least square method or the linear interpolation method, and generates the coefficients of polynomials based on the design parameters, such as pitch, chord, and camber. CAVOPT-BASE finally determines the optimum blade design parameters, so that the propeller produces the desired thrust for the given constraints on the pressure coefficient or the allowed amount of cavitation.


2020 ◽  
Vol 65 (2) ◽  
pp. 1-14
Author(s):  
Sevil Avcıoğlu ◽  
Ali Türker Kutay ◽  
Kemal Leblebicioğlu

Subspace identification is a powerful tool due to its well-understood techniques based on linear algebra (orthogonal projections and intersections of subspaces) and numerical methods like singular value decomposition. However, the state space model matrices, which are obtained from conventional subspace identification algorithms, are not necessarily associated with the physical states. This can be an important deficiency when physical parameter estimation is essential. This holds for the area of helicopter flight dynamics, where physical parameter estimation is mainly conducted for mathematical model improvement, aerodynamic parameter validation, and flight controller tuning. The main objective of this study is to obtain helicopter physical parameters from subspace identification results. To achieve this objective, the subspace identification algorithm is implemented for a multirole combat helicopter using both FLIGHTLAB simulation and real flight-test data. After obtaining state space matrices via subspace identification, constrained nonlinear optimization methodologies are utilized for extracting the physical parameters. The state space matrices are transformed into equivalent physical forms via the "sequential quadratic programming" nonlinear optimization algorithm. The required objective function is generated by summing the square of similarity transformation equations. The constraints are selected with physical insight. Many runs are conducted for randomly selected initial conditions. It can be concluded that all of the significant parameters can be obtained with a high level of accuracy for the data obtained from the linear model. This strongly supports the idea behind this study. Results for the data obtained from the nonlinear model are also evaluated to be satisfactory in the light of statistical error analysis. Results for the real flight-test data are also evaluated to be good for the helicopter modes that are properly excited in the flight tests.


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