Igor N. Egorov

Multi-objective optimization of turbomachinery cascades for minimum loss, maximum loading, and maximum gap-to-chord ratio

This paper illustrates an automatic multi-objective design optimization of a two-dimensional airfoil cascade row having a finite number of airfoils. The objectives were to simultaneously minimize the total pressure loss, maximize total aerodynamic loading (force tangent to the cascade), and minimize the number of airfoils in the finite cascade row. The constraints were: fixed mass flow rate, fixed axial chord, fixed inlet and exit flow angles, fixed blade cross-section area, minimum allowable thickness distribution, minimum allowable lift force, and a minimum allowable trailing edge radius. This means that the entire airfoil cascade shape was optimized including its stagger angle, thickness, curvature, and solidity. The analysis of the performance of intermediate airfoil cascade shapes were performed using an unstructured grid based compressible Navier-Stokes flow-field analysis code with k-e turbulence model. A robust stochastic algorithm was used in the automatic multi-objective constrained shape design process that had 18 design variables, 5 nonlinear constraints, and 3 objectives. Simultaneous reductions of the total pressure loss, increases of the total loading, and decreases of the number of airfoils were achieved using this method on a VKI high subsonic exit flow axial turbine cascade. 1Graduate Research Assistant. Student member AIAA. 2 Professor. Member of Russian Academy of Sciences. 3 Visiting Research Associate. 4 Professor. Director of MAIDO Laboratory. Associate Fellow AIAA. 5 Associate Professor.

OPTIMIZATION OF A LARGE NUMBER OF COOLANT PASSAGES LOCATED CLOSE TO THE SURFACE OF A TURBINE BLADE

A constrained optimization of locations and discrete radii of a large number of small circular cross-section straight-through coolant flow passages in internally cooled gas turbine vane was developed. The objective of the optimization was minimization of the integrated surface heat flux penetrating the airfoil thus indirectly minimizing the amount of coolant needed for the removal of this heat. Constraints were that the maximum temperature of any point in the vane is less than the maximum specified value and that the distances between any two holes or between any hole and the airfoil surface are greater than the minimum specified value. A configuration with maximum of 30 passages was considered. The presence of external hot gas and internal coolant was approximated by using convection boundary conditions for the heat conduction analysis. A parallel three-dimensional thermoelasticity finite element analysis (FEA) code from the ADVENTURE project at University of Tokyo was used to perform automatic thermal analysis of different vane configurations. A robust semi-stochastic constrained optimizer and a parallel genetic algorithm (PGA) were used to solve this problem using an inexpensive distributed memory parallel computer. 1 Research scientist. ASME member.

PARALLEL THERMOELASTICITY OPTIMIZATION OF 3-D SERPENTINE COOLING PASSAGES IN TURBINE BLADES

An automatic design algorithm for parametric shape optimization of three-dimensional cooling passages inside axial gas turbine blades has been developed. Smooth serpentine passage configurations were considered. The geometry of the blade and the internal serpentine cooling passages were parameterized using surface patch analytic formulation, which provides very high degree of flexibility, second order smoothness and a minimum number of parameters. The design variable set defines the geometry of the turbine blade coolant passage including blade wall thickness distribution and blade internal strut configurations. A parallel three-dimensional thermoelasticity finite element analysis (FEA) code from the ADVENTURE project at the University of Tokyo was used to perform automatic thermal and stress analysis of different blade configurations. The same code can also analyze nonlinear (large/plastic deformation) thermoelasticity problems for complex 3-D configurations. Convective boundary conditions were used for the heat conduction analysis to approximate the presence of internal and external fluid flow. The objective of the optimization was to make stresses throughout the blade as uniform as possible. Constraints were that the maximum temperature and stress at any point in the blade were less than the maximum allowable values. A robust semi-stochastic constrained optimizer and a parallel genetic algorithm were used to solve this problem while running on an inexpensive distributed memory parallel computer.Copyright

Optimizing chemistry of bulk metallic glasses for improved thermal stability

Thermo-mechanical-physical properties of bulk metallic glasses (BMGs) depend strongly on the concentrations of each of the chemical elements in a given alloy. The proposed methodology for simultaneously optimizing these multiple properties by accurately determining proper concentrations of each of the alloying elements is based on the use of computational algorithms rather than on traditional experimentation, expert experience and intuition. Specifically, the proposed BMG design method combines an advanced stochastic multi-objective evolutionary optimization algorithm based on self-adapting response surface methodology and an existing database of experimentally evaluated BMG properties. During the iterative computational design procedure, a relatively small number of new BMGs need to be manufactured and experimentally evaluated for their properties in order to continuously verify the accuracy of the entire design methodology. Concentrations of the most important alloying elements can be predicted so that new BMGs have multiple properties optimized in a Pareto sense. This design concept was verified for superalloys using strictly experimental data. Thus, the key innovation here lies in arriving at the BMG compositions which will have the highest glass forming ability by utilizing an advanced multi-objective optimization algorithm while requiring a minimum number of BMGs to be manufactured and tested in order to verify the predicted performance of the predicted BMG compositions.

Evolutionary Optimization of Chemistry of Bulk Metallic Glasses

Metallic glass is basically an alloy whose metallic species are “frozen” in amorphous glassy state rather than forming a standard crystalline structure. Metallic glasses have no grain boundaries and no dislocations and stacking faults. They are several times stronger than steel and considerably harder and more elastic. Formation of metallic glasses by extremely high cooling (~105 K/sec) of the melt was first accomplished in 1960s. The resulting metallic glass thickness was limited to extremely thin ribbons. In the 1990s, researchers formed new classes of metallic glasses in bulk. The bulk metallic glasses (BMGs) are composed of three or more metals in the alloy melt and a few diatomatous earth ingredients in order to lower the cooling rate. Cooling rates of the new alloys are from 100 K/s to 1 K/s. The possible thickness of these newer metallic glasses increased from micrometers to centimeters. One of the keys to lowering the cooling speed and creating larger specimens is that bulk metallic glasses should have ingredients with atomic species having large size and chemical differences. Thus, multiple thermo-mechanical properties and the cooling speed of bulk metallic glass alloys depend strongly on the concentrations of each of the chemical elements in a given alloy. The proposed methodology for accurately determining concentration of each of the important alloying elements is based on the use of a combination of a robust multiobjective optimization algorithm and on traditional experimentation. Specifically, the proposed alloy design method combines an advanced stochastic multi-objective evolutionary optimization algorithm based on self-adapting response surface methodology and a relatively very small data set of thermo-mechanical properties and the corresponding concentrations of alloying elements. During the iterative computational design procedure, new metallic glass alloys need to be manufactured and experimentally evaluated for their properties in order to continuously verify the accuracy of the entire design methodology. This metallic glass alloy design optimization method thus minimizes the need for costly and time-consuming experimental evaluations of new metallic glass alloys to fewer than 200 new alloys.

Self-organizing maps for pattern recognition in design of alloys

ABSTRACT A combined experimental–computational methodology for accelerated design of AlNiCo-type permanent magnetic alloys is presented with the objective of simultaneously extremizing several magnetic properties. Chemical concentrations of eight alloying elements were initially generated using a quasi-random number generator so as to achieve a uniform distribution in the design variable space. It was followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic protocol. These experimental data were used to develop meta-models capable of directly relating the chemical composition with desired macroscopic properties of the alloys. These properties were simultaneously optimized to predict chemical compositions that result in improvement of properties. These data were further utilized to discover various correlations within the experimental dataset by using several concepts of artificial intelligence. In this work, an unsupervised neural network known as self-organizing maps was used to discover various patterns reported in the literature. These maps were also used to screen the composition of the next set of alloys to be manufactured and tested in the next iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys. This approach helps significantly reducing the time and the number of alloys needed in the alloy development process.