Design Optimization
Design optimization is at the fundamental core of what we do at our Singapore office in BroadTech Engineering.
Featured Design Optimization Case Studies
Shape Optimization of a Turbo Prop Aircraft Wing
Objective: Objective of this research project was to create an optimization framework and find the optimal shape of a wing with a propeller for the minimum induced drag under cruise conditions.
Methodology: As interest was to reduce the induced drag, we used the flow governing equations as Euler equations and it was solved to constrain the control variables. the control variables used were the control points of NURBS. Gradients needed for optimization cycle were calculated by an adjoint method and the grid was deformed using inverse distance method in every cycle. All the codes were written in C++ from scratch. however, for the optimization cycle, an open source library called NLopt was used.
Results: Optimization cycle was performed on a wing with propeller and a new shape of a wing was shown to give 8 counts of drag reduction for cruise conditions.
Shape Optimization of Lower Aspect Ratio Wing
Objective: The objective was to optimize a wing shape for a surveillance drone aircraft of national aerospace laboratory for incompressible flow conditions.
Methodology:
As reference from my earlier project, this was a continuation of it. But in this case, as the flow conditions were incompressible flows I used fluently to solve the flow for the cruise conditions and was used in the same setup as mentioned earlier. The fluent software was coupled with in-house codes using user-defined functions to create a framework of shape optimization of propeller-driven aircraft.
Results: we achieved 3 counts drag reduction in the optimization. this framework is being used by National aerospace laboratory Bangalore for shape optimization in various cases.
Surrogate Modeling for Parasite Drag Calculations
Objective: The project was to create a surrogate model to calculate parasite drag.
Methodology: We used a python library called PythonMacros in order to generate an approximation model for calculating parasite drag of aircraft on the basis of statistical training input data set. Parametric space of the data was identified and a python code was written such a way that various kriging and approximation methods could be used through the python library.
Conclusion: The model was capable of calculating parasite drag with an acceptable accuracy and much faster than the actual drag calculation on the aircraft. Hence is being appreciated and used by the commercial aircraft methods and tools department.
Optimization of steam Generator Outlet and Inlet Nozzle Design for Minimum Pressure Drop
The objective of this simulation study was to minimize pressure drop in a steam generator. The CFD study for the baseline design indicated that the major contribution to the total pressure is due to a pressure drop at inlet and outlet nozzle. The specific design requirements forced a pipe in pipe (annular) configuration for the inlet and outlet nozzle for this steam generator.
Three most significant geometrical parameters were considered as variables for optimization study with the objective to reduce pressure drop at inlet and outlet nozzle (major contributors to overall pressure). A full factorial DOE based model was developed (using the pressure drop predicted by CFD simulations) to arrive at the geometrical parameters for the minimum pressure drop in the nozzles. The optimized design has led to about 25% drop in the total pressure drop.
Design Optimisation of Centrifugal Pump for Water & Centrifugal Separator
– NG series pumps (8 pumps designed varying from high discharge and hing head)
– Regenerative pump Simulation
– Centrifugal Separator for separation of multi-fluid scenario.
The methodology was based on achieving best possible efficiency and feasibility of the design. The approach was to make best possible use of using CFD tool, to help achieve the efficiency with minimum changes to the design.
All the design made were manufacturable and had shown good result in the actual environment. The experimental result matched with the CFD result with not less than 2-4% tolerance limit.
Design Optimization of Salmon Pump
Design Enhancements to a heavy-duty pump to Safely Extract Salmon from the ocean.
An initial dual pump system was analyzed for potential design flaws with a RANS simulation.
The mesh-independent results showed a significant mismatch between the CFD results and the required pressure head from the pump.
In order to increase head, an enhancement in the number of pumps was suggested and was increased from a dual pump to a 4 pump system.
This system was constructed by the clients and was significantly satisfied with the performance.
Shape Geometry Design Optimization of Hyperloop Capsule
Objective: Shape Optimization of Hyperloop Capsule (Hyperloop is a new mode of transportation being developed by various startup around the world – https://en.wikipedia.org/wiki/Hyperloop)
Methodology: 3D model created in SolidWorks and parametrized length, tube and capsule diameters, front and rear nose angles (which can be imported in Ansys)
– Imported in Ansys and set up CFD simulation at very low pressure, P = 100 Pa (absolute), T = 288 K, Entry speed = 0.1 to 1 Mach
– SIMPLE solution method with pressure-velocity coupling and k-epsilon turbulence model
– Used optimization software, modeFrontier, to automate design exploration process, i.e., created 100+ combination of input variables and automatically ran simulation through each design point
– Kantrowitz limit, which is an important limit for Hyperloop system, has been kept in check for all the simulations. A logical expression has been created for the same.
Outcome & Conclusion:
– Generated 4D Pareto set for minimum drag force, minimum air volume inside tube, maximum travel speed, and ratio of bypass area to tube area obtained
– Pareto chart shows optimized solutions will be combination of design tradeoffs between each variable
– Entry Speed vs Area Ratio plot: this plot suggests that available range of area ratio is narrowing with increasing travel speed
– Correlation matrix: effects of input and output variable on each other is visualized with this matrix. We can determine the importance of each design variable through a correlation matrix. Overall design process has been sped up by using this matrix
– Design distribution: Number of designs for a particular range of output variables was obtained. A maximum number of designs occurred for the lowest drag force range.
– Higher values of area ratio are not acceptable as we will have very less space available for passenger compartment even after building larger tubes.
– Optimum travel speed has been found to be 0.4-0.5 Mach with area ratio ranging from 0.4-0.5.
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