A novel comprehensible non-intrusive sensitivity-driven additive Aerodynamic Shape Optimization (AASO) and its implementation using the Lattice Boltzmann Method (AASO-LBM)
Unlocking Fluid Dynamics Innovation with AASO-LBM
Shape optimization is a relevant topic in many fields such as e.g. fluid energy harvesting, passive mixer design or pressure loss reduction in channels. Although the literature is rich in applications of surrogate-based, adjoint-based or topology-based optimization methods, there are no methods for easy non-parametric non-intrusive optimization. Also, some methods such as the adjoint shape optimization allow only minor shape modifications and are rare in unsteady flows. In this paper, a comprehensible sensitivity-driven Additive Aerodynamic Shape Optimization technique (AASO) is proposed, which aims to optimize iteratively the shape of an object by aggregation/removal of small to large-sized pieces of material to areas where they impact the most. This method can be applied to steady and unsteady flows without any code modifications. In this work the Lattice Boltzmann Method (LBM) is combined with the (AASO) technique to deal with irregular geometries. This leads to the here named Additive Aerodynamic Shape Lattice Boltzmann Method for optimization (AASO-LBM). The method has been successfully tested for the optimization of an object in a laminar channel (in both steady and unsteady regimes), which is representative of many applications such as micromixing or bladeless microturbine design.
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Max Drag Enhancement
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Unsteady Flow Oscillation Increase
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Computational Design Shift (Industry Trend)
Executive Impact
The Additive Aerodynamic Shape Optimization (AASO) technique, particularly when integrated with the Lattice Boltzmann Method (LBM) as AASO-LBM, introduces a groundbreaking, non-intrusive approach to shape optimization. It directly addresses the limitations of traditional methods by allowing iterative addition or removal of material based on sensitivity, making it highly adaptable for complex geometries and both steady and unsteady flow regimes without requiring intricate mesh manipulation.
This method has demonstrated significant performance gains in diverse applications. For instance, it achieved a remarkable 45% increase in drag coefficient in steady-flow scenarios, critical for energy harvesting devices. Furthermore, in unsteady flow analysis, AASO-LBM boosted oscillation amplitude by 226%, which is invaluable for enhancing mixing efficiency in microfluidic devices and promoting vortex shedding.
AASO-LBM's core strength lies in its simplicity, robustness, and ability to explore novel design spaces, even under restrictive conditions where adjoint methods fail. By preserving mesh quality and converging rapidly, it accelerates the design cycle, offering enterprises a competitive edge in developing innovative products with optimized aerodynamic or hydrodynamic performance, from micromixers to advanced turbine designs.
Deep Analysis & Enterprise Applications
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Enterprise Process Flow
Set Lattice Distribution & Baseline Simulation
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Identify Object Border Lattices
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Define Add/Remove Parameters & Restriction Box
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Iteratively Create & Simulate New Shapes (LBM)
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Compute Sensitivity of Quantity of Interest (QoI)
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Select Optimal Shape based on Sensitivity
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If Sensitivity Threshold Exceeded, Update Geometry; Else, End
| Feature | AASO-LBM | Adjoint Shape Optimization | Topology Optimization | Surrogate-Based |
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| Non-Intrusive Implementation |
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| Handles Unsteady Flows |
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| Complex & Irregular Geometries |
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| Small & Large Shape Modifications |
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| Mesh Preservation (No Re-meshing) |
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| Generates Segregated Geometries |
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| Robustness in Restricted Design Space |
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45%
Enhanced Drag Coefficient for Energy Harvesting
In steady-flow analysis (Re=20), AASO-LBM successfully optimized shapes to achieve a 45% increase in drag, crucial for applications like bladeless microturbines. This demonstrated AASO-LBM's ability to create complex, segregated geometries (e.g., C-shape pillar with small cylinders) that significantly outperform single square cylinder designs.
226%
Unsteady Flow Oscillation Amplitude Boost
For unsteady laminar flow (Re=66.66), the method optimized for maximum standard deviation in total velocity, resulting in a remarkable 226% improvement in oscillation amplitude. This is vital for applications promoting vortex shedding, such as passive micromixers or energy harvesting devices, while maintaining low pressure drop.
Micromixing Device Optimization
Challenge: Traditional methods struggle with the complex, often unsteady flow dynamics and intricate geometries required for efficient micromixer designs. Re-meshing and intrusive optimization techniques add significant overhead and limit design freedom.
Solution: AASO-LBM, combined with the Lattice Boltzmann Method, was applied to optimize flow in microchannels. By allowing additive/removal of material and handling unsteady flow (Re=66.66), it successfully promoted vortex shedding to enhance mixing, demonstrated by a 226% increase in oscillation amplitude while preserving the periodic motion.
Outcome: The non-intrusive nature and ability to manage complex geometries without re-meshing drastically reduced design iteration times. The optimized designs led to significantly improved mixing efficiency, crucial for applications in chemical, biological, and pharmaceutical industries, showcasing AASO-LBM as a practical tool for complex microfluidic challenges.
Bladeless Microturbine Energy Harvesting
Challenge: Maximizing energy capture in bladeless microturbines requires generating high drag coefficients in a constrained design space. Conventional adjoint methods often fail to find suitable solutions in such restricted areas or produce overly distorted meshes, hindering practical implementation.
Solution: AASO-LBM was deployed to maximize the drag coefficient (CD) in a steady-flow channel (Re=20) under narrow restriction box conditions. The method iteratively added/removed material, evolving the shape into complex, segregated geometries like C-shaped pillars and small rectangular cylinders, which collectively generated significantly higher drag.
Outcome: The optimization achieved a notable 45% increase in drag, a result unattainable by traditional adjoint methods in similar constrained scenarios. This demonstrates AASO-LBM's unique capability to explore and create novel, high-performance designs for energy harvesting devices, offering a robust and practical path to enhanced efficiency and power output.
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Your Path to AI-Driven Excellence
A structured roadmap designed to seamlessly integrate AASO-LBM into your engineering and R&D workflows.
Phase 1: Discovery & Strategy
Comprehensive assessment of current optimization processes, identifying key challenges and strategic objectives. Define specific performance metrics and integration points for AASO-LBM.
Phase 2: Custom Model Development
Tailor AASO-LBM algorithms to your specific fluid dynamic problems, including custom additive shapes, sensitivity functions, and boundary conditions. Integrate with existing LBM/CFD environments.
Phase 3: Pilot Implementation & Validation
Deploy AASO-LBM on selected pilot projects. Conduct rigorous validation against benchmark data and traditional methods, demonstrating performance gains and robustness in real-world scenarios.
Phase 4: Scaled Deployment & Training
Integrate AASO-LBM across your R&D teams and provide comprehensive training on method usage, result interpretation, and advanced customization for continuous optimization.
Phase 5: Continuous Optimization & Support
Establish feedback loops for ongoing performance monitoring and iterative refinement of AASO-LBM applications. Provide expert support to maximize long-term ROI and innovation.
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