Designing an Aerodynamic Mini-Supercar: Spiata CFD Analysis

# Introduction: The Quest for Aerodynamic Perfection What if you could combine the lightweight agility of a Mazda Miata MX-5 with the aerodynamic sophistication of a McLaren Speedtail? That's the vision behind **Spiata**—a mini-supercar design project that merges iconic automotive design with cutting-edge computational fluid dynamics (CFD) analysis. For the Thermofluids module in Year 2 at Imperial College London, we were tasked with creating a vehicle using CAD software and validating its aerodynamic performance through CFD simulation. The goal? Calculate the drag coefficient and compare it with real-world wind tunnel data of similar vehicles. ## Design Philosophy **Spiata** takes inspiration from two automotive legends: - **Mazda Miata MX-5**: The world's best-selling roadster, known for lightweight design and driving purity - **McLaren Speedtail**: The most aerodynamic car ever produced by McLaren The challenge was to create a mini-supercar that captures the essence of both—nimble like a Miata, yet slippery through the air like a Speedtail. # Design Process: From Concept to CAD ## Phase 1: Conceptualization Before touching CAD software, I studied the aerodynamic features of both inspirational vehicles: **From the Miata MX-5:** - Low-slung profile - Compact dimensions - Simple, organic curves - Minimal frontal area **From the McLaren Speedtail:** - Teardrop-shaped body (optimal for drag reduction) - Smooth underbody - Streamlined rear with integrated airflow management - Covered rear wheels (reducing turbulence) The goal: Combine these principles into a cohesive design that prioritizes aerodynamic efficiency. ## Phase 2: Surface Modeling in Fusion 360 I used **Fusion 360** for the CAD design, employing advanced surface modeling techniques—a powerful method for creating complex, organic automotive shapes. ### Surface Modeling vs. Solid Modeling **Why surface modeling?** Traditional solid modeling (extrude, revolve, sweep) works well for mechanical parts but struggles with the flowing curves of automotive design. Surface modeling allows: - **Freeform organic shapes**: Create compound curves found in car bodies - **Precise control**: Manipulate individual surfaces for exact aerodynamic profiles - **Complex curvature**: Achieve the smooth transitions critical for airflow ### Key Modeling Techniques Used **1. Loft Surfaces** Created the main body by lofting between profile curves: ``` Front profile → Mid-body sections → Rear taper ``` This technique ensures smooth transitions and continuous curvature—essential for laminar airflow. **2. Sculpting Tools** Used Fusion 360's sculpt mode to refine: - Hood contours - Roof line - Side character lines - Rear deck tapering **3. Patch and Trim** - Filled surface gaps with patch surfaces - Trimmed intersecting surfaces for clean body panels - Ensured G2 continuity (curvature continuity) at surface junctions ### Design Features for Aerodynamics **Front End:** - Low nose to reduce frontal area - Smooth integration of headlight volumes - Gentle slope to direct air over the hood **Side Profile:** - Streamlined cabin with minimal tumblehome (inward taper) - Flush door handles (modeled as surface depressions) - Covered rear wheel arches (reducing drag by 3-5%) **Rear Design:** - Extended tail inspired by Speedtail's teardrop shape - Integrated spoiler (subtle, not aggressive) - Smooth underbody transition to prevent flow separation ## Phase 3: Model Preparation for CFD Before CFD analysis, the CAD model required specific preparation: **1. Watertight Geometry** - Ensured all surfaces formed a closed volume - No gaps or overlapping faces - Critical for CFD mesh generation **2. Simplified Details** - Removed minor features (door handles, badges) - Focused on aerodynamically significant geometry - Reduced computational load **3. Export to Solidworks** - Converted Fusion 360 model to STEP format - Imported into Solidworks Flow Simulation - Verified scale and units # CFD Analysis: Computational Fluid Dynamics ## Setting Up the Simulation **Software:** Solidworks Flow Simulation **Simulation Parameters:** - **Fluid**: Air at standard atmospheric conditions (1.225 kg/m³) - **Velocity**: 30 m/s (~67 mph / 108 km/h) - **Reynolds Number**: ~4.5 million (turbulent flow regime) - **Turbulence Model**: k-ε (standard for external aerodynamics) - **Boundary Conditions**: - Velocity inlet - Pressure outlet - No-slip wall on car surface - Symmetry plane (half-car simulation for efficiency) ## Meshing Strategy Created a computational mesh with: - **Base mesh**: 1.2 million cells - **Refinement zones**: - Around car body (boundary layer capture) - Wake region (turbulence resolution) - Near wheels (complex flow features) - **Boundary layer**: 10 layers with inflation (y+ < 1) ## Results and Analysis ### Flow Visualization **1. Side Flow Trajectories** The streamline visualization revealed: - Smooth airflow over the hood and roof - Minimal flow separation until the rear deck - Well-attached flow along the Speedtail-inspired tail **2. Pressure Distribution** **2D Pressure Density Plot** showed: - High pressure (red/yellow) at the nose: Stagnation point - Low pressure (green/blue) over the roof: Accelerated flow - Pressure recovery at the rear: Gradual (good for low drag) Key observation: The gentle pressure gradient at the rear indicates effective flow management—abrupt pressure changes cause separation and drag. **3. Velocity Field** **2D Velocity Plot** demonstrated: - Flow acceleration over the roof (Venturi effect) - Boundary layer growth along the body - Wake velocity deficit (indicates drag) **4. Isometric Flow Trajectories** The 3D visualization highlighted: - Symmetric flow pattern (good design balance) - Vortex formation at rear wheel arches - Clean wake with minimal turbulence ### Drag Coefficient Calculation From the CFD simulation: **Drag Force (F_d):** 185 N **Drag Coefficient Formula:** ``` C_d = F_d / (0.5 × ρ × V² × A) ``` Where: - F_d = Drag force (185 N) - ρ = Air density (1.225 kg/m³) - V = Velocity (30 m/s) - A = Frontal area (1.2 m²) **Calculated C_d: 0.28** ### Performance Benchmarking Comparing Spiata's C_d = 0.28 with real vehicles: | Vehicle | Drag Coefficient | |---------|-----------------| | **Spiata (CFD)** | **0.28** | | McLaren Speedtail | 0.278 | | Mazda Miata MX-5 (ND) | 0.31 | | Porsche 911 | 0.29 | | Tesla Model S | 0.24 | | Average sedan | 0.30-0.35 | **Result:** Spiata achieves supercar-level aerodynamics, nearly matching the McLaren Speedtail! ## Key Aerodynamic Features That Worked ### 1. Teardrop Profile The elongated rear (Speedtail influence) allowed gradual pressure recovery, reducing form drag significantly. ### 2. Smooth Underbody Flat underbody with minimal protrusions prevented turbulent airflow beneath the car—a major drag contributor. ### 3. Covered Rear Wheels Reduced wheel-induced turbulence. Open wheels can contribute 10-15% of total drag. ### 4. Optimized Frontal Area Small frontal area (1.2 m²) combined with streamlined shape minimized drag force. # Technical Deep Dive: CFD Theory ## Understanding Drag Components Total drag consists of: **1. Form Drag (Pressure Drag)** - Caused by flow separation - Creates wake region with low pressure - Dominant at high speeds **2. Skin Friction Drag** - From boundary layer shear - Proportional to surface area - Reduced with smooth surfaces **3. Induced Drag** - From lift generation (minimal for ground vehicles) - Relevant at very high speeds **Spiata's Drag Breakdown (estimated from CFD):** - Form drag: ~65% - Skin friction: ~30% - Other (wheels, mirrors): ~5% ## Reynolds Number and Turbulence ``` Re = (ρ × V × L) / μ ``` For Spiata: - L (characteristic length) ≈ 3.2 m - μ (dynamic viscosity) = 1.81 × 10⁻⁵ Pa·s - **Re ≈ 6.5 million** (fully turbulent) This high Reynolds number confirms: - Turbulent boundary layer over most of the car - k-ε turbulence model is appropriate - Results are representative of real-world conditions ## Mesh Independence Study To ensure result accuracy, I performed a mesh sensitivity analysis: | Mesh Density | Cell Count | C_d | Δ C_d | |--------------|-----------|-----|-------| | Coarse | 600k | 0.292 | - | | Medium | 1.2M | 0.280 | -4.1% | | Fine | 2.4M | 0.278 | -0.7% | The change from medium to fine mesh is <1%, confirming mesh independence at 1.2M cells. # Challenges and Learnings ## Challenge 1: Surface Continuity **Problem:** Initial CAD model had G1 continuity (tangent) but not G2 (curvature) **Impact:** CFD showed unexpected flow separation at surface transitions **Solution:** - Rebuilt critical surfaces using spline curves - Ensured G2 continuity using Fusion 360's curvature analysis - Re-ran CFD: C_d improved from 0.32 to 0.28 ## Challenge 2: Mesh Quality **Problem:** Poor mesh near wheel arches caused convergence issues **Impact:** Simulation failed to converge after 500 iterations **Solution:** - Increased local refinement around wheels - Added boundary layer inflation - Adjusted mesh growth rate from 1.5 to 1.2 - Achieved convergence in 280 iterations ## Challenge 3: Computational Resources **Problem:** Full-car CFD simulation estimated 18+ hours on available hardware **Solution:** - Used symmetry plane (half-car model) - Reduced domain size while maintaining 3× car length wake - Simulation time: 4.5 hours (75% reduction) # Results and Impact ## Academic Performance - **Drag coefficient**: 0.28 (excellent for a compact vehicle) - **Validation**: Within 5% of similar production vehicles - **Grade achievement**: High marks in Thermofluids module ## Skills Developed **CAD Proficiency:** ✅ Advanced surface modeling techniques ✅ Automotive design principles ✅ G-continuity management **CFD Expertise:** ✅ Simulation setup and boundary conditions ✅ Mesh generation and refinement ✅ Results interpretation and validation **Engineering Process:** ✅ Iterative design optimization ✅ Performance benchmarking ✅ Technical documentation ## Design Insights Key learnings about automotive aerodynamics: 1. **Small changes, big impact**: 5° change in rear taper angle → 8% drag reduction 2. **Underbody matters**: Smooth underbody contributed 15% drag improvement 3. **Flow visualization is crucial**: CFD reveals non-intuitive flow behavior 4. **Inspiration + innovation**: Combining proven designs with original ideas # Future Improvements If continuing this project, next steps would include: ## Aerodynamic Refinements 1. **Active aero**: Deployable rear spoiler for high-speed stability 2. **Diffuser design**: Rear diffuser for downforce without drag penalty 3. **Wheel fairings**: Optimize covered rear wheel design ## Advanced Analysis 1. **Transient CFD**: Study unsteady aerodynamics (crosswinds, overtaking) 2. **Aero-acoustic**: Predict wind noise 3. **Thermal management**: Integrate cooling airflow ## Physical Validation 1. **Wind tunnel testing**: Validate CFD with scaled model 2. **Coast-down testing**: Real-world drag measurement 3. **3D printing**: Create scaled physical model # Conclusion: Engineering Meets Art Spiata represents the intersection of engineering analysis and automotive design. By combining iconic design elements from the Mazda Miata and McLaren Speedtail with rigorous CFD analysis, I created a mini-supercar concept that achieves: ✅ **C_d = 0.28** - Supercar-level aerodynamics ✅ **Validated design process** - CFD confirms design decisions ✅ **Aesthetic + performance** - Beautiful form following aerodynamic function ## Key Takeaways **For aspiring automotive engineers:** - Surface modeling is essential for organic shapes - CFD is a powerful validation tool, not just analysis - Iterative design leads to optimal solutions - Real-world benchmarking provides context **For design enthusiasts:** - Aerodynamics shapes automotive beauty - Every curve serves a purpose - The best designs balance form and function This project deepened my understanding of: - **CAD techniques** for complex surfaces - **Fluid dynamics** in automotive contexts - **Simulation methodology** for design validation Most importantly, it proved that with the right tools and knowledge, anyone can design vehicles that rival production supercars in aerodynamic efficiency. --- **Technical Details:** - **Software**: Fusion 360 (CAD), Solidworks Flow Simulation (CFD) - **Model**: Half-car symmetry, 1.2M cells - **Conditions**: 30 m/s, Standard atmosphere, k-ε turbulence - **Result**: C_d = 0.28 (validated against benchmarks) *This project was completed as part of the Thermofluids module (Year 2) at Imperial College London.*