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Drag Reduction Achieved through Heavy Vehicle Platooning Thesis Defense

Andrew Watts

04/15/2015

Topics Covered • Introduction & Motivation • Meshing and Simulation Methodology • Simplified Car Body

▫ One body ▫ Two body

• Single Heavy Vehicle ▫ Baseline model ▫ Three vehicle geometry

• Multiple Heavy Vehicle ▫ Two vehicle ▫ Three vehicle ▫ Multiple geometry two vehicle

• Conclusions & Future Work

Introduction to Platooning • What is a heavy vehicle?

▫ A large tractor-trailer combination vehicle that is used for goods transportation

▫ Primary focus of this research • What is platooning?

▫ A group of two or more aligned vehicles in a leader-follower configuration • Takes advantage of a phenomenon referred to as “drafting”

▫ Also known as “slipstreaming” ▫ Reduces aerodynamic drag, saves fuel

Figure 1. Heavy Vehicle Platoon [1]

Drafting • Drafting provides aerodynamic drag reduction for

follower vehicle • Lead vehicle encounters “wall of air,” follow vehicle

encounters highly disrupted flow ▫ Fluid dynamics perspective: lower mean flow velocity

• At highway speeds, aerodynamic drag accounts for over 70% of total drag force ▫ Aerodynamic force scales with speed squared

Figure 2. Geese in V formation [1]

Figure 3. Cyclists drafting [2]

𝐹𝐹𝐷𝐷 = 𝐶𝐶𝐷𝐷 1 2 𝜌𝜌∞𝑣𝑣∞2 𝐴𝐴

• Benefits well-known and utilized in real world scenarios ▫ Geese in V formation ▫ Cyclists ▫ NASCAR Drivers

• Reduced drag translates directly to improved fuel economy

Motivation: Improved Fuel Economy • 2012 Transportation industry:

▫ $1.33 Trillion ▫ 8.5% national GDP ▫ Extremely competitive

• Crude oil is a finite commodity ▫ Highly variable market ▫ Continually rising prices ▫ Primary fuel for foreseeable future

• Improved fuel economy ▫ Allows marketplace advantage ▫ Complies with DOT / EPA regulations [3]

• If the FedEx fleet (25,000 tractors) improved gas mileage by 1% it would generate $20 million USD savings per year

Figure 4. Crude Oil Price – 10 yr [4]

Motivation • Previously unfeasible due to human physiological limitations

▫ Driver reaction time ▫ Limited visibility ▫ 80,000 lb loaded, 400-500 ft stopping distance

• Cooperative Adaptive Cruise Control (CACC) removes barriers ▫ Offers longitudinal vehicle automation via throttle / braking

control ▫ Driver still controls lateral movement (steering)

• Under development by Auburn University ▫ Grant awarded as part of the Exploratory Advanced Research

Program by the Federal Highway Administration

CACC • Sensor and display package installed on existing tractor • Automatically monitors and adjusts distance between

vehicles via Dedicated Short Range Communication • System recognition and response time orders of

magnitude lower than human senses • Allows driver to observe metrics and roadway ahead of

lead vehicle

Figure 5. CACC Communication [5]

Existing Literature • S. Ahmed, “Some Salient Features Of The Time-

Averaged Ground Vehicle Wake” ▫ 1984 wind tunnel tests of simplified car body ▫ Well-known, common reference for validation of bluff body

analysis • Surprisingly limited research done on vehicle platooning

▫ Particularly limited computational work • Society of Automotive Engineers published the majority

of platooning work • Interesting problem but previously no practical

applications ▫ Manual platooning unsafe ▫ Illegal in many states, “tailgating”

Topics Covered • Introduction & Motivation • Meshing and Simulation Methodology • Simplified Car Body

▫ One body ▫ Two body

• Single Heavy Vehicle ▫ Baseline model ▫ Three vehicle geometry

• Multiple Heavy Vehicle ▫ Two vehicle ▫ Three vehicle ▫ Multiple geometry two vehicle

• Conclusions & Future Work

Aerodynamic Force Modeling • Two types of aerodynamic forces

▫ Normal force resulting from pressure on the surface ▫ Shear force from viscosity (skin friction)

• Determining force requires knowledge of velocity and pressure fields

• Navier-Stokes equations govern these variables ▫ Conservation of Mass

𝜕𝜕𝜌𝜌 𝜕𝜕𝜕𝜕

+ 𝛻𝛻 ⋅ 𝜌𝜌�⃑�𝑣 = 0 ▫ Conversation of Momentum

𝜕𝜕 𝜕𝜕𝜕𝜕

𝜌𝜌�⃑�𝑣 + 𝛻𝛻 ⋅ 𝜌𝜌�⃑�𝑣 �⃑�𝑣 = −𝛻𝛻𝑝𝑝 + 𝛻𝛻 ⋅ ̅𝜏𝜏 + 𝜌𝜌�⃑�𝑔 + �⃑�𝐹 • Low speed, incompressible flow negates requirement for use of

conservation of energy and equation of state • No closed form analytic solution • Discretize and numerically solve, known as Computational Fluid

Dynamics (CFD)

Simplified Car Body • First vehicle modeled, colloquially known as “Ahmed

body,” after 1984 wind tunnel test [10] • Designed to represent a simplified, generic bluff body • 0° rear slant used to more closely represent tractor-trailer • Used as validation case for one and two body simulations

Figure 6. Ahmed body reference dimensions [6]

Figure 7. Ahmed Body Isometric View

Figure 8. Ahmed body refinement zones

Meshing

• A continuous domain cannot be used ▫ Navier-Stokes equations are numerically solved ▫ Discretize volume around structures ▫ Treat each discretized cell as a control volume

• Unstructured gridding to better capture the complex nature of the tractor-trailer • Global parameters

▫ Parameters that apply to the entire domain, particularly relevant in the far field • Refinement zones

▫ Near body regions have large property gradients and must be properly resolved to maintain solution fidelity

• Inflation layer ▫ Quasi-Cartesian elements in near surface regions that are used to resolve boundary layers

Figure 6. Ahmed body inflation layer

Meshing Metrics • Number of elements

▫ Used to determine fineness or coarseness of mesh ▫ Only limited by hardware (RAM available)

• Skewness ▫ Measure of deviation from equiangular polyhedron ▫ High skewness causes interpolation error ▫ Average skewness

Represents overall element quality Desired average: 0.25

▫ Maximum skewness Represents worst element quality Elements with too large skewness unsolvable Maximum allowable: 0.90

Figure 9. Low skew vs. high skew element

Skewness Element Quality

0 Ideal

0.01 – 0.25 Excellent

0.26 – 0.50 Good

0.51 – 0.75 Fair

0.76 – 0.90 Poor

0.91 – 0.99 Bad (Sliver)

1 Degenerate

Table 1. Element Quality [7]

Flow Simulation • Unstructured CFD solver Fluent used for simulations

▫ Version 15.0, Produced by ANSYS Inc. • Pressure-based solver used (incompressible flow) • Second Order Upwind method preferred

▫ Faster convergence, more complex computation • Cell face pressure calculated using weighted average of cell center values • Pressure-Velocity solved using “coupled” algorithm

▫ Does not use predictor-correction scheme ▫ Allows a single matrix which can be solved through Algebraic Multigrid

• Relaxation factors ▫ Introduced to account for the fact that the non-linear Navier-Stokes are being

modeled linearly ▫ Directly effects rate of convergence / convergence ability ▫ Explicit – direct variable manipulation ▫ Implicit – introducing selective amounts of variables into equations ▫ A low relaxation parameter represents a tightly controlled variable / equation ▫ Complex bluff body geometry generates local high skewness regions, which

requires low explicit relaxation to achieve convergence

Boundary Conditions • Velocity inlet

▫ Very far away from bodies – considered “freestream”

▫ Incompressible flow allows only a velocity to be specified

▫ 30 m/s (67.1 mph) for most simulations • Pressure outlet

▫ Freestream assumption allows 0 gauge pressure

▫ Reference pressure is 1 atm • Solid Wall

▫ Solid surfaces in the domain ▫ No slip condition: flow cannot move

relation to wall ▫ No tangential velocity

• Symmetry Wall ▫ Can be used to represent far field parallel

boundary condition ▫ “Slip wall” ▫ No tangential velocity

Figure 10. Boundary conditions for two vehicle simulation

Turbulence Modeling • Turbulence is a phenomenon that

occurs every day on a variety of scales

• Difficult to model ▫ Irregular and chaotic in nature ▫ Highly nonlinear ▫ Adds several variables to the

Navier-Stokes equations • Two models considered herein:

▫ Realizable k-𝜀𝜀 ▫ Detached Eddy Simulation

Figure 11. Wingtip vortex turbulence [8]

Figure 12. Solar wind turbulence [9]

Realizable k-𝜀𝜀 (RKE) • Reynolds-averaged Navier-Stokes (RANS) based approach

▫ Assumes any variable can be decomposed into a fluctuation and an average • Two equation model: adds