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The Shape of Speed: How AI is Forging the Future of Automotive Design

The Shape of Speed: How AI is Forging the Future of Automotive Design - The Evolution of Car Aerodynamics

The quest for speed has driven automotive aerodynamic innovation since the early days of the automobile. As engineers pushed cars to go faster, they realized the vehicle body itself created substantial drag forces impeding velocity. This birthed the field of automotive aerodynamics, which focuses on optimizing airflow to reduce drag and improve stability.

In the 1920s and 30s, automakers made their first forays into aerodynamic body styling with teardrop-shaped cars like the Tatra T77 and Chrysler Airflow. These models demonstrated a dawning awareness of streamlining, though some of their curved forms polarised buyers. By the 1950s, aerodynamics research enabled real leaps forward with concept cars like GM's Firebird and Ford's Seattle-ite XXI. The aviation and space industries also drove progress in wind tunnel testing and computational fluid dynamics.

The 1970s energy crisis made fuel efficiency paramount, compelling automakers to refine aerodynamics for reduced drag. Wedge-like profiles emerged and integrated spoilers became commonplace. Formula One engineers, seeking maximum downforce within strict rules, mastered the dark art of managing airflow with wings, splitters and diffusers. Their innovations filtered down to production supercars.

Today, digital simulations coupled with wind tunnel validation enable astounding aerodynamic achievements. The Bugatti Chiron's 0.38 drag coefficient matches many economy sedans. Active aero elements like retractable spoilers further expand optimization. The emergent field of computational fluid dynamics-based generative design takes things even further. AI systems can iterate countless subtle tweaks to create shapes that manipulate airflow in ways once unimaginable.

The Shape of Speed: How AI is Forging the Future of Automotive Design - AI-Optimized Body Styling

Aerodynamic optimization used to be a game of incremental tweaks and intuition. Designers sculpted clay models by hand, studied wind tunnel smoke flows, and relied on their innate senses for what looked “slippery.” Modern AI-powered generative design upends this tradition by enabling rapid iteration of radical new forms.

Algorithmic topology optimization, a subset of generative design, automatically sculpts 3D geometry for maximum airflow efficiency. The AI considers an immense number of permutations, far beyond human capabilities, and converges on alien yet functional designs. Applied to automotive styling, it creates shapes maximizing downforce or minimizing drag based on set parameters.

Aston Martin notably employed generative design to style their Valkyrie hypercar. Their process paired AI-based optimization with designer oversight to balance aesthetics and aerodynamics. The result is a spaceship-like form both otherworldly yet undeniably eye-catching. Jaguar Land Rover also tapped topology optimization to design parts like side mirrors and brake cooling guides. Their “virtual wind tunnel” reduced development time by over a year.

BMW took things further by creating a fully AI-designed concept car exterior. Their goal was an emotional yet aerodynamic shape respecting BMW styling tenets. Designers defined key parameters and constraints before letting the algorithms roam free. While intriguing, the result was somewhat generic, indicating the value of human-AI collaboration.

As autonomy and electrification advance, AI-enabled styling may move beyond performance to also consider functional needs. Door and wheel placement can optimize interior space. Creative lighting integrations can enhance pedestrian awareness and communication. By transcending style solely for style’s sake, AI could birth a new era of mobility-focused design.

The Shape of Speed: How AI is Forging the Future of Automotive Design - Digital Wind Tunnels Simulate Real-World Conditions

Physical wind tunnels have long provided invaluable aerodynamic insights, enabling iterative testing with sensor instrumentation and smoke visualization. However, constructing and operating such facilities demands substantial investments of time, money and space. Wind tunnels also introduce scaling challenges when translating reduced-scale model behaviors to full-size vehicles.

Modern computational fluid dynamics (CFD) alleviates these constraints through simulated digital wind tunnels. Leveraging massive compute power, CFD software solves the complex Navier-Stokes equations governing fluid flows. Engineers specify digital 3D models of the test vehicle along with simulated wind speed, turbulence variables and other real-world conditions. The program then essentially emulates the physics of airflow over, under and around the form.

BMW enthusiastically adopted virtual wind tunnels in developing their AI-designed concept car. They credit the technology with accelerating the design process and providing new levels of aerodynamic refinement. Tesla also employed CFD simulations to optimize airflow for their Cybertruck, targeting a remarkably low 0.30 drag coefficient despite the vehicle’s blocky profile.

For their wedge-shaped I.D. R electric racer, Volkswagen paired CFD modeling with real-world track testing. They cycled between optimizing the digital model, 3D printing scale model parts for wind tunnel validation, and refining the full-size vehicle based on driver feedback. This integrated workflow enabled record-shattering success, including climbing Pikes Peak faster than any combustion car in history.

CFD delivers granular localized airflow data impossible to gather through physical testing alone. Computational models reveal vortex swirls, pressure distributions and other insights that can inspire targeted design tweaks. Some automakers even simulate interior airflow to optimize HVAC systems and maintain laminar cockpit airflows for enhanced comfort.

The Shape of Speed: How AI is Forging the Future of Automotive Design - Generative Design - Iterating Beyond Human Limitations

Generative design represents a paradigm shift in product development, transcending human cognitive limits through automated iteration. While designers once manually sculpted shapes, this emerging technology enables exploration of thousands of design permutations in a fraction of the time. For complex engineering challenges like aerodynamic optimization, generative algorithms achieve results simply unattainable through traditional methods.

Automaker Xpeng Motors utilized generative design to create an industry-leading drag coefficient of 0.27 for their P7 sports sedan. Their process evaluated about 300 iterations balancing aesthetics and aerodynamics. A designer likely explores 10-20 options in the same timeframe, severely limiting the scope for improvement. Xpeng’s AI-enabled workflow resulted in creative solutions like an embedded rear spoiler and vapor chamber for brake cooling.

A key advantage of generative systems is their ability to harness massive computing power through parallel processing. Multiple design variants can be simulated simultaneously to rapidly gather comparative performance data. Topology optimization algorithms leverage this capability by spawning a wide array of permutations, then culling poorer performers through each loop. Through this survival of the fittest, forms evolve matching or exceeding the capabilities of human designers.

Generative design also enables engineers to incorporate real-world physics simulations into the looping process. Computational fluid dynamics software can evaluate aerodynamic drag and cooling efficacy for each generated shape option. Structural analysis assesses feasibility and durability. By incorporating realistic performance metrics, generative systems produce designs optimized for their intended operating conditions, not just conceptual appeal.

The Shape of Speed: How AI is Forging the Future of Automotive Design - From Clay Models to Dynamic 3D Renderings

For over 80 years, automotive designers sculpted vehicle forms out of clay, assessing reflections and highlights by hand to perfect curves and creases. This analog process restricted designers to creating a small number of labor-intensive models. Modern digital tools now facilitate a generative, iterative approach unbound from physical constraints.

Dynamic 3D rendering engines eliminate the need for clay scale models, enabling designers to visualize proposed shapes and refine designs in real-time. Ray tracing accurately simulates how light interacts with complex surfaces, replicating the way fluctuations in reflection intensity and hue play across clay as it gets sculpted. Designers gain immediate visual feedback without spending months shaping each model variant.

The digital model itself becomes dynamic clay, mutable with a few clicks. Adjusting control points modifies the underlying geometry, updating the rendered visualization accordingly. Designers can fluidly sharpen or soften a character line, alter the rake of a windshield, or experiment with more dramatic modifications. Photorealistic renders with multiple lighting configurations and backdrops aid in evaluating design direction.

Augmented reality adds another dimension by projecting digital models into the physical environment. Designers can walk around a life-size holographic concept car right in their studio, gaining a feel for shapes and proportions impossible with a small clay model. They can even experience the interior space while making tweaks in real-time. AR systems like Microsoft HoloLens further enable collaborative design, with multiple participants viewing and interacting with the same holographic prototype.

Generative design exponentially expands the possibilities by algorithmically generating novel form options. Designers define parameters then launch autonomous iteration loops, evaluating hundreds of designs through systematic digital optimization. Such exponential ideation freedom stands utterly unachievable through traditional clay modeling.

The Shape of Speed: How AI is Forging the Future of Automotive Design - Interior Ergonomics Get a Digital Makeover

Ergonomics focuses on designing interfaces optimized for human use. In car interiors, this entails strategically positioning controls and optimizing seat shapes for comfort, visibility and access. Digital tools now facilitate ergonomic design refinements impossible through traditional methods.

Clay models provide limited ergonomic feedback since the static interior lacks real components. Digital prototyping enables designers to virtually experience the cabin space. They can adjust their seat position and reach for controls, gaining spatial awareness difficult to assess through static drawings. Human factors experts can also import digital mannequins representing a wide span of body sizes. Simulating different occupants reaching across their body for seat belt buckles reveals potential comfort issues early in design.

Digital ergonomic validation goes far beyond what static mannequins provide. Researchers at Ford utilized virtual reality headsets to analyze interior packaging. Test subjects donned VR gear to immerse themselves in digital cockpits. As they mimed actions like tuning the radio, the VR system tracked movements down to the hand joints. This enabled engineers to assess where controls fell outside ideal comfort and visibility envelopes for different populations. Virtual ergonomic studies require far less time and money compared to mock-up testing.

Holographic evaluations take digital ergonomics another leap forward. Companies like Stellantis use the Microsoft HoloLens to project full-size car interior holograms. Engineers step into the AR space and interact with virtual seats and controls overlaid into their real workspace. They can assess sightlines, feel for awkward movements in real-time, and get feedback from colleagues also viewing the hologram. This method provides an experiential richness static models and even VR cannot match.

Generative design opens radical new possibilities for ergonomic optimization. Engineers can input parameters like required control proximity zones and desirable fields of vision. Algorithms then iterate countless design arrangements assessing ergonomic factors like posture and ease of movement. While designers may conceive of a few new layouts, AI can generate hundreds or thousands of options. The most promising combinations that balance aesthetics, space efficiency and ergonomics filter to the top.

The Shape of Speed: How AI is Forging the Future of Automotive Design - Self-Learning Algorithms Refine Designs Over Time

The rapid iteration enabled by generative design is only the beginning. Self-learning algorithms build on this capability by continuously optimizing designs based on new data. Just as humans refine our ideas and skills through experience, neural networks improve their proficiency by ingesting ever-growing data sets.

For automotive design, this implies AIs can autonomously refine vehicle shapes and components as more aerodynamic simulation results, wind tunnel tests, and even real-world vehicle performance data pours in. Hyundai put this into practice when developing their Sonata Hybrid. They fed computational fluid dynamics results into a neural network and let it churn through design variations, reaching drag coefficients surpassing their own engineers. The system accounted for sensitivities a human designer may overlook, like the exact curve of a tail light cluster's impact on trailing vortices.

AI's data immersion also enables identifying subtle optimizations and flaws human evaluations could miss. A Windcatch AI system revealed that small tweaks to hatchback rear light positioning could reduce drag 4%, findings validated in tunnel tests. The platform models aerodynamics with such precision that it predicted how driving dynamics alone alter a shape's drag performance.

As electric vehicles become prevalent, self-learning algorithms can factor in new data like battery drainage patterns under different real-world conditions. An unusual 1-2% range loss on a short trip could prompt an AI to analyze operational factors like drag, temperature, route elevation, and SoC variances. It might then suggest design tweaks enhancing aerodynamic efficiency in crosswinds that human analysts overlooked.

Vehicle dynamics and industrial design teams at BMW have already explored using sensor data from test drives to improve designs. Their systems identify relationships between operating conditions and factors like stability and noise. Adjustments to aerodynamic elements, body stiffness, and drivetrain mounting then optimize real-world performance. Add self-learning algorithms to the mix and vehicles evolve generationally like smartphones.

The Shape of Speed: How AI is Forging the Future of Automotive Design - AI and the Democratization of Car Customization

For over a century, automotive design has been the providence of large manufacturers and elite coachbuilders. Personalization was limited to choosing between pre-determined trim levels and option packages. But emerging generative AI technologies promise to democratize automobile customization like never before. Soon, anyone may be able to digitally craft their dream vehicle or commission bespoke creations tailored to their unique tastes.

Automakers have long offered configurators to let buyers customize colors and add options within defined constraints. But exploring more radical modifications required hiring a coachbuilder at tremendous expense. AI generative systems like Autodesk DreamRunner lower these barriers by synthesizing photorealistic renderings matching simple text descriptions. DreamRunner recently produced hundreds of unique pickup truck variants for Xite Labs based on adjectives like “rugged” and “aerodynamic.”

For just $20, DreamRunner can generate dozens of realistic images of your outlandish dream car. Specify a lifted teal DeLorean with massive tires and dual rear wings, and AI can visualize the concept. While these are just static images, the ease of iteration empowers new realms of experimentation for enterprising designers and builders.

Startup companies also aim to open AI design freedom to the masses. IAMAUTO lets users sketch a shape or select a base model like the Toyota Supra, then customize elements like fenders, wings, and lights. Their algorithms generate Photoshop-quality renderings of your unique creation. For those lacking inspiration, IAMAUTO even offers an AI-driven “creative assistant” to collaboratively brainstorm designs.

While independent customization presents new opportunities, most modify their daily drivers rather than build concept cars. Here too AI aids realization. For example, Air Design America feeds client images of their existing vehicle into their CarMD neural network. Clients specify modification goals like improved stability or a track-inspired look. CarMD then generates Photoshop renderings of modifications including optimal spoiler and splitter geometries.

Of course, physical realization remains challenging for radical modifications. But again, technology aids democratization. 3D printing enables fabricating highly customized parts based on AI-optimized CAD files. Automotive Unity aims to provide a comprehensive customization platform combining generative design, part simulation, and 3D printing coordination. Their tools envision custom body kits and interior components feasible for small suppliers and skilled individuals alike.