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As cooling efficiency is emerging as a key constraint in scaling AI data center infrastructure, Michael Fuller, Executive Chairman of Conflux Technology and former F1 engineer, explores how Formula One’s system-level approach to thermal management could help data centers balance cooling performance with energy efficiency and operational demands.
As AI infrastructure scales, most conversations around data center performance still focus on compute capacity: faster GPUs, denser racks and increasing power availability. However, a critical constraint is quietly emerging alongside these advances – cooling efficiency.
While the industry has largely acknowledged that liquid cooling is necessary to support next-generation hardware, less attention has been paid to how efficiently those systems operate at scale. That distinction – between simply cooling hardware and engineering an efficient thermal system – is one Formula One has been refining for decades.
Perhaps Formula One’s greatest lesson isn’t simply how to engineer an efficient cooling system, but how to engineer one that can evolve. Every race weekend brings new components, revised aerodynamic packages and evolving thermal demands, leaving engineers with little time to redesign, test and validate solutions. That relentless cycle has fostered an engineering mindset built around rapid iteration, where cooling systems are expected to evolve alongside the hardware they support. AI data centers are now entering a similar phase. Successive generations of GPUs are arriving faster than ever, each with different thermal profiles, power densities and packaging constraints, meaning cooling solutions can no longer remain static while the technology around them advances.
That evolution also changes the engineering challenge. It is no longer enough for cooling systems to remove the required amount of heat. They must do so with maximum efficiency.
AI workloads are pushing rack power densities to unprecedented levels, and simply meeting thermal requirements does not guarantee an optimal system. Instead, operators must consider how much energy is required to deliver that cooling capacity.
This shifts the focus from peak performance to system efficiency, a change that has significant implications for both operational cost and infrastructure design.
One of the most important, and often overlooked, aspects of cooling performance is pressure drop.
In liquid cooling systems, pressure drop has a significant influence on the energy required to move coolant through the system. While small, perhaps even negligible, at the component level, its impact becomes significant when multiplied across thousands of servers.
Inefficient flow paths can increase pump power requirements, raise energy consumption and reduce overall system flexibility. In extreme cases, they can become a limiting factor in scaling infrastructure.
Components such as cold plates are often assessed in isolation, focusing on metrics like heat transfer performance and flow rate. However, as systems become more complex, this approach can be misleading. Formula One engineers have long understood that a component delivering impressive standalone performance can still compromise the performance of the car as a whole.
A component that delivers strong thermal performance but induces high hydraulic resistance may degrade overall system efficiency. The net result is higher energy use, even if individual components appear optimised. To address this, cooling must be treated as a system-level engineering challenge, where thermal and hydraulic performance are considered together rather than separately.
Effective liquid cooling requires managing a fundamental trade-off between improving heat transfer and maintaining low resistance to flow. Design approaches that increase turbulence or surface area can enhance thermal performance, but they often do so at the cost of higher pressure drop. The challenge lies in achieving both low thermal resistance and low hydraulic resistance at the same time.
This balance demands precision engineering at both macro and micro levels. The most effective solutions focus on directing coolant exactly where it is needed, rather than applying uniform cooling across an entire component.
At Conflux, for example, our latest single-phase cold plate designs achieve thermal resistance as low as 0.0076°C/W at 6 LPM across a 2,500 mm² footprint, while maintaining exceptionally low pressure drop – demonstrating that high thermal performance and hydraulic efficiency can be achieved together, rather than traded off against one another. It’s the same engineering philosophy that underpins Formula One: the best solution isn’t the one that excels in a single metric, but the one that delivers the greatest overall system performance.
One of Formula One’s defining characteristics is that engineers do not evaluate components in isolation. Every design decision is based on its impact on overall system performance. A component that performs exceptionally well in isolation can still reduce the overall performance of the car if it compromises weight, reliability or aerodynamic efficiency.
This principle is directly relevant to data centers, where cooling systems must balance competing objectives under tight constraints. This is why cooling design is increasingly moving beyond component optimisation towards system-level engineering, where every design decision is judged by its impact on overall performance.
Delivering that level of optimisation demands equally sophisticated design tools and manufacturing techniques.
Achieving high levels of efficiency in liquid cooling begins with how fluid flow is controlled within components.
Modern engineering approaches use simulation tools such as computational fluid dynamics to analyse and refine internal geometries. These tools make it possible to optimise coolant distribution, minimise losses and improve heat transfer where it matters most.
It’s an approach familiar to Formula One, where advanced simulation and rapid manufacturing allow engineers to continually refine components in pursuit of marginal gains.
In liquid cooling, those same principles are enabling a new generation of highly optimised cold plate designs.
Additive manufacturing makes this possible by enabling complex internal channels and geometries that would be difficult or impossible to produce using conventional manufacturing methods. This gives engineers far greater control over coolant flow, allowing thermal performance and pressure drop to be optimised together rather than traded against one another.
It also enables cold plates to be produced as single, monolithic components rather than assemblies of multiple parts. By eliminating bonded interfaces, potential leak paths are eliminated while improving structural integrity – a significant advantage in mission-critical data center environments, where reliability is just as important as performance.
Formula One has always been a sport of marginal gains, where countless small engineering improvements combine to create a meaningful competitive advantage. AI data centers are entering a similar era.
As processor power continues to increase, the question is no longer simply whether cooling systems can remove enough heat. It is whether they can do so with maximum efficiency, balancing thermal performance, pressure drop, energy consumption and reliability across the entire system.
As a result, the next phase of data center innovation may be defined not only by faster processors, but by smarter and more efficient ways of keeping them cool.
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Michael Fuller’s career was launched off the back of his boyhood dream of designing racing cars. In 1999 he migrated to Europe as a Design Engineer, and over 15 years he held various design engineering and management roles in motorsport organisations competing in series as varied as the World Endurance Championship, World Rally Championship, and the Formula 1 World Championship. As an early adapter of Additive Manufacturing (AM) in the motorsport industry he was acutely aware of the opportunities available to enhance performance.
After returning to Australia, he pursued a design idea on an AM heat exchanger that proved to have ground-breaking performance potential and launched Conflux soon thereafter. Conflux has now grown into a world leading organisation that combines specialisations in heat transfer and additive manufacturing.
He is actively involved in all aspects of the business with significant effort in design engineering, relationship building, partnership research projects and industry speaking engagements. A considerable amount of his time is invested in strategy development and ensuring the right resources are in place to fulfil our vision.
The post Why AI Data Centers Should Think More Like Formula One Teams appeared first on Data Center POST.
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