The heat exchange
Algorithmic Engineering of heat exchangers
Published 15 November 2021

Project Breakdown
Industry: Advanced Manufacturing
Product: Heat Exchanger
Partner: TRUMPF

The Vision

Heat exchangers are at the heart of modern heating and cooling, which accounts for 40% (13.2 Gt) of energy-related CO2 emissions. As global temperatures rise, due to a vicious cycle of higher carbon emissions and an increasing demand for air conditioning, we are working against the clock to heat and cool our world more efficiently.

Nature offers us valuable insights into how heat exchange governs the way some natural structures are grown. For example, the circulatory and respiratory systems of some animals have counter current configurations to maintain optimum body temperatures and fluid levels in extreme environments.

Manmade heat exchanging structures, on the other hand, pale in comparison. The simple designs of shell-and-tube or plate heat exchangers that are used in everyday items — from our smartphones to cars and fridges — no longer make the cut for our future needs.

With Additive Manufacturing, the complexities of objects and machines can now be independent of manufacturing costs, allowing for more design freedom for us to create structures that transfer thermal energy more efficiently. That is why we are seeing more efficient designs that are more complex, lower in weight, smaller in size, and more reliable as an indirect result of being printed in one piece.

Here comes the challenge. Imagine that an engineer has just spent some time designing a novel heat exchanger to optimize the evaporator and condenser within an air-conditioning unit. Can the same design be adapted for the radiators used in cars? Maybe, but that may require going back to the drawing board. At this instance, the engineer has suddenly become the bottleneck. That is why, going forward, the design paradigm with which we create objects will become more important than the designs of the objects themselves.

This presented an opportunity for Hyperganic and our partner TRUMPF, the German industrial machine manufacturing giant, to radically accelerate the innovation of heat exchangers and their design process — we envision a future where heat, especially heat produced in advanced manufacturing processes, is managed effectively with a new design paradigm that is as versatile, functional and sustainable as nature.

Achieving the Vision with Algorithmic Engineering

While many complex, additively manufactured heat exchangers have emerged in recent years, a scalable solution made for their flexible design and serial production has not been accomplished – until now.

Together with TRUMPF, Hyperganic developed an automated workflow that algorithmically designs heat exchangers based on physical principles found in nature. These heat exchangers are meant to be placed within industrial 3D printers to cool the shielding gas that is used to overcome atmospheric impurities in the printing process, where excess heat may affect the quality of the prints.

The process begins with framing the design goals with mathematical and physical models on Hyperganic Core — Hyperganic’s software platform for Algorithmic Engineering. Such goals include maximized thermal efficiency, minimized pressure drop of hot fluid, a constant flow rate and a minimized length.

The beauty of designing with such a software paradigm is that the engineer can now focus on achieving the goals through higher-level thinking that applies to all relevant heat exchanger designs, and not get lost in the time-consuming process of firming up details of one particular design.

The above design goals are then framed within the environment that our specific heat exchanger will operate in, which also dictates its outer appearance. In our case, we defined the boundary conditions of a target volume, an input diameter that had to be 50mm, argon as the shielding gas, and water as the cooling medium.

For the “growth” strategy of the pipes in the heat exchangers, we selected fractal branching due to its simplicity — a change in performance, like pressure drop, can easily be explained by a corresponding change in a parameter in the Algorithmic Engineering model. For instance, if the pressure drop is too high, this directly translates into actionable design directions such as having fewer branches, bigger branches, or a larger total cross section of the heat exchanger. To get the highest number of equally spaced points within a circle, we took inspiration from the Fibonacci sequence that is found in the recurrent structures and forms of plants, flowers or fruits. In our case, the pipes are spreaded out very similarly to the way sunflowers arrange seeds at their cores.

The point distribution is then clustered and bundled into the main parent branch, and this is repeated as a staged process until an inlet with only one point is reached. This data structure was then used to route spline curves across the stages. After giving the spline curves a thickness and hollowing them, the branching structure comes to life, and we have an algorithmically designed heat exchanger.

To tap into Algorithmic Engineering’s strength of rapid iteration, key design elements such as the number of stages of branching and the diameter of pipes are turned into parameters so that multiple designs can then be generated at once. The best-performing heat exchanger, within a given environment, can then be created efficiently.

Our algorithmically designed heat exchanger has a surface area that is 14 times higher than a cylindrical tube of the same dimensions. The pressure drop goals were reached after just a couple of design iterations and the overall heat exchange properties exceeded initial targets.

This heat exchanger is just the first of a whole suite of objects and machines that can now be algorithmically engineered. For example, should we need another structure that requires heat exchanging properties, algorithms used to create the current heat exchanger can easily be re-used.

As more engineers get onboard with this new paradigm for design, more building blocks of algorithms will recombine and morph into objects that are ever more complex, sustainable and functional. This dramatic acceleration in innovation of physical objects will move us a step closer towards a more sustainable world.

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