Design of HVAC systems

Introduction to the design of HVAC systems through numerical simulation

 
Air quality is one of the main aspects to consider when creating closed environments and spaces. The need for comfortable and healthy environments is even more felt in recent times, due to Covid. In addition, the stricted standards imposed by the authorities in terms of energy saving and sustainability complicate the task of engineers and architects in the design and management phases of enclosed spaces.

For closed spaces we refer to the building sector (residential buildings and offices, large environments such as theaters, auditoriums, museums, cinemas, etc.) and to all environments that require controlled air conditioning: from the transport industry (airplane cabins, cars, train carriages, etc.) to industrial plants, from spaces with unbalanced thermal loads, such as data center rooms, to environments where air quality and microclimate must be subject to very specific regulations, such as in surgical rooms.

HVAC systems (Heating, Ventilation and Air Conditioning) play a fundamental role in ensuring the desired climate control. They are devices with HW and SW components with a high level of connectivity and digitalization, which exploit the movement of air between the internal and external areas to provide proper air conditioning of the environment.

The goal of HVAC systems is no longer just the achievement of certain levels of air quality. The focus is increasingly shifting towards maximizing their energy efficiency.

Numerical simulation is an appropriate tool to support the design of HVAC systems. Knowing the motion and thermal fields and the distributions of any pollutants is a precious added value in this sense: without requiring expensive experimental activities, they allow you to evaluate different design solutions directly from your PC.

What can Dofware do

1) FFD simulation

Computational fluid dynamics (CFD) is used in HVAC design for its accuracy in predicting flow velocity and thermal fields. Once the geometry, boundary and initial conditions have been specified, the designers are able to produce virtual representations containing all the relevant quantities for assessing indoor air quality and for testing solutions.

At the same time, CFD has a great disadvantage, namely the high computational burden. In some cases it even takes several days to arrive at the result of a single simulation. And, in addition, often the necessary simulations are very numerous, affecting their applicability.

A more rapid alternative is represented by lumped parameter models. However, their speed of use is contrasted by an accuracy that is not always up to the need.

To obtain a quick and effective solution, Dofware offers Fast Fluid Dynamics (FFD) technology, which is an often appropriate compromise between the agility of lumped-parameter models and the accuracy of CFD.

Born in the computer-graphics field, Fast Fluid Dynamics (FFD) was developed to virtually reproduce the behavior of motion and thermal fields. Today this method is able in real time to simulate the evolution of comfort and air quality inside closed environments, thus becoming an advantageous alternative to CFD, thanks also to a negligible loss of accuracy.

Once the layout of the environment has been defined, through the FFD it is possible to verify the correct positioning and functioning of the HVAC systems, carry out the study of thermal phenomena, calculate the temperature trend and the polluting diffusion, all taking into account the environmental conditions. outside the domain of interest.

Figure 1: Results of an FFD simulation represented in the real environment through Augmented Reality techniques.

The code implemented by Dofware can be integrated into embedded or custom applications and the R&D team can support the customer in defining the context, creating the model and analyzing the simulation results.

2) The combined approach: physical data and simulated data

Figure 2: Example of co-simulation in which the numerical model is put in direct communication with the data collected from the field to increase the accuracy of the results.

In the IoT era, sensors are the direct way to monitor physical fields within closed environments. However, to obtain a detailed representation of the overall environmental conditions it is necessary to increase the number of devices, resulting in high infrastructure costs.

Alternatively, Dofware proposes a hybrid approach: the sensors are placed in collaboration with the numerical models for monitoring the internal environment in time and space. On the one hand, numerical models provide complete and continuous information on the entire domain. On the other hand, the sensor field data allow to reduce the gaps due both to the intrinsic precision limits in the numerical approach and to the inability to manage unexpected actions, such as opening / closing windows.

Dofware aims to define a co-simulation architecture in which numerical models exploit sensor data as input to be supplied to the numerical model at fixed time intervals. Between two consecutive samples, the simulation runs autonomously. When field data is provided, the model inputs are updated and the simulation restarts from the last calculated field. The numerical model, based on the coupling of lumped parameter units (developed in Modelica) with the detailed 3D treatment provided by the FFD, must for its part be faster than real time, having to keep pace with field data.

3) Visualization of results with Augmented Reality

Figure 3: Example of a heat map obtained with the FFD and represented using Augmented Reality techniques.

Dofware is able to create software tools for sharing the results of numerical simulations in Augmented Reality (AR). This technology allows the visualization of 3D fluid dynamics results on real images of the environment in question.

This system is very effective in sharing results within heterogeneous work teams, with actors with different skills. By superimposing virtual results on real images, AR in fact allows even a non-expert technician to understand and analyze the fluid dynamic results.