Introduction to the Design of HVAC Systems through Numerical Simulation

The Issue

The 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 strict standards imposed by the authorities in terms of energy saving and sustainability complicate the work of engineers and architects. 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 (data center rooms, etc.) to environments where air quality and microclimate must be subject to strict regulations (surgical rooms, etc.).

The Approach

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. They exploit the movement of air between internal and external areas to provide proper air conditioning. The goal of HVAC systems is no longer just the achievement of certain levels of air quality. The focus is increasingly expanding towards maximizing their energy efficiency.

Numerical simulation is an appropriate tool to support the design of HVAC systems. Knowing the velocity and thermal fields and the distributions of pollutants are precious added values in this sense. Without requiring expensive experimental activities, they allow to evaluate different design solutions directly from your PC.

In the following, a list of some key competencies DOFWARE can make available to Customers:

FFD Fluid Dynamic Simulation: Why

Computational Fluid Dynamics (CFD) is used in HVAC design for its accuracy in predicting flow velocity and thermal fields. Once the geometry, the boundary and the 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 different 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 its 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 needs.

FFD Fluid Dynamic Simulation: How

To obtain a quick and effective solution, DOFWARE offers Fast Fluid Dynamics (FFD) technology. It 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 recently devoted to virtually reproduce the behavior of velocity and thermal fields. Today, this method is able to simulate in real-time the evolution of comfort and air quality inside closed environments. It is becoming an advantageous alternative to CFD, thanks also to a negligible loss of accuracy.

Once the layout of the environment has been defined, by means of the FFD it is possible to achieve several tasks. It allows to verify the correct positioning and functioning of the HVAC systems, to carry out the study of thermal phenomena, to calculate the temperature trend and the pollutant diffusion, all taking into account the environmental conditions outside the domain of interest. The software implemented by DOFWARE can be integrated into embedded or custom applications. The R&D team can support the Customer in defining the context, in creating the virtual model, and in analyzing the simulation results.

Results of an FFD simulation represented in the virtual environment.

The Combined Approach: Physical Data + Simulated Data

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 made collaborate 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 numerical model gaps due both to the intrinsic precision limits and to the inability to manage unexpected actions (window opening/closing, etc.).

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

Example of co-simulation where the numerical model (FFD + Modelica) receives the field data sampled by sensors (input blocks on the left) for reproducing the domain temperature field (output on the right).

Visualization of Results with Augmented Reality

DOFWARE is able to create software tools for sharing the results of numerical simulations by means of the Augmented Reality (AR). This technology allows to overlap 3D fluid dynamic results to real images of the target environment. This system is very effective in sharing results within heterogeneous work teams, with actors having different skills. By superimposing virtual results on real images, AR in fact allows even a technician non-expert in numerical simulations to understand and to analyze the fluid dynamic results.

Temperature map where the FFD results are represented on a cut-plane within the analyzed room by AR.
Velocity pathlines where the FFD results are represented within the analyzed room volume by AR.