An optimum HVAC system provides occupant thermal comfort and good indoor air
quality throughout the year, irrespective of the outside weather conditions. Besides
achieving these performance criteria, HVAC systems should also be energy efficient. If
these performance criteria aren't met, the indoor environment can impede
productivity, cognition, and the general health and well-being of occupants.
Reason why the problem is occuring
In a typical HVAC design process, an engineer uses Excel sheets or 1D tools to
calculate heat loads, airflow volumes, and ventilation requirements. While this
approach can help to size HVAC equipment capacities and parameters, it can’t assess
the airflow patterns, which is critical to accurately model occupant thermal comfort
and indoor air quality.
The challenge with this design method is that HVAC systems can only be evaluated for
comfort once the design is implemented. If the system must be redesigned and
implemented again to correct a thermal comfort or indoor air quality problem, it
results in extra costs, project delays, and bad client experiences.
The best solution is to analyze the systems better at the design stage, which is possible
through Computational Fluid Dynamics (CFD) simulations.
CFD as a Diagnostic Tool
CFD comes to the rescue in this situation, allowing precise forecasting of the
performance of HVAC systems by modeling the physical phenomena, ambient
conditions, and the detailed geometry of the space.
This helps designers make decisions about design optimization without needing to
physically build and install the system.
In the HVAC industry, CFD has the following principal applications -
1. For better Indoor Air Quality
Understanding Mean Age of Air
2. For optimum Thermal Comfort
Understanding temperature distribution
Understanding relative humidity
Understanding local effects arising due to air drafts
CFD empowers an engineer to visualize different parameters virtually at every point in
space. After post-processing, engineers can visualize data differently using Surface
Plots, Contour Plots, Iso-Volume Plots, Iso-Surface Plots, and Flowlines to diagnose the
We will examine examples of how CFD can be used as a diagnostic tool.
For Indoor Air Quality
Indoor Air Quality (IAQ) refers to the air quality within and around buildings and
structures, especially concerning the health and comfort of building occupants. If
too little outdoor air enters indoors, pollutants can accumulate to levels that can
pose health and comfort problems making HVAC systems critical in ensuring
indoor air quality isn't unhealthy.
For CO2 Concentration
Indoor carbon dioxide concentrations are driven by a combination of outdoor CO2,
indoor breathing, and the ventilation rate of the building. To make ventilation systems
more energy efficient, conditioned air is recirculated rather than being exhausted to
the environment. As per ASHRAE 62.2, CO2 concentrations should be below 1000
ppm in a space. Concentrations over 1000 ppm can lead to occupants feeling
drowsy and losing attention, and increased heart rate.
Iso-Volume Plot of CO2
Mean Age of Air
The mean age of air refers to the average time spent by air in a building zone—the
Greater the time, the more significant the chances of contaminants accumulating in a
space. The mean age of air can help gauge the ventilation effectiveness of a system.
The lesser the average time spent by air, the higher the indoor air quality.
In the below-shown contour plot, the orange-colored sections show regions in the
space where circulated air is getting accumulated for more time. This will decrease
the IAQ in this region for the occupants. A remedy for this issue can be adding a
supply diffuser to lower the mean age of air, improving IAQ.
Contour Plot of Mean Age of Air
Measuring Occupant Thermal Comfort-
Designing the HVAC system to get optimized thermal comfort is an iterative
process.‘Comfort’ is a state of mind or a personal feeling - not a quantifiable metric.
Thanks to Prof. Fanger for providing us with the parameters to evaluate occupant
comfort. The various parameters that can determine thermal comfort are Predicted
Mean Vote (PMV), Predicted Percentage Dissatisfaction (PPD), Draft Rating Index
(DR), and Effective Draft Temperature (EDT). With AHC 2023, one can design for
Thermal Comfort by considering all these parameters.
Role of PPD and PMV in assessing Thermal Comfort
Predicted mean vote (PMV) is an index that predicts the mean value of a large group
of persons' thermal sensation votes (self-reported perceptions) on a sensation scale
expressed from –3 to +3. PMV values for space should lie between +0.5 to -0.5 to
ensure occupant thermal comfort, according to ASHRAE 55.
Predicted percentage of dissatisfied (PPD) is an index that establishes a quantitative
prediction of the percentage of thermally dissatisfied people determined by PMV.
Comfort criteria are achieved when PPD values are below 10 %. PPD levels below
10% don't ensure that all the occupants in the space are comfortable. 10% of the
occupants will still feel uncomfortable.
Predicted Mean Vote (PMV) thermal comfort criteria
Predicted Percentage Dissatisfaction vs Predicted Mean Vote
PMV Contour Plot
PPD Comfort Cloud
Role of Draft Rating and Effective Draft Temperature in assessing
Draft Rating and Effective Draft Temperature assess local effects from high-velocity
drafts of extremely cold or hot air.
A draft is an unwanted local cooling of the body caused by air movement. Draft
rating is an index that establishes a quantitative prediction of the percentage of
occupants dissatisfied due to the draft. Comfort Criteria are achieved when the
percentage of dissatisfied people due to draft is below 20%, as mentioned in the
Effective draft temperature is a calculated temperature difference that combines the
effect of air temperature and air motion on the human body. Comfort Criteria are
achieved when the effective draft temperature (EDT) is between -3 °F to +2 °F, and
the air velocity is less than or equal to 70 fpm.
Below are the horizontal slices at occupant neck level (seated) for effective draft
temperature. Simulations are done for 2 configurations of sidewall supply diffuser
locations. In configuration 1, the diffusers are located at a height, while in
configuration 2, diffusers are located near the floor. The results of flowlines for supply
air help visualize the airflow and air diffusion parameters like throw, spread, and
drop. One can also visualize if the path of supply air is obstructed due to furniture, as
in images 1 and 3.
Draft Rating Contour Plot
In configuration 1, the draft ratings of the space were higher than 20% making it
uncomfortable for occupants. This has been resolved by changing the location of
diffusers which can be viewed in configuration 2. The results of draft ratings are below
20%, ensuring minimum occupant discomfort due to air drafts which can also be
confirmed using EDT contour plots.
EDT Contour Plot
Role of Relative Humidity in assessing Thermal Comfort
In places with too dry or too humid air, humidity control is critical in ensuring occupant
thermal comfort. In AHC, one can design an HVAC system for humidity control using
Relative Humidity contour plots as described in the images below. It’s recommended to
have humidity levels between 40% and 60% to achieve thermal comfort. The contour
plot below shows the relative humidity levels in the space and lies between a range of
40% to 60% ensuring maximum comfort.
Relative Humidity Contour Plot
Temperature Distribution in space
Temperature plots from CFD simulation can be viewed to visualize temperature
distribution across space. One can use them to view hot and cold spots in space.
Temperature Contour Plot
Mean Radiant Temperature
The mean radiant temperature (MRT) expresses the influence of surface temperatures
on occupant comfort. Mean radiant is a dominant element in the thermal comfort
equation which is an integral part of indoor environmental quality and building
performance. Heat gain in the form of radiation from all the surfaces can be calculated
using MRT contour plots. MRT plots can ensure that space occupants will be
comfortable in different sections of the space. The below image shows MRT results
near exposed surfaces like walls and windows to understand the effect of solar
Mean Radiant Temperature Contour Plot
Traditional CFD Simulation workflow
Now that we have established the importance of CFD in designing optimum HVAC
systems, let's understand the workflow for performing a CFD simulation in any
commercially available software.
1. Modeling the building geometry
2. Model flow domain and cad clean-up
3. Generate mesh
4. Establish boundary conditions and initial conditions
5. Choose turbulence models and solver schemes
6. Perform simulation
7. Iterative convergence
8. Post-processing to visualize results
9. Iteration for performance parameters
Comparison between conventional CFD and Autonomous HVAC CFD
Why are HVAC designers not using CFD widely?
Lack of CFD knowledge, extensive training requirements, expensive software licenses,
and the need for in-house, high-performance computing equipment have kept CFD
from being widely implemented.
How does Autonomous HVAC CFD help?
What if I tell you that there is an application that will automate this entire process for
you and help you achieve the performance parameters with ease of mind? Presenting
to you the Autonomous HVAC CFD (AHC) application, which does all the heavy lifting
of the CFD simulation process (fluid volume extraction, meshing, selecting the suitable
numerical models and solver schemes, monitoring convergence, and postprocessing)
autonomously. The only inputs the app needs are the Building Information Models
(BIMs) of indoor spaces and information about the HVAC system, weather conditions,
and occupant density.
AHC app is designed to help HVAC solution providers and HVAC manufacturers design
suitable airside systems by accurately evaluating thermal comfort and indoor air
quality in conditioned spaces.
It provides vital information like Predicted Mean Vote(PMV), Percentage of People
Dissatisfied (PPD), Relative Humidity, Air Velocity, Temperature, Mean Radiant
Temperature of Air, Mean Age of Air, and CO2 levels for any conditioned space.
Nothing demonstrates the power of a product more than its real-world examples. View case studies of how AHC is used for estimating thermal comfort.
Wish to explore Autonomous HVAC CFD to optimize your HVAC design for IAQ and thermal? Sign-up now and get free credits worth $500 for 90 days. You can explore the product and evaluate HVAC designs for Indoor Air Quality and estimate thermal comfort upto space area of 5000 square feet.
Ruturaj is a product analyst at Centre for Computational Technologies Private Limited (CCTech). At CCTech he is keenly interested in learning the upcoming new technologies in the field of Computational Fluid Dynamics and Machine Learning. His areas of interest are Heating Ventilation and Air Conditioning along with Computation Fluid Dynamics. He holds a Bachelor's degree in Mechanical Engineering from University of Mumbai. He enjoys reading about Building services and green building related stuff.
Ruturaj is a product analyst at the Centre for Computational
Technologies Private Limited (CCTech), Pune. He loves to work in the
fields of physics and mathematics. He holds a Bachelor's degree in
Mechanical Engineering from Savitribai Phule Pune University. His areas
of interest are Heat Transfer, Fluid Mechanics, Computational Fluid
Dynamics, Numerical Methods, and Operation Research modeling.
Watching Formula One, traveling, and playing football are his hobbies,
and he likes to explore nature.