Why CFD Analysis Matters for Thermal Comfort

Thermal Comfort Analysis predicts whether occupants will find a space thermally acceptable. Zone-based energy models assume a well-mixed room, so they cannot capture:

• Thermal stratification in high-bay volumes
• Short-circuiting between supply diffusers and return grilles
• Local drafts above 0.2 m/s in occupied zones
• Asymmetric radiant exposure from glazing, roofs and process equipment
• Hot and cold plumes from equipment and solar-heated surfaces

CFD analysis resolves the full 3D velocity, temperature and radiation fields, making it the most reliable way to validate comfort against ASHRAE 55, ISO 7730 and EN 16798-1 before a design is frozen.

SuperDesignTech has performed Thermal Comfort Analysis since 2009, over 15 years of simulation experience across commercial, industrial, healthcare, data center and infrastructure projects.

The Project: Challenge and Scope

Client: Fortune 500 company (name withheld under NDA)
Location: Gauteng province, South Africa
Facility: [approx. 18,000 m²] multi-zone facility with a [14 m] high-bay production hall, office mezzanine and control room
Climate: Highveld subtropical highland, with high diurnal swing and intense solar radiation

Engineering challenges

1. High solar load on large roof and façade areas, risking elevated radiant temperature at workstations.
2. Stratification in a large volume, with hot air at roof level and occupied zones needing to stay in the comfort band.
3. Mixed-mode ventilation: natural openings, roof extractors and mechanical supply operating together.
4. Process heat gains of [approx. 450 kW] distributed across the hall.
5. Dual-season compliance: peak summer cooling and winter morning heating/draft.
6. Schedule pressure: HVAC procurement was on a fixed timeline.

Deliverable: Thermal Comfort CFD Report

• PMV and PPD contour maps at 0.1 m, 0.6 m, 1.1 m and 1.7 m above floor
• Operative temperature and mean radiant temperature (MRT) distributions
• Air velocity and draft rating
• Air Diffusion Performance Index (ADPI)
• Age of Air and ventilation effectiveness
• Design recommendations with quantified before/after comparison

Technical Methodology

1. Geometry and CAD preparation

Architectural and MEP models were imported and defeatured: details below 10 mm irrelevant to flow were removed, while diffusers, louvers, flow-affecting structure, glazing and equipment envelopes were retained. Diffusers were modeled with validated momentum-source and effective-area methods using manufacturer data to preserve throw and entrainment.

2. Meshing strategy

• Mesh type: polyhedral-dominant unstructured with prism inflation layers
• Cell count: [approx. 14 million]
• Near-wall: y+ ≈ 1 on key surfaces, enhanced wall treatment elsewhere
• Local refinement: diffuser throw zones, glazing, occupied zone
• Verification: three-level mesh independence study using the Grid Convergence Index (GCI below [3%] for key comfort variables)

3. Physics and solver setup (ANSYS Fluent)

4. Boundary conditions

• Supply diffusers: mass-flow and temperature inlets
• Return/extract: pressure or mass-flow outlets
• Natural openings: pressure boundaries from wind-pressure coefficients
• Equipment: volumetric or surface heat flux
• Occupants: heat sources of [approx. 100 W/person; 1.2 met]
• Envelope: conjugate heat transfer through roof and walls using specified U-values

5. Comfort post-processing

Using user-defined functions and custom field functions in Fluent, PMV and PPD were computed per ISO 7730 and ASHRAE 55 from local CFD fields: air temperature, mean radiant temperature, air velocity and relative humidity, with metabolic rate and clothing insulation (0.5 clo summer, 1.0 clo winter).

Target criteria: PMV within ±0.5 and PPD below 10% (Category B) in the occupied zone, vertical air temperature difference below 3 K (head to ankle), and draft rate within standard limits.

6. Cloud HPC on Rescale

SuperDesignTech is an early adopter and pioneer of Rescale cloud simulation. On this project:

• Cases ran on compute-optimized cloud nodes with [InfiniBand] interconnect
• Design variants ran in parallel rather than queued
• Solver licensing was managed alongside our licensed ANSYS Fluent and DCX Software tools
• Run time per case fell from [days on in-house workstations] to [approx. hours on cloud HPC]

Key Findings

Baseline design

• Stratification: roof-level air [approx. 8 K] above occupied-zone temperature
• Local discomfort: [18%] of occupied floor area exceeded PPD 10% at peak summer afternoon
• Radiant asymmetry: western glazing raised MRT by [approx. 3–4 K] near workstations
• Draft: velocities above 0.3 m/s in the mezzanine near two diffusers
• Dead zones: poor Age of Air in a control room corner

Optimization iterations

Across [6] design variants we evaluated: relocating and re-orienting diffusers; adjusting supply temperature and flow; adding destratification fans; modifying roof extractor position and capacity; adding external shading to western glazing; and rebalancing mezzanine supply air.

Final design performance

Recommendations were implemented before procurement, so the client avoided costly retrofits and received a documented, standards-based compliance record.

Why Choose SuperDesignTech for CFD Analysis?

• Since 2009: 15+ years of focused Thermal Comfort Analysis
• Dedicated team: 6 full-time CFD engineers working 24/7 for global customers
• Licensed tools: ANSYS Fluent and DCX Software
• Cloud-first: early user and pioneer of Rescale
• Standards-driven: ASHRAE 55, ISO 7730, EN 16798-1, with IGBC/GRIHA/LEED documentation support
• Transparent reporting: mesh sensitivity, convergence evidence and assumptions in every report

How SuperDesignTech Uses AI to Reduce CFD Cost and Delivery Time

We apply AI where it removes repetitive work, while engineers remain responsible for physics, verification and sign-off.

1. ML surrogate models trained on completed parametric CFD runs predict comfort metrics for new diffuser positions or flow rates in seconds, reducing full CFD runs.
2. AI-assisted meshing and setup automates geometry cleanup, mesh-quality checks and boundary-condition templates.
3. Automated convergence monitoring flags stalled or oscillating residuals early so cloud compute is not wasted.
4. Automated post-processing and reporting generates PMV/PPD maps and compliance tables; language models draft narratives that engineers review and correct.
5. Cloud cost optimization through workload-aware scheduling on Rescale matches each job to suitable hardware.

Result: faster turnaround and lower cost per study at the same accuracy standard [add measured figures, e.g. "up to X% faster on repeat project types"].

Frequently Asked Questions

What is Thermal Comfort Analysis?

It evaluates whether occupants will feel comfortable based on air temperature, radiant temperature, air velocity, humidity, metabolic rate and clothing. CFD predicts these in 3D, and PMV/PPD are calculated from them.

How is CFD different from energy simulation?

Energy models predict loads using well-mixed zones. CFD resolves local airflow, temperature gradients and radiation, showing where and why discomfort occurs.

How long does a CFD thermal comfort study take?

Typically [2–4 weeks], depending on size and number of design variants. Cloud HPC and AI-assisted workflows shorten this.

Which standards do you follow?

ASHRAE 55, ISO 7730, EN 16798-1 and relevant local codes.