ABALBERTO
BRAMBILA

HYDROGEN / SPECIES TRANSPORT / AUTOMATION

Hydrogen dilution
across three exhaust towers.

Diluting hydrogen is also a flow-design problem: how should the exhaust mix with air, and how much resistance does the system add? This example makes those questions visible across three extraction towers.

Three-tower hydrogen distribution at 4 and 10 mol% inlet H2, with the same displayed 0 to 0.20 mol% color scale
H₂ distribution at 4 and 10 mol% inlet concentration, on the same display scale. Enlarge ↗

Alberto Brambila, PhD · Species mixing, scenario comparison, Python/PyFluent meshing and solver setup.

PROFESSIONAL PROJECTS / FUEL-CELL TEST BENCHES

Balance dilution, flow resistance and extraction.

The problem

Two early hydrogen projects examined different parts of the exhaust system: a restrictive baffle and a fan-driven ventilation unit. Both required local H₂ concentrations to be considered alongside the flow conditions.

My contribution

I compared baffle layouts and exhaust operating points, then studied reduced fan flow, inlet H₂ concentration and swirl. Species fields and pressure results helped separate mixing problems from hydraulic restrictions.

Engineering outcome

The baffle study supported a lower-resistance layout. The ventilation study identified total extraction flow as a stronger dilution driver than the tested swirl changes, helping focus the design assessment.

This three-tower model demonstrates methods from those two projects. Its geometry, 4/10 mol% cases and plotted results are separate from the industrial studies; it is not a safety certification.

01 / THE ENGINEERING QUESTION

One exhaust stream.
Three mixing paths.

A system-wide flow rate does not show whether hydrogen is distributed evenly. The useful comparison follows the mixture through the manifold and examines each discharge separately.

COMMON MANIFOLD

Fuel-cell exhaust mixture

H₂ + H₂O + O₂ + N₂
One inlet · 1.315 kg/s total · 343.15 K

Dilution air →

Branch 1

Mixing + fan pressure jump + swirl

↓ Outlet 1
Dilution air →

Branch 2

Mixing + fan pressure jump + swirl

↓ Outlet 2
Dilution air →

Branch 3

Mixing + fan pressure jump + swirl

↓ Outlet 3

Topology schematic, not a geometry drawing. Air enters through pressure boundaries; the fan model and system resistance determine its flow.

Change the composition

The study compares inlet H₂ mole fractions of 4% and 10%, with nitrogen adjusted to keep the mixture sum at one. The author’s study description holds geometry, fan settings, temperatures and exhaust mass flow fixed.

Keep the relevant physics

Non-reacting species transport resolves H₂/H₂O/O₂/N₂ mixing. Energy transport accounts for the hot exhaust and cooler dilution air. The reconstructed setup uses k–ω SST and pressure-jump fans with swirl.

This is a mixing study. It does not model combustion, ignition or a fuel cell’s electrochemistry.

02 / FROM FIELDS TO A MEASUREMENT

Inspect the local peak
at every outlet.

The saved monitor reports the maximum H₂ mass fraction on each outlet surface. This complements the field view and avoids hiding a local concentration behind a surface average.

Outlet H2 mass-percent peaks for the two inlet cases; dots show iteration 1500 and vertical lines show substantial variation over iterations 1301 to 1500
Calculated from the supplied Fluent monitors. Dots are the last saved iteration; vertical lines show the observed minimum–maximum over the last 200 iterations. They are iteration variation, not confidence intervals. Inlet labels use mol%; plotted outlet values use mass%.
Peak H₂ at each outlet · iteration 1500 · mass percent
Inlet conditionOutlet 1Outlet 2Outlet 3
4 mol% H₂0.00454%0.00245%0.00304%
10 mol% H₂0.00934%0.01091%0.00839%

At the final iterate, outlet 1 has the highest value in Case 1 and outlet 2 in Case 2. The histories still fluctuate, so this snapshot does not establish a stable outlet ranking. Download the displayed values and ranges (CSV) ↗

Read the concentration basis: mass fraction and mole fraction differ because molecular weights differ. The hero and monitor chart therefore answer related questions on different scales. No conversion is inferred without the local mixture composition, and these outlet reports do not provide a maximum over the entire domain.

03 / CHECK THE EVIDENCE

A final number needs
its iteration history.

Plotting the monitors reveals what a single contour or endpoint cannot: whether the quantity being interpreted has settled.

Inspect the monitor histories and remaining checks
Outlet concentration histories from iterations 1000 to 1500 showing persistent fluctuations in both cases
Saved solver iterations 1000–1500, not physical time. No smoothing is applied. The full source monitors contain 1,500 records per case.

What the records support

Over iterations 1301–1500, the highest peak across the three outlets is 0.01383 mass% for Case 1 and 0.01457 mass% for Case 2. These ranges show why the final iterate must be interpreted with care.

What the next decision needs

Review residuals, species conservation, backflow composition and fan direction, then establish mesh and solution sensitivity. The supplied records do not include a domain-maximum monitor or a closed species balance.

04 / REPEATABLE SIMULATION

From manual setup
to configurable Python drivers.

Separate mesh and solver scripts make the modeling choices inspectable and reusable. Engineering checks remain part of that workflow.

  1. 01

    Build the mesh

    Discovery geometry → Watertight Geometry workflow → four connected fluid regions, polyhedral cells and smooth-transition layers.

  2. 02

    Reconstruct the model

    Explicit species, materials, fan interfaces, boundary conditions, expressions and outlet reports. The setup driver does not read a SET file at runtime.

  3. 03

    Inspect the outputs

    Check real zone types and fan assignments, then parse the monitor histories with Python and retain units, iterations and provenance.

How the scripts were checked

Verify boundary and fan assignments

The prior development record describes a meshing update that reported completion while leaving boundary types unchanged. Supplying the workflow’s starting-state arguments resolved it.

A second check found fan interfaces left as ordinary internal faces. Valid outlet reports had not detected the missing fan model. Explicitly inspecting those interfaces made the check meaningful.

Keep configuration recoverable

The separate PyFluent drivers expose their configuration near the top of each script. WFT and SET references are retained privately to preserve the original modeling choices alongside the code.

This demonstrates automation of repeated preparation and checking. No measured time saving or automatic full operating-matrix run is claimed.

Mesh, setup and visualization evidence

The hero uses a 0–0.20 mol% display range, not a domain-maximum report. The retained SET supports Case 2; the Case 1 label follows the author’s context and archive. Supplied histories are separate from the setup-only script checks.

The prior reconstruction record reports 2,606,549 reference cells and 2,606,683 rebuilt cells, minimum orthogonal quality 0.24 and maximum surface skewness 0.48. Similar cell counts do not establish mesh independence.

The retained driver uses 20–30 mm surface sizing and approximately 87.4 mm maximum volume size. Its turbulence and material choices include assumptions documented during reconstruction. Report evaluation used hybrid initialization; the driver does not run the supplied 1,500-iteration campaigns. No Fluent solve was performed for this publication.

05 / EXPERIENCE THAT TRANSFERS

Hydrogen systems,
mixing and engineering automation.

Species transport, hydraulic reasoning and controlled scenario comparisons apply to other gas-handling systems. The transferable skill is connecting a concentration field to a design decision.

Species transport

Connect mixture composition and flow distribution to local concentrations. Applicable to gas dilution, ventilation and mixing systems.

See the physical model ↑

Scenario design

Define what changes between cases, then compare the same measurement at every outlet. The professional work also examined baffle and fan-operating changes.

See the comparison ↑

Numerical interpretation

Distinguish mass from mole fraction, local maxima from averages, and saved iterates from converged results.

See the checks ↑

Python / PyFluent automation

Turn the mesh, setup and report-processing sequence into configurable steps with explicit verification.

See the workflow ↑
Scope of the public example

Alberto’s professional project record includes baffle/back-pressure comparisons and ventilation studies varying fan flow, inlet H₂ and swirl. Those are separate studies with their own conditions and evidence. Their industrial results and acceptance decisions are not attributed to this demonstration.

The selected geometry and author-supplied visualization illustrate related engineering skills. No commercial equipment identity or certified manufacturer fan curve is asserted. Implementation files and complete histories remain private.

← Throttle-body flowNext: CVD reactor →

Selected engineering evidence is public. Scripts, CAD, detailed settings and complete source histories are maintained privately.