CFD + AI · Engineering better outcomes

Better designs.
Better processes.
Fewer costly iterations.

Resolve performance bottlenecks, evaluate design changes and anticipate scale-up challenges with CFD and AI—without building a simulation toolchain or becoming a modelling specialist.

Locate the hot spot.FLOW · HEAT · DESIGN
“How do we eliminate hot spots without increasing cooling power?”Better thermal performance. The same energy budget.

What you gain

Improve performance.
Make each iteration count.

Identify limiting factorsCompare alternativesUnderstand trade-offs

Focus experiments and design effort on the changes that matter.

Test the possibilities in a model.
Focus physical trials where they matter.

Identify the flow, heat and transport effects holding performance back. Compare design and operating changes before committing materials, equipment time and experimental effort.

01 —Find the bottlenecks that hold performance back.
02 —Compare designs against performance and energy targets.
03 —Prioritise physical trials with evidence and clear assumptions.

From design improvement to process scale-up

Better outcomes start
with the right physics.

Improve cooling, preserve process performance at scale, or assess equipment loads. ResEt AI selects the physics and AI each problem needs, with geometry, meshing and solver setup handled behind the scenes.

Eliminate hot spots. Keep cooling power in check.

Compare layout and flow choices against thermal constraints.

Behind the answer

Geometry + mesh→CFD→AI comparison

ResEt AI models flow and heat transfer, adding structural analysis when fluid or thermal loads affect the equipment.

In practice · Fermentation scale-up

The cells haven’t changed.
Their environment has.

Why does a process that works in the lab lose performance in a larger vessel?

01 / Kinetics

What the
cells need.

Growth, substrate use and oxygen demand.

Demand informs the flow model.
Local conditions feed back into growth.

ONE CONNECTED PROCESS MODEL

02 / CFD

What the
vessel delivers.

Mixing, oxygen transport and local temperature.

Reveal the gradients a vessel-average measurement can miss.

03 / Preserve performance as you scale.Compare feed, aeration and mixing changes before the next scale-up trial. AI uses batch data to refine the models, helping you focus on changes that address the process limits.

ResEt AI starts with balances and kinetics, adds CFD when local conditions matter, and checks the models against process measurements.

Explore / Interactive engineering

Make the physics tangible.

All labs & explainers →
Heat exchanger schematic
Flow + heat transfer

CFD Heat Transfer Lab

Explore how flow and heat transfer shape cooling performance in a simplified heat exchanger.

ONE CONNECTED PROCESS MODEL
Kinetics + transport

Fermenter Lab

Explore the oxygen and cooling constraints that can limit fermentation performance at scale.

Engineering + energy

Draught Fan Lab

Explore how airflow and pressure requirements translate into fan power and energy demand.

Insights / From the company

Our thinking on AI.
Grounded in engineering.

Perspectives from the ResEt AI team on physics-informed models, useful data and better decisions.

Read our insights →
Physics + AI

Physics in Forecasting: When Nature Becomes Your Mentor ↗

Problem-first thinking

The Real Bottleneck: Integration Is a Distraction ↗

Understanding AI

Understanding RAG: Solving the Case ↗

Better performance starts
with your next engineering decision.

Start a conversation ↗