Key Takeaways
- Dolphin Global Holdings has integrated SimScale’s AI‑driven physics platform to accelerate radiator and heat‑exchanger development.
- AI‑assisted CFD reduces set‑up time by up to 70 % and can cut simulation cycles from 8 h to ≈2 h.
- Early results show 10‑15 % reductions in cooling‑system volume and weight while maintaining or improving heat‑transfer performance.
- The technology also predicts clog‑risk in dusty environments, giving OEMs a data‑backed safety margin.
- Dolphin will exhibit the AI‑optimized designs at InnoTrans 2026 (Berlin, 20‑23 April), highlighting real‑world CFD gains.
Overview
Dolphin Global Holdings announced a strategic partnership with cloud‑based simulation leader SimScale to embed its Physics‑AI suite into the design workflow for heat exchangers, radiators, and related cooling hardware. The move targets original equipment manufacturers (OEMs) that demand lighter, more compact cooling solutions without sacrificing thermal efficiency or durability in harsh, particulate‑laden settings.
Why AI Simulation Matters
Faster Turn‑around
Traditional computational fluid dynamics (CFD) requires manual mesh generation, boundary‑condition tuning, and iterative solver runs. SimScale’s AI engine automates meshing and pre‑configures solver settings, shrinking set‑up time from an average 8–10 hours to under 2 hours per model. This speed enables engineers to explore 5‑10× more design variants within a typical project window.
Higher Fidelity with Less Effort
The AI module continuously learns from prior simulations, delivering mesh quality that meets Y⁺ ≤ 1 for turbulent wall‑functions—a benchmark often only achieved after manual refinement. Resulting predictions of heat‑transfer coefficients (h) and pressure drops (ΔP) stay within ±3 % of wind‑tunnel test data, matching the accuracy of fully manual CFD setups.
Benefits for Heat‑Exchanger and Radiator Design
| Metric | Traditional CFD | AI‑Assisted CFD (SimScale) |
|---|---|---|
| Set‑up time | 8–10 h | 1.5–2 h |
| Mesh generation | Manual, 30 % of effort | Automated, <5 % effort |
| Design iterations | 3–5 per project | 12–15 per project |
| Weight reduction | 0–5 % (baseline) | 10–15 % (reported) |
| Volume reduction | 0–4 % | 8–12 % |
| Clog‑risk prediction | Not standard | Integrated particulate‑flow model |
| Cost per simulation | $2,500‑$4,000 (on‑prem) | $500‑$800 (cloud‑hour) |
The AI‑driven workflow also incorporates a particulate‑deposition model that quantifies fouling risk in environments with dust concentrations up to 150 mg m⁻³, a capability previously limited to specialized research labs.
Impact on OEM Partnerships
OEMs such as Siemens Mobility and Alstom have expressed interest in the lighter radiator cores Dolphin is now able to deliver. Preliminary data from a joint pilot with Siemens showed a 12 % drop in radiator mass (from 4.3 kg to 3.8 kg) while maintaining a thermal resistance (Rₜ) of 0.018 K W⁻¹, identical to the legacy part.
InnoTrans 2026 Showcase
At InnoTrans 2026 in Berlin (April 20‑23), Dolphin will present a live demo of the AI‑optimized designs. Attendees can view side‑by‑side CFD visualizations of:
- Core geometry – optimized tube‑pitch and fin‑height achieving a 15 % increase in heat‑transfer area.
- Airflow paths – simulated with turbulent kinetic energy (k) reductions of 22 %, indicating smoother flow and lower fan power consumption.
The booth will also host a hands‑on SimScale sandbox, allowing engineers to run a quick AI‑mesh generation on a sample radiator within 5 minutes.
Bottom Line
Dolphin’s adoption of SimScale’s AI‑powered physics platform marks a decisive step toward faster, lighter, and more reliable cooling systems for the rail and heavy‑industry sectors. By slashing simulation set‑up time, delivering high‑accuracy predictions, and adding fouling‑risk analytics, the partnership equips OEMs with a competitive edge in a market where every kilogram saved translates to lower energy use and higher payload capacity. The upcoming InnoTrans 2026 exhibition will serve as a real‑world proof point, showcasing how AI‑augmented CFD can turn theoretical efficiency gains into tangible product improvements.