zerothX / by JJ Cooling

Computational
Thermal Intelligence.

Our thermal modeling work covers temperature, fluid flow and phase change. The display below is a demo; engineering calculations are carried out with our in-house solvers.

Our in-house simulation capabilities
zerothXJJ COOLING / PHYSICS INTELLIGENCE
DEMO · NO SOLVER
MODEL FOUNDATION11reported solver families
PUBLISHED VALIDATION2.1 Kmean junction-temperature error
IEEE · 2021Published thermosyphon experiment
T(x,y) / TEMPERATUREREV. 01
REFERENCE POINT0.000.000.250.250.500.500.750.751.001.00x / NORMALIZED POSITIONy / NORMALIZED POSITION
RELATIVE SCALELOW → HIGH
GOVERNING PRINCIPLEq = −k ∇T
ENGINEERING OUTPUTST, ΔP, Re — not calculated
Validation benchmark: heated 16 cm² copper pseudo-CPU, R1233zd(E), 100–700 W; 25–30 °C inlet air, 40–178 CFM. Mean error 2.1 K; maximum 5.8 K. Solver-family count is company-reported.
01

Define & simulate

Translate geometry, materials, loads and boundary conditions into a consistent physical model.

02

Explore & optimize

Compare architectures and sensitivities. Resolve the trade-offs between temperature, pressure loss and system constraints.

03

Correlate & validate

Challenge the model with measured data. Establish the operating envelope before relying on a design.

Published solver validation
The next step

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