💻 Microchannel Heat Sink Sizer (MCHE)

Design microchannel heat sinks for high-power electronics (CPU/GPU/SiC): junction temperature Tj, total thermal resistance Rth (K/W), heat flux density (W/cm2), and pressure drop.

⚡ Fortran 90 Engine Double Precision (IEEE 754) ✓ ISO / ASME Validated

📊 Solver Telemetry ● ACTIVE

👁️ Consultations 22

⚡ Calculs faits 21

💾 Téléchargements 135 📦 Code Fortran 4.1 KB

📅 Mise en service Jun 2026

⏱️ Latence < 1 ms

🔬 High-Density Microchannels & Thermal Heat Spreading

Real-time visual simulation: Micro-fin array with liquid coolant streams and heat conduction vectors

📝 Configuration & Presets

🤖 700W AI Datacenter GPU 🖥️ 350W Server Direct-to-Silicon 🚗 1200W EV SiC Inverter 🧪 FC-72 Immersion Sink

⚡ Heat Source & Die Geometry

Thermal Dissipation Q [W]:

Substrate Material:

Die Width W [mm]:

Die Length L [mm]:

🔬 Microchannel Array Microgeometry

Channel Width W_c [μm]:

Channel Height H_c [μm]:

Micro-Fin Width W_w [μm]:

💧 Coolant Medium & Flow Rate

Coolant Medium:

Coolant Flow Rate [mL/min]:

Inlet Temperature T_in [°C]:

Tuckerman & Pease Microchannel Formulation:
• Total Resistance: R_th = R_conv + R_cond + R_caloric [K/W]
• Convective Resistance: R_conv = 1 / (h · A_eff) [K/W]
• Fin Efficiency: η_fin = tanh(m·H_c) / (m·H_c)
• Junction Temperature: T_j = T_in + Q · R_th [°C].

📊 Thermal Performance Results

📊 Output Summary
💾 Fortran Source

Chip Peak Junction Temperature (T_j)

44.9 °C

Thermal Resistance: 0.0284 K/W (C/W) | Heat Flux: 112.0 W/cm²

EXCELLENT (TJ < 70°C)

Convective Heat Coeff (h) 18167 W/(m²·K) Microchannel convective slot

Microchannel Pressure Drop 34.1 kPa 0.34 bar pump head

Number of Microchannels 125 channels 120μm slot / 80μm fin

Temperature Rise (ΔT_j-in) 19.9 °C Inlet = 25 °C

📈 Junction Temp Tj (°C) vs Coolant Flow (mL/min)

📉 Total Thermal Resistance Rth (K/W) vs Channel Width (μm)

=================================================================
 THERMOFLUIDCALC — MICROCHANNEL HEAT SINK (MCHE) SIZING REPORT
=================================================================
Case Title                 : 700W AI Datacenter GPU Microchannel Cold Plate
Chip Heat & Dimensions     : Q = 700.0 W, Footprint = 25.0x25.0 mm (Heat Flux = 112.0 W/cm2)
Microchannel Array         : 125 channels (Wc = 120 um, Hc = 800 um, Ww = 80 um)
Coolant & Substrate        : Deionized Water (Optimal Thermal Performance) / Oxygen-Free Copper (k = 398 W/m·K)
Flow & Inlet Temperature   : V_flow = 1200.0 mL/min, Tin = 25.0 deg C
-----------------------------------------------------------------
CHIP JUNCTION TEMPERATURE  : 44.91 deg C (EXCELLENT (TJ < 70°C))
TOTAL THERMAL RESISTANCE   : 0.02844 K/W (deg C/W)
Convective Heat Coeff (h)  : 18166.9 W/(m2.K)
Microchannel Pressure Drop : 34.12 kPa (0.341 bar)
=================================================================

📘 Calculation Methodology & Electronics Cooling Standards

Tuckerman & Pease Microchannel Paradigm

By reducing hydraulic diameter down to tens of micrometers, the convective heat transfer coefficient $h \propto k/D_h$ reaches astronomical values (> 15,000 W/m²·K), enabling heat removal from ultra-dense processors exceeding $100\,\text{W/cm}^2$.

Three-Component Thermal Resistance Budget

The total junction-to-fluid thermal resistance includes $R_{cond}$ (substrate base conduction), $R_{conv}$ (fin convective resistance), and $R_{cal}$ (caloric fluid temperature rise along channel).

Key Engineering Assumptions

Laminar flow with Kwak rectangular duct aspect-ratio corrections.
1D fin conduction efficiency across high aspect ratio micro-fins.
Applicable to NVIDIA/AMD AI GPUs, Intel Xeon/AMD EPYC CPUs, and automotive EV SiC traction inverters.

ThermoFluidCalc Engineering Report