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1. Data Center
Two-Phase Immersion Cooling (2PIC)
▶ Instability analysis of TPCT using cellulose nanofiber (CNF) and hydrophilic surface modification
▶ Heat performance analysis of TPCT applying super-hydrophobic and super-hydrophilic surface modification
A study on the heat transfer performance of TPCT according to the type of fluids and surface modification at the inner surface of tube.
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Working Fluids (DI water & CNF fluids)
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Surface modification in condenser(hydrophobic, super-hydrophobic), in evaporator(hydrophilic, super-hydrophilic)
앵커 2
2. EV Charger Cable Cooling System
▶ Investigation of Cooling system for EV super-fast charger
- Development of a phase change cooling system using subcooled flow boiling and dielectric fluid
- When the power is increased for fast charging, the amount of heat generated increases. By developing a cooling system, charging time can be effectively reduced.
- Unlike existing air/liquid cooling methods, cooling performance is effectively improved by using boiling because of latent heat.
- Develop a cooling system that directly cools by utilizing the electrical insulation properties of dielectric fluid.
앵커 3
3. HEAT PIPE
Two-Phase Closed Thermosyphon (TPCT)
▶ Instability analysis of TPCT using cellulose nanofiber (CNF) and hydrophilic surface modification
▶ Heat performance analysis of TPCT applying super-hydrophobic and super-hydrophilic surface modification
A study on the heat transfer performance of TPCT according to the type of fluids and surface modification at the inner surface of tube.
-
Working Fluids (DI water & CNF fluids)
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Surface modification in condenser(hydrophobic, super-hydrophobic), in evaporator(hydrophilic, super-hydrophilic)
앵커 4
4. CONDENSATION
▶ Condensation heat transfer enhancement induced by surface modification (Self-Assembled monolayer) on multi copper tubes.
- An experiment to compare the heat transfer coefficients of a SAM coated copper tube and a bare copper tube.
- Unlike the existing single-tube experiment, five tubes are installed to compare heat transfer coefficients by various factors depending on the installation location.
앵커 5
5. BOILING AND QUENCHING
▶ Enhance stability of heat transfer by conducting boiling in CNF (Cellulose Nano Fiber).
▶ Increase CHF (Critical Heat Flux) by using CNF and surface modification during both boiling and quenching process.
▶ Examination on quenching process in solution of CNF in water
- Denser the solution of CNF in water,
higher heat transfer stability
- Increment of CHF during transition period of quenching
- Control unwanted leftovers produced after oxidation during quenching
앵커 6
6. Supercritical Carbon Dioxide Cycle
▶ Small 12kW S-CO2 transcritical cycle loop experiment
- Small scale S-CO2 experimental facility was designed and manufactured in our lab.
- A experiment of transcritical cycle efficiency for small heat source(such as solar power) power cycle
- Designed Cycle Efficiency : 6.98%
- After installing IHX : 18.75%
▶ S-CO2 Brayton Cycle Design
- The supercritical CO2(S-CO2) power cycle research is a next generation technology that will replace conventional steam rankine and gas turbine cycles.
- S-CO2 has both liquid and gas properties, which can significantly reduce the scale of the cycle and enable the establishment of a power generation system with high economic feasibility and efficiency.
앵커 7
7. EAF(Electric Arc Furnace)
Efficiency Enhancement - AI Project
▶ A statistical model to predict the Electronic Energy(EE) consumption of EAF
- The main task is to find inefficient momentum in the process process in terms of energy consumption and to find key variables that are highly related to power consumption to reduce energy loss and maximize process efficiency.
▶ A statistical model to predict molten steel temperature of EAF
- The main task is to predict the temperature prediction of molten steel during steelmaking.
The operators predict and check the temperature of molten steel in the furnace based on their experiences. Repeated measurements of the temperature of the molten steel can cause heat and loss of productivity. The prediction will reduce the number of measurements, which will increase the productivity and efficiency significally.
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