Performance Analysis of Single-Phase Space Thermal Radiators and Optimization through Taguchi-Neuro-Genetic Approach

In the thermal management of spacecraft, space thermal radiators play a vital role as heat sinks. A serial radiator with proven advantages in ground applications is proposed and analyzed for space applications. From the performance analysis, specific heat rejection of serial radiator is found to be higher than parallel radiator by 80% for maximum diameter of tube, 47% for maximum thickness of fin, and 75% for maximum pitch of tubes under consideration. Also, serial radiator requires four times higher pumping power than parallel radiator with geometric parameters and a maximum mass flow rate under consideration. In serial radiators, the cross conduction between the fins has a significant effect on its thermal performance. Thus, conjugate heat transfer simulations and optimization operations are to be performed iteratively to optimize the serial radiator, which is computationally costly. To reduce the computational time, Artificial Neural Network is trained using conjugate heat transfer simulations data and combined with the genetic algorithm to perform optimization. Taguchi's orthogonal arrays provided the partial fraction of conjugate heat transfer simulations set to train the ANN. Taguchi-Neuro-Genetic approach, a process that combines the features of three powerful techniques in different optimization phases, is used to optimize both parallel and serial radiators. The optimization aims to obtain a configuration that provides the lowest mass and lowest pumping power requirement for given heat rejection. Optimization results show that the conventional parallel radiator is about 20% heavier and requires about 35% more pumping power than the proposed serial radiator.

Experimental Analysis and Modelling of a Novel Thermosyphon System for Electronics Cooling

In an era of ever-growing digitalisation, the absorbed power of processing units is becoming an actual challenge for cooling systems. The effectiveness is imperative, but compactness and passiveness are driving factors in the design as well. The goal of the present paper is twofold: 1) to present a detailed experimental campaign on a thermosyphon system for high-heat-load electronics; 2) to propose a model of the thermosyphon system using a Machine Learning approach. The thermosyphon system is composed of a micro-channel evaporator plate directly attached to the heat-generating device and an air-cooled multiport condenser. The height between the evaporator and condenser inlets is 12 cm. The condenser is also proposed in two solutions: the first one has a footprint heat exchange area of 180 x 120 mm2, which allows a single fan's placement; the second one has a footprint area of 240x120 mm2, allowing the placement of two fans. The working fluid used in the system is R1234ze(E) with different charges. The experimental results show that the single-fan condenser reached a maximum heat rejection of 330 W, corresponding to a heat flux of 21.9 W/cm2. The double-fan condenser bore a maximum heat rejection of 570 W (37.7 W/cm2). The model, constructed purely via a Machine Learning tool, shows a very satisfactory agreement between experimental and predicted data.

The Sandia National Laboratories Natural Circulation Cooler

Sandia National Laboratories (SNL) is developing a cooling technology concept — the Sandia National Laboratories Natural Circulation Cooler (SNLNCC) — that has potential to greatly improve the economic viability of hybrid cooling for power plants. The SNLNCC is a patented technology that holds promise for improved dry heat rejection capabilities when compared to currently available technologies. The cooler itself is a dry heat rejection device, but is conceptualized here as a heat exchanger used in conjunction with a wet cooling tower, creating a hybrid cooling system for a thermoelectric power plant. The SNLNCC seeks to improve on currently available technologies by replacing the two-phase refrigerant currently used with either a supercritical fluid — such as supercritical CO2 (sCO2) — or a zeotropic mixture of refrigerants. In both cases, the heat being rejected by the water to the SNLNCC would be transferred over a range of temperatures, instead of at a single temperature as it is in a thermosyphon. This has the potential to improve the economics of dry heat rejection performance in three ways: decreasing the minimum temperature to which the water can be cooled, increasing the temperature to which air can be heated, and increasing the fraction of the year during which dry cooling is economically viable. This paper describes the experimental basis and the current state of the SNLNCC.