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The variety of new electronic packaging technologies has grown significantly over the last 20 years as a result of market demands for higher device performance at lower costs and in less space. Those demands have pushed for heterogeneous packaging, where computer chips with different stack heights are closely packed, creating nonuniform heat flux and temperature and additional challenges for thermal management. Without implementing an appropriate thermal management strategy for heterogeneous packages, large temperature gradients can be observed within the package, which would increase the thermal stresses on the chip and raise reliability issues. To mimic this real-life scenario of such packaging, an experimental setup was designed and built. The design of the new experimental setup consists of four identical 1.2 cm × 1.2 cm ceramic heaters, each of which is connected to a separate power supply and can reach a heat flux of 140 W/cm2. Accordingly, this mock package is capable of delivering different power levels to mimic different multicore microprocessor conditions. To give the heater the ability to move precisely in the x-, y-, and z-directions, each heater is mounted to an XYZ linear stage. Deionized water (DI) was used as the working fluid, and a pin-fin heat sink was used to run the initial steady-state tests on the experimental rig. The tests showed how different flow rates at a constant fluid temperature and input power affect the temperatures of the heaters and the thermohydraulic performance of the heat sink. In addition, a three-dimensional numerical model has been developed and validated with experimental data in terms of heat sink pressure drop and the temperatures of the heaters.
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Experimental Evaluation of Data-Driven Predictive Indoor Thermal Management
This paper considers the problem of thermal management in a typical shared indoor space that may be equipped with multiple heterogeneous heat sources and have different temperature require- ments in different sections (thermal zones) of the shared space. Utilizing an on-campus smart conference room as a testbed, we discuss the practical challenges involved in real-time data-driven model learning, when a simple first-order dynamical model is used to capture the dependencies between the heat controls and the air temperatures measured at sensor locations. The data-driven model is then utilized for predictive control of the thermal environment towards minimizing the error between the desired and attained temperatures, and the integrated solution is evaluated against a standard thermal control employed by the BMS.
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- Award ID(s):
- 1827546
- PAR ID:
- 10107135
- Date Published:
- Journal Name:
- Proceedings of the Tenth ACM International Conference on Future Energy Systems
- Page Range / eLocation ID:
- 531 to 535
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3307772.3331031
