An Application Adaptation Approach to Mitigate the Impact of Dynamic Thermal Management on Video Encoding

2015 ◽  
Vol 20 (4) ◽  
pp. 1-27 ◽  
Author(s):  
Ali Mirtar ◽  
Sujit Dey ◽  
Anand Raghunathan
Keyword(s):  
Thermal Management ◽  
Video Encoding ◽  
Dynamic Thermal Management ◽  
Application Adaptation ◽  
The Impact

scholarly journals Investigating the impact of on-chip interconnection noise on Dynamic Thermal Management efficiency

2015 ◽  
Author(s):  
Somayeh Rahimipour ◽  
Wameedh Nazar Flayyih ◽  
Noor Ain Kamsani ◽  
Mircea Stan ◽  
Fakhrul Zaman Rohani
Keyword(s):  
Thermal Management ◽  
Dynamic Thermal Management ◽  
Management Efficiency ◽  
On Chip ◽  
The Impact

High-Efficiency Transient Temperature Calculations for Applications in Dynamic Thermal Management of Electronic Devices

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Maxat N. Touzelbaev ◽  
Josef Miler ◽  
Yizhang Yang ◽  
Gamal Refai-Ahmed ◽  
Kenneth E. Goodson
Keyword(s):  
Thermal Management ◽  
Impulse Response ◽  
Semiconductor Devices ◽  
Temperature Response ◽  
Infinite Impulse Response ◽  
Transient Temperature ◽  
Modeling Tools ◽  
Dynamic Thermal Management ◽  
The Impact ◽  
Power Densities

The highly nonuniform transient power densities in modern semiconductor devices present difficult performance and reliability challenges for circuit components, multiple levels of interconnections and packaging, and adversely impact overall power efficiencies. Runtime temperature calculations would be beneficial to architectures with dynamic thermal management, which control hotspots by effectively optimizing regional power densities. Unfortunately, existing algorithms remain computationally prohibitive for integration within such systems. This work addresses these shortcomings by formulating an efficient method for fast calculations of temperature response in semiconductor devices under a time-dependent dissipation power. A device temperature is represented as output of an infinite-impulse response (IIR) multistage digital filter, processing a stream of sampled power data; this method effectively calculates temperatures by a fast numerical convolution of the sampled power with the modeled system's impulse response. Parameters such as a steady-state thermal resistance or its extension to a transient regime, a thermal transfer function, are typically used with the assumption of a linearity and time-invariance (LTI) to form a basis for device thermal characterization. These modeling tools and the time-discretized estimates of dissipated power make digital filtering a well-suited technique for a run-time temperature calculation. A recursive property of the proposed algorithm allows a highly efficient use of an available computational resource; also, the impact of all of the input power trace is retained when calculating a temperature trace. A network identification by deconvolution (NID) method is used to extract a time-constant spectrum of the device temperature response. We verify this network extraction procedure for a simple geometry with a closed-form solution. In the proposed technique, the amount of microprocessor clock cycles needed for each temperature evaluation remains fixed, which results in a linear relationship between the overall computation time and the number of temperature evaluations. This is in contrast to time-domain convolution, where the number of clock cycles needed for each evaluation increases as the time window expands. The linear dependence is similar to techniques based on FFT algorithms; in this work, however, use of z-transforms significantly decreases the amount of computations needed per temperature evaluation, in addition to much reduced memory requirements. Together, these two features result in vast improvements in computational throughput and allow implementations of sophisticated runtime dynamic thermal management algorithms for all high-power architectures and expand the application range to embedded platforms for use in a pervasive computing environment.

scholarly journals Investigating the impact of on-chip interconnection noise on dynamic thermal management efficiency

2015 ◽  
Author(s):  
Somayeh Rahimipour ◽  
Wameedh Nazar Flayyih ◽  
Noor Ain Kamsani ◽  
Mircea Stan ◽  
Fakhrul Zaman Rohani
Keyword(s):  
Thermal Management ◽  
Dynamic Thermal Management ◽  
Management Efficiency ◽  
On Chip ◽  
The Impact

scholarly journals Wide range continuously tunable and fast thermal switching based on compressible graphene composite foams

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Tingting Du ◽  
Zixin Xiong ◽  
Luis Delgado ◽  
Weizhi Liao ◽  
Joseph Peoples ◽  
...  
Keyword(s):  
Thermal Management ◽  
Dynamic Control ◽  
Operating Conditions ◽  
Dynamic Thermal Management ◽  
Graphene Composite ◽  
Continuous Tuning ◽  
Composite Foams ◽  
Wide Range ◽  
Thermal Switches ◽  
Thermal Switching

AbstractThermal switches have gained intense interest recently for enabling dynamic thermal management of electronic devices and batteries that need to function at dramatically varied ambient or operating conditions. However, current approaches have limitations such as the lack of continuous tunability, low switching ratio, low speed, and not being scalable. Here, a continuously tunable, wide-range, and fast thermal switching approach is proposed and demonstrated using compressible graphene composite foams. Large (~8x) continuous tuning of the thermal resistance is achieved from the uncompressed to the fully compressed state. Environmental chamber experiments show that our variable thermal resistor can precisely stabilize the operating temperature of a heat generating device while the ambient temperature varies continuously by ~10 °C or the heat generation rate varies by a factor of 2.7. This thermal device is promising for dynamic control of operating temperatures in battery thermal management, space conditioning, vehicle thermal comfort, and thermal energy storage.

Perimeter Cooling Unit and Localized Row-Based Cooling Unit Transient Air Flow Effects Modeling and Characterization in Data Centers

2015 ◽  
Author(s):  
Tianyi Gao ◽  
James Geer ◽  
Bahgat G. Sammakia ◽  
Russell Tipton ◽  
Mark Seymour
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Data Centers ◽  
Cooling System ◽  
Air Flow ◽  
Cooling Systems ◽  
Dynamic Thermal Management ◽  
Hybrid Cooling ◽  
Cooling Unit ◽  
Cooling Air

Cooling power constitutes a large portion of the total electrical power consumption in data centers. Approximately 25%∼40% of the electricity used within a production data center is consumed by the cooling system. Improving the cooling energy efficiency has attracted a great deal of research attention. Many strategies have been proposed for cutting the data center energy costs. One of the effective strategies for increasing the cooling efficiency is using dynamic thermal management. Another effective strategy is placing cooling devices (heat exchangers) closer to the source of heat. This is the basic design principle of many hybrid cooling systems and liquid cooling systems for data centers. Dynamic thermal management of data centers is a huge challenge, due to the fact that data centers are operated under complex dynamic conditions, even during normal operating conditions. In addition, hybrid cooling systems for data centers introduce additional localized cooling devices, such as in row cooling units and overhead coolers, which significantly increase the complexity of dynamic thermal management. Therefore, it is of paramount importance to characterize the dynamic responses of data centers under variations from different cooling units, such as cooling air flow rate variations. In this study, a detailed computational analysis of an in row cooler based hybrid cooled data center is conducted using a commercially available computational fluid dynamics (CFD) code. A representative CFD model for a raised floor data center with cold aisle-hot aisle arrangement fashion is developed. The hybrid cooling system is designed using perimeter CRAH units and localized in row cooling units. The CRAH unit supplies centralized cooling air to the under floor plenum, and the cooling air enters the cold aisle through perforated tiles. The in row cooling unit is located on the raised floor between the server racks. It supplies the cooling air directly to the cold aisle, and intakes hot air from the back of the racks (hot aisle). Therefore, two different cooling air sources are supplied to the cold aisle, but the ways they are delivered to the cold aisle are different. Several modeling cases are designed to study the transient effects of variations in the flow rates of the two cooling air sources. The server power and the cooling air flow variation combination scenarios are also modeled and studied. The detailed impacts of each modeling case on the rack inlet air temperature and cold aisle air flow distribution are studied. The results presented in this work provide an understanding of the effects of air flow variations on the thermal performance of data centers. The results and corresponding analysis is used for improving the running efficiency of this type of raised floor hybrid data centers using CRAH and IRC units.

Cellular nonlinear network controller for dynamic thermal management

2012 ◽  
Vol 48 (10) ◽  
pp. 555
Author(s):  
A. Dasu ◽  
S. Chandrasekaran
Keyword(s):  
Thermal Management ◽  
Dynamic Thermal Management ◽  
Nonlinear Network ◽  
Network Controller

Design and Analysis of an Aircraft Thermal Management System Linked to a Low-Bypass Ratio Turbofan Engine

2021 ◽  
Author(s):  
Robert A. Clark ◽  
Mingxuan Shi ◽  
Jonathan Gladin ◽  
Dimitri Mavris
Keyword(s):  
Thermal Management ◽  
Management System ◽  
Environmental Control ◽  
Cooling System ◽  
Heat Transfer Fluid ◽  
Turbofan Engine ◽  
Thermal Management System ◽  
Air Stream ◽  
The Impact ◽  
Bypass Ratio

Abstract The design of an aircraft thermal management system (TMS) that is capable of rejecting heat loads into the bypass stream of a typical low-bypass ratio turbofan engine, or a ram-air stream, is investigated. The TMS consists of an air cycle system (ACS), which is similar to the typical air cycle machines (ACMs) used on current aircraft, both military and commercial. This system turbocharges compressor bleed air and uses heat exchangers in a ram air stream or the engine bypass stream to cool the engine bleed air prior to expanding it to low temperatures suitable for heat rejection. In this study, a simple low-bypass ratio afterburning turbofan engine was modeled in NPSS to provide boundary conditions to the TMS system throughout the flight envelope of a typical military fighter aircraft. The engine was sized to produce sea level static (SLS) thrust roughly equivalent to that of an F-35-class engine. Two different variations of the TMS system, a ram air cooled and a bypass air cooled, were sized to handle a given demanded aircraft heat load, which might include environmental control system (ECS) loads, avionics cooling loads, weapons system loads, or other miscellaneous loads. The architecture and modeling of the TMS is described in detail, and the ability of the sized TMS to reject these demanded aircraft loads throughout several key off-design points was analyzed, along with the impact of ACS engine bleeds on engine thrust and fuel consumption. A comparison is made between the cooling capabilities of the ram-air stream versus the engine bypass stream, along with the benefits and drawbacks of each cooling stream. It is observed that the maximum load dissipation capability of the TMS is tied directly to the amount of engine bleed flow, while the level of bleed flow required is set by the temperature conditions imposed by the aircraft cooling system and the heat transfer fluid used in the ACS thermal transport bus. Furthermore, the higher bypass stream temperatures significantly limit the thermodynamic viability and capability of a TMS designed with bypass air as the ultimate heat sink. The results demonstrate the advantage that adaptive, variable cycle engines (VCEs) may have for future military aircraft designs, as they combine the best features of the two TMS architectures that were studied here.

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