CPU Failure Research Articles

Design and Optimization of Heatsink for an Active CPU Cooler using Numerical Simulations

Heat generation in CPUs with high computing performance has been a very serious issue that deteriorates their performance. To ensure that CPUs operate at their maximum potential, it is essential to maintain their temperature below 80 °C. Forced convection coolers comprising of a heatsink and fan are considered the most effective means of satisfying operating temperature requirements for a CPU in order to ensure its maximum performance. A heatsink design paired with a fan with airflow speed of 80 cubic feet per minute (CFM) for a CPU Cooler targeted at CPUs with maximum heat generation of 380 watts operating in ambient temperature of 25 °C, is developed using numerical methods of Computational Fluid Dynamics (CFD) and topology optimization using ANSYS Mechanical and ANSYS Fluent. Various fin profiles, fin arrangements, fin numbers and heatsink materials are comparatively analyzed. The best results from the comparative analyses are combined to present a base design capable of keeping the CPU temperature below 80 °C, which is the requirement for ensuring maximum computing performance. A 30 fin heatsink with covered rectangular plate fins in an arc-shaped placement configuration is determined to provide the maximum cooling performance. In materials, Silicon Carbide resulted in the lowest CPU temperature of 78°C, followed by copper at 84 °C. Silicon Carbide heatsink successfully managed to satisfy the requirements for maximum CPU performance. Copper heatsink is unlikely to cause CPU failure, but it fails to meet conditions for maximum CPU performance. In addition, this base design is then optimized for material cost reduction using topology optimization which resulted in 13% reduction in material cost with a negligible cooling performance reduction of only 0.32%. The overall design of the cooler can be improved by incorporating fan design and various CPU load conditions into the design parameters in future research.

Intrinsic fault tolerance of multilevel Monte Carlo methods

Monte Carlo (MC) and multilevel Monte Carlo (MLMC) methods applied to solvers for Partial Differential Equations with random input data are proved to exhibit intrinsic failure resilience. Sufficient conditions are provided for non-recoverable loss of a random fraction of MC samples not to fatally damage the asymptotic accuracy versus work of a MC simulation. Specifically, the convergence behavior of MLMC methods on massively parallel hardware with runtime faults is analyzed mathematically and investigated computationally. Our mathematical model assumes node failures which occur uncorrelated of MC sampling and with general sample failure statistics on the different levels and which also assume absence of checkpointing, i.e., we assume irrecoverable sample failures with complete loss of data. Modifications of the MLMC with enhanced resilience are proposed. The theoretical results are obtained under general statistical models of CPU failure at runtime. Particular attention is paid to node failures with so-called Weibull failure models on massively parallel stochastic finite volume computational fluid dynamics simulations are discussed. We discuss the resilience of massively parallel stochastic Finite Volume computational fluid dynamics simulations.

RELIABILITY ANALYSIS OF DIGITAL SYSTEMS IN A PROBABILISTIC RISK ANALYSIS FOR NUCLEAR POWER PLANTS

To assess the risk of nuclear power plant operation and to determine the risk impact of digital systems, there is a need to quantitatively assess the reliability of the digital systems in a justifiable manner. The Probabilistic Risk Analysis (PRA) is a tool which can reveal shortcomings of the NPP design in general and PRA analysts have not had sufficient guiding principles in modelling particular digital components malfunctions. Currently digital I&C systems are mostly analyzed simply and conventionally in PRA, based on failure mode and effects analysis and fault tree modelling. More dynamic approaches are still in the trial stage and can be difficult to apply in full scale PRA-models. As basic events CPU failures, application software failures and common cause failures (CCF) between identical components are modelled.The primary goal is to model dependencies. However, it is not clear which failure modes or system parts CCF:s should be postulated for. A clear distinction can be made between the treatment of protection and control systems. There is a general consensus that protection systems shall be included in PRA, while control systems can be treated in a limited manner. OECD/NEA CSNI Working Group on Risk Assessment (WGRisk) has set up a task group, called DIGREL, to develop taxonomy of failure modes of digital components for the purposes of PRA. The taxonomy is aimed to be the basis of future modelling and quantification efforts. It will also help to define a structure for data collection and to review PRA studies.