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Lumped-parameters Control-oriented Gray-box Modelling of Liquid Immersion Cooling Systems

机译:集总参数控制的浸液冷却系统灰箱建模

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Liquid cooling systems have better heat dissipation capabilities than air based ones, and are expected to become a standard choice in future data centers, due to the ever increasing power density and heat rejection needs of the compute infrastructure. A convenient side-effect of implementing liquid cooling is that it facilitates the efficient recovery of the heat waste. However designing and managing these heat recovery infrastructures benefit from having control-oriented models that can accurately describe how different operating conditions of the to-be-cooled heat sources will affect the thermal status of the coolant. The aim of this manuscript is to derive control-oriented models of liquid immersion cooling systems, i.e., systems where the compute infrastructure is immersed in a vessel filled with dielectric fluid. More specifically we derive, starting from physical interpretations, a general lumped-parameters gray box dynamical model that has - as inputs - the electrical consumption of the heat sources and the working point of the heat recovery system, and has - as outputs - the temperature distribution of the coolant in the most relevant points of the system. Beyond proposing this modelling methodology we also validate the generalization capabilities of the obtainable models. In specific, we test the achievable statistical performances in a field case, plus compare with the ones of classical black box system identification strategies. We thus report that in the considered field case our gray box model reached a fit index of 91.08% when simulating test sets, while the best black box model we have been able to identify reached (on the same test sets) fit indexes of only 72.56%.
机译:液体冷却系统具有比空气冷却系统更好的散热能力,并且由于计算基础设施的功率密度和散热需求不断增长,因此有望成为未来数据中心的标准选择。实施液体冷却的一个方便的副作用是,它有助于有效地回收废热。然而,设计和管理这些热回收基础设施得益于具有面向控制的模型,该模型可以准确地描述要冷却的热源的不同运行状况将如何影响冷却液的热状态。该手稿的目的是推导液体浸没冷却系统(即将计算基础设施浸入装有电介质的容器中的系统)的面向控制的模型。更具体地说,我们从物理解释出发,得出一个通用的集总参数灰盒动力学模型,该模型具有-作为输入-热源的电耗和热回收系统的工作点,并具有-作为输出-温度冷却液在系统最相关点的分布。除了提出这种建模方法之外,我们还验证了可获得模型的泛化能力。具体来说,我们在现场情况下测试可实现的统计性能,并与经典黑匣子系统识别策略进行比较。因此,我们报告说,在考虑的现场案例中,我们的灰盒模型在模拟测试集时达到了91.08%的拟合指数,而我们已经能够识别出的最佳黑盒模型(在相同的测试集上)仅达到72.56的拟合指数。 %。

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