Numerical investigation of serpentine earth-to-air heat exchanger for passive building heating systems by recovery criteria

This paper investigates the performance of the earth-to-air heat exchanger (EAHE) system in the winter for Mashhad. A three-dimensional model of novel serpentine geometry for use in the passive building heating system is proposed. A new and visual method of recovery analysis is performed for regaining the soil energy by considering a period of time to stop the system. The thermal performance of the system is evaluated by analyzing the derating factor, knee point, and heat transfer evaluation criteria for a serpentine model of EAHE. Results demonstrate that the thermal conductivity of the soil and the duration of EAHE's continuous operation has a significant impact on its transient thermal performance. By employing soil thermal conductivities of 1, 2, and 4 W/m.K, the temperature of the outlet reduced 21 %, 12 %, and 6 %, respectively. Finally, the results indicate the highly better performance of the system in soils with higher thermal conductivity during long-term. The temperature of the outlet air in the 24-hours operation mode decreases compared with the 1-hour operation mode. A new method of recovery analysis is performed for regaining soil energy. This paper aims to develop a new model of EAHE system to maximize the energy use of buildings

Fixed-point Iteration Approach to Spark Scalable Performance Modeling and Evaluation

Companies depend on mining data to grow their business more than ever. To achieve optimal performance of Big Data analytics workloads, a careful configuration of the cluster and the employed software framework is required. The lack of flexible and accurate performance models, however, render this a challenging task. This paper fills this gap by presenting accurate performance prediction models based on Stochastic Activity Networks (SANs). In contrast to existing work, the presented models consider multiple work queues, a critical feature to achieve high accuracy in realistic usage scenarios. We first introduce a monolithic analytical model for a multi-queue YARN cluster running DAG-based Big Data applications that models each queue individually. To overcome the limited scalability of the monolithic model, we then present a fixed-point model that iteratively computes the throughput of a single queue with respect to the rest of the system until a fixed-point is reached. The models are evaluated on a real-world cluster running the widely-used Apache Spark framework and the YARN scheduler. Experiments with the common transaction-based TPC-DS benchmark show that the proposed models achieve an average error of only 5.6% in predicting the execution time of the Spark jobs. The presented models enable businesses to optimize their cluster configuration for a given workload and thus to reduce their expenses and minimize service level agreement (SLA) violations. Makespan minimization and per-stage analysis are examined as representative efforts to further assess the applicability of our proposition