Exergoeconomic analysis of solar organic rankine cycle for geothermal air conditioned net zero energy buildings

This study is an attempt at achieving Net Zero Energy Building (NZEB) using a solar Organic Rankine Cycle (ORC) based on exergetic and economic measures. The working fluid, working conditions of the cycle, cycle configuration, and solar collector type are considered the optimization parameters for the solar ORC system. In the first section, a procedure is developed to compare ORC working fluids based on their molecular components, temperature-entropy diagram and fluid effects on the thermal efficiency, net power generated, vapor expansion ratio, and exergy efficiency of the Rankine cycle. Fluids with the best cycle performance are recognized in two different temperature levels within two different categories of fluids: refrigerants and non-refrigerants. Important factors that could lead to irreversibility reduction of the solar ORC are also investigated in this study. In the next section, the system requirements needed to maintain the electricity demand of a geothermal air-conditioned commercial building located in Pensacola of Florida is considered as the criteria to select the optimal components and optimal working condition of the system. The solar collector loop, building, and geothermal air conditioning system are modeled using TRNSYS. Available electricity bills of the building and the 3-week monitoring data on the performance of the geothermal system are employed to calibrate the simulation. The simulation is repeated for Miami and Houston in order to evaluate the effect of the different solar radiations on the system requirements. The final section discusses the exergoeconomic analysis of the ORC system with the optimum performance. Exergoeconomics rests on the philosophy that exergy is the only rational basis for assigning monetary costs to a system’s interactions with its surroundings and to the sources of thermodynamic inefficiencies within it. Exergoeconomic analysis of the optimal ORC system shows that the ratio Rex of the annual exergy loss to the capital cost can be considered a key parameter in optimizing a solar ORC system from the thermodynamic and economic point of view. It also shows that there is a systematic correlation between the exergy loss and capital cost for the investigated solar ORC system.

Controls on sensible heat and latent energy fluxes from a short-hydroperiod Florida Everglades marsh

Little is known of energy balance in low latitude wetlands where there is a year-round growing season and a climate best defined by wet and dry seasons. The Florida Everglades is a highly managed and extensive subtropical wetland that exerts a substantial influence on the hydrology and climate of the south Florida region. However, the effects of seasonality and active water management on energy balance in the Everglades ecosystem are poorly understood. An eddy covariance and micrometeorological tower was established in a short-hydroperiod Everglades marsh to examine the dominant environmental controls on sensible heat (H) and latent energy (LE) fluxes, as well as the effects of seasonality on these parameters. Seasonality differentially affected H and LE fluxes in this marsh, such that H was principally dominant in the dry season and LE was strongly dominant in the wet season. The Bowen ratio was high for much of the dry season (1.5–2.4), but relatively low (H and LE fluxes across nearly all seasons and years (). However, the 2009 dry season LE data were not consistent with this relationship () because of low seasonal variation in LE following a prolonged end to the previous wet season. In addition to net radiation, H and LE fluxes were significantly related to soil volumetric water content (VWC), water depth, air temperature, and occasionally vapor pressure deficit. Given that VWC and water depth were determined in part by water management decisions, it is clear that human actions have the ability to influence the mode of energy dissipation from this ecosystem. Impending modifications to water management under the Comprehensive Everglades Restoration Plan may shift the dominant turbulent flux from this ecosystem further toward LE, and this change will likely affect local hydrology and climate.

Host and network optimizations for performance enhancement and energy efficiency in data center networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.

Host and Network Optimizations for Performance Enhancement and Energy Efficiency in Data Center Networks

Modern data centers host hundreds of thousands of servers to achieve economies of scale. Such a huge number of servers create challenges for the data center network (DCN) to provide proportionally large bandwidth. In addition, the deployment of virtual machines (VMs) in data centers raises the requirements for efficient resource allocation and find-grained resource sharing. Further, the large number of servers and switches in the data center consume significant amounts of energy. Even though servers become more energy efficient with various energy saving techniques, DCN still accounts for 20% to 50% of the energy consumed by the entire data center. The objective of this dissertation is to enhance DCN performance as well as its energy efficiency by conducting optimizations on both host and network sides. First, as the DCN demands huge bisection bandwidth to interconnect all the servers, we propose a parallel packet switch (PPS) architecture that directly processes variable length packets without segmentation-and-reassembly (SAR). The proposed PPS achieves large bandwidth by combining switching capacities of multiple fabrics, and it further improves the switch throughput by avoiding padding bits in SAR. Second, since certain resource demands of the VM are bursty and demonstrate stochastic nature, to satisfy both deterministic and stochastic demands in VM placement, we propose the Max-Min Multidimensional Stochastic Bin Packing (M3SBP) algorithm. M3SBP calculates an equivalent deterministic value for the stochastic demands, and maximizes the minimum resource utilization ratio of each server. Third, to provide necessary traffic isolation for VMs that share the same physical network adapter, we propose the Flow-level Bandwidth Provisioning (FBP) algorithm. By reducing the flow scheduling problem to multiple stages of packet queuing problems, FBP guarantees the provisioned bandwidth and delay performance for each flow. Finally, while DCNs are typically provisioned with full bisection bandwidth, DCN traffic demonstrates fluctuating patterns, we propose a joint host-network optimization scheme to enhance the energy efficiency of DCNs during off-peak traffic hours. The proposed scheme utilizes a unified representation method that converts the VM placement problem to a routing problem and employs depth-first and best-fit search to find efficient paths for flows.