Matching Items (5)
Filtering by
- All Subjects: Sustainability
Description
Wind measurements are fundamental inputs for the evaluation of potential energy yield and performance of wind farms. Three-dimensional scanning coherent Doppler lidar (CDL) may provide a new basis for wind farm site selection, design, and control. In this research, CDL measurements obtained from multiple wind energy developments are analyzed and a novel wind farm control approach has been modeled. The possibility of using lidar measurements to more fully characterize the wind field is discussed, specifically, terrain effects, spatial variation of winds, power density, and the effect of shear at different layers within the rotor swept area. Various vector retrieval methods have been applied to the lidar data, and results are presented on an elevated terrain-following surface at hub height. The vector retrieval estimates are compared with tower measurements, after interpolation to the appropriate level. CDL data is used to estimate the spatial power density at hub height. Since CDL can measure winds at different vertical levels, an approach for estimating wind power density over the wind turbine rotor-swept area is explored. Sample optimized layouts of wind farm using lidar data and global optimization algorithms, accounting for wake interaction effects, have been explored. An approach to evaluate spatial wind speed and direction estimates from a standard nested Coupled Ocean and Atmosphere Mesoscale Prediction System (COAMPS) model and CDL is presented. The magnitude of spatial difference between observations and simulation for wind energy assessment is researched. Diurnal effects and ramp events as estimated by CDL and COAMPS were inter-compared. Novel wind farm control based on incoming winds and direction input from CDL's is developed. Both yaw and pitch control using scanning CDL for efficient wind farm control is analyzed. The wind farm control optimizes power production and reduces loads on wind turbines for various lidar wind speed and direction inputs, accounting for wind farm wake losses and wind speed evolution. Several wind farm control configurations were developed, for enhanced integrability into the electrical grid. Finally, the value proposition of CDL for a wind farm development, based on uncertainty reduction and return of investment is analyzed.
ContributorsKrishnamurthy, Raghavendra (Author) / Calhoun, Ronald J (Thesis advisor) / Chen, Kangping (Committee member) / Huang, Huei-Ping (Committee member) / Fraser, Matthew (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the ever-increasing demand for high-end services, technological companies have been forced to operate on high performance servers. In addition to the customer services, the company's internal need to store and manage huge amounts of data has also increased their need to invest in High Density Data Centers. As a result, the performance to size of the data center has increased tremendously. Most of the consumed power by the servers is emitted as heat. In a High Density Data Center, the power per floor space area is higher compared to the regular data center. Hence the thermal management of this type of data center is relatively complicated.
Because of the very high power emission in a smaller containment, improper maintenance can result in failure of the data center operation in a shorter period. Hence the response time of the cooler to the temperature rise of the servers is very critical. Any delay in response will constantly lead to increased temperature and hence the server's failure.
In this paper, the significance of this delay time is understood by performing CFD simulation on different variants of High Density Modules using ANSYS Fluent. It was found out that the delay was becoming longer as the size of the data center increases. But the overload temperature, ie. the temperature rise beyond the set-point became lower with the increase in data center size. The results were common for both the single-row and the double-row model. The causes of the increased delay are accounted and explained in detail manner in this paper.
Because of the very high power emission in a smaller containment, improper maintenance can result in failure of the data center operation in a shorter period. Hence the response time of the cooler to the temperature rise of the servers is very critical. Any delay in response will constantly lead to increased temperature and hence the server's failure.
In this paper, the significance of this delay time is understood by performing CFD simulation on different variants of High Density Modules using ANSYS Fluent. It was found out that the delay was becoming longer as the size of the data center increases. But the overload temperature, ie. the temperature rise beyond the set-point became lower with the increase in data center size. The results were common for both the single-row and the double-row model. The causes of the increased delay are accounted and explained in detail manner in this paper.
ContributorsRamaraj, Dinesh Balaji (Author) / Gupta, Sandeep (Thesis advisor) / Hermann, Marcus (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2015
Description
The combination of rapid urban growth and climate change places stringent constraints on multisector sustainability of cities. Green infrastructure provides a great potential for mitigating anthropogenic-induced urban environmental problems; nevertheless, studies at city and regional scales are inhibited by the deficiency in modelling the complex transport coupled water and energy inside urban canopies. This dissertation is devoted to incorporating hydrological processes and urban green infrastructure into an integrated atmosphere-urban modelling system, with the goal to improve the reliability and predictability of existing numerical tools. Based on the enhanced numerical tool, the effects of urban green infrastructure on environmental sustainability of cities are examined.
Findings indicate that the deployment of green roofs will cool the urban environment in daytime and warm it at night, via evapotranspiration and soil insulation. At the annual scale, green roofs are effective in decreasing building energy demands for both summer cooling and winter heating. For cities in arid and semiarid environments, an optimal trade-off between water and energy resources can be achieved via innovative design of smart urban irrigation schemes, enabled by meticulous analysis of the water-energy nexus. Using water-saving plants alleviates water shortage induced by population growth, but comes at the price of an exacerbated urban thermal environment. Realizing the potential water buffering capacity of urban green infrastructure is crucial for the long-term water sustainability and subsequently multisector sustainability of cities. Environmental performance of urban green infrastructure is determined by land-atmosphere interactions, geographic and meteorological conditions, and hence it is recommended that analysis should be conducted on a city-by-city basis before actual implementation of green infrastructure.
Findings indicate that the deployment of green roofs will cool the urban environment in daytime and warm it at night, via evapotranspiration and soil insulation. At the annual scale, green roofs are effective in decreasing building energy demands for both summer cooling and winter heating. For cities in arid and semiarid environments, an optimal trade-off between water and energy resources can be achieved via innovative design of smart urban irrigation schemes, enabled by meticulous analysis of the water-energy nexus. Using water-saving plants alleviates water shortage induced by population growth, but comes at the price of an exacerbated urban thermal environment. Realizing the potential water buffering capacity of urban green infrastructure is crucial for the long-term water sustainability and subsequently multisector sustainability of cities. Environmental performance of urban green infrastructure is determined by land-atmosphere interactions, geographic and meteorological conditions, and hence it is recommended that analysis should be conducted on a city-by-city basis before actual implementation of green infrastructure.
ContributorsYang, Jiachuan (Author) / Wang, Zhihua (Thesis advisor) / Kaloush, Kamil (Committee member) / Myint, Soe (Committee member) / Huang, Huei-Ping (Committee member) / Mascaro, Giuseppe (Committee member) / Arizona State University (Publisher)
Created2016
Description
This dissertation introduces FARCOM (Fortran Adaptive Refiner for Cartesian Orthogonal Meshes), a new general library for adaptive mesh refinement (AMR) based on an unstructured hexahedral mesh framework. As a result of the underlying unstructured formulation, the refinement and coarsening operators of the library operate on a single-cell basis and perform in-situ replacement of old mesh elements. This approach allows for h-refinement without the memory and computational expense of calculating masked coarse grid cells, as is done in traditional patch-based AMR approaches, and enables unstructured flow solvers to have access to the automated domain generation capabilities usually only found in tree AMR formulations.
The library is written to let the user determine where to refine and coarsen through custom refinement selector functions for static mesh generation and dynamic mesh refinement, and can handle smooth fields (such as level sets) or localized markers (e.g. density gradients). The library was parallelized with the use of the Zoltan graph-partitioning library, which provides interfaces to both a graph partitioner (PT-Scotch) and a partitioner based on Hilbert space-filling curves. The partitioned adjacency graph, mesh data, and solution variable data is then packed and distributed across all MPI ranks in the simulation, which then regenerate the mesh, generate domain decomposition ghost cells, and create communication caches.
Scalability runs were performed using a Leveque wave propagation scheme for solving the Euler equations. The results of simulations on up to 1536 cores indicate that the parallel performance is highly dependent on the graph partitioner being used, and differences between the partitioners were analyzed. FARCOM is found to have better performance if each MPI rank has more than 60,000 cells.
The library is written to let the user determine where to refine and coarsen through custom refinement selector functions for static mesh generation and dynamic mesh refinement, and can handle smooth fields (such as level sets) or localized markers (e.g. density gradients). The library was parallelized with the use of the Zoltan graph-partitioning library, which provides interfaces to both a graph partitioner (PT-Scotch) and a partitioner based on Hilbert space-filling curves. The partitioned adjacency graph, mesh data, and solution variable data is then packed and distributed across all MPI ranks in the simulation, which then regenerate the mesh, generate domain decomposition ghost cells, and create communication caches.
Scalability runs were performed using a Leveque wave propagation scheme for solving the Euler equations. The results of simulations on up to 1536 cores indicate that the parallel performance is highly dependent on the graph partitioner being used, and differences between the partitioners were analyzed. FARCOM is found to have better performance if each MPI rank has more than 60,000 cells.
ContributorsBallesteros, Carlos Alberto (Author) / Herrmann, Marcus (Thesis advisor) / Adrian, Ronald (Committee member) / Chen, Kangping (Committee member) / Huang, Huei-Ping (Committee member) / Lopez, Juan (Committee member) / Arizona State University (Publisher)
Created2019
Description
The world has been continuously urbanized and is currently accommodating more than half of the human population. Despite that cities cover only less than 3% of the Earth’s land surface area, they emerged as hotspots of anthropogenic activities. The drastic land use changes, complex three-dimensional urban terrain, and anthropogenic heat emissions alter the transport of mass, heat, and momentum, especially within the urban canopy layer. As a result, cities are confronting numerous environmental challenges such as exacerbated heat stress, frequent air pollution episodes, degraded water quality, increased energy consumption and water use, etc. Green infrastructure, in particular, the use of trees, has been proved as an effective means to improve urban environmental quality in existing research. However, quantitative evaluations of the efficacy of urban trees in regulating air quality and thermal environment are impeded by the limited temporal and spatial scales in field measurements and the deficiency in numerical models.
This dissertation aims to advance the simulation of realistic functions of urban trees in both microscale and mesoscale numerical models, and to systematically evaluate the cooling capacity of urban trees under thermal extremes. A coupled large-eddy simulation–Lagrangian stochastic modeling framework is developed for the complex urban environment and is used to evaluate the impact of urban trees on traffic-emitted pollutants. Results show that the model is robust for capturing the dispersion of urban air pollutants and how strategically implemented urban trees can reduce vehicle-emitted pollution. To evaluate the impact of urban trees on the thermal environment, the radiative shading effect of trees are incorporated into the integrated Weather Research and Forecasting model. The mesoscale model is used to simulate shade trees over the contiguous United States, suggesting how the efficacy of urban trees depends on geographical and climatic conditions. The cooling capacity of urban trees and its response to thermal extremes are then quantified for major metropolitans in the United States based on remotely sensed data. It is found the nonlinear temperature dependence of the cooling capacity remarkably resembles the thermodynamic liquid-water–vapor equilibrium. The findings in this dissertation are informative to evaluating and implementing urban trees, and green infrastructure in large, as an important urban planning strategy to cope with emergent global environmental changes.
This dissertation aims to advance the simulation of realistic functions of urban trees in both microscale and mesoscale numerical models, and to systematically evaluate the cooling capacity of urban trees under thermal extremes. A coupled large-eddy simulation–Lagrangian stochastic modeling framework is developed for the complex urban environment and is used to evaluate the impact of urban trees on traffic-emitted pollutants. Results show that the model is robust for capturing the dispersion of urban air pollutants and how strategically implemented urban trees can reduce vehicle-emitted pollution. To evaluate the impact of urban trees on the thermal environment, the radiative shading effect of trees are incorporated into the integrated Weather Research and Forecasting model. The mesoscale model is used to simulate shade trees over the contiguous United States, suggesting how the efficacy of urban trees depends on geographical and climatic conditions. The cooling capacity of urban trees and its response to thermal extremes are then quantified for major metropolitans in the United States based on remotely sensed data. It is found the nonlinear temperature dependence of the cooling capacity remarkably resembles the thermodynamic liquid-water–vapor equilibrium. The findings in this dissertation are informative to evaluating and implementing urban trees, and green infrastructure in large, as an important urban planning strategy to cope with emergent global environmental changes.
ContributorsWang, Chenghao (Author) / Wang, Zhihua (Thesis advisor) / Myint, Soe W. (Committee member) / Huang, Huei-Ping (Committee member) / Mascaro, Giuseppe (Committee member) / Arizona State University (Publisher)
Created2019