Practical application of CFD for wind loading on tall buildings

This paper is concerned with assessing the scope of appicabiity for computational fluid dynamics(CFD) in the field of structural engineering, with a particular reference to tall buildings. Modern design trends and advances in engineering materials have encouraged the demand for taller and more slender structures. This pattern induces inherent structural flexibility; these cases exceed the limitations of the quasi-static method offered by current codes of practice. Wind tunnel testing is the traditional solution for such dynamically sensitive structures. However, even this scaled modelling approach is clouded by some uncertainties, including scaling the Reynolds number and assuming damping values for the aeroelastic model. While CFD cannot be used as a replacement for wind tunnel testing, there are results within the literature to suggest it has the potential to act as a complimentary tool - provided it is used within its capabilities. The paper outlines the various turbulence models that are available and summarises the extent of their application in a practical structural engineering sense. It also details the user-defined criteria that must be satisfied and discusses the potential for simplified models in tall building CFD analyses, with a view to promoting more efficient and practical solutions

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid

The concept of Grid computing is becoming the most important research area in the high performance computing. Under this concept, the jobs scheduling in Grid computing has more complicated problems to discover a diversity of available resources, select the appropriate applications and map to suitable resources. However, the major problem is the optimal job scheduling, which Grid nodes need to allocate the appropriate resources for each job. In this paper, we combine Fuzzy C-Mean and Genetic Algorithms which are popular algorithms, the Grid can be used for scheduling. Our model presents the method of the jobs classifications based mainly on Fuzzy C-Mean algorithm and mapping the jobs to the appropriate resources based mainly on Genetic algorithm. In the experiments, we used the workload historical information and put it into our simulator. We get the better result when compared to the traditional algorithms for scheduling policies. Finally, the paper also discusses approach of the jobs classifications and the optimization engine in Grid scheduling