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COS 504: Simulations for Physical Systems

Course Title	Simulations for Physical Systems
Course Code	COS 504
Course Type	Elective
Level	PhD
Instructor’s Name	Assoc. Prof. Giannis Koutsou (Lead Instructor), Dr. Simone Bachhio Prof. Constantia Alexandrou
ECTS	5
Lectures / week	2 (90 min. each) 4.5 weeks
Laboratories / week	2 (90 min. each) 2.5 weeks
Course Purpose and Objectives	The course aims at teaching students to apply high-performance computing and data analysis approaches to solve complex physical systems. Students will learn to handle a range of applications from condensed matter and biophysics to particle and nuclear physics.
Learning Outcomes	Students will: - learn to describe and analyze non-linear systems and systems with many degrees of freedoms - develop algorithms, optimize and implement them on large computers - learn state-of-the-art simulations approaches such as Markov Chain Monte Carlo - study phase transitions and critical behavior using simulations and deep learning approaches - implement crowd simulation such as particle and agent based models for a range of self-organized dynamics of structures - use a range of data analysis methods such as jackknife and bootstrap resampling, Bayesian statistical analysis, - aquire a set of the High Performance Computing and data analysis skills and employ them for solving physical systems. These skills are applicable to a range of problems in chemistry, biology and engineering.
Prerequisites	None
Background Requirements	Knowledge of a low-level programming languages such as Fortran, C, C++ and parallel programing including MPI
Course Content	Week 1-2 Numerical solution of partial differential equations, such as the wave, diffusion and Schrödinger’s equations Week 3 Introduction to minimization algorithms Week 4 Data analysis of correlated data sets, resampling and Bayesian approaches Week 5-6 Phase transitions in physical systems, critical behaviour, identification using deep learning methods Week 7 Markov processes and Monte Carlo methods for many body systems
Teaching Methodology	- 9 x 1.5 h lectures - 5 x 1.5 h hands-on sessions - 3 homework assignments - Presentation of final project
Bibliography	- Course notes - Monte Carlo Methods, Malvin H. Kalos and Paula A. Whitlock
Assessment	The following assessment methods will be combined for the final grade: - Coursework - A final project
Language	English

August 2016
September 2016

For the initial monitoring of the building the SUI CyI research team installed a weather station on the roof, placed sensors inside the building and performed a thermal comfort assessment survey.

1. Climate data monitoring

Exterior
A Vantage Pro2 weather station was placed on the building roof to collect data from the SUI area. Regularly updated information about the climatic conditions could be found on the station webpage: http://www.weatherlink.com/user/suitepakcy/. The weather station base was designed and installed by the CyI technical equipment development team.

Interior
Twelve (12) HOBO data-loggers were placed in the building, for collecting temperature, humidity and light data. The data are collected every 30 minutes and will be elaborated with the HOBOware software.

2. Thermal comfort assessment questionnaire

A survey on occupant comfort satisfaction with the indoor environment in summer was conducted. The questionnaire was based on templates proposed by the HSE (http://www.hse.gov.uk/temperature/index.htm). A third of the total employees were asked and complementary measurements were made using a Heat Stress WBGT (Wet Bulb Globe Temperature) Meter, both for the indoor and the outdoor climatic conditions at the time of the survey.

Images below, from left to right:
Left: The Vantage Pro2 weather station
Middle: HOBO data logger placed in the working place – 1st floor
Right: Completed questionnaire (first page) – Ground floor.

View the embedded image gallery online at:
https://www.cyi.ac.cy/index.php/sui-activities/sui-building-monitoring/cos-504-simulations-for-physical-systems.html#sigProGalleria86fa08f526

1. Thermal imaging

Thermal images were taken using a T440 Flir thermal camera in order to find missing, damaged, or inadequate insulation, building envelope air leaks, moisture intrusion and other problems. Also, the main sources of radiant temperature were determined.

2. HOBO sensors data collection and analysis

The temperature, humidity, air movement and dew point data from the HOBO sensors were collected and analysed. The mean predicted vote (PMV) was calculated using the CBE Thermal Comfort Tool (http://comfort.cbe.berkeley.edu/EN). In all the spaces under monitoring the people are expected to be in thermal comfort, since all values are within the thermal comfort range of -0.7

Images below, from left to right:
Left: A thermal image from the mezzanine
Right: Graph. Thermal Comfort PMV

View the embedded image gallery online at:
https://www.cyi.ac.cy/index.php/sui-activities/sui-building-monitoring/cos-504-simulations-for-physical-systems.html#sigProGalleria1b1c874984