QUANTITATIVE SCHLIEREN MEASUREMENT OF 3 DIMENSIONAL TEMPERATURE, CONCENTRATION AND VELOCITY FIELDS IN A GAS FLOW

Background Oriented Schlieren (BOS) estimates the flow behaviour that exists between the camera and background from the shift in the background image features due to the change in the transfer channel function. The current optical flow techniques used to find the deflection vectors of the change in background images rely on two main assumptions: global constant value of intensity and continuity of local motion. The global intensity invariance assumption hardly works for BOS technique when imaging a self luminous flow. In this thesis, an optical flow equation which takes the change in intensity into account and an estimation motion model that considers both translational and rotational deflections were developed. The results showed that for a transparent gas jet all the tested optical flow algorithms worked well. However the proposed model gave better results for BOS images taken through natural gas flames and smoke from a fog generator. The developed deflection vector estimation algorithm and optical tomography served as a tool to extract the index of refraction of the gaseous fields. The Gladstone-Dale relationship was used to show the direct correlation between the index of refraction and density of the flow. Three different types of axi-symmetric flows were used as gas sample media. These were a CNG injected fuel jet, an open methane flame and a hot air jet. Based on the measured index of refraction the species mole fractions of CNG injected jet and methane flame were measured. In addition, the three dimensional temperature fields of the methane flame and the hot air were also measured and displayed. The other main contribution of this research was the use of Background Oriented Schlieren (BOS) technique for the measurement of the velocity field of a variable density round jet. The density field was further exploited to extract the axial and radial velocity vectors for six different jet-exit temperature values with the aid of the continuity and energy equations. Results of the measured temperature and velocity vector fields were compared with thermocouples and hot wire anemometry readings respectively and showed good agreements

QUANTITATIVE SCHLIEREN MEASUREMENT OF 3 DIMENSIONAL TEMPERATURE, CONCENTRATION AND VELOCITY FIELDS IN A GAS FLOW

Background Oriented Schlieren (BOS) estimates the flow behaviour that exists between the camera and background from the shift in the background image features due to the change in the transfer channel function. The current optical flow techniques used to find the deflection vectors of the change in background images rely on two main assumptions: global constant value of intensity and continuity of local motion. The global intensity invariance assumption hardly works for BOS technique when imaging a self luminous flow. In this thesis, an optical flow equation which takes the change in intensity into account and an estimation motion model that considers both translational and rotational deflections were developed. The results showed that for a transparent gas jet all the tested optical flow algorithms worked well. However the proposed model gave better results for BOS images taken through natural gas flames and smoke from a fog generator. The developed deflection vector estimation algorithm and optical tomography served as a tool to extract the index of refraction of the gaseous fields. The Gladstone-Dale relationship was used to show the direct correlation between the index of refraction and density of the flow. Three different types of axi-symmetric flows were used as gas sample media. These were a CNG injected fuel jet, an open methane flame and a hot air jet. Based on the measured index of refraction the species mole fractions of CNG injected jet and methane flame were measured. In addition, the three dimensional temperature fields of the methane flame and the hot air were also measured and displayed. The other main contribution of this research was the use of Background Oriented Schlieren (BOS) technique for the measurement of the velocity field of a variable density round jet. The density field was further exploited to extract the axial and radial velocity vectors for six different jet-exit temperature values with the aid of the continuity and energy equations. Results of the measured temperature and velocity vector fields were compared with thermocouples and hot wire anemometry readings respectively and showed good agreements

Application of Rain Intensity Dependent Rain Admittance Factor (RAF) in Hygrothermal Performance Assessment of Wall Systems

Wind-driven rain (WDR) is one of the main moisture loading sources on the exterior enclosures. The direct impact of wind-driven rain on the hygrothermal performance of building envelope has been well documented. Rain admittance factor (RAF) and rain penetration values characterize the amount of water reaching the exterior surface and the exterior surface of the water-resistive barrier respectively based on measured horizontal rain intensity. In common RAF factor calculation from horizontal rainfall data procedures, such as ASHRAE 160, RAF values are not affected by the intensity of the rainfall. However, a previous study shows RAF coefficients are sensitive to the rainfall intensity. Thus it is important to investigate how the sensitivity of using horizontal rainfall intensity dependent RAF factors and the subsequent rain penetration relates to hygrothermal performance assessment of building envelope components. This study is based on five years of WDR and horizontal rainfall data collected at different orientations of façades at a two-story test building in a mild coastal climate. The data is categorized into two sets based on rain intensity. The correlation between the measured moisture content on the sheathing board of a building envelope at different points utilizing RAF values based on the proposed approach and the overall measured RAF values is studied using WUFI simulation. Results show that an average percentage difference between the moisture content values of a sheathing board using RAF values of the rain intensity dependent approach and the overall RAF measured value can be as large as 9 %

Highly Insulated Wall Systems with Exterior Insulation of Polyisocyanurate under Different Facer Materials: Material Characterization and Long-Term Hygrothermal Performance Assessment

The application of exterior insulation in both new construction and retrofits is a common practice to enhance the energy efficiency of buildings. In addition to increased thermal performance, the rigid insulation can serve to keep the sheathing board warm and serve as a water-resistive barrier to keep moisture-related problems due to condensation and wind-driven rain. Polyisocyanurate (PIR) rigid boards have a higher thermal resistance in comparison to other commonly used exterior insulation boards. However, because of its perceived lower permeance, its use as exterior insulation is not very common. In this study, the hygrothermal property of PIR boards with different facer types and thicknesses is characterized. The material data obtained through experimental test and extrapolation is used in a long term hygrothermal performance assessment of a wood frame wall with PIR boards as exterior insulation. Results show that PIR with no facer has the smallest accumulated moisture on the sheathing board in comparison to other insulation boards. Walls with a bigger thickness of exterior insulation perform better when no vapor barrier is used. The PIR exterior insulation supports the moisture control strategy well in colder climates in perfect wall scenarios, where there is no air leakage and moisture intrusion. In cases where there is trapped moisture, the sheathing board has a higher moisture content with PIR boards with both aluminum or fiberglass type facers. An innovative facer material development for PIR boards can help efforts targeting improved energy-efficient and durable wall systems

Application of Machine Learning to Assist a Moisture Durability Tool

The design of moisture-durable building enclosures is complicated by the number of materials, exposure conditions, and performance requirements. Hygrothermal simulations are used to assess moisture durability, but these require in-depth knowledge to be properly implemented. Machine learning (ML) offers the opportunity to simplify the design process by eliminating the need to carry out hygrothermal simulations. ML was used to assess the moisture durability of a building enclosure design and simplify the design process. This work used ML to predict the mold index and maximum moisture content of layers in typical residential wall constructions. Results show that ML, within the constraints of the construction, including exposure conditions, does an excellent job in predicting performance compared to hygrothermal simulations with a coefficient of determination, R2, over 0.90. Furthermore, the results indicate that the material properties of the vapor barrier and continuous insulation layer are strongly correlated to performance