Ranjit Shrestha

Windows play a crucial role in maintaining indoor thermal comfort, influenced by occupant behavior, passive design strategies, and advanced technologies that contribute to sustainable building practices. Despite advancements in adaptive and occupant-centric design, critical gaps remain unresolved in understanding of multi-climate adaptability, the complex interrelation between window operation and occupant behavior, and the integration of occupant roles into energy-related strategies under emerging technologies. This scoping review synthesizes peer-reviewed studies to assess the importance of window design (geometry, glazing, shading), operational strategies (manual control to AI-driven systems), and technological approaches (passive to smart systems) on thermal comfort, energy performance, and occupant behavior. Using bibliometric and scientometric analyses, the review focuses on four primary research clusters: thermal comfort and occupant behavior, window operation strategies, their impact on energy performance, and sustainability, with an emphasis on emerging trends. The findings highlight that glazing technologies, shading systems, and operational choices have a significant impact on both comfort and energy efficiency. The study develops a framework linking thermal comfort to window operation, occupant behavior, and climate context while conceptualizing a comprehensive design matrix and outlining future research directions aligned with the Sustainable Development Goals (SDG 3: health and well-being, SDG 7: clean energy, and SDG 11: sustainable cities and communities).

Windows are crucial for indoor thermal comfort, influenced by occupant behavior, passive design, and advanced technologies, significantly contributing to sustainable building practices. This systematic review, synthesizing peer-reviewed studies, assesses the impacts of window design - encompassing geometric configurations, glazing systems, and shading; operational strategies - manual to artificial intelligence (AI) driven controls; and technologies - passive to smart systems on thermal comfort, energy efficiency, and occupant behavior. Using bibliometric and scientometric analyses, the review focuses on four primary research clusters: thermal comfort and occupant behavior; window strategies for natural ventilation and passive cooling; windows behavior and its impact on energy performance; and sustainability along with emerging trends. The review highlights that window behaviour, glazing technologies, shading systems, and design strategies significantly influence thermal comfort. Despite advancements in adaptive design and occupant-centric strategies, critical gaps remain in understanding multi-climate adaptability, the complex interrelations between window operation and occupants' behavior, and the integration of occupant roles in energy-related window design strategies. This review addresses these gaps by providing a comprehensive framework linking window operation, occupant behavior, and sustainable design practices. It also traces the historical evolution of thermal comfort and offers climate-specific, occupant-centric considerations with future research aligned with SDGs 3, 7, and 11 to promote affordable, energy-efficient, and healthy building practices for sustainable communities.

Infrared thermography is a non-destructive technique that uses infrared radiation to visualize surface temperature variations. It is a versatile tool that can be used to detect a variety of problems in buildings, including insulation deficiencies, moisture intrusion, structural compromise, and electrical faults. The review paper discusses the fundamental principles of infrared thermography, the different types of infrared approaches, and the condition monitoring of buildings using infrared imaging techniques. It also discusses research showing how infrared thermography has been applied to recognize and solve different building-related problems. The article highlights the potential for infrared thermography to advance while also acknowledging its current limits. Infrared thermography is predicted to become an even more effective technique for building diagnostics with the development of more sensitive cameras and the incorporation of artificial intelligence.

Modelling and simulation have now become standard methods that serve to cut the economic costs of R&D for novel advanced systems. This paper introduces the study of modelling and simulation of the infrared thermography process to detect defects in the hydroelectric penstock. A 3-D penstock model was built in ANSYS version 19.2.0. Flat bottom holes of different sizes and depths were created on the inner surface of the model as an optimal scenario to represent the subsurface defect in the penstock. The FEM was applied to mimic the heat transfer in the proposed model. The model’s outer surface was excited at multiple excitation frequencies by a sinusoidal heat flux, and the thermal response of the model was presented in the form of thermal images to show the temperature contrast due to the presence of defects. The harmonic approximation method was applied to calculate the phase angle, and its relationship with respect to defect depth and defect size was also studied. The results confirmed that the FEM model has led to a better understanding of lock-in infrared thermography and can be used to detect subsurface defects in the hydroelectric penstock.

Recently, thermographic testing has been applied as a non-destructive evaluation technology for composite materials due to its remarkable advantages, such as its non-contact, non-destructive, and high-speed properties, it is harmless to the human body and easy to use, as well as having both qualitative and quantitative inspection performance. In this paper, multiple lock-in frequency modulated thermography is applied to investigate defects on the subsurface of polypropylene/glass fiber composites, and experimental investigations were conducted on various defect conditions for qualitative and quantitative estimation. A thin rectangle-shaped PP-GF plate of dimension 100 × 180 mm and a depth of 3 mm with various 24 defects conditions was considered as a test sample. The sample was stimulated at multiple modulation frequencies, viz., 1 Hz, 0.2 Hz, 0.1 Hz, 0.05 Hz, 0.03 Hz, and 0.01 Hz. For qualitative and quantitative evaluation, two representatives, multiple lock-in frequency modulated algorithms of lock-in thermography, HA and FFT, were applied to the raw data. Moreover, their detectability performance was compared in terms of phase contrast and signal-to-noise ratio.

Thermal imaging plays a vital role in structural health monitoring of various materials and provides insight into the defect present due to aging, deterioration, and fault during construction. This study investigated the effectiveness of spatial filters during pre-processing of thermal images and a correlation technique in post-processing, as well as exploited its application in non-destructive testing and evaluation of defects in steel structures. Two linear filters (i.e., Gaussian and Window Averaging) and a non-linear filter (i.e., Median) were implemented during pre-processing of a pulsed thermography image sequence. The effectiveness of implemented filters was then assessed using signal to noise ratio as a quality metric. The result of pre-processing revealed that each implemented filter is capable of reducing impulse noise and producing high-quality images; additionally, when comparing the signal to noise ratio, the Gaussian filter dominated both Window Averaging and Median filters. Defect size was determined using a correlation technique on a sequence of pulsed thermography images that had been pre-processed with a Gaussian filter. Finally, it is concluded that the correlation technique could be applied to the fast measurement of defect size, even though the accuracy may depend on the detection limit of thermography and defect size to depth ratio.

The growing use of composite honeycomb structures in several industries including aircraft has demonstrated the need to develop effective and efficient non-destructive evaluation methods. In recent years, active thermography has attracted great interest as a reliable technology for non-destructive testing and evaluation of composite materials due to its advantages of non-contact, non-destructive, full-area coverage, high speed, qualitative, and quantitative testing. However, non-uniform heating, low spatial resolution, and ambient environmental noise make the detection and characterization of defects challenging. Therefore, in this study, lock-in thermography (LIT) was used to detect water ingress into an aircraft composite honeycomb sandwich structure, and the phase signals were binarized through the Otsu algorithm. A square composite honeycomb with dimensions of 210 mm × 210 mm along with 16 different defective areas of various sizes in groups filled with water by 25%, 50%, 75%, and 100% of the cell volume was considered. The sample was excited at multiple modulation frequencies (i.e., 1 Hz to 0.01 Hz). The results were compared in terms of phase contrast and CNR according to the modulation frequency. In addition, the detectability was analyzed by comparing the number of pixels of water ingress in the binarized image and the theoretical calculation.