Saudi Cultural Missions Theses & Dissertations
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Item Restricted Investigation into the Environmental Improvement of Passive Solar Design for Cooling (Comfort) in Residential Buildings in Saudi Arabia(Saudi Digital Library, 2025-01) Albalawi, Abeer; Grant, JohnThis dissertation investigates the environmental improvement of passive solar design for cooling residential buildings within Saudi Arabia’s unique climatic and cultural context. The research employs a mixed-methods approach, incorporating case studies and surveys, to evaluate the integration of passive solar techniques and renewable energy technologies, such as photovoltaic systems and heat pumps. Key findings reveal that hybrid systems combining passive solar design with photovoltaics can reduce energy consumption by up to 70.7%, while ground-source heat pumps achieve a 34% reduction in CO₂ emissions compared to air-source systems. The study also highlights the feasibility and cultural compatibility of integrating traditional architectural elements, like Mashrabiya, with modern sustainable solutions. These results underscore the potential of passive solar design and hybrid systems to align with Saudi Arabia’s Vision 2030 sustainability goals. However, challenges such as high initial costs, limited public awareness, and expertise gaps necessitate targeted policy interventions, capacity building, and public education initiatives. This research provides valuable insights into sustainable housing practices, offering actionable recommendations for enhancing energy efficiency, reducing carbon emissions, and advancing environmentally responsible construction in extreme climates.13 0Item Restricted Evaluation of Prefabricated Construction Systems and Materials' Thermal Performance with Reference to Housing Construction in Saudi Arabia.(University of Nottingham, 2025) Alkelani, Abdulaziz; Gadi, MohamedIn light of recent revisions to international standards, such as those advocated by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), the prioritisation of improved air circulation to support more energy-efficient ventilation systems has become evident. These systems simultaneously enhance occupant satisfaction and thermal comfort. Within this framework, the current research systematically examines the thermal performance and comfort of prefabricated houses in Saudi Arabia, a subject of increasing importance given the rise in summertime temperatures, consistent with the global increase in temperatures. The investigation distinctly outlines the implications of individual components of prefabricated buildings, particularly building envelope components, on the comprehensive thermal performance in the extreme climate conditions prevalent in Saudi Arabia. It ventures to shape innovative prospects in the Saudi prefabricated construction industry, emphasising the reduction of energy expenses while elevating the quality of the indoor environment through the introduction of high-performance prefabricated building components and systems. In Saudi Arabia, characterised by a harsh and hot climate, the residential sector accounts for nearly 50% of national energy consumption. With energy demand expected to rise further, this research investigates the thermal performance and thermal comfort potential of prefabricated housing as a sustainable alternative. The study prioritises optimising building envelope components, developing high-performance precast systems, and providing design guidelines to reduce energy consumption and enhance indoor thermal comfort. It is evident that the study centred its investigation on natural ventilation from the initial stage. Consequently, it revealed a significant reduction in total discomfort hours across various cities in Saudi Arabia. Optimal performance, characterised by minimal total discomfort hours, was observed in cities characterised by lower humidity levels. This suggests that cities with higher relative humidity, exemplified by Jeddah, exhibit extended discomfort hours and encounter challenges in achieving markedly low discomfort hours compared to drier cities like Riyadh, the capital city of Saudi Arabia. The research employs field observations of existing prefabricated houses in Saudi Arabia and simulation tools to evaluate and optimise thermal performance. Findings reveal substantial reductions in total discomfort hours across various zones, with optimisations achieving up to 32% reductions in specific zones. Key innovations include the use of phase change materials (PCMs) with a melting point of 23°C, improved insulation strategies, and optimised window-to-wall ratios, achieving reductions of up to 48% in cooling loads, 99.95% in heating loads, and 51.6% in annual energy consumption for air conditioning. The study culminates in a tangible design product: a high-performance precast system tailored for extreme climates, offering transformative solutions for sustainable construction practices in Saudi Arabia.56 0Item Restricted A VALUE-BASED MODELING FRAMEWORK FOR SOLAR ENERGY UTILIZATION AND MONITORING(Saudi Digital Library, 2023-12-08) Alanizi, Muslat Abdulrahman; Jololian, LeonWe have developed and presented a value-based modeling (VBM) framework for optimal solar energy utilization and monitoring. Our model adopts a universal approach that prioritizes values to ensuring a comprehensive analysis of solar energy systems by recognizing the complexities and intricacies of the renewable energy landscape. To determine the robustness and applicability of our VBM framework, we subjected it to a real-world test through a detailed case study focusing on Net-Metering Monitoring System. This validation reinforced the model's efficacy and showcased its potential as a dynamic tool for decision-making in solar energy. Using Shannon's entropy method, we recorded the optimal efficiency in solar power usage of the case study. These results, in terms of entropy values, highlight the stable and efficient use of solar energy after the implication of our value-based modeling framework. Additionally, our model has proven highly predictive, delivering accurate forecasts for net-metering values. Such predictive accuracy emphasizes the model's potential to assist utility providers, policymakers, and consumers make informed decisions about solar energy utilization. Hence, we introduce a pioneering Value-based Modeling framework for solar energy and highlight its practical significance and potential impact in optimizing and monitoring solar energy systems. Ultimately, we encouraged a sustainable and value-driven energy future. Keywords: Value-based Modeling, Solar Energy, Monitoring, Enterprise Systems, Net-metering, Process Improvement31 0Item Restricted RUN-TIME CONFIGURABLE APPROXIMATE MULTIPLIER DESIGN(Saudi Digital Library, 2023-08-14) Haddadi, Ibrahim; Shafik, Rishad; Xia, Fei; Yakovlev, AlexThe complexity of arithmetic continues to be an issue in the design of high-performance and energy-efficient hardware. The problem is further exacerbated in systems powered by variable power levels can limit their computation capabilities. Multipliers constitute a major component of these applications with complex logic design and a large gate count compared to other arithmetic units. As such, there is significant interest in designing new approaches to low-complexity multipliers. Recently, approximate arithmetic, in particular approximate adders and multipliers, have shown notable advantages to benefit from a wide spectrum of naturally imprecise-tolerant applications, such as image processing, pattern recognition, and machine learning (ML). The concept of approximate arithmetic involves replacing system components of normal degrees of complexity with less complex components, which may provide reduced accuracy. Compared to the adder, the multiplier is a crucial component of these applications with complex logic design and a large gate count. This thesis investigates the possibility and profitability to trade accuracy for energy at run-time by using configurable approximate arithmetic hardware. In the first approach, a configurable adaptive approximation method for multiplication is proposed. The extra overheads associated with in the configuration circuits prove to be negligible compared to the multiplier’s costs. Central to the proposed approach is a significance-driven logic compression (SDLC) multiplier architecture that can dynamically adjust the level of approximation depending on the run-time power/accuracy constraints. The architecture can be configured to operate in the exact mode (no approximation) or in progressively higher approximation modes (i.e. 2 to 4-bit SDLC). In the second approach, a novel ML hardware design method centred around multiply–accumulate (MAC) units is presented. Core to the configurable MAC design is a configurable multiplier. In the third approach, a configurable modified activation function is proposed to minimize the prediction error of the configurable MAC design. To evaluate and validate the trade-offs, the three approaches (configurable multiplier, MAC unit and modified activation function) are designed in System-Verilog and synthesized using Synopsys Design Compiler, employing a UMC 90nm digital complementary metal-oxide semiconductor (CMOS) technology as well as on Field Programmable Gate Arrays (FPGAs), and then compared with other available methods. These improvements come at the expense of errors introduced into the circuit and investigated. The efficacy of the first approach (configurable multiplier) technique is evaluated with a real life image processing application, which consists of additions and multiplications using the proposed three multiplier configurations (Exact, 2- and 4-bit SDLC). The analysis considers the Gaussian blur filter since it is widely used in image processing application, typically to reduce image noise and artifacts by acting as a low-pass filter. Additionally, the second and third approaches are evaluated as the key processing blocks in a multi-layer perceptron (MLP) network in order to validate the dynamic tunability between accuracy and power consumption. As case studies, the MLP is trained using well-known machine learning (ML) datasets. The configurable multiplier design (first approach) can be suitably used for energy-efficient multiplier designs, where quality requirements can be relaxed. The second and the third approaches (configurable MAC unit and activation function) can also be used within the power-adaptive neuron modules with a minimal loss in output quality compared to those used in previous studies.40 0