Search results for: Amgad A. Farghal

Authors: Amgad A. Farghal, Dina I. El Desouki

Abstract:

Energy consumption and users’ satisfaction were compared in three LEED certified office buildings in turkey and an office building in Egypt. The field studies were conducted in summer 2012. The measured environmental parameters in the four buildings were indoor air temperature, relative humidity, CO2 percentage and light intensity. The traditional building is located in Smart Village in Abu Rawash, Cairo, Egypt. The building was studied for 7 days resulting in 84 responds. The three rated buildings are in Istanbul; Turkey. A Platinum LEED certified office building is owned by BASF and gained a platinum certificate for new construction and major renovation. The building was studied for 3 days resulting in 13 responds. A Gold LEED certified office building is owned by BASF and gained a gold certificate for new construction and major renovation. The building was studied for 2 days resulting in 10 responds. A silver LEED certified office building is owned by Unilever and gained a silver certificate for commercial interiors. The building was studied for 7 days resulting in 84 responds. The results showed that all buildings had no significant difference regarding occupants’ satisfaction with the amount of lighting, noise level, odor and access to the outdoor view. There was significant difference between occupants’ satisfaction in LEED certified buildings and the traditional building regarding the thermal environment and the perception of the general environment (colors, carpet and decoration. The findings suggest that careful design could lead to a certified building that enhances the thermal environment and the perception of the indoor environment leading to energy consumption without scarifying occupants’ satisfaction.

Keywords: energy consumption, occupants’ satisfaction, rating systems, office buildings

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Authors: Amgad Rezk, Heba Ahmed, Leslie Yeo

Abstract:

Ultrasound presents a powerful means for material synthesis. In this talk, we showcase a new field demonstrating the possibility for harnessing sound energy sources at considerably higher frequencies (10 MHz to 1 GHz) compared to conventional ultrasound (kHz and up to ~2 MHz) for crystalising and manipulating a variety of nanoscale materials. At these frequencies, cavitation—which underpins most sonochemical processes—is largely absent, suggesting that altogether fundamentally different mechanisms are at dominant. Examples include the crystallization of highly oriented structures, quasi-2D metal-organic frameworks and nanocomposites. These fascinating examples reveal how the highly nonlinear electromechanical coupling associated with high-frequency surface vibration gives rise to molecular ordering and assembly on the nano and microscale.

Keywords: high-frequency acoustics, microfluidics, crystallisation, composite nanomaterials

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Authors: Alaa A. Osman, Amgad M. Bayoumy Aly, Ismail El baialy, Osama E. Abdellatif, Essam E. Khallil

Abstract:

In this paper, a numerical simulation of a finned store separating from a wing-pylon configuration has been studied and validated. A dynamic unstructured tetrahedral mesh approach is accomplished by using three grid sizes to numerically solving the discretized three dimensional, inviscid and compressible Navier-stokes equations. The method used for computations of separation of an external store assuming quasi-steady flow condition. Computations of quasi-steady flow have been directly coupled to a six degree-of-freedom (6DOF) rigid-body motion code to generate store trajectories. The pressure coefficients at four different angular cuts and time histories of various trajectory parameters during the store separation are compared for every grid size with published experimental data.

Keywords: CFD modelling, transonic store separation, quasi-steady flow, moving-body trajectories

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Authors: Omar A. Farghal

Abstract:

This research aimed to experimentally and analytically investigate the contribution of bonded web carbon fiber reinforced polymer (CFRP) sheets to the shear strength of reinforced concrete (RC) T-beams. Two strengthening techniques using CFRP strips were applied along the shear-span zone: the first one is vertical U-jacket and the later is vertical strips bonded to the beam sides only. Fibers of both U-jacket and side sheets were vertically oriented (θ = 90°). Test results showed that the strengthening technique with U-jacket CFRP sheets improved the shear strength particularly. Three mechanisms of failure were recognized for the tested beams depending upon the end condition of the bonded CFRP sheet. Although the failure mode for the different beams was a brittle one, the strengthened beams provided with U-jacket CFRP sheets showed more or less a ductile behavior at a higher loading level up to a load level just before failure. As a consequence, these beams approved an acceptable enhancement in the structural ductility. Moreover, the obtained results concerning both the strains induced in the CFRP sheets and the maximum loads are used to study the applicability of the analytical models proposed in this study (ACI code) to predict: the nominal shear strength of the strengthened beams.

Keywords: carbon fiber reinforced polymer, wrapping, ductility, shear strengthening

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Authors: Philipp Sommer, Amgad Agoub

Abstract:

The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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