Physics الفيزياء
Permanent URI for this collection
Browse
Recent Submissions
- ItemA COMPARATIVE STUDY OF MAGNETIC CHARACTERIZATION FOR MAGNETOCALORIC MATERIALS(Al-Quds University, 2024-12-14) Nihal Mahmoud Abd Alhamed Abuassba; نهال محمود عبد الحميد ابو عصبهThe demand for an alternative to traditional refrigeration systems has grown during a period of rising energy consumption, which is predicted to triple by 2050. This is particularly true given that traditional refrigerators contribute to the ozone layer's thinning. However, research is still ongoing to obtain the best magnetic material and overcome the materials relating issues like mechanical stability, hysteresis/reversibility, or formability. This thesis presents the current trend of magnetocaloric materials and highlights the top families of compounds and alloys that have received attention in recent years. It also introduces a comparative analysis of the magnetocaloric and magnetic properties of the current trend materials (La(Fe,Si)13, Ni2–Mn-X-based Heusler alloys (H•A•s), and the High entropy alloys (HEAs)) according to their physical form, synthesis method, the elements of compounds and their proportions, and the effect of doping on those properties. In Ni2MnGa, the bulk single crystal and polycrystalline bulk show good magnetocaloric properties in contrast to the microwires and nanowires. And in La(Fe,Si)13, the melt spinning and ball milling show huge quantities of La(Fe,Si)13 with a high purity in a very short amount of annealing time in contrast to the arc melting, which shows a large annealing time due to the slow cooling process. Also, the Curie temperature of La(Fe,Si)13 can be increased by doping with Al or the light element H as a result of the strong exchange interaction that overcomes the kinetic energy of the itinerant electrons. In contrast to RE-free High entropy alloys, which exhibit a Curie temperature near room temperature but a small magnetic entropy change, RE-based High entropy alloys exhibit a low Curie temperature because of the weak exchange interaction and large magnetic entropy change caused by the large magnetic moments present in the rare earth elements. This study focused on the bulk properties of those MCE materials. To examine structural and magnetic properties in detail, one can use advanced techniques like X-ray absorption spectroscopy, high magnetic field measurements, and neutron scattering because they provide localized information and focus on specific atomic and magnetic environments.
- ItemSecuring electricity needs from rooftop solar PV systems: A case study of three schools in Beit Fajjar-Bethlehem-Palestine(Al-Quds University, 2023-12-23) Ali yousef Taqatqa; علي يوسف طقاطقةIn Palestine the energy sector suffers due to the various policies of the Israeli occupation, and the energy sector in Palestine is considered expensive as a result of the dependence of the industrial sector in Palestine directly on electricity, so it is necessary to resort to sources of renewable energy as a new source of energy. Solar energy systems are one of the most important renewable energy sources in the world, and the countries of the world today seek to exploit this method in securing their energy needs. To exploit solar energy must be available the spaces on which solar projects should be placed, hence the idea of this study in exploiting the roofs of almost empty schools in the generation of electricity through solar photovoltaic projects, because schools close their doors about 180 days a year, and have a period of working a day with a maximum of 7 hours, as well as the low energy consumption, all of these stimulate the exploitation of these spaces available on school roofs to generate solar energy. In order to study the feasibility of exploiting the roofs of buildings to generate electrical energy, in this study, PVsyst software was used to build and design three photovoltaic systems on the roofs of three schools of different area and amount of consumption, and then calculate the income and payback period. The total area used in the three projects combined was about 901 m2, the total number of panels used was 345 panels, and the capacity of the three systems 189.75 Kw, so the annual production 314681 kwh. From the electricity company’s readings, it was found that the three schools consumes an amount of electricity amounting to 39401 kwh, so the annual surplus amount from the three schools is about 268653 kwh, and its annual selling price reaches 150445.68 NIS. The number of schools in Palestine 3075 When this project is implemented on the roofs of these schools, they will produce 322.54 Gwh, which represents 4.98% of the annual consumption of electricity in Palestine, in addition to the schools’ energy consumption, which represents 0.6% of the annual consumption. The total cost of the project will reach 816 million NIS with a payback period of about 5 years, and the total annual income from selling the surplus electricity for schools will be 154 million NIS. This will help reduce the bill for purchasing electricity from Israel, as it will save the Palestinian treasury an amount of 150. 5 million NIS after about 5 years
- ItemWind Speed Prediction Using Machine Learning Algorithms: A Case Study of Using ANFIS and KNNR(Al-Quds University, 2023-07-26) Khalil Shiban Mahmoud Abuayyash; خليل شيبان محمود ابو عياشWind speed prediction using machine learning algorithms is crucial for various applications, such as wind energy planning and urban development. This paper presents a case study on wind speed prediction in Jerusalem using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and K-Nearest Neighbors Regression (KNNR) algorithms. The study evaluates their performance using multiple metrics, including root mean square (RMSE), bias, and coefficient of determination R2. ANFIS demonstrates good accuracy with lower RMSE (0.196) and minimal bias (0.0003). However, there is room for improvement in capturing overall variability (R2 = 0.15). In contrast, KNNR exhibits a higher R2 (0.4093), indicating a better fit, but with a higher RMSE (1.4209). This study provides insights into the applicability of ANFIS and KNNR in wind speed prediction for Jerusalem and suggests future research directions. The outcomes have practical implications for wind energy planning, urban development, and environmental assessments in similar regions.
- ItemMeasurement of Radioactivity in Some Liquid Foodstuffs Samples Collected from Palestinian Markets(Al-Quds University, 2023-07-26) Anwar Mohammad Ali Al-Jaradat; أنوار محمد علي الجراداتRadon has been identified as one of mankind's health dangers because long-term radon exposure raises the risk of getting lung cancer. The objectives of this thesis to measurement the concentration of radon and radium in 57 samples of liquid foodstuffs collected from different markets from West Bank and describes radioactivity as an alpha particle created by the decay of radium to radon, by using passive technique - Solid State Nuclear Detectors SSNTDs (CR39) which were etched chemically after 90 days of exposure by using 6.25 M of NaOH at 〖70〗^°, then optical microscope was used to calculate the average number of tracks per unit area then calculated the radon concentrations , the effective radium contents, the annual effective Dose for inhalation and ingestion of radon and effective dose equivalent.This study showed the results of both the concentration of radon and radium, and it was as follows; The results of radon concentration levels in fruit juice samples are getting between 51.0 Bq/m^3 to 278.8 Bq/m^3 and the concentration of radium getting between 0.714 Bq⁄kg to 3.90 Bq⁄kg . The results of radon concentration levels in milk samples are getting between 61.2 Bq/m^3 to 258.4 Bq/m^3 , the concentration of radium getting between 0.85 Bq⁄kg to 3.61 Bq⁄kg with average 2.6 Bq⁄kg . The results of levels of radon concentration in soft drinks and other liquid foodstuffs samples are getting between 64.6Bq/m^3 to 224.4 Bq/m^3 , and the concentration of radium getting between 0.90 Bq⁄kg to 3.14 Bq⁄kg . The obtained values show that the levels of radon and radium concentration of all samples are less than the safe limits as recommended by ICRP, there is no health hazards related to radiation.
- ItemWind Speed Prediction Using Machine Learning Algorithms: A Case Study of Using ANFIS and KNNR(2023) hKhalil Shiban Mahmoud Abuayyash; خليل شعبان محمود ابو عياشWind speed prediction using machine learning algorithms is crucial for various applications, such as wind energy planning and urban development. This paper presents a case study on wind speed prediction in Jerusalem using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and K-Nearest Neighbors Regression (KNNR) algorithms. The study evaluates their performance using multiple metrics, including root mean square (RMSE), bias, and coefficient of determination R2. ANFIS demonstrates good accuracy with lower RMSE (0.196) and minimal bias (0.0003). However, there is room for improvement in capturing overall variability (R2 = 0.15). In contrast, KNNR exhibits a higher R2 (0.4093), indicating a better fit, but with a higher RMSE (1.4209). This study provides insights into the applicability of ANFIS and KNNR in wind speed prediction for Jerusalem and suggests future research directions. The outcomes have practical implications for wind energy planning, urban development, and environmental assessments in similar regions.