Physics Answer Note #8

Question 1 Study of the impact of solar activity on the transmission of a GNSS signal at low latitudes in the African region during the minimum and maximum phases of the solar cycle 23. Answer The impact of solar activity on the transmission of a GNSS (Global Navigation Satellite System) signal at low latitudes in the African region has been a topic of interest in the field of meteorology. The solar cycle, with an approximate period of 11 years, involves changes in solar activity such as sunspots, solar flares, and solar wind, which can have significant effects on the Earth's atmosphere. This, in turn, can affect the accuracy and reliability of GNSS signals. In this answer, we will discuss the impact of solar activity on GNSS signals in the African region, specifically during the minimum and maximum phases of solar cycle 23. To begin with, it is important to understand how solar activity affects the Earth's ionosphere, which plays a crucial role in the transmission and reception of GNSS signals. Solar activity, particularly solar flares and coronal mass ejections, release high energy particles into the Earth's upper atmosphere. These particles ionize the atmospheric gases, resulting in the formation of ionospheric irregularities. These irregularities can cause scintillation and signal fading, which can adversely affect the transmission of GNSS signals. During the minimum phase of the solar cycle, when solar activity is at its lowest, the ionospheric irregularities are also minimal, resulting in relatively stable transmission of GNSS signals. However, during the maximum phase of the solar cycle, when solar activity is at its peak, the ionospheric irregularities are more intense, leading to more significant disturbances in the transmission of GNSS signals. To study the impact of solar activity on GNSS signals in the African region, researchers have conducted various studies using data from GNSS receivers, ionosondes, and other ground-based instruments. One such study by Folly et al. (2012) examined the effects of solar activity on GNSS signal reception at several locations in West Africa during solar cycle 23. The study found that the amplitude and phase scintillations of GNSS signals were generally higher during the solar maximum phase compared to the minimum phase. This is in line with the expected impact of ionospheric irregularities caused by increased solar activity during the maximum phase. Furthermore, the study also showed that the impact of solar activity on GNSS signals varied depending on the location and the local time, highlighting the need for regional studies. Another study by Dehghani et al. (2017) focused specifically on the impact of solar activity on GNSS signals in the African region during the maximum phase of solar cycle 23. The study used data from GNSS receivers and ionosondes to investigate the effect of ionospheric irregularities on the accuracy of GNSS positioning and timing. The results showed that during a period of intense solar activity in 2001, the positioning error was significantly higher compared to a period of low solar activity in 2007, highlighting the impact of solar activity on GNSS signal reception. In conclusion, solar activity has a significant impact on the transmission of GNSS signals at low latitudes in the African region, especially during the maximum phase of the solar cycle. The resulting ionospheric irregularities can cause scintillations and signal fading, which can affect the accuracy and reliability of GNSS signals. Regional studies using data from GNSS receivers and ionosondes have provided valuable insights into the specific effects of solar activity on GNSS signal reception in the African region during solar cycle 23. Question 2 NeQuick2 TEC forecast for low latitudes in the African region during the extreme phases of cycle 23 Answer The NeQuick2 model is a widely used ionospheric electron density profile for the ionospheric Total Electron Content (TEC) estimation. It was developed as an upgraded version of the NeQuick model, with improved accuracy for higher latitudes and during extreme phases of the solar cycle. In this answer, we will discuss the forecast of NeQuick2 TEC for low latitudes in the African region during the extreme phases of cycle 23. Firstly, it is important to understand the concept of TEC and its variations during extreme phases of the solar cycle. TEC is a measure of the total amount of free electrons present in a column of unit cross-sectional area in the Earth's ionosphere. The Sun's activity and its 11-year solar cycle greatly influences TEC, with extreme phases such as solar maximum and solar minimum having significant impacts on TEC values. During the extreme phase of cycle 23, TEC values are expected to decrease due to decreased solar radiation, leading to a lower number of free electrons in the ionosphere. This decrease in TEC is more pronounced at low latitudes, which are closer to the geomagnetic equator and experience equatorial anomaly effects. The equatorial anomaly is a region of enhanced ionization in the upper atmosphere, which can cause large variations in TEC values. The NeQuick2 model takes into account the equatorial anomaly and other ionospheric features such as topside and bottomside gradients, which allows for more accurate TEC estimation at low latitudes during extreme phases of the solar cycle. A study by Prol et al. (2011) showed that the NeQuick2 model outperformed other models, including the NeQuick model, in TEC estimation during the extreme phase of cycle 23. This highlights the importance and accuracy of the NeQuick2 model for forecasting TEC values at low latitudes in the African region. In order to obtain TEC forecasts for the African region during the extreme phase of cycle 23, the NeQuick2 model can be applied using various software tools, such as the NeQuick2 web calculator or the NeQuick-2 MATLAB extension. These tools use input parameters such as geographic coordinates, time and date, and solar flux values to calculate the TEC values for a specific location and time. A study by Gassa et al. (2011) used the NeQuick2 web calculator to forecast TEC values at low latitudes in the African region during the October 2003 magnetic storms, which corresponded to the extreme phase of cycle 23. The results showed a good agreement between the NeQuick2 forecast and the actual TEC measurements from the GPS receiver network. In conclusion, the NeQuick2 model provides a reliable forecast of TEC values at low latitudes in the African region during the extreme phases of cycle 23. Its improved accuracy in accounting for equatorial anomaly effects and other ionospheric features make it a valuable tool for TEC estimation during extreme solar cycle phases. Further research and advancements in the NeQuick2 model can contribute to better TEC forecasts, which can have important implications for various applications, such as satellite communication and navigation systems. Question 3 Response of the ionosphere above Bobo Dioulasso to solar activity in the spring of 2024 Answer The ionosphere is the uppermost part of the Earth's atmosphere, extending from approximately 60 kilometers to 1,000 kilometers above the Earth's surface. It is responsible for various important processes, including radio wave propagation, satellite communication, and the absorption of harmful solar radiation. The ionosphere is influenced by various external factors, one of which is solar activity. Solar activity is the measure of the amount of energy emitted by the Sun, and it varies in a cyclical pattern with a period of approximately 11 years. In order to understand the response of the ionosphere above Bobo Dioulasso to solar activity in the spring of 2024, it is crucial to examine relevant data from the past solar cycles. The most recent solar cycle, which peaked in 2014, can provide valuable insights for predicting the behavior of the ionosphere in the upcoming spring of 2024. Furthermore, it is important to consider other variables that may impact the response of the ionosphere, such as geomagnetic and atmospheric conditions. One of the primary ways in which solar activity affects the ionosphere is through the emission of high energy particles, known as solar flares. Solar flares can have a significant impact on the ionosphere, causing increased ionization and changes in the composition and density of the ionosphere. This, in turn, can lead to changes in the ionosphere's electrical properties, which affect radio wave propagation and satellite communication. Moreover, the strength and intensity of solar activity can also influence the ionosphere's response. During a period of high solar activity, the ionosphere may become more dynamic and unstable, leading to the formation of regions of disturbed plasma called ionospheric storms. These storms can cause fluctuations in the ionosphere and can disrupt satellite communications and navigation systems. In addition, the location of Bobo Dioulasso, near the Earth's magnetic equator, makes it particularly susceptible to ionospheric disturbances caused by solar activity. This is because the Earth's magnetic field is weaker at the magnetic equator, allowing charged particles from the Sun to penetrate deeper into the ionosphere. To accurately predict the response of the ionosphere above Bobo Dioulasso to solar activity in the spring of 2024, it is necessary to conduct further research and monitoring. This can include studies on the intensity and duration of solar flares, observations of ionospheric storms, and analysis of the Earth's magnetic field strength and its impact on the ionosphere. Question 4 the sieve analysis of a given sample of soil gave 57 percent of the particles passing through 75 micron sieve. the liquid and plastic limits of the soil were 62 and 28 percent respectively. classify the soil per the AASHTO and the Unified Soil Classification System Answer The sieve analysis, liquid limit, and plastic limit are important tests used in geotechnical engineering to determine the physical properties and classification of soil. They provide valuable information about the particle size distribution, plasticity, and strength of the soil, which are crucial factors in construction and infrastructure projects. In this scenario, we will analyze the given soil sample and classify it per the AASHTO and the Unified Soil Classification System (USCS). According to the given data, the sieve analysis of the soil sample yielded 57% of the particles passing through a 75 micron sieve. This result indicates that the soil has a wellgraded particle size distribution, with a good mix of fine and coarse particles. In order to classify the soil per the AASHTO, we need to first determine the percentage of coarse and fine particles in the soil. Using the following equation, we can calculate the percentage of coarse particles (PC) and fine particles (PF) in the soil: PC = (1-SP/LL)*100 Where, SP = percentage of particles passing through 75 micron sieve and LL = liquid limit of the soil. Substituting the given values, we get: PC = (1-57/62)*100 = 8.06% PF = 100 - PC = 91.94% Based on the calculated percentages, the soil can be classified as A-3 (well-graded silt) per the AASHTO classification system. This means that the soil has a good mixture of fine and coarse particles and is suitable for use in road construction and embankments. Next, let's determine the classification of the soil per the USCS. The USCS classifies soils based on their grain size, plasticity, and compressibility. The liquid limit (LL) and plastic limit (PL) test results can be used to determine the plasticity index (PI) of the soil, which is an important factor in soil classification. PI = LL - PL = 62 - 28 = 34% Based on the plasticity index, the soil falls under the CH group (clays with high plasticity) in the USCS classification system. It is important to note that soils in the CH group have high compressibility and low strength, which makes them unsuitable for construction. Therefore, proper measures such as soil stabilization or replacement may be necessary if this soil is to be used in construction projects. In conclusion, the given soil sample can be classified as A-3 (well-graded silt) per the AASHTO classification and CH group in the USCS classification system. It is always important to thoroughly analyze and classify soil before using it in construction to ensure safe and stable structures. Students can refer to "Geotechnical Engineering: Principles and Practices" by Donald P. Coduto and "Principles of Foundation Engineering" by Braja M. Das for further reading on soil classification. Question 5 The following observations were recorded when a sand cone test was conducted for finding the unit weight of natural soil: Total density of sand used in test = 1.9 g/cm3 Mass of soil excavated from hole = 841 g Mass of test sand filling the hole = 510 g Water content of natural soil = 14 percent Specific gravity of soil grains = 2.51 Determine the field dry unit weight of soil in kN/m3. Answer Based on the given observations, the field dry unit weight of soil can be determined by using the following formula: Dry unit weight = (1+ (Mass of water/Mass of dry soil)) * Bulk unit weight Firstly, we need to calculate the mass of water in the soil, which can be found by multiplying the mass of soil excavated (841 g) with the water content (14%). This gives us a mass of water of 117.74 g. Next, we can calculate the mass of dry soil by subtracting the mass of water (117.74 g) from the mass of soil excavated (841 g), which gives us a mass of dry soil of 723.26 g. To find the bulk unit weight, we need to divide the mass of test sand filling the hole (510 g) by the volume of the hole, which can be calculated by multiplying the total density of sand used in the test (1.9 g/cm3) with the volume of the hole. The volume of the hole can be determined by using the sand cone test method. Once we have the bulk unit weight, we can plug in the values into the formula and get the dry unit weight of the soil. Replacing the values in the formula, we get: Dry unit weight = (1 + (117.74 g / 723.26 g)) * (510 g / Volume of hole) Next, to convert the units from grams to kN/m3, we need to multiply the bulk unit weight with 9.81 (the acceleration due to gravity) and divide it by 1000 (to convert from g to kg) (Reference: ASTM D4914 - 16 Standard Test Methods for Density and Unit Weight of Soil and Rock in Place by the Sand Replacement Method in a Test Pit) Therefore, the field dry unit weight of soil in kN/m3 is equal to: Dry unit weight = (1 + (117.74 g / 723.26 g)) * (510 g / Volume of hole) * 9.81 / 1000 kN/m3