Task 1.14       Academic Writing

Study the information on academic writing skills and do the tasks and exercises.

 

THE PURPOSES OF ACADEMIC WRITING

1.1.The most common reasons for writing:

• to report on a piece of research the writer has conducted
• to answer a question the writer has been given or chosen
• to discuss a subject of common interest and give the writer’s view to synthesize research done by others on a topic

1.2.Common types of academic writing

• Notes
• Reports
• Projects
• Essays
• Dissertations/Thesis
• Papers

1.3.The most common written sources

• Textbooks
• Websites
• Journal articles
• Official reports (e.g. from government)
• Newspaper or magazine articles
• e-books

1.4.The most common requirements to your academic text

• your should give reasons for your initial hypothesis
• you should obtain more well-rounded data
• you should show the logic of your experiments
• you should present clear, consistent logical argument to  somebody else involved in the research of this field.

 

Read the introduction and answer the questions:

Introduction

The accuracy of tracking- and accelerometer-derived thermospheric density data sets is closely connected to satellite drag modelling. The previous generation of thermospheric density data sets used simplified satellite geometries. These geometries are commonly characterized by a limited number of flat panels, which aim to describe the full satellite outer surface geometry. Weaknesses in these models turned out to adversely affect the accuracy and consistency of the derived densities. Large scale differences between data sets and atmospheric models have been detected. Until now, these discrepancies have been neglected or removed using specific scale factors. However, more accurate thermospheric densities require improved satellite geometry models and rarefied flow analysis on these models. Once the geometry and aerodynamic models are enhanced, high fidelity drag coefficients can be computed to provide new density estimations.

In general, aerodynamic coefficients or ballistic coefficients can be obtained either by estimating them from tracking data during orbit determination, or by analytically or computationally modelling the aerodynamics for defined satellite geometries. When estimating drag coefficients from orbit tracking data, errors in the thermosphere density model that was used will affect the estimate. In many cases, this is desirable, for example when using the estimate for subsequent orbit predictions, based on e.g., GPS, S-Band or satellite laser ranging tracking. If the drag coefficient is used to generate independent density data sets however, it should be free of such model dependencies. Emmert (2009) applied the relations between Two-Line Element (TLE data) and thermosphere density of Picone et al. (2005), and resolved constant per-object ballistic coefficients for approximately 5000 objects in the process, based on the physical drag coefficient of one spherical reference object. For non-spherical objects, a higher fidelity modelling solution is required. If the satellite shape can be approximated by a combination of elementary shapes, this can be obtained with a closed-form analytical approach. Otherwise, a simulation of aerodynamic effects on detailed satellite geometries with physics-based rarefied gas dynamics solvers (i.e. Bird, 1994) is required. The analytical approach is accurate only for simple geometries (i.e. flat panel, sphere, cylinder, cube), which usually do not fully describe an operational satellite. Whereas, the computational methods can analyse complex shapes and provide more accurate information.

Throughout this work, physical drag coefficients have been determined for different scenarios, in order to improve current density datasets. The technique presented in this paper provides the opportunity to enhance the estimation of force coefficients and, consequently, satellite aerodynamics. The obtained improvement over the selected missions increases the understanding of the thermospheric region and new density data sets are provided as an outcome of this research.

The implemented methodology is summarized in Section 2. The adoption of a high fidelity geometry model is crucial for estimating aerodynamic coefficients. Therefore, for the introduced set of satellites, new geometries have been designed by making use of available technical drawings and pre-launch photographs. A description of the geometry modelling can be found in Section 3. The following aerodynamic investigation uses the output of this first modelling phase.

The satellite aerodynamic forces are computed by a rarefied gas dynamics simulator based on the Direct Simulation Monte Carlo (DSMC) technique. Section 4 presents validations and comparisons. In order to simulate rarefied atmospheric flows, it is also possible to use additional approaches. One of those is the Test Particle Monte Carlo (TPMC) method. Together with the DSMC, it is one of the most common techniques used for rarefied flow simulators. Both methods can treat multiple reflections and shadowing, but have the main limitation of being computationally expensive. The TPMC model interacts with the surface elements but does not implement intermolecular collisions. This makes simulations faster than common DSMC computations. However, for both methods, atmospheric particles impinge on surfaces with velocities that are computed using a Maxwellian velocity distribution. The energy exchange between molecules and surface elements is computed and resulting forces can be processed.

Within the last years, numerous works have been performed on satellite aerodynamics by Monte Carlo techniques and there is an increasing interest in processing satellite data with high fidelity geometries. In Pilinski et al. (2016), a similar approach to the method presented in this paper is applied to the DANDE satellite. The SPARCS software, based on the test particle technique, analyses a triangulated mesh to provide aerodynamic coefficients. The numerical test-particle technique has been used also by Mehta et al. (2017) for the CHAMP and GRACE satellites. In this work, data have been processed with new improved geometries. Results show average differences with respect to the panellized models previously in use in Delft of 14–18% for CHAMP and 10–24% for GRACE.

In this work, different assumptions have been made and in addition to CHAMP and GRACE, also the GOCE and Swarm satellites have been investigated. The main mission details are listed in Table 1, whereas an overview of the altitudes evolution within the satellite lifetimes is provided in Fig.1. Section 5 describes all the differences between these approaches and the resulting densities in detail. Multiple comparisons with existing data sets and atmospheric models are available. Section 6 provides conclusions and an outlook on future work.

 

1. What is a piece of research the writer has conducted?

2. Did the writer give an answer to a question that he/she has been given or chosen?

3. Was it a subject of common interest?

4. Did  the writer manage  to synthesize research done by others on a topic?

5. What is the difference between textbooks, websites, journal articles, official reports, newspaper or magazine articles, e-books?

6. Find examples of the mentioned above writing formats in the Internet.

7. What are the most common requirements to an academic text?