Engineering dis 1 - N/a PDF

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Advantages: 1. More torque 2. Last Longer (Due to the cycle being completed less) 3. Higher efficiency in the ratio of fuel to output power Disadvantages 1. Complicated Design compared to 2 stroke (Due to having valves) 2. Half as powerful as two stroke engines 3. More expensive than 2 stroke 4. Intake/Compression: 5. The inlet port opens and the exhaust port will close. 6. Compressed fuel-air mixture is drawn into the cylinder and fresh charge into the crankcase. The pistons upward movement provides further compression as the piston approaches TBD 7. Power/ Exhaust: 8. 9. Ignition causes the fuel-air mixture to combust. 10. Piston moves downward compressing fuel-air mixture in the crankcase. 11. Exhaust port opens allowing gases to exit in an exhaust blow down process 12. Inlet port opens; causing the fresh air to compress in the crankcase, which flows into the cylinder. The shape of the ports and piston are to deflect the incoming charge from flowing directly into the exhaust ports and scavenging. Advantages: 1. Simple design and Low weight 2. Two stroke engines fire once every revolution while four stroke every other revolution 3. 2 strokes can work in any position due to no concern of oil flow  Disadvantages 1. 2 stroke engines do not last as long as four stroke engines; there is no lubrication system in a two stroke engine so parts wear out a lot faster. 2. Two stroke engines produce a lot of pollution 3. Two stroke engines produce a lot of pollution, and the way the engine is designed that part of the air/fuel leaks out of the chamber through the exhaust port, which is why you see a small thin film, or sheen, of oil around any two stroke outboard motor, and this leaking oil is a real mess for the environment. This is the reason why two stroke engines are used only in application where the motor is not used very often and a fantastic power-to-weigh ratio is important.

Introduction: The following project covers the major parts of the assessment brief which is outlined for the module KB7044, it intends on covering in detail the learning outcomes for this module. The following tasks one to six are based upon a case study that involves the design and manufacture of a material that supports machinery. The tasks from seven to ten consist of another case study involving the analysis of a concrete truss which is subject to a compressive load. All ten tasks will be described in detail through graphs and tables throughout the assignment, and then the data from the results will be interpreted. The additional independent learning task will consist of a literature review into the knowledge of uncertainty modelling in engineering cost estimation. Furthermore, the independent learning task will have an example of an engineering project which will investigate the risks and uncertainty using data analysis modelling. Task 1: Case 1 Background: The company in question ABC, designs and builds support structures for machinery, whereby several complaints about yielding of the materials have been made. It appears to be a concern due to the components in question, failure could cause significant loss of life and damage to public and commercial buildings. The measurements of safety are important because instilling trustworthiness in products ensures excellence time and time again to customer specifications. Understanding similarities of reliability, quality and risk are essential for performance factors. The equation that describes stress can be seen in Eq 1:

Eq 1:σ =

F A

σ =Sigma , F=Force, A=Area As with design practices, it is known that safety factors are used to ensure structural integrity as any structure or component can be made to fail if subject to excessive loading. This design is subject directly to compression loading. A Maximum loading condition should not exceed the material yield strength and ultimate strength, by definition the appropriate factor of safety in design is considered to be between 1.5 for yield; and 2-3 for ultimate strength depending on uncertainty or depending on load and material application (Burdekin, 2007). Equation 2 describes the design stress:

Eq 2: σ d =

SF . Load A σ d =Design stress

,

SF=Safety Factor ,

A= Area , L= Load

The practitioner is tasked to investigate if there is any correlation between uncertainty and actual stress. The initial stage of the investigation is to determine 22 data samples obtained from the overall population to determine certain data as seen in Table 1. If there were any suspected reasons believed that correlation exists between factors then regression analysis would be performed. Table 1: Data for Correlation and Regression Analysis

Data organisation: Table 2 has 22 data samples and has a mean load variation of between 83 KN to 101 KN. The data observed in Table 2 is structured in such a way to easily observe the load variation factor and mean load, smallest to largest. The load variation is in ascending order 1 to 3, from the organised data the range of the three load factors 1 to 3 as well as the design stress, actual and ratio range in Table 3. Table 2: Data Organisation

Table 3: Data Range Summary of Variables Load Variation 1 2 3

Design Stress range

120 ≤σd ≤130 120 ≤σd ≤130 120 ≤σd ≤130

Actual Stress Range

78 ≤ σd ≤105 104 ≤ σd ≤ 150 180 ≤σd ≤247

Ratio

0.65 ≤ σd ≤ 0.808 0.8 ≤ σd ≤ 1.25 1.5 ≤σd ≤2.058

The ratio σa/σd seen in Table 2 and 3 indicates the relationship between design stress and actual stress. If the value of the ratio is about 1, it would suggest that the actual stress is higher than the designed sample stress maximum. For example: Design stress = 120 MPa, Actual Stress = 180 MPa , Ratio = 1.5 This example shows that the actual stress is 50% higher than the design stress indicating a failure with the sample. Descriptive analysis Correlation and Regression Analysis: The task involved the organisation and sorting of data to determine patterns when observed. As Engineers it is essential to understand the concepts of correlation and regression for the reasons of how one another is significantly affected by other variables in data analysis. The terms correlation and regression are two analytical applications based upon multivariate distribution. Correlation explores the statistical relationship between two variables whereas regression analysis explores the relationship between each variable, whereby an independent and dependant variable is present (Kupper and Muller, 2020). The correlation between the design and actual test results are of significance indicating structural issues. There is correlation between the factors with...