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Multidisciplinary Design & Engineering Optimisation Assignment Help

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KB7043 Multidisciplinary Design & Engineering Optimisation Assignment

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Assessment Title: Individual Project - Design Optimisation Problems

Learning Outcomes assessed in this assessment -

LO1. Appraise key features of modern engineering design concepts, theories and methods and develop critiques of them.

Answer: Modern engineering design concepts, theories, and methods have evolved significantly, incorporating advancements in technology and problem-solving approaches. Key features include a focus on user-centered design, emphasizing the needs and experiences of end-users; systems thinking, considering the interconnectedness of components within a design; and sustainability, aiming to minimize environmental impact and promote resource efficiency. Additionally, the integration of digital tools and techniques, such as computer-aided design (CAD) and simulation, has revolutionized the design process, enabling rapid prototyping and iterative refinement. While these modern approaches offer numerous benefits, they also present challenges. For instance, user-centered design can be time-consuming and resource-intensive, and systems thinking may require complex analysis and modeling. Moreover, the rapid pace of technological change can make it difficult to keep up with emerging trends and best practices. Despite these critiques, modern engineering design concepts, theories, and methods continue to shape the development of innovative and impactful solutions across various industries.

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LO2. Plan the design optimisation processes for complex engineering design problems.

Answer: Design optimization for complex engineering problems involves a systematic process that aims to identify the best possible solution within given constraints. This process typically includes several key steps: 1) Problem Formulation: Clearly defining the design objectives, constraints, and variables involved in the problem. 2) Design Space Exploration: Identifying the range of possible values for each design variable. 3) Objective Function Definition: Quantifying the performance of the design based on the objectives and constraints. 4) Optimization Algorithm Selection: Choosing an appropriate optimization algorithm, such as genetic algorithms, simulated annealing, or gradient-based methods, based on the problem's characteristics. 5) Optimization Process Execution: Iteratively evaluating different design solutions using the selected algorithm and updating the design variables to improve the objective function. 6) Sensitivity Analysis: Assessing the impact of changes in design variables on the objective function to identify critical parameters. 7) Result Validation and Verification: Ensuring that the optimized design meets all requirements and constraints. 8) Implementation and Testing: Implementing the optimized design and conducting thorough testing to validate its performance. Throughout this process, it is crucial to consider factors such as computational resources, design complexity, and uncertainty in design parameters. Additionally, incorporating techniques like multi-objective optimization and robust design can help address complex engineering problems with multiple objectives and uncertainties.

LO3. Conduct essential calculations for reliability driven design problems

Answer: Reliability-driven design requires careful consideration of component failure rates, operating conditions, and maintenance strategies. Key calculations include: Failure Rate Analysis: Determining the individual failure rates of components using historical data, industry standards, or reliability databases. Component Reliability Calculation: Calculating the reliability of each component based on its failure rate and operating time. System Reliability Calculation: Determining the overall system reliability using methods like series, parallel, or complex network analysis, depending on the system's architecture. Mean Time Between Failures (MTBF): Calculating the average time between component failures to assess system availability. Mean Time To Repair (MTTR): Estimating the average time required to repair or replace a failed component. Availability: Calculating the percentage of time a system is operational, considering both MTBF and MTTR. Reliability Growth Modeling: Using statistical models to predict how reliability will improve over time with corrective actions and design changes. Risk Assessment: Identifying potential failure modes and their associated consequences to prioritize reliability improvement efforts. By conducting these calculations, engineers can make informed decisions about component selection, redundancy strategies, and maintenance schedules to ensure the desired level of system reliability.

LO4. Formulate for a given design problem the corresponding optimisation problem, identifying the best applicable search method and carrying out essential calculations to find the optimum solution.

Answer: To formulate an optimization problem for a given design, we must first define the objective function, which quantifies the desired outcome (e.g., minimizing cost, maximizing performance). Next, we establish constraints that limit the design space (e.g., material properties, manufacturing limitations). Once these are defined, we can express the optimization problem as:

Minimize (or Maximize) Objective Function
Subject to: Constraints

Choosing the best search method depends on the problem's characteristics. For example:

Gradient-based methods are suitable for continuous, differentiable functions with a single global optimum.
Metaheuristics like genetic algorithms or simulated annealing are effective for complex, non-convex problems with multiple local optima.
Specialized algorithms (e.g., simplex method for linear programming) might be applicable for specific problem types.

The essential calculations involved in solving the optimization problem vary based on the chosen method. However, they typically include:

Evaluating the objective function for different design points.
Updating design variables based on the search method's rules.
Checking if constraints are satisfied.
Terminating the search when a satisfactory solution is found or a predefined stopping criterion is met.

By following these steps and selecting the appropriate search method, we can effectively solve optimization problems and identify optimal solutions for various design challenges.

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LO5. Carry out design under uncertainties for a given problem, making critical decisions and performing essential calculations.

Answer: Designing under uncertainties involves acknowledging and addressing potential variations in design parameters, operating conditions, and external factors. Key steps include: Risk Identification: Identifying potential risks and their associated probabilities. Uncertainty Quantification: Quantifying the range of possible values for uncertain parameters using methods like sensitivity analysis, Monte Carlo simulation, or fuzzy logic. Robust Design: Incorporating design features that minimize the impact of uncertainties on performance, such as redundancy, fault tolerance, or adaptive control. Decision Making: Making informed decisions based on risk assessment and uncertainty analysis, considering factors like cost, safety, and performance. Essential Calculations: Performing calculations to assess the impact of uncertainties on design objectives, such as reliability analysis, worst-case scenario analysis, or probabilistic safety assessment. By proactively considering uncertainties and implementing robust design strategies, engineers can create systems that are more resilient to unexpected challenges and better able to meet their intended goals.

Assessment Task - Choose a design optimisation problem from the attached list of design problems. Write a report with no more than 7000 words and no more than 15 A4 pages in the main body.

Design Optimisation Problems - Select ONE of the options below and follow the instructions given on the assignment brief.

Problem 1 - Optimal sizing of a standalone Wind-PV-Battery-Diesel hybrid renewable energy system

For an arbitrary site with known load and resource profile, find optimum size of each component (including inverter/converter) leading to minimum levelised cost of energy subject to a series of constraints including a number of arbitrary end- user requirements.

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Problem 2 - Design optimisation of a flat finned heat exchanger

For an arbitrary capacity (heat transfer rate in W), ambient temperature and maximum allowable temperature, find the optimal material and size for the finned heat sink below leading to minimum cost.

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Problem 3 - Design optimisation of an adaptive passive beam vibration absorber

For an arbitrary set of data (beam length, cross-section and material), find the optimal configuration and characteristics of a string-mass absorber that maximises the absorber operation range.

Problem 4 - Design optimisation of a nanofluid flat solar collector

Find the optimum configuration (tube type, tube size, tube surface roughness, type of nanoparticle, size of nanoparticle, mass flow rate, tube distribution configuration, glazing type and size, insulation size and type) of a nanofluid flat solar collector which maximises the efficiency per unit area.

Problem 5 - Design optimisation of hybrid photovoltaic-thermal collectors

Find the optimum configuration (see figure below) of a hybrid photovoltaic-thermal collector integrated in a domestic hot water heating system with the objective of cost.

Note - No more than 7,000 words or 15 A4 pages, with single line spacing, 11pt Calibri (Body) font. Referencing Style: British Standard or Harvard. Students will make use of MATLAB for their coding.

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