GSOE9340 Life Cycle Engineering


THE UNIVERSITY OF NEW SOUTH WALES
Faculty of Engineering
GSOE9340 – Life Cycle Engineering

Assignment One - Term 2-2024
Assignment weight: 30%
Due date: Thursday, Week 5, 27 June 2024

Purpose: The purpose of this assignment is to develop an understanding of the first step
of operationalizing the framework for LCE, which is the total space allocation for the
selected industry, organisation and a product based on allocation principles discussed in
week2. This will increase your familiarity with how the environmental space is allocated
to achieve the 2°C reduction goal, and the relevant reduction pathways towards
decarbonization. As a result of completing this assignment, you will be able to determine
the environmental space allocated or the emission budget of your industry and product, and
the reduction required to meet the 2°C reduction goal.

Task: The assignment will require week 1 and week 2 lectures. Therefore, you should
attempt the following tasks by using both week’s lecture materials. Before you attempt the
following tasks, find an industry sector, a company and a product that you want to
investigate, ideally from your technical background. It is also critical to remember that the
assignments throughout the term will be a rolling one, therefore, the chosen product will
be used for assignments 2 and 3 as well. The annual Share of the Safe Operating Space for
climate (SoSclimate) in 2022 reference year is estimated to be 52.069 MtCO2eq/year based
on IPCCs SSP1-1.9 scenario (IPCC, 2018). By using this and the chosen product and the
company:
 Determine SoS for the sector (SoSsector), the company (SoScompany) by using
one of the allocation principles discussed in the week 2 lecture for the
reference year 2020 after clearly justifying the choice of allocation principle.

 By using another allocation principle from the week 2 lecture notes,
determine the SoS for your chosen product (SoSproduct) for the reference year
2020 after clearly justifying the choice of allocation principle.

 Determine the estimated volume growth / year of your chosen organization
and the product from 2022 to 2050 by using the publicly available data. You
may use simple regression analysis to do this by using past data if the data is
not available. Resulting growth should be shown as a graph after providing
data set or regression model used.

 Determine the change in the SoSproduct from 2022 to 2050 and show a graph
after providing data set or regression model used.
 Comment on the advantages and disadvantages of the chosen SoS allocation
method and discuss potential implications for your organization, sector and
for the planet.
Note:
 You may make assumptions if you need one after clearly stating them.
 All tasks are not equal value.

Assessment Criteria:
NOTE: Everything needs to be clearly documented. If it is not in your report, it does
NOT exist. Your intention is not evidence for the task.
 Providing a detailed references for the background data used and demonstrating rigorous data
analysis.
 Providing a formula for the allocation methods used for both chosen organisation and
product.
 Providing a formula for volume prediction, predicted volumes for each year and estimation of
 volume growth annually.
 Providing a graph to show the change in the SoSproduct
 Providing all calculations, in addition to graphs, so that the results can be replicated

【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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