Design Tool for Cost Effective Reduction of Noise from Enclosed Power Generators (DRONE)

其目的是开发一种多学科的“设计工具”，包括在噪声源识别和封闭式发电机组传输方面的最先进知识，以获得更具竞争力的环境噪声性能。目前，在英国的发电行业中，没有可用的设计工具能够优化杂乱的外壳设计，以最低的制造成本降低噪音，以满足噪声指令2000/14/EC的要求。关于概念设计阶段性能的定量信息尚不清楚。必须制造昂贵的模型来确定它们的性能，这对交货期和利润产生了不利影响。发电机组早期概念设计的虚拟声学验证和制造成本计算是非常具有挑战性的。具体地说，市场上还没有工具将概念设计参数、这些参数的声学分析与制造成本联系起来。该项目的目标市场是发电行业，特别是音质衰减发电机组，该行业竞争激烈，2000kVA以下产品的全球价值估计为27亿美元。工业界和学术界在这个项目中的结合将确保在噪音和制造成本方面整合虚拟设计和验证过程的能力。该项目将由FG·威尔逊使用6西格玛DMEDI方法领导。在设计阶段拥有这种能力将导致对产品的新理解，从而优化性能与成本。开发的灵活的参数化设计界面将为使用该工具包提供一个用户友好的平台。参与该项目的合作伙伴在他们的作用、专业知识和战略动机方面的组合是建立该财团的主要考虑因素。合作伙伴的总体平衡包括大型用户、中小企业、软件工具开发人员和学术专家。这一组合代表了一群合作伙伴，他们每个人在项目中都有强烈的个人动机。技术方法将涉及使用计算间接边界元(IBE)和实验方法来模拟通过封闭式柴油发电机组产生和传播的声音的辐射和散射，该发电机组包括发动机、交流发电机和冷却风扇。此外，还将开发和实施基于减振和使用多层声学衬垫的降噪减振方法。LMS UK Ltd将提供他们的全套声学预测软件并进行开发，而牵头组织FG Wilson将提供不同额定功率的发电机组。在外部，将使用由后进航空航天公司提供的地面电源单元(GPU)作为独立的验证工作。总体而言，这项技术将是高度通用的，范围很广，从汽车、航空航天、铁路和船舶到建筑行业，在这些行业中，环境降噪成本的权衡是一个相关的问题。该项目的关键创新在于在一个环境下，使用数值建模技术、经验算法、工业制造成本数据建模，以及为下一阶段的评估和设计数据生成进行实验，从多个技术学科获取和利用专家知识。这将为工程师提供一个有形的环境，在其中综合设计固有的杂乱无章的封闭式发电机组，满足交货期、有竞争力的成本目标和噪音立法目标。

The aim is to develop a multidisciplinary 'design tool' that encompasses the state-of-the-art knowledge in noise source identification and transmission from enclosed generator sets, for more competitive environmental noise performance. Currently, within the UK based power generation industry, there are no design tools available that have the capability to optimise the design of cluttered enclosures for noise reduction at minimum manufacturing cost, in addressing the noise directive 2000/14/EC. Quantitative information about the performance at conceptual design stage is not known. Expensive mock-ups have to be manufactured to determine their performance, which adversely affects the lead-time and profits. Virtual acoustic validation and costing of manufacture for early conceptual design of power generating sets is very challenging. Specifically there are no tools on the market that link conceptual design parameters, acoustic analysis of those parameters and manufacturing cost. The market targeted for the project is the power generation industry, specifically sound attenuated generator sets, which is highly competitive and globally worth an estimated 2.7billion for products up to 2000kVA. The combination of Industry and Academia in this project will ensure an integrated capability for merging a virtual design and validation process, in terms of noise and manufacturing cost. The project will be led by FG Wilson using 6 Sigma DMEDI methodology. Having this capability at the design stage will result in a new understanding of the product, hence optimised performance weighted against cost. The developed flexible parametric design interface will provide a user-friendly platform to use the toolkit. The mix of partners involved in this project in terms of their role, expertise and strategic motivation has been a major consideration in setting up the consortium. The overall balance of partners includes large users, SMEs, software tool developers and academic experts. This mix represents a group of partners who each have a strong personal motivation in the project. The technical approach will involve the use of computational Indirect Boundary Element (IBE) and experimental approaches to simulate the radiation and scattering of the sound generated and transmitted through an enclosed diesel generator set, which includes an engine, an alternator, and cooling fans. In addition, methods for noise and vibration reduction based on damping and using multi-layer acoustic liner will be developed and implemented. LMS UK Ltd will supply their comprehensive suite of software for acoustic predictions and undertake development, while FG Wilson, the lead organisation, will supply generator sets, of different power ratings. Externally, a Ground Power Unit (GPU) supplied by Houchin Aerospace, will be used as an independent validation exercise. In general, this technology will be highly generic with wide scope ranging from automotive, aerospace, railway and marine through to the construction industry where trade-off in the cost of environmental noise reduction is such a relevant issue. The key innovation of this project hinges on the capture and exploitation of expert knowledge, from multiple technical disciplines, under one environment using numerical modelling techniques, empirical algorithms, industrial manufacturing cost data modelling, and experimentation for evaluation and design data generation for next stage. This will provide engineers with a tangible environment from which to synthetically design inherently cluttered enclosed generator sets, meeting lead-time, competitive cost targets and noise legislative targets.