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”，包括噪声源识别和封闭发电机组传输方面的最新知识，以提高环境噪声性能。目前，在英国的发电行业中，没有设计工具能够优化杂乱外壳的设计，以最低的制造成本降低噪音，以解决噪音指令2000/14/EC。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，这对交货时间和利润产生不利影响。发电机组早期概念设计的虚拟声学验证和制造成本是一个非常具有挑战性的问题。具体来说，市场上没有将概念设计参数、声学分析参数和制造成本联系起来的工具。该项目的目标市场是发电行业，特别是衰减发电机组，这是一个竞争激烈的行业，全球价值估计为27亿美元，产品高达2000kVA。在这个项目中，工业界和学术界的结合将确保在噪音和制造成本方面合并虚拟设计和验证过程的综合能力。该项目将由FG Wilson领导，采用6西格玛DMEDI方法。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相权衡。开发的灵活参数化设计界面将提供一个用户友好的平台来使用该工具包。这个项目的合作伙伴在他们的角色、专业知识和战略动机方面的组合是建立财团的主要考虑因素。合作伙伴的总体平衡包括大用户、中小企业、软件工具开发商和学术专家。这种组合代表了一组合作伙伴，他们每个人在项目中都有很强的个人动机。技术方法将包括使用计算间接边界元（IBE）和实验方法来模拟声音的辐射和散射，这些声音产生并通过一个封闭的柴油发电机组传播，该发电机组包括发动机、交流发电机和冷却风扇。此外，将开发和实施基于阻尼和使用多层声学衬垫的降噪和减振方法。LMS英国有限公司将提供其声学预测和开发的综合软件套件，而FG Wilson公司将提供不同额定功率的发电机组。外部，一个地面动力单元（GPU）提供的Houchin宇航，将被用作一个独立的验证演习。总的来说，这项技术将是高度通用的，范围广泛，从汽车、航空航天、铁路和船舶到建筑行业，在这些行业中，降低环境噪音的成本是一个非常重要的问题。该项目的关键创新在于获取和利用来自多个技术学科的专家知识，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。这将为工程师提供一个有形的环境，从这个环境中，他们可以综合设计固有的杂乱封闭发电机组，满足交付时间、竞争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.