Virginia Tech Optical Inlet Sensor for Particle Detection: Rolls Royce M250 Turboshaft Demonstration

IF 1.4 4区 工程技术 Q3 ENGINEERING, MECHANICAL Journal of Engineering for Gas Turbines and Power-transactions of The Asme Pub Date : 2023-11-06 DOI:10.1115/1.4063584
Brittney Antous, Gwibo Byun, K. Todd Lowe, C. Frederic Smith
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Abstract

Abstract Propulsion systems are exposed to environmental ingestion hazards that can cause significant damage and decrease performance. Particles are ingested in a wide range of flight environments that can cause immediate engine failure or long-term damage. An accurate measurement technique has been developed to quantify particle ingestion and aid engine health monitoring. This sensor utilizes scattering and extinction techniques along with machine learning models to measure particle characteristics based on a robust and versatile library. The capabilities of this sensor have been demonstrated using solid quartz particles on the Rolls-Royce M250-C20B particle ingestion turboshaft test engine. To the authors' knowledge, this work presents the first demonstration and validation of optical solid particle sensing in a turbine engine. CSPEC sand (Mil-E-5007C) was ingested for the validation test at two different feed rates using a sand feeder. The sand concentrations were 45 mg/m3 and 22 mg/m3. The sensor outputs the particle characteristics of aspect ratio (AR), size distribution (σ), Sauter mean diameter (D32), and the particle mass flowrate. The Sauter mean diameter and mass flowrate of ingested sand were calculated using the machine learning model outputs and validated by independent measurements. The sensor produced a 0.1 g/min RMS error compared to the validation measurement.
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弗吉尼亚理工大学粒子检测光学入口传感器:劳斯莱斯M250涡轮轴演示
推进系统暴露于环境摄取危害中,可造成重大损害并降低性能。颗粒在各种飞行环境中被摄入,可能导致发动机立即故障或长期损坏。一种精确的测量技术已经开发出来,以量化颗粒的摄入和帮助发动机健康监测。该传感器利用散射和消光技术以及机器学习模型来测量基于鲁棒和通用库的粒子特性。该传感器的性能已经在劳斯莱斯M250-C20B颗粒吸入涡轴测试发动机上使用固体石英颗粒进行了验证。据作者所知,这项工作提出了光学固体颗粒传感在涡轮发动机中的首次演示和验证。CSPEC砂(Mil-E-5007C)以两种不同的进料速率使用给砂器进行验证测试。砂的浓度分别为45 mg/m3和22 mg/m3。传感器输出颗粒的长径比(AR)、粒径分布(σ)、Sauter平均直径(D32)和颗粒质量流量等特性。利用机器学习模型的输出结果计算了沙的Sauter平均直径和质量流量,并通过独立测量进行了验证。与验证测量值相比,传感器产生0.1 g/min的RMS误差。
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来源期刊
CiteScore
3.80
自引率
20.00%
发文量
292
审稿时长
2.0 months
期刊介绍: The ASME Journal of Engineering for Gas Turbines and Power publishes archival-quality papers in the areas of gas and steam turbine technology, nuclear engineering, internal combustion engines, and fossil power generation. It covers a broad spectrum of practical topics of interest to industry. Subject areas covered include: thermodynamics; fluid mechanics; heat transfer; and modeling; propulsion and power generation components and systems; combustion, fuels, and emissions; nuclear reactor systems and components; thermal hydraulics; heat exchangers; nuclear fuel technology and waste management; I. C. engines for marine, rail, and power generation; steam and hydro power generation; advanced cycles for fossil energy generation; pollution control and environmental effects.
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