A one-dimensional stage un-stacking approach to reveal flow angles and speeds in a multistage axial compressor at the design operating point

Stage stacking methods commonly use a one-dimensional (1D) through flow analysis at the mean line to design individual axial compressor stages and stack these to form a multistage axial compressor. This phase of design exerts a great influence on each stage's pressure and temperature ratio. The...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Koh, Alan Fu Hai, Ng, Eddie Yin Kwee
مؤلفون آخرون: Energy Research Institute @ NTU (ERI@N)
التنسيق: مقال
اللغة:English
منشور في: 2019
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/10356/90003
http://hdl.handle.net/10220/49888
الوسوم: إضافة وسم
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المؤسسة: Nanyang Technological University
اللغة: English
الوصف
الملخص:Stage stacking methods commonly use a one-dimensional (1D) through flow analysis at the mean line to design individual axial compressor stages and stack these to form a multistage axial compressor. This phase of design exerts a great influence on each stage's pressure and temperature ratio. The design process for an individual stage is usually guided by design values and rules developed in previous designs. This study develops a 1D stage un-stacking method (SUSM), which uses a minimal set of data from an actual axial compressor, while reducing the needed number of assumptions. Proceeding from the premise that an actual axial compressor design fulfills all thermodynamic requirements, velocity triangle requirements and design guidelines simultaneously, this proposed SUSM calculates the pressure, temperature, velocities and flow angles as a set of dependent data at each stage of the axial compressor. In approximating a possible axial compressor design for the LM2500 gas turbine that achieves the known pressure ratio distribution, the suggested stage loading coefficient (SLC) distribution is more appropriately considered an initial well-informed estimate and further improvements to this SUSM are needed to infer the actual SLC distributions used.