Hot section components life usage analyses for industrial gas turbines

Industrial gas turbines generally operate at a bit stable power levels and the hot section critical components, especially high pressure turbine blades are prone to failure due to creep. In some cases, plants are frequently shut down, thus, in addition to creep low cycle fatigue failure equally sets in. Avoiding failure calls for proper monitoring of how the lives of these components are being consumed. Efforts are thus being made to estimate the life of the critical components of the gas turbine, but, the accuracy of the life prediction methods employed has been an issue. In view of the above observations, in this research, a platform has been developed to simultaneously examine engine life consumption due to creep, fatigue and creep-fatigue interaction exploiting relative life analysis where the engine life calculated is compared to a reference life in each failure mode. The results obtained are life analysis factors which indicate how well the engine is being operated. The Larson-Miller Parameter method is used for the creep life consumption analysis, the modified universal slopes method is applied in the low cycle fatigue life estimation while Taira's linear accumulation method is adopted for creep-fatigue interaction life calculation. Fatigue cycles counting model is developed to estimate the fatigue cycles accumulated in any period of engine operation. Blade thermal and stress models are developed together with a data acquisition and pre-processing module to make the life calculations possible. The developed models and the life analysis algorithms are implemented in PYTHIA, Cranfield University's in-house gas turbine performance and diagnostics software to ensure that reliable simulation results are obtained for life analysis. The developed life analysis techniques are applied to several months of real engine operation data, using LM2500+ engine operated by Manx Utilities at the Isle of Man to test the applicability and the feasibility of the methods. The developed algorithms provide quick evaluation and tracking of engine life. The lifing algorithms developed in this research could be applied to different engines. The relative influences of different factors affecting engine life consumption were investigated by considering each effect on engine life consumtion at different engine operation conditions and it was observed that shaft power level has significant impact on engine life consumption while compressor degradation has more impact on engine life consumption than high pressure turbine degradation. The lifing methodologies developed in this work will help engine operators in their engine conditions monitoring and condition-based maintenance

Pemodelan Hubungan Kondisi Kerusakan Jalan Pada Perkerasan Lentur Jalan By Pass Kecamatan Krian, Sidoarjo Akibat Volume Kendaraan

Abstrak Kondisi kerusakan jalan merupakan kondisi ketika jalan tak mampu memberikan pelayanan optimal untuk kebutuhan masyarakat terhadap kendaraan yang melintasi ruas jalan. Penyebab terjadinya kerusakan jalan salah satunya disebabkan oleh peningkatan volume kendaraan. Jalan By Pass Krian merupakan jalan strategis sebagai jalur transportasi antar kota seperti Surabaya-Madiun-Solo-Jogja dan memiliki potensi wilayah seperti perindustrian, pendidikan, perdagangan. Hal tersebut mengakibatkan semakin meningkatnya volume dari variasi moda transportasi yang berpotensi menurunkan kondisi jalan. Tujuan penelitian ini untuk menemukan hubungan volume kendaraan terhadap nilai kondisi jalan menggunakan regresi dengan software SPSS. Pemilihan metode PCI dikarenakan memberikan cara lebih rinci penilaian kondisi jalan dan penggunaan SPSS memberi kemudahan analisis data. Hasil yang diperoleh model regresi yang sesuai dalam penelitian ini adalah regresi linear. Light Vehicles didapatkan persamaan Y = 91,167-0,091 X dengan R2 0,588. Medium Heavy Vehicle didapatkan persamaan Y = 83,159-0,131 X dengan R2 0,734. Heavy Vehicles didapatkan persamaan Y =87,917– 0,162X dengan R2 0,802. Motor Cycle didapatkan persamaan Y =69,932 – 0,015X dengan R2 0,347. Hasil regresi linear berganda merupakan pengaruh antara LV, MHV, HV (No MC) terhadap nilai kondisi jalan didapatkan persamaan Y = 90,892 – 0,016 X1-0,033 X2-0,110 X3 dengan R2 0,827. Hasil regresi dengan hubungan terkuat dimiliki oleh LV, MHV, HV, MC terhadap nilai kondisi jalan dengan persamaan Y = 91,329– 0,017 X1-0,029X2-0,107X3-0,001X4 dengan R2 0,830 Hasil tersebut menandakan bahwa semakin banyak volume dari variasi moda transportasi berpengaruh menurunkan kondisi jalan sehingga semakin tinggi volume jenis kendaraan maka kondisi jalan semakin buruk.Kata Kunci: model, volume kendaraan, kondisi kerusakan jalan, metode PCI, analisis regresi. AbstractThe condition of road damage is a condition where the road is not able to provide optimal service for the needs of the community for vehicles that cross the road. One of the causes of road damage is caused by an increase in the volume of vehicles. Jalan By Pass Krian is a strategic road as a transportation route between cities such as Surabaya-Madiun-Solo-Jogja and has regional potential such as industry, education, trade. This results in an increase in the volume of variations in transportation modes that have the potential to reduce road conditions. The purpose of this study was to determine the relationship between vehicle volume and road condition values using regression with SPSS software. The PCI method was chosen because it provides a more detailed way to assess road conditions and the use of SPSS makes data analysis easier. The results of the right regression model in this study is linear regression. Light Vehicles obtained the equation Y = 91,167-0,091 X with R2 0,588. Medium Heavy Vehicles obtained the equation Y = 83,159-0,131 X with R2 0.734. For Heavy Vehicles, the equation Y = 87,917–0,162X with R2is 0,802. Motor Cycle obtained equation Y = 69,932 – 0,015X with R2 0,347. The result of multiple linear regression is the effect of LV, MHV, HV (No MC) on the value of road conditions, the equation Y = 90,892 – 0,016 X1-0,033 X2-0,110 X3 with R2 0,827. The regression results with the strongest relationship are LV, MHV, HV, MC on the value of road conditions with the equation Y = 91,329– 0,017 X1-0,029X2-0,107X3-0,001X4 with R2 0,830. It reduces road conditions so that the higher the volume of vehicle types, the worse the road conditions.Keywords: model, vehicle volume, road damage condition, PCI method, regression analysi

Genuine Process Logic

The Genuine Process Logic described here (abbreviation: GPL) places the object-bound process itself at the center of formalism. It should be suitable for everyday use, i.e. it is not primarily intended for the formalization of computer programs, but instead, as a counter-conception to the classical state logics. The new and central operator of the GPL is an action symbol replacing the classical state symbols, e.g. of equivalence or identity. The complete renunciation of object-language state expressions also results in a completely new metalinguistic framework, both regarding the axioms and the expressive possibilities of this system. A mixture with state logical terms is readily possible

Henri Temianka photographs, professional acquaintances

https://digitalcommons.chapman.edu/temianka_photos/1188/thumbnail.jp