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Abstrak :
Pertumbuhan jejak aliran tak-mantap fluida kental inkompresibel melalui silinder bulat pada Re 11 000 diamati dan dianalisis secara numerik dan eksperimental. Pengujian eksperimental dilakukan dengan teknik visualisasi aliran menggunakan zat pewarna sedangkan simulasi numerik dilakukan dengan bantuan paket program CFD (computational fluid dynamics), yaitu melalui penyelesaian persamaan Navier-Stokes dengan metoda finite volume. Pertumbuhan ukuran symmetrical close wake di belakang silinder menunjukkan kecenderungan yang sesuai dengan hasil penelitian lain pada Re yang berbeda, yaitu semakin tinggi Re maka ukuran maksimum close wake semakin kecil. Panjang symmetrical close wake ditentukan berdasar pengamatan plotting vektor kecepatan dan distribusi kecepatan pada center wake. Awal pecahnya symmetrical close wake dijelaskan berdasar posisi inti vorteks dan nilai vortisitas di belakang silinder, sedangkan titik separasi ditentukan berdasarkan nilai nol vortisitas dinding (wall vorticity). Fenomena vortex shedding hasil visualisasi eksperimental dibandingkan terhadap hasil simulasi numerik pada selang waktu yang sama untuk mendapatkan perbandingan kualitatif pota aliran di belakang silinder. Hasil simulasi numerik dalam tesis ini memberikan gambaran mengenai fenomenafenomena dasar yang berkaitan dengan pertumbuhan jejak aliran tak-mantap yang sulit diperoleh secara eksperimental.

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The wake growth process of the unsteady flows of a viscous incompressible fluid past a circular cylinder with Re 11000 were observed and analyzed by means of both flow visualization experiment and numerical study. The color dye track technique was employed to visualize the flows, while the numerical simulation was performed by means of CFD package based on a finite volume of the unsteady Navier-Stokes equations. The size growth of the main vortex,i.e., the symmetrical close wake behind the cylinder has trendline that agrees well with the existing experimental study. The length of the main vortex can be determined by both vector plotting and velocity distribution at the center wake. The beginning of symmetrical close wake collapse can be explained to base on the vortex center position and the vorticity value behind the cylinder. However the separation point is found by calculation of zero value of wall vorticity. The vortex shedding phenomenon by flow visualization is then compared with that of numerical simulation. The present numerical study elucidates more detail about fundamental mechanisms corresponding to the wake growth that is very difficult to obtain experimentally.

Abstrak :
Tte h water hemma pro,one the most -important question of unsteady flow in pipelines.....

Abstrak :
Numerical analysis has been performed of time-space structures in a large turning angle axial cascade subject to unsteady incoming wake exciation....

Abstrak :
In this project, Physics-Informed Neural Networks (PINNs) will be used to predict 2D unsteady flows. PINNs is a deep learning application to solve partial differential equations where neural networks learn from data and from physics. In this project, PINNs will be used to predict the velocity and pressure fields of a 2D unsteady flow by learning from the pressure and velocity data from flow simulations and fitting the output velocity and pressure data and its derivatives to the Navier-Stokes equations (NSE) as the governing equations. PINNs learns from data by developing a model that performs nonlinear regression on a set of training velocity and pressure data to produce a predicted output velocity and pressure values that is close to the training pressure and velocity data values. To learn from physics, the derivatives of the output velocity and pressure fields are computed which will be fitted into the Navier-Stokes equations. This becomes an optimization problem where the neural network needs to minimize the error of the predicted and training data and the error from fitting the data and the derivatives to the Navier-Stokes equations. PINNs will be implemented in 3 different scenarios, which are super resolution, data noise- filtering and pressure gradient prediction, and finally, time series prediction. In super resolution, the neural network will be trained with low resolution pressure and velocity field data to reconstruct accurate high-resolution velocity and pressure fields. In data noise- filtering and pressure gradient prediction, the neural network will be trained only with data from noisy velocity fields and no pressure data, mimicking data processing of Particle Image Velocimetry (PIV) measurements which will produce an accurate noise free velocity field and pressure gradient data. In time series prediction, the network will train with velocity and pressure fields at a limited time range and must predict the velocity and pressure data beyond the time range. The result of this project shows that PINNs make excellent tools to the field of experimental and computational fluid dynamics. PINNs can reconstruct accurate high- resolution velocity and pressure fields with less than 0.01 normalized error even if training data has a resolution 10 times smaller than the validation data. PINNs can also remove noise with a normalized error of less than 0.02 despite the noisy data having a 0.25 mean squared error. PINNs are however not effective enough to predict flows in a domain without training data or boundary conditions.

Abstrak :
Aliran air tanah mengalir disebabkan oleh perbedaan tinggi hidraulik. Dalam menurunkan persamaan, massa jenis sering diasumsikan konstan. Persamaan ini dapat dan sering diaplikasi di tempat yang jauh dari wilayah pantai. Namun, persamaan tersebut tidak dapat diaplikasikan di wilayah/akuifer pantai karena massa jenis merupakan sebuah fungsi dari konsentrasi. Air laut yang mengandung garam NaCl menginduksi air tanah sehingga menyebabkan perubahan massa jenis. Peristiwa ini disebut transpor pencemar. Perbedaan massa jenis menyebabkan perubahan tinggi hidraulik. Dalam kondisi aliran tidak berubah menurut waktu (steady), transpor pencemar tidak terlalu berpengaruh terhadap perubahan tinggi hidraulik. Sebaliknya, dalam kondisi aliran yang berubah menurut waktu (unsteady), transpor pencemar sangat berpengaruh. Hal ini menyebabkan terjadi aliran air tanah didorong oleh perbedaan massa jenis (driven density). Untuk memodelkan masalah ini, digunakan metode numerik beda hingga dan diterapkan pada spread sheet program Microsoft Excel. ......Groundwater flow caused by hydraulic head. In term of governing equation, density often assumed as a constant value. This equation often used far away from coastal area. However, those equation can?t be used in coastal area/aquifers because density is a function of concentration. Saltwater which is containing salt (NaCl) induces freshwater so that causing changes in the density of water. This phenomenon called contaminant transport. The changing of density caused changes in the hydraulic head. At steady condition, contaminant transport does not significantly affect to the hydraulic head. Otherwise, contaminant transport significantly affect to the hydraulic head. This is caused driven density groundwater flow. To solving this problem, is used numerical method (finite difference method) and applied to the spread sheet of Microsoft Excel.