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"ABSTRAKData statistik ketenagalistrikan pada tahun 2018 menyebutkan bahwa, masih terdapat masyarakat Indonesia yang belum mendapatkan akses khususnya provinsi Nusa Tenggara Timur yang masih bernilai 61.9%. Tidak tersedianya akses distribusi akibat mahalnya biaya instalasinya dan sulitnya akses menuju lokasi. Oleh karena itu, pembangkit listrik mandiri berbahan baku renewable energy merupakan solusi yang tepat untuk mengatasi krisis energi di daerah terpencil di Indonesia. Disamping itu, potensi energi air di Indonesia mempunyai total potensi sebesar 75 GW. Ini menjadi dasar dari beberapa hasil kajian, turbin pikohidro merupakan solusi yang tepat untuk meningkatkan rasio elektrifikasi di daerah terpencil. Tujuan dalam studi ini adalah memverifikasi fenomena aliran yang terdapat pada sudu turbin piko hidro jenis cross flow pada CFD berdasarkan metode eksperimen Particle Image Velocimetry. Simulasi akan menggunakan ANSYS Fluent 18.1 dengan fitur steady dengan tinggi jatuh air sebesar 1.4 m dan debit aliran 0.01167 m3/s. Ukuran timestep yang digunakan adalah 0.002. Metode eksperimen menggunakan Particle Image Velocimetry dengan Dantec Dynamic laser system. Hasil komputasi divalidasi dengan metode PIV, ditunjukkan oleh distribusi kecepatan pada nosel dan fenomena separasi fluida yang terjadi pada bagian raner turbin arus lintang. Studi ini, menyimpulkan bahwa fenomena pada kondisi sebenarnya dapat digambarkan oleh metode Particle Image Velocimetry dengan proporsi seeding particle dengan diameter maksimum 100 micron.

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ABSTRACT

Based on electricity data in 2018, there are still Indonesian people who do not have access to electricity by 2% (Indonesia). As many as 2% of people who have not received electricity are in remote areas. This is due to the unavailability of distribution access due to the very high installation costs and access to difficult locations. Thus, an independent power plant made from renewable energy is the right solution to overcome the energy crisis in remote areas in Indonesia. furthermore, the potential of water energy in Indonesia has a total potential of 75 GW. This is the basis of several studies, pico hydro turbines are the right solution to increase the electrification ratio in remote areas (Diah; Sarasa; Sari). The purpose of this study is to verify the flow phenomena contained in the crossflow turbine blades on CFD based on the Particle Image Velocimetry experimental method. The simulation will use ANSYS Fluent 18.1 with the steady feature with a head of 1.4 m and a flow rate of 0.01167 m3/ s. The timestep size used is 0.002. The experimental method uses Particle Image Velocimetry with the Dantec Dynamic laser system. The results of computation are then validated by the PIV method, shown by the velocity distribution in the nozzle and the flow phenomenon in the crossflow turbine runner section. This study concludes that the phenomenon of actual conditions can be described by the Particle Image Velocimetry method with the proportion of seeding particles with a maximum diameter of 100 microns.

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"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."

"ABSTRAKSumber energi merupakan salah satu sektor penting dalam kehidupan, terutama dalam sektor bahan bakar. Oleh karena itu untuk mengatasi krisis bahan bakar diperlukan alternatif lain dari bahan terbarukan yaitu biodiesel. Penelitian ini memanfaatkan high shear (geseran fluida yang disebabkan gradien kecepatan yang tinggi) dalam campuran dengan menumbukkan jet fluida dari nosel secara vertikal ke campuran reaksi dalam kolom untuk memperkuat pengadukan campuran dan memperoleh konversi biodiesel yang tinggi. Pada penelitian ini akan di uji hidrodinamika untuk mengetahui velocity gradient. Proses ini direalisasikan dengan menggunakan reaktor kolom gelembung pancaran (jet bubble column reactor) dan komponen tambahan seperti laser, lensa plano konkaf dan glass beads untuk pengujian dengan metode particel image velocity (PIV). Dalam mensimulasi reaksi biodiesel fluida yang digunakan (campuran air dan gliserol) memiliki rasio mol setara dengan rasio mol metanol/CPO 6:1 hingga 4:1 dan kecepatan jet 3,7 m/s hingga 7,7 m/s. Pada penelitian ini, data yang diambil berupa video dan image calibrasi dengan menggunakan kamera high speed 800fps selama 5 detik dan kemudian diolah dengan menggunakan software Virtualdub (untuk memecah gambar), PIVView 1,7 (untuk menghitung kecepatan dan shear/velocity gradient) dan Tecplot (untuk mempresentasikan kontur kecepatan dan shear pada bidang pengambilan gambar). Hasil penelitian menunjukan jika kecepatan jet semakin diperbesar, shear semakin besar. Oleh karena itu pencampuran yang terjadi semakin besar (high mixing). Jika rasio mol makin diperbesar, shear semakin besar pula. Dari aspek hidrodinamika yang telah dilakukan, dapat dinyatakan bahwa shear berbanding lurus dengan koefisien perpindahan massa. Semakin tinggi koefisein perpindahan massa semakin tinggi pula perpindahan massa yang terjadi. Oleh karena itu shear yang terjadi semakin besar (high shears). Dari aspek hidrodinamika, fluida yang memberikan efek keteraturan, nilai shears yang baik di setiap kecepatan jet yang memberikan shear terbesar yaitu pada fluida yang memiliki rasio mol paling besar.

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ABSTRACTEnergy source is one of the important sectors of life, especially in the fuel sector. Therefore, to solve the fuel crisis an alternative materials that renewable biodiesel is needed. This study utilizing high shear (shear fluid caused a high velocity gradient) in a mixture with a pound of fluid jet from nozzle vertically into the reaction mixture in the column to reinforce the stirring mixture and obtain a high conversion biodiesel. In this study will be tested to determine the hydrodynamic velocity gradient. This process is realized using a jet bubble column reactors and additional components such as lasers, plan concave lenses and glass beads for testing by the method particle image velocity (PIV). In reaction to simulate the fluid used biodiesel (a mixture of water and glycerol) has a mole ratio of the equivalent mole ratio of methanol/CPO 6:1 to 4:1 and the jet velocity 3.7 m/s to 7.7 m/s. In this study, data taken in the form of video and image calibration using the camera 800 fps high speed for 5 seconds and then processed using VirtualDub software (for split images), PIVView 1.7 (to calculate the velocity and shear / velocity gradient) and Tecplot (to present the contours of velocity and shear in the field of shooting). The results show increasingly magnified if the jet velocity, shear increases. Therefore it becomes high mixing. If the mole ratio increases, the shears increases also. From the aspect of hydrodynamics that has been done, it can be concluded that shear is directly proportional to the mass transfer coefficient. The higher the mass transfer coefficient the higher the mass transfer occurs. Therefore it becomes high shears. From the aspect of hydrodynamics, the fluid which gives the effect of regularity, Shears good value at each jet velocity that provide the greatest shear in the fluid that has a mole ratio of at most. "