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All Journal Bionatura Bulletin of Scientific Contribution : Geology Journal of Geoscience, Engineering, Environment, and Technology Jurnal Elektronika dan Telekomunikasi
Irwan Ary Dharmawan
Department Of Geophysics, Faculty Of Mathematics And Natural Science, Universitas Padjadjaran, Jatinangor, Indonesia.
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Physics

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NONLINEAR PHOTONIC CRYSTAL FOR ALL-OPTICAL SWITCHING APPLI Ayi Bahtiar; Yayah Yuliah -; Irwan Ary Dharmawan
Bionatura Vol 9, No 3 (2007): Bionatura Nopember 2007
Publisher : Direktorat Sumber Daya Akademik dan Perpustakaan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

An all-optical switching device is a crucial component for developing high speed data transmission and signal processing in telecommunication network. The device is based on nonlinear optical material, whose refractive index depends on light intensity. Recently, photonic crystals have been considerable interest both theoretically and experimentally for optical switching devices. Due to the practical reason, we studied one-dimensional nonlinear photonic crystal for all-optical switching devices. We use transfer matrix method and nonlinear coupled mode equation to determine photonic bandgap and optical switching process. We applied them to different structures: nonlinear Distributed Bragg Reflector (DBR) and nonlinear photonic crystals which has similar linear refractive index but has opposite sign of nonlinear refractive index. By using an appropriate combination of refractive indices, it was found that the first structure can be used for all-optical switching at telecommunication wavelength (1.55 m). The second structure can be used both for all-optical switching and optical limiter at the wavelength of 1 m.Keywords: all-optical switching, optical limiter, nonlinear photonic crystal, transfer matrix, nonlinear coupled mode equation.
Electronic and Thermoelectric Properties on Rutile SnO2 Under Compressive and Tensile Strains Engineering Budi Adiperdana; Nadya Larasati Kartika; Irwan Ary Dharmawan
Jurnal Elektronika dan Telekomunikasi Vol 22, No 2 (2022)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.506

Abstract

SnO2 has the potential to be an environmentally friendly thermoelectric material. To obtain the optimum properties of this material, strain engineering is used to investigate the electronic and thermoelectric properties. In this study, we used compressive and tensile strains with -5%, -2%, 0%, 2%, 5%, and 10% in three schemes; they are triaxial (ɛabc), biaxial (ɛab), and uniaxial (ɛc) strains. All model structures are calculated based on density functional theory (DFT) with several exchange-correlation functionals. The presented results show that strain engineering enhances the Seebeck coefficient for a compressive strain parameter since the energy gap between the conduction and valence band increased due to the strong covalent bonding at the conduction band. From several comparisons in bandgap and thermoelectric properties calculation between PBEsol and PBE0, this study suggests that PBE0 is effectively used to calculate the energy gap. Meanwhile, for thermoelectric properties, PBEsol gave the best-estimated value. In addition, this study explained that the largest or the smallest bandgap could be achieved by varying strain simply on the c-axis as the optimum manipulation of the SnO2 structure. Furthermore, this paper also revealed that the simulation strategy could be determined from the desired result, whether to enhance the Seebeck coefficient or the electrical conductivity by manipulating the ab-axis and the c-axis, respectively.
Machine Learning Application of Two-Dimensional Fracture Properties Estimation Ardian Nurcahya; Aldenia Alexandra; Satria Zidane Zainuddin; Fatimah Az-Zahra; M. I. Khoirul Haq; Irwan Ary Dharmawan
Journal of Geoscience, Engineering, Environment, and Technology Vol. 8 No. 02-2 (2023): Special Issue from “The 1st International Conference on Upstream Energy Te
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2023.8.02-2.13874

Abstract

Fractures are substantial contributors to solute transport sedimentary systems that form pathways. The pathway formed in a fracture has two physical parameters, there are mean aperture and surface roughness. Mean aperture is the thickness of the pathway that the fluid will pass through, and surface roughness is the roughness of the fracture pathway. The two physical parameters of the fracture are important to determine since they affect the permeability value in petroleum reservoir analysis. We developed a machine learning algorithm based on the Convolutional Neural Network (CNN) to predict those two parameters. Furthermore, image processing analysis is performed to generate the datasets. The results show that the CNN algorithm shows good agreement with the reference results. In addition, the algorithms showed efficient performance in terms of computational time. CNN is a type of deep neural designed to perform analysis on multi-channel images that can classify fracture geometry. The best model was determined using a benchmark dataset with a CNN model provided by Keras. The results of experiments conducted on fracture geometry images show that the machine learning model created is able to predict the mean aperture and surface roughness values.
Machine learning prediction of tortuosity in digital rock Fadhillah Akmal; M. Cisco Ramadhan Dzulizar; Muhammad Faizal Rafli; Fatimah Az-Zahra; M. I. Khoirul Haq; Irwan Ary Dharmawan
Journal of Geoscience, Engineering, Environment, and Technology Vol. 8 No. 02-2 (2023): Special Issue from “The 1st International Conference on Upstream Energy Te
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2023.8.02-2.13875

Abstract

Physical rock property measurement is an important stage in energy exploration, both for hydrocarbons and geothermal sources. The value of physical rock properties can provide information about reservoir quality, and one of these properties is tortuosity. Tortuosity is an intrinsic property of porous materials that describes the level of complexity of the porous arrangement when a fluid passes through it. Conventionally, tortuosity values are measured through laboratory analysis and numerical simulation, but these measurements can take a long time. An alternative method for measuring tortuosity is using machine learning with a convolutional neural network (CNN). A CNN is a type of deep neural network designed to analyze multi-channel images and has been applied successfully to classification and non-linear regression problems. By training a CNN on a dataset of digital rock samples that have been simulated using numerical computation to obtain their tortuosity values, it is possible to demonstrate that CNNs can accurately predict the tortuosity of digital rock. The result is that the CNN model can predict tortuosity values with the Xception model being the most accurate with the lowest RMSE value of 0.90962.
INVESTIGASI LAPISAN BATUAN KAWASAN PENDIDIKAN UNIVERSITAS PADJADJARAN JATINANGOR BAGIAN UTARA BERDASARKAN ELECTRICAL RESISTIVITY TOMOGRAPHY (ERT) Kusnahadi Susanto; Muhammad Zharfan Azzam; Zhafirah Nurul Syarafina; Kartika Hajar Kirana; Irwan Ary Dharmawan; Asep Harja
Bulletin of Scientific Contribution: GEOLOGY Vol 21, No 2 (2023): Bulletin of Scientific Contribution : GEOLOGY
Publisher : Fakultas Teknik Geologi Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/bsc geology.v21i2.48042

Abstract

Pemeriksaan lapisan batuan serta kondisi bawah permukaan memegang peran yang sangat penting dalam upaya pemanfaatan lahan, baik untuk mendukung infrastruktur diatasnya maupun pemanfaatan sumber daya alam yang terkandung didalamnya. Pemanfaatan lahan berhubungan erat dengan pengembangan infrastruktur yang direncanakan. Agar dapat mengurangi risiko potensial dari bencana, penting untuk memperhatikan kualitas dan kemampuan dukungan lahan yang diperlukan. Artikel ini mengulas tentang hasil studi mengenai identifikasi lapisan batuan yang dilakukan kampus Universitas Padjadjaran Jatinangor di bagian utara. Metode yang digunakan dalam penelitian ini adalah metode Electrical Resistivity Tomography (ERT) karena merupakan metode yang memiliki resolusi yang baik dalam pengukuran geofisika dangkal. Data yang dihasilkan dari pengukuran ini adalah penampang kontras resistivitas tanah dan batuan yang berasosiasi pada berbagai aspek seperti kandungan air dalam pori, jenis batuan serta struktur bawah permukaan. Pengukuran ERT dilakukan dengan membuat 11 lintasan baik yang saling memotong maupun saling sejajar. Hasil pengukuran geolistrik resistivitas di daerah penelitian ini menunjukkan variasi kontras tahanan jenis batuan mulai dari 0,5 Ωmeter sampai lebih dari 500 Ωmeter, namun demikian dalam interpretasi yang kami lakukan, rentang nilai tersebut dibagi menjadi beberapa rentang kategori yang disesuaikan dengan maksud penelitian serta kondisi lapangan yang sedang diteliti. Hasil interpretasi terhadap data resistivitas memunculkan informasi dugaan tiga lapisan batuan di antara kedalaman 0 sampai dengan 70 dan struktur patahan minor di sekitar daerah penelitian.  
Co-Authors Aldenia Alexandra Ardian Nurcahya Asep Harja Ayi Bahtiar Budi Adiperdana Fadhillah Akmal Fatimah Az-Zahra Fatimah Az-Zahra Kartika Hajar Kirana Kusnahadi Susanto M. Cisco Ramadhan Dzulizar M. I. Khoirul Haq M. I. Khoirul Haq Muhammad Faizal Rafli Muhammad Zharfan Azzam Nadya Larasati Kartika Satria Zidane Zainuddin Yayah Yuliah - Zhafirah Nurul Syarafina