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Abstrak :
Penelitian ini mengevaluasi performa dua metode utama untuk menentukan konduktivitas termal pada kuningan dan stainless steel menggunakan Inverse Heat Conduction Problem (IHCP) 2 dimensi: dekomposisi LU dengan batasan dan Algoritma iterasi Conjugate Gradient Method (CGM) dengan Backpropagation Learning. Tujuan penelitian adalah menganalisis dampak variasi material, konfigurasi insulasi, tebakan nilai awal konduktivitas termal, dan daya pemanas terhadap hasil akhir konduktivitas termal. Metode CGM dipilih karena efisiensinya dalam menangani sistem persamaan linier besar, sementara dekomposisi LU efektif untuk matriks pentadiagonal. Simulasi dan eksperimen dilakukan untuk memvalidasi metode ini, dengan variasi daya pemanas dan tebakan awal nilai konduktivitas termal. Hasil menunjukkan bahwa pada pelat kuningan, prediksi konduktivitas termal simulasi berada diantara 110,157-111,659 dengan kesalahan absolut maksimum sebesar 0,76% dan prediksi konduktivitas termal eksperimen berada diantara 109,802-111,382 dengan kesalahan absolut maksimum sebesar 1,08% sedangkan pada pelat stainless steel, prediksi konduktivitas termal simulasi berada diantara 13,502-13,933 dengan kesalahan absolut maksimum sebesar 1,99% dan prediksi konduktivitas termal eksperimen berada diantara 13,502-13,951 dengan kesalahan absolut maksimum sebesar 2,16%. Peningkatan daya pemanas tidak mempengaruhi nilai konduktivitas termal, tetapi tebakan awal konduktivitas termal mempengaruhi jumlah iterasi yang dibutuhkan untuk mencapai nilai konduktivitas termal yang akurat. Penelitian ini juga menyoroti batasan seperti analisis dalam kondisi steady-state dan skala laboratorium eksperimen. Rekomendasi untuk penelitian mendatang mencakup pengaturan sistem tertutup pada eksperimen untuk mengontrol suhu lingkungan serta penerapan algoritma machine learning guna meningkatkan akurasi prediksi konduktivitas termal material. Secara keseluruhan, penelitian ini menegaskan bahwa metode yang digunakan efektif dalam memprediksi konduktivitas termal kuningan dan stainless steel, meskipun material dengan konduktivitas rendah cenderung memiliki kesalahan yang lebih besar dalam pengukuran dengan metode IHCP. ......This research evaluates the performance of two primary methods for determining thermal conductivity in brass and stainless steel using the 2-dimensional Inverse Heat Conduction Problem (IHCP): LU decomposition with constraints and the iterative Conjugate Gradient Method (CGM) with Backpropagation Learning. The study aims to analyze the impact of material variation, insulation configuration, initial thermal conductivity guesses, and heating power on the final thermal conductivity results. CGM was chosen for its efficiency in handling large linear equation systems, while LU decomposition is effective for pentadiagonal matrices. Simulations and experiments were conducted to validate these methods, with variations in heating power and initial thermal conductivity guesses. The results show that for brass plates, simulated thermal conductivity predictions range between 110.157 and 111.659 with a maximum absolute error of 0.76%, and experimental predictions range between 109.802 and 111.382 with a maximum absolute error of 1.08%. For stainless steel plates, simulated thermal conductivity predictions range between 13.502 and 13.933 with a maximum absolute error of 1.99%, and experimental predictions range between 13.502 and 13.951 with a maximum absolute error of 2.16%. An increase in heating power does not affect the thermal conductivity values, but the initial thermal conductivity guesses influence the number of iterations required to achieve accurate thermal conductivity values. This research also highlights limitations such as the steady-state analysis and the laboratory-scale experiments. Recommendations for future research include implementing a closed-system setup in experiments to control ambient temperature and applying machine learning algorithms to improve the accuracy of thermal conductivity predictions. Overall, this study confirms that the methods used are effective in predicting the thermal conductivity of brass and stainless steel, although materials with low conductivity tend to have higher measurement errors with the IHCP method.

Abstrak :
Penelitian ini mengevaluasi performa algoritma dekomposisi LU dengan batasan serta metode iterasi Conjugate Gradient Method (CGM) dalam menentukan konduktivitas termal aluminium dan besi menggunakan metode Inverse Heat Conduction Problem (IHCP). IHCP digunakan untuk menyelesaikan masalah konduksi panas dengan menentukan parameter yang tidak diketahui seperti kondisi batas dan konduktivitas termal bergantung pada temperatur. Algoritma dekomposisi LU dengan batasan diimplementasikan dalam IHCP 2D untuk mengoptimalkan perhitungan distribusi temperatur. Simulasi pada pelat aluminium menunjukkan kesalahan absolut maksimum 1,22%, sementara eksperimen dengan isolasi penuh menunjukkan 1,83%. Prediksi konduktivitas termal menggunakan tembakan 10, 50, dan 100 W/mK menghasilkan nilai konduktivitas aluminium antara 233,693 hingga 240,659 W/mK dengan kesalahan maksimum 1,83%, dan besi antara 78,84 hingga 80,38 W/mK dengan kesalahan maksimum 1,74%. Kesimpulannya, variasi material, nilai konduktivitas termal, fluks panas, dan kondisi sistem tidak berdampak signifikan pada prediksi konduktivitas termal. Peningkatan peralatan uji dan metode pengukuran yang lebih akurat diperlukan untuk aplikasi praktis. ......This study aims to evaluate the performance of the LU decomposition algorithm with constraints and the Conjugate Gradient Method (CGM) iteration in determining the thermal conductivity of aluminum and iron materials using the Inverse Heat Conduction Problem (IHCP) method. IHCP is applied to solve heat conduction problems, determining unknown parameters such as boundary conditions and temperature-dependent thermal conductivity. In this research, the LU decomposition algorithm with constraints was implemented in a 2D IHCP to optimize forward calculations for temperature distribution. Simulations on aluminum plates showed a maximum absolute error of 1.22%, while experiments with full insulation showed 1.83%. Thermal conductivity prediction using shots of 10, 50, and 100 W/mK revealed values for aluminum ranging from 233.693 to 240.659 W/mK with a maximum error of 1.83%, and for iron from 78.84 to 80.38 W/mK with a maximum error of 1.74%. The study concludes that material variation, thermal conductivity values, heat flux, and system conditions do not significantly impact thermal conductivity prediction. Therefore, more accurate testing equipment and measurement methods are necessary for practical applications.

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Penelitian ini merupakan penelitian komprehensif menggunakan analisis balik yang dilakukan terhadap settlement preloading dengan pemasangan prefabricated vertical drain (PVD) dengan studi kasus proyek di Gresik, Jawa Timur. Tujuan dari penelitian ini adalah mengetahui faktor yang menyebabkan terjadinya perbedaan settlement pada 16 titik uji settlement plate di lapangan. Metode yang digunakan untuk menganalisis settlement merupakan Metode Kombinasi dan Finite Difference Method dengan menggunakan program CONSOL. Hasil dari penelitian ini menunjukkan bahwa nilai Cv ekuivalen dari seluruh settlement plate bernilai 10-30 kali lipat Cv laboratorium. Nilai Cc hasil pemodelan bernilai 0.5-0.9 dengan rata-rata rasio perbandingan antara Cc pemodelan yang menggambarkan Cc lapangan terhadap Cc laboratorium adalah sebesar 0.66. Rasio tersebut berarti bahwa rata-rata nilai Cc lapangan adalah sebesar 66% dari nilai Cc yang didapatkan dari tes laboratorium. Rasio Cv/Ch yang didapatkan berkisar 0.3-0.67, berarti drainase arah horizontal lebih besar dibandingkan drainase arah vertikal. Hal yang mempengaruhi perbedaan tersebut adalah letak titik pantau, tinggi timbunan, kecepatan penimbunan, dan smear effect. hr> ABSTRACT
This study is a comprehensive study using back analysis conducted on preloading settlement with the installation of prefabricated vertical drain (PVD) with a project case study in Gresik, East Java. The purpose of this study was to determine the factors that cause differences in settlement on settlement plate of 16 field test points. The methods used to analyze the settlement are Combination Method and Finite Difference Method using software CONSOL. The result of this study shows that the value of equivalent Cv is 10-20 times larger than Cv acquired from laboratory test. The value of compression index (Cc) ranged from 0.55-0.9 and the ratio of Cc obtained from modelling which represents actual Cc value in field compared with Cc obtained from laboratory test is 0.66. The ratio shows that Cc obtained in field is 66% of the Cc obtained in laboratory tests. Ratio of Cv/Ch ranged from 0.3-0.67, it shows that radial drainage is larger than vertical drainage. The factors affecting that difference is the location of the observed area, height of embankment, speed of embankment construction, and smear effect

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Pengisian air melalui waduk resapan di Universitas Indonesia, Depok telah menimbulkan pencemaran. Dari hasil pengujian melalui laboratorium, parameter DO, COD, Nitrogen dan Phosfat melebihi nilai yang disyaratkan menurut Peraturan Pemerintah No.82 Tahun 2001 (Kelas I) Tentang Pengelolaan Kualitas Air dan Pengendalian Pencemaran Air. Untuk mengetahui sebaran pencemaran maka diperlukan pemodelan. Pemodelan didasarkan pada hukum kekekalan massa dengan mempertimbangkan proses adveksi dan dispersi. Pemodelan komputer dalam Visual Basic Application for Excel dibuat dengan pendekatan metode beda hingga dan menghasilkan visualisasi berupa grafik konsentrasi dan jarak. Dari berbagai variasi didapat jarak pencemaran terjauh 3500 m dengan konsentrasi tertinggi 370,30 mg/l. ......Water recharge through artificial pond at university of indonesia, Depok caused water pollution. From the laboratory test, DO, COD, Nitrogen and phosphat parameter exceed the value that admitted by Goverment Regulation No. 82 year 2001 (class I) about Water quality management and water pollution control. For that, to understand the pollution distribution, it is necessary a numerical simulation. Numerical simulation based on the conservation of mass law by considering the advection and dispersion process. Numerical simulation by computer in visual basic application for excel made with finite-difference method approach and produce concentration and distance graphic visualitation. from many variation result that the longest pollution distance 3500 m with the highest concentration 370,30 mg/l.

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.

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This book contains manuscripts of topics related to numerical modeling in Civil Engineering (Volume 1) as part of the proceedings of the 1st International Conference on Numerical Modeling in Engineering (NME 2018), which was held in the city of Ghent, Belgium. The overall objective of the conference is to bring together international scientists and engineers in academia and industry in fields related to advanced numerical techniques, such as FEM, BEM, IGA, etc., and their applications to a wide range of engineering disciplines. This volume covers industrial engineering applications of numerical simulations to Civil Engineering, including: Bridges and dams, Cyclic loading, Fluid dynamics, Structural mechanics, Geotechnical engineering, Thermal analysis, Reinforced concrete structures, Steel structures, Composite structures.

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This book gathers outstanding papers on numerical modeling in Mechanical Engineering (Volume 2) as part of the proceedings of the 1st International Conference on Numerical Modeling in Engineering (NME 2018), which was held in Ghent, Belgium. The overall objective of the conference was to bring together international scientists and engineers in academia and industry from fields related to advanced numerical techniques, such as the finite element method (FEM), boundary element method (BEM), isogeometric analysis (IGA), etc., and their applications to a wide range of engineering disciplines. This book addresses various industrial engineering applications of numerical simulations to Mechanical and Materials Engineering, including: Aerospace applications, Acoustic analysis, Biomechanical applications, Contact problems and wear, Heat transfer analysis, Vibration and dynamics, Transient analysis, Nonlinear analysis, Composite materials, Polymers, Metal alloys, Fracture mechanics, Fatigue of materials, Creep behavior, Phase transformation, and Crystal plasticity.