Article Per Year (5 Year)
p-Index From 2021 - 2026
1.642
P-Index
This Author published in this journals
All Journal Journal of Mechanical Engineering Science and Technology KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Dinamis Mechanical Engineering for Society and Industry Jurnal Pengabdian Masyarakat Abdimas Dinamis
Marianingsih, susi
Unknown Affiliation
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences Transportation Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Journal of Mechanical Engineering Science and Technology

 1 
Artificial Neural Network-Based Modeling of Performance Spark Ignition Engine Fuelled with Bioethanol and Gasoline Marianingsih, Susi; Mar’i, Farhanna; Nanlohy, Hendry Y.
Journal of Mechanical Engineering Science and Technology (JMEST) Vol 7, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um016v7i22023p190

Abstract

Machine learning technology can distinguish the relationship between engine characteristics and performances. Therefore, the goal of the present work is to predict the performance parameters of a single-cylinder 4-stroke gasoline engine at different ignition timings using a blended mixture of gasoline and bioethanol by an artificial neural network (ANN). Experimental data for training and testing in the proposed ANN was obtained at a dynamic speed and full load condition. An ANN model was developed based on standard Back-Propagation algorithm for the spark ignition engine. Multi-layer perception network (MLP) was used for non-linear mapping between the input and output parameters. An optimizer in the family of quasi-Newton methods (lbfgs) and the rectified linear unit function were used to assess the percentage error between the desired and the predicted values. The network input parameters are engine speed, fuel, and ignition timing. Furthermore, torque, power, specific fuel consumption (SFC), thermal efficiency (ηth), and energy consumption (EC) are taken as output parameters. The results show that ANN is the proper method for predicting SIE performance because it has accurate prediction results that are very similar to experimental results. Moreover, from the observation results, the ANN model can predict the engine performance quite well with correlation coefficient (R)=0.962139 and MSE=0.003967 for data testing.
Computational Fluid Dynamics Analysis of Temperature Distribution in Solar Distillation Panel with Various Flat Plate Materials Trismawati, Trismawati; Nanlohy, Hendry Y.; Riupassa, Helen; Marianingsih, Susi
Journal of Mechanical Engineering Science and Technology (JMEST) Vol 8, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um0168i12024p108

Abstract

As the world population continues to grow, the demand for clean water is increasing daily, making it a crucial resource to access. However, there are ways to harness abundant resources like solar energy and seawater to produce clean water. The present studies have conducted experimental investigations to convert seawater into freshwater using solar stills, where solar energy is utilized as the primary heat source for evaporation. The temperature distribution inside the solar stills was analyzed using a flat plate made of three different materials: copper, stainless steel, and aluminum. To examine the temperature distribution and performance of the solar stills, researchers employed computational fluid dynamics simulations (Ansys R15.0). The results showed variations in temperature distribution among the three plate materials. Copper flat plates achieved the highest temperature, approximately 44.5 Celsius, followed by aluminum at 43.91 Celsius, while stainless steel exhibited the lowest temperature at around 42.01 Celsius. The average heat flux across the three materials was approximately 581 W/m2. Additionally, observations indicated that the amount of convection occurring in copper flat plates was 121.108 Watts; in aluminum, it was 118.517 Watts; and in stainless steel, it was 105.05 Watts. The radiation energy for stainless steel flat plates was 29.93 W; for copper, 16.14 W; and for aluminum, 13.49 W.
Co-Authors Abdul Basit Armanda Putri, Fellicia Roshita Haurissa, Jusuf Helen Riupassa Hendry Y. Nanlohy Indrati, Wahyu Iriyanto, Santje Magdalena Islami, Firsta Angelia Manullang, Evanita Veronica Mar'i, Farhanna Mustafafi, Wanda Zahratul Novitasari, Riza Pieter, Marla Sheilamita Shalin Ramadhani, Ismi Suci Safira, Meilani Tayane, Rosalin Theophilia Tayane, Rosaline Theophilia Trismawati, Trismawati Usman, Syamsudin Utaminingrum, Fitri Widodo Widodo Yulius Palumpun