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All Journal Teknomekanik Journal of Engineering Researcher and Lecturer
Nelvi Erizon
Department of Mechanical Engineering, Faculty of Engineering, Universitas Negeri Padang, Indonesia
Chemical Engineering, Chemistry & Bioengineering Education Engineering Mechanical Engineering

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Optimizing vertical-axis wind turbine designs: A comparative CFD analysis of savonius, darrieus, and savonius-darrieus configurations Nelvi Erizon; Refdinal Refdinal; Jasman Jasman; Irzal Irzal; Yufrizal A; Muhammad Shadiq Fahrezi; Firza Fernanda; Egi Fadillah; Ma Leona Maye B. Pepito
Teknomekanik Vol. 8 No. 2 (2025): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/teknomekanik.v8i2.33172

Abstract

This study aims to evaluate the performance of vertical-axis wind turbines (VAWTs) with three different configurations, including Savonius, Darrieus, and a Savonius-Darrieus hybrid wind turbine, using Computational Fluid Dynamics (CFD) simulations. The methodology involves 3D geometry modeling, simulation parameter setup, meshing, and post-simulation analysis using SolidWorks 2022 software. The simulation results indicate that the Savonius turbine achieves the highest power coefficient (Cp) and torque coefficient (Ct) among the three designs, with a maximum Cp value of 0.5 at a Tip-Speed Ratio (TSR) of 0.4. Conversely, the hybrid turbine demonstrates lower efficiency, although it theoretically offers potential for improving performance at low wind speeds. Pressure and flow velocity distributions reveal that the Savonius turbine maintains the most stable pressure pattern compared to the other configurations. These findings highlight the potential of the Savonius turbine as a small-scale renewable energy solution, particularly in urban areas with low wind speeds. Further research is recommended to optimize hybrid turbine designs using machine learning approaches and empirical validation through field experiments to support the achievement of Sustainable Development Goals (SDGs), particularly Goal 7, affordable and clean energy.
Enhancing vocational education through augmented reality: Android-based learning media for CNC TU-2A instruction in technical and vocational high schools Rezky Azhari Salim; Syahril Syahril; Nelvi Erizon
Journal of Engineering Researcher and Lecturer Vol. 4 No. 2 (2025): Regular Issue
Publisher : Researcher and Lecturer Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58712/jerel.v4i2.188

Abstract

The rapid evolution of digital technologies has opened new opportunities for transforming vocational education, particularly in CNC (Computer Numerical Control) machining. Despite its vital role in preparing skilled workers, CNC instruction in Indonesian vocational schools remains constrained by teacher-cantered methods, static materials, and limited access to costly machines, resulting in low engagement and achievement. This study aimed to design, implement, and evaluate Android-based Augmented Reality (AR) learning media for CNC TU-2A machines to enhance students’ cognitive performance, psychomotor skills, and classroom participation. Using a Classroom Action Research (CAR) model across two cycles, the research involved 31 eleventh-grade Mechanical Engineering students at SMK Negeri 5 Padang. Data were collected through cognitive tests, student activity observations, and surveys. Results showed significant improvements: average cognitive scores rose from 75.91 to 82.47, classical mastery increased from 54.83% to 100%, and psychomotor scores improved by 3.24 points. Student learning activities also climbed from 72.2% to 80.4%, with discussion and collaboration showing the highest gain (17%). While barriers such as device limitations and technical issues arose, they were addressed through device sharing, offline content, and teacher mentoring. Findings confirm AR as an effective, scalable tool for modernizing CNC instruction, fostering engagement, and preparing vocational students for Industry 4.0 learning demands.
Co-Authors Egi Fadillah Firza Fernanda Irzal Irzal Jasman Jasman Ma Leona Maye B. Pepito Muhammad Shadiq Fahrezi Refdinal Refdinal Rezky Azhari Salim Syahril Syahril Yufrizal Yufrizal