Hamidreza Mirzaeian

PhD Researcher in Wind Energy, focusing on the power of Machine Learning to unlock a cleaner future!! Driven by a curiosity for real-world challenges in wind energy, I am currently focused on advancing Wind Turbine Fault Detection to enhance efficiency and sustainability.

RESEARCH, TEACHING, or OTHER INTERESTS

Renewable Energy, Sustainability and the Environment, Mechanical Engineering

Scopus Publications

Performance Improvement of Hybrid Vertical Axis Wind Turbines Equipped With J-Shaped Blades
Narges Kashani, Mojtaba Mirhosseini, Rouhollah Ahmadi, Hamidreza Mirzaeian
Energy Science and Engineering, 2025
Vertical axis wind turbines (VAWTs) are emerging as a promising solution for urban wind energy harvesting, owing to their omnidirectional capability. Hybrid VAWTs, which combine Savonius and Darrieus turbines, have shown improved power performance, yet their commercialization remains limited by challenges in self‐starting ability and operational range. The present study introduces a novel hybrid configuration using J‐shaped Darrieus blades with a NACA 0021 airfoil profile, to improve self‐starting ability, power production, and operational range. The rotor has a diameter of 1 m, and numerical simulations using the computational fluid dynamics (CFD) method with the SST k‐ω turbulence model were conducted to evaluate aerodynamic performance. Various opening ratios of 30%, 50%, 70%, and 90% were analyzed, revealing that the 30% opening ratio produces the highest power output across a broad range of tip speed ratios (TSRs). Specifically, using J‐shaped Darrieus blades with a 30% opening ratio enhances power output by 50% at TSR = 1.5% and 14.4% at TSR = 2.5, compared to the baseline model. These findings highlight the potential of J‐shaped blades to optimize hybrid VAWT performance for practical applications, offering enhanced efficiency.
Performance Analysis and Optimization of Dual-Row Vertical Axis Wind Turbines With Innovative Hybrid Blades
H. Mirzaeian, B. Ghobadian, M. Mirhosseini
Energy Science and Engineering, 2025
Vertical axis wind turbines have shown potential for urban energy harvesting but suffer from weak start‐up performance. This study introduces a novel dual‐row turbine that combines J‐shaped and conventional blades to harness both drag and lift forces, achieving enhanced performance over a wide operational range. Through computational fluid dynamics simulations and Taguchi optimization, the turbine demonstrated a higher maximum power coefficient of 0.52 and a superior power performance at low tip speed ratios. This innovative design significantly surpasses conventional hybrid Savonius‐Darrieus, as well as single‐ and dual‐row Darrieus designs. Among the five main parameters analyzed, the tip speed ratio (λ) had the strongest influence on the performance, followed by the type of airfoils (α), radial ratio (δ), solidity ratio (σratio), and angular distance (ϕ), respectively. The optimization results identified the optimal operational point of the turbine at λ = 2, ϕ = 0, δ = 1.4, σratio = 1.5, and α = 21 (utilizing J‐shaped blades in the inner row and conventional blades in the outer row). These findings highlight the potential of the hybrid dual‐row Darrieus design to enhance vertical axis wind turbine efficiency and pave the way for its application in urban wind energy.
Study on a novel dual-row vertical axis wind turbine with J-Shaped blades by using numerical methods and optimization
Hamidreza Mirzaeian, Barat Ghobadian, Mojtaba Mirhosseini
Energy Sources Part A Recovery Utilization and Environmental Effects, 2024
This study investigates the benefits of using J-shaped blades in a dual-row Darrieus wind turbine (DDWT), combining two innovative ideas whose simultaneous impact on the self-starting capability of Darrieus turbines has not been explored. The performance of the turbine was simulated using Computational Fluid Dynamics (CFD) and optimized using both the Taguchi method and a combined Machine Learning and Genetic Algorithm (ML-GA) technique. Three main operating parameters, including the tip speed ratio (λ), radial ratio (δ), and angular distance (ϕ) of dual-row turbines, were investigated across four levels using an L25 orthogonal array design. According to the Taguchi analysis, the impact of the parameters on the power output was ranked in the order of λ > δ > ϕ. After comparison, the optimized turbine predicted by the ML-GA technique was chosen due to the superior accuracy of the ML-GA approach compared to the Taguchi method. The optimal configuration of a DDWT with J-shaped blades was predicted at λ = 2.15, δ = 1.39, and ϕ = 75.36, with a Cp of 0.49 based on CFD simulations. Compared to the single-row wind turbine, the proposed turbine showed a significant increase of the Cp at low tip speed ratios resulting in a better self-starting performance.