Ralston Pinto | Mechanical Engineering Award | Best Innovation Award

Mr Ralston Pinto | Mechanical Engineering Award | Best Innovation Award

Mr Ralston Pinto, Robert Bosch GmbH,  Germany

Ralston Pinto, born on October 31, 1994, in India, is a mechanical engineer specializing in modeling and simulation of Solid Oxide Fuel Cell (SOFC) contacts. Currently pursuing a PhD at RWTH Aachen University in collaboration with Robert Bosch GmbH and Forschungszentrum Jülich, he focuses on predicting contact mechanics in manufactured cells. Ralston’s expertise extends to finite element methods, material subroutines, and automation using Python and MATLAB. He has also worked on process influences on sensing elements during his master’s thesis at Bosch and has substantial experience in project management from his tenure at Hamon Group in India. Ralston is driven by the challenge of solving real-time engineering problems and values environments that foster innovative thinking and professional growth.

Publication Profile

Orcid 

Education

Ralston Pinto is currently pursuing his PhD in Mechanical Engineering at RWTH Aachen University, with a project focused on modeling and simulation of SOFC contacts in collaboration with Robert Bosch GmbH and Forschungszentrum Jülich. He holds a Master of Science in Mechanical Engineering from Rheinwaal University of Applied Sciences, where he studied process engineering, materials, and simulation, earning a final grade of 1.8. His master’s thesis focused on understanding process influences on crack failure modes in exhaust gas sensors. Ralston completed his Bachelor of Engineering in Mechanical Engineering from the University of Mumbai, specializing in structural mechanics, fluid mechanics, simulation and CAD, thermodynamics, and process engineering. His bachelor’s thesis involved designing and assembling a pedal-powered water purification vehicle to address water scarcity in rural India.

Experience 

Ralston Pinto is currently engaged in doctoral research at Robert Bosch GmbH in Bamberg, Germany, focusing on the modeling and simulation of SOFC contacts using finite element methods. His work involves investigating the pressures on Solid Oxide Cell contacts and developing material subroutines for anisotropic plasticity. Previously, he completed a master’s thesis at Bosch in Stuttgart, Germany, exploring crack failure modes in exhaust gas sensors. Ralston also interned at Bosch, working on developing protective coatings for sensor elements. His early career includes a position as an Assistant Project Engineer at Hamon Group in Mumbai, India, where he coordinated national-level power sector projects, managed resource allocation, and controlled production processes. His diverse experiences have equipped him with a unique understanding of both project management and hands-on engineering tasks.

Awards and Honors

Ralston Pinto has been recognized for his academic excellence and professional contributions. He received the Deutschland Stipendium from the Bundesministerium für Bildung und Forschung, awarded for his outstanding academic performance at Rheinwaal University of Applied Sciences. This prestigious scholarship is given to students who demonstrate exceptional academic achievements and social commitment. During his tenure at Bosch, Ralston was involved in significant research projects that led to the implementation of his findings in the field. His contributions to the modeling and simulation of SOFC contacts and process influences on sensor failure modes have been well-received in the scientific community. Ralston’s dedication to solving real-world engineering problems and his innovative approach to research have earned him accolades and recognition from both academic and professional circles.

Research Focus 

Ralston Pinto’s research primarily focuses on the modeling and simulation of Solid Oxide Fuel Cell (SOFC) contacts. His doctoral thesis at RWTH Aachen University, in collaboration with Robert Bosch GmbH and Forschungszentrum Jülich, aims to predict the contact mechanics of manufactured cells, incorporating non-ideal aspects like tolerance distributions and uneven profiles. Ralston employs finite element methods, homogenization techniques, and anisotropic plasticity subroutines in his simulations. He also integrates Python and MATLAB for automation and data generation, utilizing machine learning methods for optimization. His master’s research at Bosch involved understanding process influences on crack failure modes in exhaust gas sensors, where he developed experimental methods and analyzed empirical data. Ralston’s broad research interests include computational fluid dynamics (CFD), materials science, process engineering, and the development of innovative solutions for real-world engineering challenges.

Publication Top Notes

A constitutive model for homogenized solid oxide cell contacts with dimensional tolerances

Homogenization of fuel cell interconnects to determine the contacting configuration in a stack

Sihao Han | Mechanical Engineering Award | Best Researcher Award

Dr Sihao Han | Mechanical Engineering Award | Best Researcher Award

Dr Sihao Han , South China University of Technology , China

Publication profile 

Google Scholar

Education and Experience

Sihao Han is currently pursuing his PhD in Mechanics at South China University of Technology, building upon his Bachelor’s degree in Mechanical Engineering from Southwest Jiaotong University. His academic journey is marked by a commitment to excellence and a passion for pushing the boundaries of knowledge in his field.

Academic Achievements 

Sihao’s research is centered on solid mechanics, with a keen focus on understanding the wave characteristics and impact behavior of acoustic/mechanical metamaterials. Additionally, he is dedicated to advancing the development of multifunctional metamaterials. His goal is to leverage the power of artificial intelligence to revolutionize the application of metamaterials in demanding engineering contexts.

 

Research Focus

Sihao Han’s research centers on the innovative field of metamaterials, with a particular emphasis on their applications in energy absorption, vibration insulation, and wave propagation. His work involves the design and optimization of re-entrant honeycomb metamaterials and phononic crystals, leveraging advanced techniques like deep learning and reinforcement learning. Sihao’s studies aim to enhance the performance of these materials in demanding engineering applications, contributing to areas such as impact mitigation and load-bearing capacity. Through his interdisciplinary approach, Sihao bridges mechanical engineering with artificial intelligence to push the boundaries of materials science. 🚀🛠️

Publication Top Notes
  1. 📚 “Hierarchical re-entrant honeycomb metamaterial for energy absorption and vibration insulation” – N Ma, Q Han, S Han, C Li, International Journal of Mechanical Sciences 250, 108307, 2023 (47 citations)
  2. 📚 “Design and reinforcement-learning optimization of re-entrant cellular metamaterials” – S Han, Q Han, N Ma, C Li, Thin-Walled Structures 191, 111071, 2023 (10 citations)
  3. 🧠 “Deep-learning-based inverse design of phononic crystals for anticipated wave attenuation” – S Han, Q Han, C Li, Journal of Applied Physics 132 (15), 2022 (9 citations)
  4. 🧠 “Inverse design of phononic crystals for anticipated wave propagation by integrating deep learning and semi-analytical approach” – S Han, Q Han, T Jiang, C Li, Acta Mechanica 234 (10), 4879-4897, 2023 (8 citations)
  5. 🔄 “Complex dispersion relations and evanescent waves in periodic magneto-electro curved phononic crystal plates” – S Han, Q Han, T Jiang, C Li, Applied Mathematical Modelling 119, 373-390, 2023 (3 citations)
  6. 📐 “Design and optimization of the dual-functional lattice-origami metamaterials” – T Jiang, S Han, Q Han, C Li, Composite Structures 327, 117670, 2024 (2 citations)
  7. ⚡ “Parametric electro-mechanical dispersive behaviors of guided waves in the functionally graded piezoelectric annular plate” – C Li, D Xiao, S Han, Q Han, ZAMM-Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte, 2023 (1 citation)
  8. 🛠️ “Machine learning-based optimal design of an acoustic black hole metaplate for enhanced bandgap and load-bearing capacity” – S Han, N Ma, Q Han, C Li, Mechanical Systems and Signal Processing 215, 111436, 2024
  9. 📏 “Compressive response and optimization design of a novel hierarchical re-entrant origami honeycomb metastructure” – N Ma, S Han, W Xu, Q Han, C Li, Engineering Structures 306, 117819, 2024
  10. 📏 “Design and compressive behaviors of the gradient re-entrant origami honeycomb metamaterials” – N Ma, S Han, Q Han, C Li, Thin-Walled Structures 198, 111652, 2024
  11. 🔧 “A novel multi-resonator honeycomb metamaterial with enhanced impact mitigation” – H Zheng, S Han, S Li, Q Han, C Li, European Journal of Mechanics-A/Solids, 105272, 2024