Computational Thermo-kinetics of Rigid Polyurethane Foams

Theory and Applications
Erscheinungsjahr 2025
ISBN: 978-981-962076-0
Verlag: Springer Nature Singapore

Buch, Englisch, 114 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 219 g

Reihe: SpringerBriefs in Applied Sciences and Technology

This book presents a detailed exploration of advanced computational modeling techniques in the design, testing, and applications of rigid polyurethane foams (RPUFs). By leveraging modern approaches such as database-driven predictions, iterative simulations, and emerging innovations in computational material engineering, it offers a more accurate and efficient way to model the thermo-kinetic behavior of RPUFs. The necessity for computational tools in materials science is intertwined with the growth of the polyurethane market, with many academic and industrial researchers seeking to adopt these methods. The book comprehensively discusses the advancement in bridging the gap between traditional empirical methods and cutting-edge computational techniques specifically applied to RPUFs. Furthermore, it is a comprehensive guide to the computational modeling of the thermo-kinetics of RPUFs, making it an essential resource for researchers, engineers, and academicians seeking to innovate in material science and engineering. This book addresses a niche yet critical area within this broader scope.

Zielgruppe

Research

Autoren/Hrsg.

Lubguban, Arnold A.

Dumancas, Gerard G.

Alguno, Arnold C.

Malaluan, Roberto M.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Naturwissenschaften Physik Physik Allgemein Theoretische Physik, Mathematische Physik, Computerphysik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde
• Naturwissenschaften Physik Quantenphysik

Inhaltsverzeichnis

.- Foundations of Computational Thermokinetics in RPUF.

.- Modeling Techniques.

.- Key Factors in Computational Modeling of RPUF.

.- Implications and Future Outcomes.

Über Autor(innen)

Dr. Arnold A. Lubguban is a professor of Chemical Engineering and senior researcher at the Mindanao State University-Iligan Institute of Technology (MSU-IIT) in the Philippines. With a Ph.D. in chemical engineering, his expertise lies in the development of petroleum-based and sustainable polymers, focusing on industrial, environmental, biomedical, and nanotechnology applications. Dr. Lubguban's research is dedicated to advancing renewable materials, with the goal of creating sustainable solutions for industrial and medical sectors. He obtained his Ph.D. from the University of Missouri.

Dr. Arnold C. Alguno is a professor of Materials Science and Engineering program at the Mindanao State University-Iligan Institute of Technology (MSU-IIT), specializing in semiconductor physics, solar cell technology, and nanotechnology. He obtained his Ph.D. in physics at Tohoku University, Japan. His research spans a broad range of fields, including sensor technology, quantum dots, and advanced functional materials. 

Dr. Roberto M. Malaluan is a professor of Chemical Engineering at the Mindanao State University-Iligan Institute of Technology (MSU-IIT) and a pioneering researcher in the field of sustainable polymers and supercritical fluid extraction. He completed his  Doctor of Engineering at Tohoku University, Japan. Dr. Malaluan has dedicated his career to teaching and research, particularly focusing on the supercritical carbon dioxide extraction of natural products, a technique he has applied to drug discovery and industrial applications.

Dr. Gerard Dumancas is a professor of Chemistry and associate dean of Research and Program Innovation at North Carolina Agricultural and Technical State University. He obtained his Ph.D. in Analytical Chemistry (Chemometrics) from Oklahoma State University. His research interests are in the areas of chemometrics, molecular genetics, analytical chemistry, analytical method development, STEM education, and machine learning techniques as applied to innovative bioenergy solutions.