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Dr Najam ul Qadir
Assistant Professor
Department of Design and Manufacturing Engineering

SMME
National University of Sciences and Technology (NUST)
H-12, Islamabad
Tel : +925190856039

Specialization
Solar Adsorption Refrigeration, Gradient Based Optimization

Education
PhD (Mechanical Engineering), King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

Dr Najam has taken a total of 33 graduate-level courses during MS and PhD programmess till date. His PhD dissertation was based on lab-scale synthesis and characterisation of two ulti-walled carbon nanotube (MWCNT) incorporated water-stable Metal Organic Frameworks (MOFs), MIL-100(Fe) and MIL-101(Cr), for use in adsorption chiller applications. He has 13 consecutive years of research experience in MATLAB programming. He is the first person to formulate exact form of Levenberg-Marquardt Algorithm using Direct Differentiation for use in Feedforward Neural Networks with higher convergence rate than the Neural Network Toolbox in MATLAB. His research interests include Design and Performance Optimisation, Solar Adsorption Refrigeration, and Modeling Manufacturing Process of Flexible fiber Polymer Composites.​

  • Design and Performance Optimisation

  • Solar Adsorption Refrigeration

  • Modelling Manufacturing Process of Flexible fiber Polymer Composites

​Publications​

  • C. J. Kim, Najam ul Qadir, Asif Mahmood, Y. –H Han and T.-H Sung, “The Effect Of BaCeO3 Nano-particles on the Current Density of a Melt-Processed YBCO Superconductor”, Physica C: Superconductivity, Vol. 463-465, 344-347, 2007.

  • Najam ul Qadir and David A. Jack, “Modeling Fiber Orientation in Short Fiber Suspensions Using the Neural Network Based Orthotropic Closure”, Composites Part A: Applied Science and Manufacturing, Vol. 40, 1524-1533, 2009.

  • Nouari Saheb, Najam Ul Qadir, Muhammad Usama Siddiqui, Abul Fazl Muhammad Arif, Syed Sohail Akhtar and Nasser Al-Aqeeli, “Characterization of Nanoreinforcement Dispersion in Inorganic Nanocomposites: A Review”, Materials, Vol. 7, No. 6, 4148-4181, 2014.

  • Najam ul Qadir and S.A.M. Said, “Structural stability of metal organic frameworks in aqueous media – Controlling factors and methods to improve hydrostability and hydrothermal cyclic stability”, Microporous and Mesoporous Materials, Vol. 201, 61-90, 2015.

  • Najam ul Qadir, S.A.M. Said, Rached B. Mansour, “Modeling the Performance of a Two-bed Solar Adsorption Chiller using a Multi-walled Carbon Nanotube/MIL-100(Fe) Composite Adsorbent”, Renewable Energy, Vol. 109, 602-612, 2017.

  • Najam U. Qadir, Syed A. M. Said, Rached B. Mansour, Khalid Mezghani, and Anwar U. Hamid, “Synthesis, Characterization, and Water Adsorption Properties of a Novel Multi-Walled Carbon Nanotube/ MIL-100(Fe) Composite”, Dalton Trans., Vol. 45, 15621-15633, 2016.

  • S. A. M. Said, N. U. Qadir, R. B. Mansour, Khaled Mezghani and H. M. Irshad, “Synthesis, and Water Sorption Properties of a Series of Exfoliated Graphene/MIL-100(Fe) Composites, RSC Adv., Vol. 7, 17353-17356, 2017.

  • Naef A. A. Qasem, Najam U. Qadir, Rached Ben-Mansour, Syed A. M. Said, “Synthesis, Characterization, and CO2 Breakthrough Adsorption of a Novel MWCNT/MIL-101(Cr) Composite”, Journal of CO2 utilization, Vol. 22, 238-249, 2017.

  • Najam ul Qadir, S.A.M. Said, Rached B. Mansour, “Numerical simulation of a Two-bed Solar Adsorption Chiller with Adaptive Cycle time using a MIL-100(Fe)/water pair – Influence of Solar Collector Configuration”, International Journal of Refrigeration, Vol. 85, 472-488, 2018.

  • Najam ul Qadir and Stephen Montgomery Smith, “Direct Differentiation Based Hessian Formulation for Training Multilayer Feedforward Neural Networks using the LM Algorithm – Performance Comparison with Conventional Jacobian-Based Learning”, Global J. Technol. Optim., Vol. 9, No.1, DOI: 10.4172/2229-8711.1000223, 2018.

  • Najam ul Qadir, S.A.M. Said, R.B. Mansour, Hussain Imran and Mushtaq Khan, “Performance Comparison of a Two-bed Solar-Driven Adsorption Chiller with Optimal Fixed and Adaptive Cycle Times using a Silica Gel/Water Working Pair”, Submitted to Renewable Energy

  • Training Feedforward Artificial Neural Networks with Direct Differentiation Based Hessian and Gradient – Influence of the Selection of Data Preprocessing Procedure (In progress)

Undergraduate Courses:

Fall semester:

  • Manufacturing Processes (ME-221)

​Spring semester:

  • Mechanics of Materials I (ME-211)

Postgraduate Courses:

Fall semester:

  • Advanced Manufacturing Processes (DME-812)

Spring semester:

  • Product Design and Development (DME-811)

  • Mar 2006 – Jul 2006: Korea Atomic Energy Research Institute

  • Dec 2003 – Feb 2004: Kahuta Research Laboratories​

  • Dec 2002 – Aug 2003: Air Weapons Complex, Hassan Abdal