Optimal design of a forced circulation solar water heating system for a residential unit in cold climate using TRNSYS

Solar water heating system proves to be an effective technology for converting solar energy into thermal energy. The efficiency of solar thermal conversion is around 70% when compared to solar electrical direct conversion system which has an efficiency of only 17%. Hence solar water heaters play a vital role in domestic as well as industrial sector due to its ease of operation and simple maintenance. Extensive works on improving the thermal efficiency of solar water heaters resulted in techniques to improve the convective heat transfer. Passive technique has been used to augment convective heat transfer. These techniques when adopted in solar water heaters proved that the overall thermal performance improved significantly. This paper reviews various techniques to enhance the thermal efficiency in solar water heater. In addition to this, a detailed discussion on the limitations of existing research, research gap and suggested possible modifications is made.

A comprehensive review on solar water heaters

Effect of thermal conductivity of absorber plate on the performance of a solar water heater

Optimal design for a thermosyphon solar water heater

Design improvements for a collector/generator/adsorber of a solid adsorption solar refrigerator

The effect of tube spacing on the cost-effectiveness of a flat-plate solar collector

Abstract Structural rearrangements play a central role in the organization and function of complex biomolecular systems. In principle, Molecular Dynamics (MD) simulations enable us to investigate these thermally activated processes with an atomic level of resolution. In practice, an exponentially large fraction of computational resources must be invested to simulate thermal fluctuations in metastable states. Path sampling methods focus the computational power on sampling the rare transitions between states. One of their outstanding limitations is to efficiently generate paths that visit significantly different regions of the conformational space. To overcome this issue, we introduce a new algorithm for MD simulations that integrates machine learning and quantum computing. First, using functional integral methods, we derive a rigorous low-resolution spatially coarse-grained representation of the system’s dynamics, based on a small set of molecular configurations explored with machine learning. Then, we use a quantum annealer to sample the transition paths of this low-resolution theory. We provide a proof-of-concept application by simulating a benchmark conformational transition with all-atom resolution on the D-Wave quantum computer. By exploiting the unique features of quantum annealing, we generate uncorrelated trajectories at every iteration, thus addressing one of the challenges of path sampling. Once larger quantum machines will be available, the interplay between quantum and classical resources may emerge as a new paradigm of high-performance scientific computing. In this work, we provide a platform to implement this integrated scheme in the field of molecular simulations. 

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D.M. Ghamari

Papers

The effect of tube spacing on the cost-effectiveness of a flat-plate solar collector

Sampling rare conformational transitions with a quantum computer