Engineering research and development, also known as R&D, is the systematic process of learning about and building new technologies to design a product.
As opposed to scientific research, engineering research is not concerned with discovering how the world works, but rather how things can be made to function for a given purpose.
Such research might involve much scientific study; however, as engineers work to create design solutions to real-world problems.
The development part of engineering research and development refers to the attempt to build the final product; many stages of development, each with various degrees of readiness, may be required for the design to succeed.
Examples of Engineering Research
Haque, Hasan, and Rahman (2008) describe in their paper entitled ‘Design, construction and testing of a hybrid solar room heating system’ how they developed solar room heating systems with the utilization of renewable energy, where solar energy is converted to heat energy and transported to the target room or space to be conditioned.
The air, in this case, is the medium. In the ancient period, as the authors assert, the shelters were constructed to let sunlight in and prevent as much heat loss as possible.
The improved version of this concept is known as the ‘greenhouse effect,’ where there is a combination of a dark surface and a transparent cover that results as an energy gap. This heat energy is then supplied into the room for heating.
The heating system was designed for a space of 27 m3. The room temperature was 20°C, which was aimed to rise at 23°C. To reach this goal, 98.136 kJ heat energy was required to supply inside the room.
Because of the unavailability of such a room, the experiment was conducted in a room that was almost three times the desired space. As a result, the desired temperature was not available.
However, the performance of the cross heating system was impressive.
In a study on the performance of leader election algorithms in mobile sensor networks, Mamun, Rahman, and Mottalib (2008) extensively studied problems in Mobile Sensor Networks.
In their study, they implemented an extended idea of an existing leader election algorithm for energy saving to arbitrary topological changes.
In this method, their focus was to reduce the number of leader election processes to make it more energy-efficient.
Unlike the previous solutions, the algorithm proposed that each node maintains a list of candidates to minimize the total number of leader elections.
Simulation results show that this Candidate Based Leader Election Algorithm for Sensor Networks has much fewer leader elections and generates a lower number of messages than the existing leader election algorithm.