Impact Of Supply Air Flow Rate On System Performance And Space Comfort Of Residential Air Conditioning Systems

The amount of air flow rate supplied to the space by the residential air conditioning system can have significant effect on the whole system performance and on the thermal comfort level. This study investigates the impact of air flow rates when they are higher or lower than the current practices or manufacture’s specified values on system performance and on space comfort. In addition, this work examines the performance operation data such as (1) the annual and hourly energy consumption, (2) the system operation hours, (3) the temperature of air supplied to the space, (4) humidity ratio of the space, (5) system efficiency, and (6) the amount of sensible / latent load that can be added / removed from the space. Experiments are conducted on the air conditioning heat pump located at the HVAC laboratory of the Civil, Architectural & Environmental Engineering (CAEE) department in order to identify experimentally the fan performance and define the fan performance curve and efficiency. Simulations are also done to investigate the whole system performance of air conditioning systems and associated thermal comfort with different USA locations based on ASHRAE climate zones. The model is developed by the energy simulation software eQuest, using the characteristics of the tested lab system for a 1600 ft2 typical residential house conditioned by 3-ton residential air conditioning heat pump system. The simulation model is tested on various air flow rates, ranging from 900 cfm to 1400 cfm and considering 1200 cfm as baseline. The results show an increase in the fan energy and total annual energy consumption with the higher airflow rate supplied. A higher temperature of the airflow causes elevated humidity in space that can become an issue in terms of space comfort, especially in humid weather locations

Impact Of Supply Air Flow Rate On System Performance And Space Comfort Of Residential Air Conditioning Systems

The amount of air flow rate supplied to the space by the residential air conditioning system can have significant effect on the whole system performance and on the thermal comfort level. This study investigates the impact of air flow rates when they are higher or lower than the current practices or manufacture’s specified values on system performance and on space comfort. In addition, this work examines the performance operation data such as (1) the annual and hourly energy consumption, (2) the system operation hours, (3) the temperature of air supplied to the space, (4) humidity ratio of the space, (5) system efficiency, and (6) the amount of sensible / latent load that can be added / removed from the space. Experiments are conducted on the air conditioning heat pump located at the HVAC laboratory of the Civil, Architectural & Environmental Engineering (CAEE) department in order to identify experimentally the fan performance and define the fan performance curve and efficiency. Simulations are also done to investigate the whole system performance of air conditioning systems and associated thermal comfort with different USA locations based on ASHRAE climate zones. The model is developed by the energy simulation software eQuest, using the characteristics of the tested lab system for a 1600 ft2 typical residential house conditioned by 3-ton residential air conditioning heat pump system. The simulation model is tested on various air flow rates, ranging from 900 cfm to 1400 cfm and considering 1200 cfm as baseline. The results show an increase in the fan energy and total annual energy consumption with the higher airflow rate supplied. A higher temperature of the airflow causes elevated humidity in space that can become an issue in terms of space comfort, especially in humid weather locations

Creative or Not? Birds and Ants Draw with Muscle

In this work, a novel approach of merging two swarm intelligence algorithms is considered – one mimicking the behaviour of ants foraging (Stochastic Diffusion Search [5]) and the other algorithm simulating the behaviour of birds flocking (Particle Swarm Optimisation [17]). This hybrid algorithm is assisted by a mechanism inspired from the behaviour of skeletal muscles activated by motor neurons. The operation of the swarm intelligence algorithms is first introduced via metaphor before the new hybrid algorithm is defined. Next, the novel behaviour of the hybrid algorithm is reflected through a cooperative attempt to make a drawing, followed by a discussion about creativity in general and the ’computational creativity’ of the swarm

Cooperation of Nature and Physiologically Inspired Mechanism in Visualisation

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised. The performance of the nature-inspired algorithms and the biologically inspired mechanisms in the hybrid algorithm is reflected through a cooperative attempt to make a drawing on the canvas. The scientific value of the marriage between the two swarm intelligence algorithms is currently being investigated thoroughly on many benchmarks and the results reported suggest a promising prospect (al-Rifaie, Bishop & Blackwell, 2011). We also discuss whether or not the ‘art works’ generated by nature and biologically inspired algorithms can possibly be considered as ‘computationally creative’

An Investigation Into the use of Swarm Intelligence for an Evolutionary Algorithm Optimisation; The Optimisation Performance of Differential Evolution Algorithm Coupled with Stochastic Diffusion Search

The integration of Swarm Intelligence (SI) algorithms and Evolutionary algorithms (EAs) might be one of the future approaches in the Evolutionary Computation (EC). This work narrates the early research on using Stochastic Diffusion Search (SDS) -- a swarm intelligence algorithm -- to empower the Differential Evolution (DE) -- an evolutionary algorithm -- over a set of optimisation problems. The results reported herein suggest that the powerful resource allocation mechanism deployed in SDS has the potential to improve the optimisation capability of the classical evolutionary algorithm used in this experiment. Different performance measures and statistical analyses were utilised to monitor the behaviour of the final coupled algorithm

An Investigation into the Merger of Stochastic Diffusion Search and Particle Swarm Optimisation

This study reports early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) to the Particle Swarm Optimiser (PSO) metaheuristic, effectively merging the two swarm intelligence algorithms. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between particles, has the potential to improve the optimisation capability of conventional PSOs

Generative music with stochastic diffusion search

This paper introduces an approach for using a swarm intelligence algorithm, Stochastic Diffusion Search (SDS) – inspired by one species of ants, Leptothorax acervorum – in order to generate music from plain text. In this approach , SDS is adapted in such a way to vocalise the agents, to hear their “chit-chat” . While the generated music depends on the input text, the algorithm’s search capability in locating the words in the input text is reflected in the duration and dynamic of the resulting musical notes. In other words, the generated music depends on the behaviour of the algorithm and the communication between its agents. This novel approach, while staying loyal to the original input text, when run each time, ‘vocalises’ the input text in varying ‘flavours’

Maximising overlap score in DNA sequence assembly problem by Stochastic Diffusion Search

This paper introduces a novel study on the performance of Stochastic Diffusion Search (SDS)—a swarm intelligence algorithm—to address DNA sequence assembly problem. This is an NP-hard problem and one of the primary problems in computational molecular biology that requires optimisation methodologies to reconstruct the original DNA sequence. In this work, SDS algorithm is adapted for this purpose and several experiments are run in order to evaluate the performance of the presented technique over several frequently used benchmarks. Given the promising results of the newly proposed algorithm and its success in assembling the input fragments, its behaviour is further analysed, thus shedding light on the process through which the algorithm conducts the task. Additionally, the algorithm is applied to overlap score matrices which are generated from the raw input fragments; the algorithm optimises the overlap score matrices to find better results. In these experiments real-world data are used and the performance of SDS is compared with several other algorithms which are used by other researchers in the field, thus demonstrating its weaknesses and strengths in the experiments presented in the paper