Mosa G K Machesa - Profile on Academia.edu (original) (raw)

Papers by Mosa G K Machesa

Results in Engineering (Elsevier), 2024

Thermoacoustic engines (TAE) offer a promising avenue for converting low-grade heat into useable ... more Thermoacoustic engines (TAE) offer a promising avenue for converting low-grade heat into useable energy. Despite its simplicity in fabrication, designing thermoacoustic engines face efficiency challenges hindering their widespread adoption. One key obstacle faced by current thermoacoustic devices is their lack of efficiency. The core components of TAE are the stack, Cold Heat Exchanger (CHX), Hot Heat Exchanger (CHX) and the resonator. These components are crucial in advancing the performance of thermoacoustic engines. The CHX keeps the working gas cool, which is essential for the engine to function efficiently and create the desired acoustic wave. However, many existing efforts in this field have not taken into consideration the effect of the CHX design which may have effect on the operating temperature and the engines performance. This study examines the influence of different CHX blockage ratios (28 %, 36 %, 42 %, 47 %, and 59 %), along with the addition of an external CHX, on performance metrics. Measurements were conducted using air at room temperature and atmospheric pressure to assess temperature difference, startup time, volumetric velocity, and sound pressure levels. Key findings indicate that Efficiency directly correlates with volumetric velocity and inversely with onset temperature. Even though decreasing the blockage ratio increases operating temperature, it lengthens startup and reduces volumetric velocity, suggesting increased axial conduction. In addition, External CHX lowers operating temperature, reducing volumetric velocity and sound pressure. Interestingly, resonance frequency remains unaffected by CHX blockage ratio changes. An optimal configuration with a 42-47 % blockage ratio achieves 5.92 m/s velocity, 136 s startup time, and 77 • C onset temperature. These findings offer valuable insights for optimizing thermoacoustic engine efficiency and advancing their potential as sustainable energy solutions.

Proceedings of the 31st Annual Conference of the Southern African Institute for Industrial Engineering (SAIIE31), 2020

Industry 4.0 is a radical transformation and innovation through technology which is gaining so mu... more Industry 4.0 is a radical transformation and innovation through technology which is gaining so much attention in virtually all sectors of the economy. Manufacturing operations at inbound and outbound settings require the optimization of resources and logistics at all levels, to enable an intelligent flow of products from source to destinations within a defined network. Autonomous robots and computer algorithm of the future is required to achieve the task by ensuring timely delivery via the shortest route. Supply chain models for effective distribution of products using unmanned aerial vehicles and autonomous ground vehicles have been developed. There is no doubt that the model if tested, would help in the effective delivery of products efficiently, effectively, and optimally within the distribution network. It would enable a reduction in prices of products at the lowest echelon, reduced risk of accident as a result of human error, and finally, the environment will be cleaner and greener.

Proceedings of the 9th International Conference on Appropriate Technology (ICAT), 2021

This research is focused on the development of a modular AGV for moving light materials from one ... more This research is focused on the development of a modular AGV for moving light materials from one location to another. The development of the system was actualized using light wooden (LW) material with remote sensing carrier which moves at a steady and constant speed by following drawn lines, using infrared sensors for effective path navigation and mapping. The system was developed to pick up and drop light materials at various stations with two degrees of freedom (DOF), using the LW manipulator. Also incorporated in the design is the ultrasonic sensor to detect and avoid obstacles. The microcontroller which serves as the brain of the system was programmed via an Arduino board using an Embedded C language to send specific commands to the system. The capabilities of the developed AGV were explored during preliminary tests. It was observed that the robot is capable of navigating along a planned path from the start point to the destination without colliding with obstacles. The tasks of material handling, especially in hazardous environments, are made easier with a well-developed AGV to enhance efficiency in the delivery of items, especially at centres for palliative care, the nuclear industry and other hazardous environments.

South African Journal of Industrial Engineering, 2020

Supplier selection in a manufacturing system is highly complex owing to the nature and structure ... more Supplier selection in a manufacturing system is highly complex owing to the nature and structure of organisations, necessitating a paradigm shift from the rule-of-thumb and classical methods of supplier selection to a reliable technique that uses a hybrid algorithm to provide greater accuracy in the selection process. This study proposes the use of a hybrid computational intelligence technique — an adaptive neuro-fuzzy inference system — for the effective identification and selection of sustainable suppliers. This hybrid modelling configuration was applied in a paint manufacturing company to select the best possible supplier. Information obtained from the company within the period of investigation was fed into the model. The result obtained shows a faster and more reliable prediction by the creative model. Professionals and business managers will benefit greatly from the selection of sustainable suppliers in an in-bound and out-bound supply chain system.

Sustainability (MDPI), 2021

settingsOrder Article Reprints Open AccessArticle Prediction of the Oscillatory Heat Transfer Coe... more settingsOrder Article Reprints
Open AccessArticle
Prediction of the Oscillatory Heat Transfer Coefficient in Thermoacoustic Refrigerators
by Mosa Machesa,Lagouge Tartibu *ORCID andModestus Okwu
Department of Mechanical & Industrial Engineering Technology, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2028, South Africa
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(17), 9509; https://doi.org/10.3390/su13179509
Submission received: 29 May 2021 / Revised: 11 August 2021 / Accepted: 16 August 2021 / Published: 24 August 2021
Downloadkeyboard_arrow_down Browse Figures Versions Notes

Abstract
Thermoacoustic refrigerators are emerging devices that make use of meaningful high-pressure sound waves to induce cooling. Despite the accelerated progress in the field of thermoacoustics, knowledge of the heat transfer process in the heat exchange of the devices is still developing. This work applies different soft computing techniques, namely, an artificial neural network trained by particle swarm optimisation (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANNs) to predict the oscillatory heat transfer coefficient in the heat exchangers of a thermoacoustic device. This study provides the details of the parametric analysis of an artificial neural network model trained by particle swarm optimisation. The solution model considers the number of neurons, the swarm population, and the acceleration factors to develop and analyse the architecture of several models. The regression model (R2) and mean squared error (MSE) were used to evaluate the accuracy of the models. The result showed that the proposed soft computing techniques can potentially be used for the modelling and the analysis of the oscillatory heat transfer coefficient with a higher level of accuracy. The result reported in this study implies that the prediction of the OHTC can be considered for the enhancement of thermoacoustic refrigerators performances.

Proceedings of the ASME 2019 International Mechanical Engineering Congress and Exposition (IMECE2019), 2019

The characterisation of heat transfer in oscillatory flow of thermo-acoustic based heat exchanger... more The characterisation of heat transfer in oscillatory flow of thermo-acoustic based heat exchangers is a cumbersome issue. This is due to the nature of the heat transfer between the gas particles moving along the device at high amplitude and the solid surface of the heat exchangers. In addition, the change in velocity, pressure and temperature induces nonlinear effect. As a result, the performance of heat exchangers negatively affects the efficiency of thermo-acoustic systems. Hence, it is necessary to determine to oscillatory heat transfer coefficient in order to measure the performance of heat exchangers in thermoacoustic systems. Although it is possible to conduct experimental investigation or perform numerical analysis in order to determine oscillatory heat transfer coefficient, the former requires costly time consuming experiment while the latter involves the resolution of complex mathematical models. In this paper, an improved adaptive neurofuzzy inference system and artificial neural network trained by particle swarm optimization are proposed to predict oscillatory heat transfer coefficient. This paper is intending to provide clarity on the benefits of these new approaches on the computation of geometrical configuration and the working parameters of heat exchangers in thermo-acoustic systems.

Neural Computing and Applications (Springer), 2022

Stirling engine is considered as one of the most promising alternatives to conventional combustio... more Stirling engine is considered as one of the most promising alternatives to conventional combustion units due to its versatility and potential to achieve relatively high efficiency. The output power and torque are the main performance indicators that depend on many variables. Many studies have pointed out that the relationship between the performance indicators of the Stirling engine and its input variables was nonlinear. This study analyses the prediction performance of power and torque in a Stirling engine system using soft computing techniques—artificial neural network (ANN) and Fuzzy Mamdani Model (FMM) and hybrid algorithms—adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained with particle swarm optimization (ANN-PSO). The performance of these approaches has been discussed using a dataset from a test conducted on an existing Stirling engine. The performance indicators of the different models considering the power and the torque were predicted and analysed. A parametric analysis has been performed for the ANN-PSO model to identify the best model configuration considering the number of neurons in hidden layers, the number of swarm size and acceleration factors. A detailed description of the process leading to the identification of the best networks architecture for the power and torque model has been provided. The comparison of the four approaches indicates that FMM exhibits the highest performance prediction considering the power while the ANN-PSO and ANFIS model exhibit the highest performance considering the torque. This study demonstrates the suitability of soft computing techniques and hybrid algorithms for the prediction of Stirling engine characteristics and its potential to optimize time and experimental cost.

Proceedings of the 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), 2020

Global energy use has increased significantly over the past few years. This increase is as a resu... more Global energy use has increased significantly over the past few years. This increase is as a result of several factors which include growth in population, improved living standards and the development of the trade and commercial industry. With the world's increased reliance on fossil fuels, various

Results in Engineering (Elsevier), 2024

Thermoacoustic generators (TAGs) present an attractive solution for converting low-grade thermal ... more Thermoacoustic generators (TAGs) present an attractive solution for converting low-grade thermal energy into useable electrical power. Despite their relatively modest fabrication, TAGs face significant efficiency challenges that impede their broader adoption. Current research efforts in thermoacoustics are primarily focused on overcoming these limitations. A critical factor influencing TAG efficiency is the design of the acoustic-to-electric (ATE) device. This component plays a vital role in converting the acoustic energy generated within the thermoacoustic engine (TAE) into electricity. Despite its importance, the impact of ATE design on overall TAG performance has often been overlooked in previous studies. This research aims to address this gap by investigating how different ATE configurations influence the efficiency of power conversion within thermoacoustic systems. Specifically, this study delves into the potential of electromagnetic devices: linear alternators and loudspeakers, as ATE converters in a Standing Wave Thermoacoustic Generator (SWTAG) framework using air at atmospheric conditions. Firstly, a comparative analysis was conducted between two types of linear alternators—moving magnet and moving coil and tested with different magnet types (rare earth and ferrie anisotropic) to evaluate their impact on voltage generation. Secondly, the influence of various factors on energy conversion using loudspeakers was investigated, including loudspeaker type (paper cone vs. polypropylene cone), housing geometry (diameter and length), housing material (galvanized steel vs. PVC), open versus closed ATE housing design and utilizing dual loudspeakers. The generated electricity was utilized to power a light bulb.

2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2019

The work presents the prediction performance results of three algorithms, namely Artificial Neura... more The work presents the prediction performance results of three algorithms, namely Artificial Neural Network (ANN), Artificial Neural Network trained with Particle Swarm Optimization (PSO) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models. ANFIS and ANN trained by PSO are applied to predict the power and torque values of a Stirling heat engine with a level controlled displacer driving mechanism. Data from experimental work done by Karabulut et al. is used to train and assess the algorithms. MATLAB is used to develop, implement and train the algorithms. The Root Mean Square Error (RMSE, Coefficient of determination (R2) and computational time are used to assess the performance of the algorithms.

International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), 2020

The growing electricity demand in the world has brought about significant challenges in her econo... more The growing electricity demand in the world has brought about significant challenges in her economic development. This concern has prompted the need for electricity generation from different technologies, such as the thermo-acoustic engines. These engines are low-cost electrical power generators. They are alternative and sustainable solutions for electricity generation in developing countries because they generate clean energy. Although the engines have good thermal efficiencies, their oscillatory heat transfer coefficient (OHTC) estimation is often a challenging task. This study, therefore, considers the evaluation of thermo-acoustic engines OHTC using artificial neural network (ANN) model. The input parameters considered are frequency and mean pressure. Experimental data from literature were used to evaluate different hidden-layer architectures of the network configuration. It was concluded that the best solution was obtained with a root mean square error of 0.64 from a model with 4-10-2 architecture.

This work explores the use of thermo-acoustic system as alternative technology for electricity ge... more This work explores the use of thermo-acoustic system as alternative technology for electricity generation. This technology is proposed as a potential replacement for low-cost electrical power generation because of its simplicity and lack of moving parts. Thermo-acoustic generators providing clean electrical energy to power small appliances. The energy conversion from heat into sound wave is done within thermo-acoustic engine. The latter is coupled to a linear alternator for electricity generation. The study investigates the influence of the geometrical configuration of the device on to the whole functionality of the generator. The paper studies the technology through experimental trails performed using a simple arrangement to simulate the generator. The experiment is conducted in phases; the first phase identifies the best geometrical configuration of the thermo-acoustic engine by measuring the sound pressure level and the temperatures. The second phase consist of measuring the electricity generated using a Loudspeaker. The results obtained show the potential for this sustainable solution for electricity generation.

Results in Engineering (Elsevier), 2024

Thermoacoustic engines (TAE) offer a promising avenue for converting low-grade heat into useable ... more Thermoacoustic engines (TAE) offer a promising avenue for converting low-grade heat into useable energy. Despite its simplicity in fabrication, designing thermoacoustic engines face efficiency challenges hindering their widespread adoption. One key obstacle faced by current thermoacoustic devices is their lack of efficiency. The core components of TAE are the stack, Cold Heat Exchanger (CHX), Hot Heat Exchanger (CHX) and the resonator. These components are crucial in advancing the performance of thermoacoustic engines. The CHX keeps the working gas cool, which is essential for the engine to function efficiently and create the desired acoustic wave. However, many existing efforts in this field have not taken into consideration the effect of the CHX design which may have effect on the operating temperature and the engines performance. This study examines the influence of different CHX blockage ratios (28 %, 36 %, 42 %, 47 %, and 59 %), along with the addition of an external CHX, on performance metrics. Measurements were conducted using air at room temperature and atmospheric pressure to assess temperature difference, startup time, volumetric velocity, and sound pressure levels. Key findings indicate that Efficiency directly correlates with volumetric velocity and inversely with onset temperature. Even though decreasing the blockage ratio increases operating temperature, it lengthens startup and reduces volumetric velocity, suggesting increased axial conduction. In addition, External CHX lowers operating temperature, reducing volumetric velocity and sound pressure. Interestingly, resonance frequency remains unaffected by CHX blockage ratio changes. An optimal configuration with a 42-47 % blockage ratio achieves 5.92 m/s velocity, 136 s startup time, and 77 • C onset temperature. These findings offer valuable insights for optimizing thermoacoustic engine efficiency and advancing their potential as sustainable energy solutions.

Proceedings of the 31st Annual Conference of the Southern African Institute for Industrial Engineering (SAIIE31), 2020

Industry 4.0 is a radical transformation and innovation through technology which is gaining so mu... more Industry 4.0 is a radical transformation and innovation through technology which is gaining so much attention in virtually all sectors of the economy. Manufacturing operations at inbound and outbound settings require the optimization of resources and logistics at all levels, to enable an intelligent flow of products from source to destinations within a defined network. Autonomous robots and computer algorithm of the future is required to achieve the task by ensuring timely delivery via the shortest route. Supply chain models for effective distribution of products using unmanned aerial vehicles and autonomous ground vehicles have been developed. There is no doubt that the model if tested, would help in the effective delivery of products efficiently, effectively, and optimally within the distribution network. It would enable a reduction in prices of products at the lowest echelon, reduced risk of accident as a result of human error, and finally, the environment will be cleaner and greener.

Proceedings of the 9th International Conference on Appropriate Technology (ICAT), 2021

This research is focused on the development of a modular AGV for moving light materials from one ... more This research is focused on the development of a modular AGV for moving light materials from one location to another. The development of the system was actualized using light wooden (LW) material with remote sensing carrier which moves at a steady and constant speed by following drawn lines, using infrared sensors for effective path navigation and mapping. The system was developed to pick up and drop light materials at various stations with two degrees of freedom (DOF), using the LW manipulator. Also incorporated in the design is the ultrasonic sensor to detect and avoid obstacles. The microcontroller which serves as the brain of the system was programmed via an Arduino board using an Embedded C language to send specific commands to the system. The capabilities of the developed AGV were explored during preliminary tests. It was observed that the robot is capable of navigating along a planned path from the start point to the destination without colliding with obstacles. The tasks of material handling, especially in hazardous environments, are made easier with a well-developed AGV to enhance efficiency in the delivery of items, especially at centres for palliative care, the nuclear industry and other hazardous environments.

South African Journal of Industrial Engineering, 2020

Supplier selection in a manufacturing system is highly complex owing to the nature and structure ... more Supplier selection in a manufacturing system is highly complex owing to the nature and structure of organisations, necessitating a paradigm shift from the rule-of-thumb and classical methods of supplier selection to a reliable technique that uses a hybrid algorithm to provide greater accuracy in the selection process. This study proposes the use of a hybrid computational intelligence technique — an adaptive neuro-fuzzy inference system — for the effective identification and selection of sustainable suppliers. This hybrid modelling configuration was applied in a paint manufacturing company to select the best possible supplier. Information obtained from the company within the period of investigation was fed into the model. The result obtained shows a faster and more reliable prediction by the creative model. Professionals and business managers will benefit greatly from the selection of sustainable suppliers in an in-bound and out-bound supply chain system.

Sustainability (MDPI), 2021

settingsOrder Article Reprints Open AccessArticle Prediction of the Oscillatory Heat Transfer Coe... more settingsOrder Article Reprints
Open AccessArticle
Prediction of the Oscillatory Heat Transfer Coefficient in Thermoacoustic Refrigerators
by Mosa Machesa,Lagouge Tartibu *ORCID andModestus Okwu
Department of Mechanical & Industrial Engineering Technology, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2028, South Africa
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(17), 9509; https://doi.org/10.3390/su13179509
Submission received: 29 May 2021 / Revised: 11 August 2021 / Accepted: 16 August 2021 / Published: 24 August 2021
Downloadkeyboard_arrow_down Browse Figures Versions Notes

Abstract
Thermoacoustic refrigerators are emerging devices that make use of meaningful high-pressure sound waves to induce cooling. Despite the accelerated progress in the field of thermoacoustics, knowledge of the heat transfer process in the heat exchange of the devices is still developing. This work applies different soft computing techniques, namely, an artificial neural network trained by particle swarm optimisation (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANNs) to predict the oscillatory heat transfer coefficient in the heat exchangers of a thermoacoustic device. This study provides the details of the parametric analysis of an artificial neural network model trained by particle swarm optimisation. The solution model considers the number of neurons, the swarm population, and the acceleration factors to develop and analyse the architecture of several models. The regression model (R2) and mean squared error (MSE) were used to evaluate the accuracy of the models. The result showed that the proposed soft computing techniques can potentially be used for the modelling and the analysis of the oscillatory heat transfer coefficient with a higher level of accuracy. The result reported in this study implies that the prediction of the OHTC can be considered for the enhancement of thermoacoustic refrigerators performances.

Proceedings of the ASME 2019 International Mechanical Engineering Congress and Exposition (IMECE2019), 2019

The characterisation of heat transfer in oscillatory flow of thermo-acoustic based heat exchanger... more The characterisation of heat transfer in oscillatory flow of thermo-acoustic based heat exchangers is a cumbersome issue. This is due to the nature of the heat transfer between the gas particles moving along the device at high amplitude and the solid surface of the heat exchangers. In addition, the change in velocity, pressure and temperature induces nonlinear effect. As a result, the performance of heat exchangers negatively affects the efficiency of thermo-acoustic systems. Hence, it is necessary to determine to oscillatory heat transfer coefficient in order to measure the performance of heat exchangers in thermoacoustic systems. Although it is possible to conduct experimental investigation or perform numerical analysis in order to determine oscillatory heat transfer coefficient, the former requires costly time consuming experiment while the latter involves the resolution of complex mathematical models. In this paper, an improved adaptive neurofuzzy inference system and artificial neural network trained by particle swarm optimization are proposed to predict oscillatory heat transfer coefficient. This paper is intending to provide clarity on the benefits of these new approaches on the computation of geometrical configuration and the working parameters of heat exchangers in thermo-acoustic systems.

Neural Computing and Applications (Springer), 2022

Stirling engine is considered as one of the most promising alternatives to conventional combustio... more Stirling engine is considered as one of the most promising alternatives to conventional combustion units due to its versatility and potential to achieve relatively high efficiency. The output power and torque are the main performance indicators that depend on many variables. Many studies have pointed out that the relationship between the performance indicators of the Stirling engine and its input variables was nonlinear. This study analyses the prediction performance of power and torque in a Stirling engine system using soft computing techniques—artificial neural network (ANN) and Fuzzy Mamdani Model (FMM) and hybrid algorithms—adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained with particle swarm optimization (ANN-PSO). The performance of these approaches has been discussed using a dataset from a test conducted on an existing Stirling engine. The performance indicators of the different models considering the power and the torque were predicted and analysed. A parametric analysis has been performed for the ANN-PSO model to identify the best model configuration considering the number of neurons in hidden layers, the number of swarm size and acceleration factors. A detailed description of the process leading to the identification of the best networks architecture for the power and torque model has been provided. The comparison of the four approaches indicates that FMM exhibits the highest performance prediction considering the power while the ANN-PSO and ANFIS model exhibit the highest performance considering the torque. This study demonstrates the suitability of soft computing techniques and hybrid algorithms for the prediction of Stirling engine characteristics and its potential to optimize time and experimental cost.

Proceedings of the 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), 2020

Global energy use has increased significantly over the past few years. This increase is as a resu... more Global energy use has increased significantly over the past few years. This increase is as a result of several factors which include growth in population, improved living standards and the development of the trade and commercial industry. With the world's increased reliance on fossil fuels, various

Results in Engineering (Elsevier), 2024

Thermoacoustic generators (TAGs) present an attractive solution for converting low-grade thermal ... more Thermoacoustic generators (TAGs) present an attractive solution for converting low-grade thermal energy into useable electrical power. Despite their relatively modest fabrication, TAGs face significant efficiency challenges that impede their broader adoption. Current research efforts in thermoacoustics are primarily focused on overcoming these limitations. A critical factor influencing TAG efficiency is the design of the acoustic-to-electric (ATE) device. This component plays a vital role in converting the acoustic energy generated within the thermoacoustic engine (TAE) into electricity. Despite its importance, the impact of ATE design on overall TAG performance has often been overlooked in previous studies. This research aims to address this gap by investigating how different ATE configurations influence the efficiency of power conversion within thermoacoustic systems. Specifically, this study delves into the potential of electromagnetic devices: linear alternators and loudspeakers, as ATE converters in a Standing Wave Thermoacoustic Generator (SWTAG) framework using air at atmospheric conditions. Firstly, a comparative analysis was conducted between two types of linear alternators—moving magnet and moving coil and tested with different magnet types (rare earth and ferrie anisotropic) to evaluate their impact on voltage generation. Secondly, the influence of various factors on energy conversion using loudspeakers was investigated, including loudspeaker type (paper cone vs. polypropylene cone), housing geometry (diameter and length), housing material (galvanized steel vs. PVC), open versus closed ATE housing design and utilizing dual loudspeakers. The generated electricity was utilized to power a light bulb.

2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI), 2019

The work presents the prediction performance results of three algorithms, namely Artificial Neura... more The work presents the prediction performance results of three algorithms, namely Artificial Neural Network (ANN), Artificial Neural Network trained with Particle Swarm Optimization (PSO) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models. ANFIS and ANN trained by PSO are applied to predict the power and torque values of a Stirling heat engine with a level controlled displacer driving mechanism. Data from experimental work done by Karabulut et al. is used to train and assess the algorithms. MATLAB is used to develop, implement and train the algorithms. The Root Mean Square Error (RMSE, Coefficient of determination (R2) and computational time are used to assess the performance of the algorithms.

International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), 2020

The growing electricity demand in the world has brought about significant challenges in her econo... more The growing electricity demand in the world has brought about significant challenges in her economic development. This concern has prompted the need for electricity generation from different technologies, such as the thermo-acoustic engines. These engines are low-cost electrical power generators. They are alternative and sustainable solutions for electricity generation in developing countries because they generate clean energy. Although the engines have good thermal efficiencies, their oscillatory heat transfer coefficient (OHTC) estimation is often a challenging task. This study, therefore, considers the evaluation of thermo-acoustic engines OHTC using artificial neural network (ANN) model. The input parameters considered are frequency and mean pressure. Experimental data from literature were used to evaluate different hidden-layer architectures of the network configuration. It was concluded that the best solution was obtained with a root mean square error of 0.64 from a model with 4-10-2 architecture.

This work explores the use of thermo-acoustic system as alternative technology for electricity ge... more This work explores the use of thermo-acoustic system as alternative technology for electricity generation. This technology is proposed as a potential replacement for low-cost electrical power generation because of its simplicity and lack of moving parts. Thermo-acoustic generators providing clean electrical energy to power small appliances. The energy conversion from heat into sound wave is done within thermo-acoustic engine. The latter is coupled to a linear alternator for electricity generation. The study investigates the influence of the geometrical configuration of the device on to the whole functionality of the generator. The paper studies the technology through experimental trails performed using a simple arrangement to simulate the generator. The experiment is conducted in phases; the first phase identifies the best geometrical configuration of the thermo-acoustic engine by measuring the sound pressure level and the temperatures. The second phase consist of measuring the electricity generated using a Loudspeaker. The results obtained show the potential for this sustainable solution for electricity generation.