Optimisation techniques to improve the drive efficiency of a mobile ventilator platform
- Authors: Imran, Mohammed Zaahid
- Date: 2024-04
- Subjects: Artificial respiration , Respirators (Medical equipment) , Topology , Medical instruments and apparatus -- Design and construction
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64555 , vital:73749
- Description: COVID-19 pandemic has underscored the indispensable role of mechanical ventilators in providing critical respiratory support to patients. This research has focused on the imperative task of optimising the articulation system of the ventilator, designed to enhance its efficiency, with particular emphasis on improving the volume displacement from the ambu bag. Several optimisation methods were explored, including generative design (GD), Design of Experiments (DOE), Shape optimisation, and topology optimisation. The study also highlights the significance of additive manufacturing and material testing in developing ventilator components. The study delves into the intricate development and fine-tuning of the ventilator setup, emphasising its pivotal role in delivering life-sustaining respiratory aid. The ventilator’s core mechanisms, featuring a two-pusher arm system powered by a servo motor, was engineered intricately to apply precise pressure on the ambu bag. The research underscores the importance of optimising both the pusher arm and pressure plates to improve air displacement within the system. A significant challenge addressed in this research was the excessive strain on the servo motor owing to the demands of the articulation system. The research employed strategies such as shape optimisation and topology optimisation to reduce the stress on the articulation system while increasing the air displacement and thus reducing the pusher arm displacement on the ventilator. The research methodology included stages such as setting performance benchmarks, calibration, and verification to ensure precision and reliability; shape optimisation for maximum efficiency; and topology optimisation for superior structural performance and reduced weight. These interconnected stages were instrumental in the comprehensive development and enhancement of the ventilator system, ensuring its effectiveness and dependability in delivering lifesaving respiratory support.This research extensively examined sensor reliability and performance through verification tests and calibrations, highlighting the precision of the servo motor and the suitability of the 5-Amps current sensor for monitoring servo motor current without additional calibration. Optimisation efforts aimed to enhance the ventilators performance by relocating the pusher arm to the bag’s centre, resulting in improved volume displacement efficiency by 7.78 % and a 25.35 % reduction in current consumption. Shape optimisation, especially with curvature-based pressure plates, increased volume displacement by 84.47 % reaching an optimal configuration outputting 1475.73 ml of volume per compression. Understanding the forces through strain gauges and FEA facilitated topology optimisation, the MAXSTIFFDS15 configuration demonstrated promising results by reducing component weight and achieving significant energy savings of 45.04 %, potentially reducing long-term costs. , Thesis (MEng) -- Faculty of Engineering, the Built Environment, and Technology, School of Engineering, 2024
- Full Text:
- Date Issued: 2024-04
- Authors: Imran, Mohammed Zaahid
- Date: 2024-04
- Subjects: Artificial respiration , Respirators (Medical equipment) , Topology , Medical instruments and apparatus -- Design and construction
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64555 , vital:73749
- Description: COVID-19 pandemic has underscored the indispensable role of mechanical ventilators in providing critical respiratory support to patients. This research has focused on the imperative task of optimising the articulation system of the ventilator, designed to enhance its efficiency, with particular emphasis on improving the volume displacement from the ambu bag. Several optimisation methods were explored, including generative design (GD), Design of Experiments (DOE), Shape optimisation, and topology optimisation. The study also highlights the significance of additive manufacturing and material testing in developing ventilator components. The study delves into the intricate development and fine-tuning of the ventilator setup, emphasising its pivotal role in delivering life-sustaining respiratory aid. The ventilator’s core mechanisms, featuring a two-pusher arm system powered by a servo motor, was engineered intricately to apply precise pressure on the ambu bag. The research underscores the importance of optimising both the pusher arm and pressure plates to improve air displacement within the system. A significant challenge addressed in this research was the excessive strain on the servo motor owing to the demands of the articulation system. The research employed strategies such as shape optimisation and topology optimisation to reduce the stress on the articulation system while increasing the air displacement and thus reducing the pusher arm displacement on the ventilator. The research methodology included stages such as setting performance benchmarks, calibration, and verification to ensure precision and reliability; shape optimisation for maximum efficiency; and topology optimisation for superior structural performance and reduced weight. These interconnected stages were instrumental in the comprehensive development and enhancement of the ventilator system, ensuring its effectiveness and dependability in delivering lifesaving respiratory support.This research extensively examined sensor reliability and performance through verification tests and calibrations, highlighting the precision of the servo motor and the suitability of the 5-Amps current sensor for monitoring servo motor current without additional calibration. Optimisation efforts aimed to enhance the ventilators performance by relocating the pusher arm to the bag’s centre, resulting in improved volume displacement efficiency by 7.78 % and a 25.35 % reduction in current consumption. Shape optimisation, especially with curvature-based pressure plates, increased volume displacement by 84.47 % reaching an optimal configuration outputting 1475.73 ml of volume per compression. Understanding the forces through strain gauges and FEA facilitated topology optimisation, the MAXSTIFFDS15 configuration demonstrated promising results by reducing component weight and achieving significant energy savings of 45.04 %, potentially reducing long-term costs. , Thesis (MEng) -- Faculty of Engineering, the Built Environment, and Technology, School of Engineering, 2024
- Full Text:
- Date Issued: 2024-04
A model to predict the development of preeclampsia in South African women
- Authors: Smith, Nathan
- Date: 2022-12
- Subjects: Medical instruments and apparatus -- Design and construction , Hypertension in pregnancy -- measurements-- South Africa , Fetus -- Physiology
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/59980 , vital:62724
- Description: Preeclampsia is the new onset of hypertension and is one of the leading causes of maternal mortality in South Africa and the world. Preeclampsia is usually diagnosed after 20 weeks’ gestation. Due to South Africa’s poor level of antenatal care, the prediction of pregnant women at risk of developing preeclampsia can be an essential component of improving the level of antenatal. This study used an antenatal care dataset from a South African obstetrician. A review of the literature and existing systems was conducted to identify the eight risk factors. These risk factors are systolic blood pressure, diastolic blood pressure, maternal age, body mass index, diabetes status, hypertension history, nulliparity, and maternal disease. This study used antenatal care datasets from a South African obstetrician. Two models were developed that could accurately predict the development of preeclampsia, one before 16 weeks’ gestation and the other within three check-ups. The model was evaluated using five evaluation metrics: classification accuracy, area under the curve, precision, recall and F-Score. The results of this study show a promising future for the use of machine learning models in health care. To the researcher’s knowledge, this model is the first machine learning model for predicting preeclampsia using a South African dataset. Future work will revolve around validating the model on data collected from field studies in hospitals and clinics around South Africa , Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2022
- Full Text:
- Date Issued: 2022-12
- Authors: Smith, Nathan
- Date: 2022-12
- Subjects: Medical instruments and apparatus -- Design and construction , Hypertension in pregnancy -- measurements-- South Africa , Fetus -- Physiology
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/59980 , vital:62724
- Description: Preeclampsia is the new onset of hypertension and is one of the leading causes of maternal mortality in South Africa and the world. Preeclampsia is usually diagnosed after 20 weeks’ gestation. Due to South Africa’s poor level of antenatal care, the prediction of pregnant women at risk of developing preeclampsia can be an essential component of improving the level of antenatal. This study used an antenatal care dataset from a South African obstetrician. A review of the literature and existing systems was conducted to identify the eight risk factors. These risk factors are systolic blood pressure, diastolic blood pressure, maternal age, body mass index, diabetes status, hypertension history, nulliparity, and maternal disease. This study used antenatal care datasets from a South African obstetrician. Two models were developed that could accurately predict the development of preeclampsia, one before 16 weeks’ gestation and the other within three check-ups. The model was evaluated using five evaluation metrics: classification accuracy, area under the curve, precision, recall and F-Score. The results of this study show a promising future for the use of machine learning models in health care. To the researcher’s knowledge, this model is the first machine learning model for predicting preeclampsia using a South African dataset. Future work will revolve around validating the model on data collected from field studies in hospitals and clinics around South Africa , Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2022
- Full Text:
- Date Issued: 2022-12
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