Variations In Input Parameters Research Articles

Influence of parameter variation intervals on pyrolysis sensitivity analysis for charring and non-charring materials

This study applies the Morris method for a sensitivity analysis to evaluate the input parameters’ influence on the heat release rate in a pyrolysis model, focusing on two materials, poly(methyl methacrylate) (non-charring) and poly(vinyl chloride) (charring), examined under a cone calorimeter. A key aspect of our exploration was the role of input parameter variation intervals on the sensitivity outcomes. We analyzed three interval-setting methods: 1. A standard ±10% deviation from the nominal value, commonly used in the literature. 2. A range determined by the experimental uncertainties for individual parameters. 3. A span from minimum to maximum values found in existing literature for each parameter. Our intensive literature review supported the framing of intervals for the latter two methods. Our findings underscore the critical role of the selected variation interval. Specifically, while a uniform ±10% variation identified activation energies as the primary influencers—consistent with prior literature—the introduction of experimental uncertainties shifted this prominence toward heats of reaction. Thus, the selected interval can drastically reshape the perceived importance of certain parameters. This original work challenges the traditionally employed variation ranges in sensitivity studies, emphasizing the need for a nuanced approach.

A Credibility Framework for Extreme Value-at-Risk

Value-at-risk estimates derived from extreme value data by fitting fat-tailed distributions can be so large that their validity is open to question. In this paper, an objective criterion, and a framework from which it was developed, are presented in order to decide whether or not a fitted distribution is inappropriate for the purpose of value-at-risk calculation. That criterion is based on established extreme value theory (principally the Pickands-Balkema-deHaan Theorem), which is used to calculate a sequence of reference value-at-risk estimates using Generalised Pareto distributions. Those estimates are used to develop a closed-form formula for calculating a theoretical ’maximum’ value-at-risk. The method is validated by generating 100 random data sets and testing them against the framework for varying input parameter values. Approximately 75% of those cases passed the validation test.

Automating data classification for label-free point-of-care biosensing in real complex samples

Surface-based affinity biosensors present a promising avenue for point-of-care (POC) pathogen detection in real-world samples. While laboratory-based devices commonly employ various techniques to mitigate noise, signal drifts, fluidic artifacts, and other system imperfections, their simple cost-effective POC counterparts designed for field use frequently lack such capabilities. This paper addresses this gap by introducing a procedure for automatically classifying pathogen presence in unprocessed liquids from direct detection data measured by a simple POC quartz crystal microbalance sensor device. The procedure integrates classical analytical tools such as filtering, data selection, baseline de-drifting, and result calculation in tailored successive steps, considering the nature of the sensor signal and the challenges posed by real-world media. We show that the developed procedure exhibits exceptional robustness across different biosensing assays and complex real-world media. Through optimizing parameters using diverse datasets encompassing Escherichia coli O157:H7 (E. coli) and SARS-CoV-2 detection in various media including food-derived matrices and cell culture media, we achieved rates of successful detection as high as 80.8 % and 90.9 % for E. coli and SARS-CoV-2, respectively, without extensive machine learning. Furthermore, we analyse the sensitivity of the procedure to variations of input parameters and with examples discuss key factors influencing overall procedure accuracy. Our results suggest that this exceptionally robust method holds potential as a straightforward tool for automating sample classification in point-of-care diagnostics, underpinning its promising broader applicability.

A comprehensive review of parametric optimization of electrical discharge machining processes using multi-criteria decision-making techniques

Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013–22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi’s L9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decision-making tools.

Fundamental frequency formulation and modeling of masonry slender structures: A comparative study of machine learning and regression techniques

This paper presents a novel method for determining the fundamental frequency and modeling slender masonry structures, which is particularly relevant in the context of structural dynamics. This research evaluates the effectiveness of machine learning techniques, specifically artificial neural networks (ANN), and regression methods, including multiple linear regression (MLR) and multiple nonlinear regression (MNLR), in predicting the fundamental frequency of structures, considering both their geometrical and mechanical properties. The objective is to provide a comprehensive comparison of these methods, emphasizing their benefits, limitations, and potential applications in structural health monitoring. The ANN model demonstrated superior performance to linear and non linear regression methods. The findings could significantly impact both theoretical understanding and practical applications in this field. Moreover, this work paves the way for future research, potentially leading to the creation of more accurate and efficient predictive models for slender masonry structures.This methodology ensures a thorough understanding of the model’s behavior and its responsiveness to variations in input parameters.

Modeling and stability analysis of the transmission dynamics of Monkeypox with control intervention

Monkeypox is a deadly disease from the Orthopox family. The paper explores a nonlinear trend in the dynamics of Monkeypox propagation. In this model, we include vaccination, treatment, and level of awareness as the main parameters of the propagation of Monkeypox. We also include the quarantined infected individuals with severe complications in the Monkeypox dynamics. We show the boundedness and positivity of the model and also demonstrate the stability of the model using the Banach fixed point theory and the Picard successive approximation method to ensure that it is meaningful mathematically and epidemiologically. We obtain the unique solution of the model under appropriate conditions. This work finds three equilibrium points: Monkeypox free, Rodents free, and endemic. We show the local stability of monkeypox free equilibria and rodent-free endemic equilibria through reproduction number. Furthermore, Dulac’s function technique demonstrates the global stability of endemic equilibrium. We show the transformation from endemic equilibria to Monkeypox-free equilibria via rodent-free. We also obtain the condition of transcritical bifurcation. We analyze the system’s dynamic behavior to develop efficient infection control strategies. We find that if we increase awareness, vaccination, treatment, and quarantine rates, we effectively control the transmission. The model is connected with a continuous model using an ordinary differential equation. We examine the complex dynamics of Monkeypox infection under diverse system input factors through numerical simulation of the proposed model with variable input parameters in reducing Monkeypox. We present the theory of Monkeypox disease control in humankind as an application in real-world problems. The work can be helpful in the vector-borne disease control system.

Modeling electron temperature profiles in the pedestal with simple formulas for ETG transport

This paper reports on the refinement (building on (Hatch D.R. et al 2022 Phys. Plasmas 29 062501)) and application of simple formulas for electron heat transport from electron temperature gradient (ETG) driven turbulence in the pedestal. The formulas are improved by (1) improving the parameterization for certain key parameters and (2) carefully accounting for the impact of geometry and shaping in the underlying gyrokinetic simulation database. Comparisons with nonlinear gyrokinetic simulations of ETG transport in the MAST pedestal demonstrate the model’s applicability to spherical tokamaks in addition to standard aspect ratio tokamaks. We identify bounds for model applicability: the model is accurate in the steep gradient region, where the ETG turbulence is largely slab-like, but accuracy decreases as the temperature gradient becomes weaker in the pedestal top. We use the formula to model the electron temperature profile in the pedestal for four experimental scenarios while extensively varying input parameters to represent uncertainties. In all cases, the predicted electron temperature profile exhibits extreme sensitivity to separatrix temperature and density, which has implications for core-edge integration. The model reproduces the electron temperature profile for high ηe=Lne/LTe scenarios but not for low η e scenarios in which microtearing modes (MTMs) have been identified. We develop a proof-of-concept model for MTM transport and explore the relative roles of ETG and MTM in setting the electron temperature profile.

The value of artificial intelligence for the treatment of mechanically ventilated intensive care unit patients: An early health technology assessment

PurposeThe health and economic consequences of artificial intelligence (AI) systems for mechanically ventilated intensive care unit patients often remain unstudied. Early health technology assessments (HTA) can examine the potential impact of AI systems by using available data and simulations. Therefore, we developed a generic health-economic model suitable for early HTA of AI systems for mechanically ventilated patients. Materials and methodsOur generic health-economic model simulates mechanically ventilated patients from their hospitalisation until their death. The model simulates two scenarios, care as usual and care with the AI system, and compares these scenarios to estimate their cost-effectiveness. ResultsThe generic health-economic model we developed is suitable for estimating the cost-effectiveness of various AI systems. By varying input parameters and assumptions, the model can examine the cost-effectiveness of AI systems across a wide range of different clinical settings. ConclusionsUsing the proposed generic health-economic model, investors and innovators can easily assess whether implementing a certain AI system is likely to be cost-effective before an exact clinical impact is determined. The results of the early HTA can aid investors and innovators in deployment of AI systems by supporting development decisions, informing value-based pricing, clinical trial design, and selection of target patient groups.

Trajectory tracking control of wheeled mobile robots with skidding and time-varying delay

This paper tackles the problem of trajectory tracking for wheeled mobile robots subject to time-varying input delay and bounded external disturbances. First, we establish a dynamic model for a wheeled mobile robot under sliding and skidding conditions, and determine the maximum allowable input delay that maintains system stability using Razumikhin-type stability analysis without prior knowledge of the variation in delay. The proposed adaptive robust controller combined with super-twisting sliding mode control is resilient to disturbances such as input delay, system uncertainty, and parameter variation. The proposed adaptive law enables real-time modification of switching gain based on tracking error without predefined knowledge of uncertainty bounds. Compared with traditional sliding mode control strategies, the super-twisting algorithm can eliminate chattering phenomenon while combined robust methods further reduce modeling uncertainties’ influence on system performance. Finally, we select an appropriate Lyapunov function to analyze and prove uniformly ultimately bounded of the closed-loop system. MATLAB simulation comparison results demonstrate that this approach achieves high tracking accuracy, faster response speed, and robustness.

A hybrid robust SBM-DEA, multiple regression, and MCDM-GIS model for airport site selection: Case study of Sistan and Baluchestan Province, Iran

This study focuses on the strategic decision-making process for selecting airport locations in developing countries, where investment constraint is a significant concern. Recognizing that airport location decisions are influenced by a multitude of factors, often in the absence of comprehensive data and amidst uncertain forecasts, there is a need for a methodology capable of adeptly handling various factors under uncertainty. The goal is to provide solutions that remain feasible and near-optimal, even with some variations in input parameters, ensuring robustness. To this end, this research introduces a decision tool incorporating Slack-Based Measurement in Robust Data Envelopment Analysis (SBM-RDEA). The Slack-based method is beneficial in cases with multiple inputs and outputs where their relationships are not strictly proportional, as in the airport location selection problem. DEA, in conjunction with Multiple Regression analysis, leverages available data on the attributes of operational regional airports to compute the significance of these criteria in shaping the success factors of these facilities. Furthermore, these weightings can be employed to evaluate the suitability of airport locations currently under development and assess their potential for contributing to regional development. Additionally, to address the uncertainty of input data, a Robust optimization approach is employed to ensure a reliable response for this strategic decision-making process. This weighting framework is utilized twice within the research methodology: first, to assess the potential of different counties in the state for hosting a new regional airport, and second, to identify the optimal location for the airport in an MCDM-GIS analysis. This methodology has been applied to a case study in Sistan and Baluchistan, Iran. The result of the proposed method has been compared with one of the most common existing methodologies. This method identifies Zahedan County as having the highest potential and selects the location (29.48° N, 60.90° E), in the east of Zahedan city, as the optimal site for airport construction. Validation results confirm the efficacy of this solution.

MIRD Pamphlet No. 29: MIRDy90-A 90Y Research Microsphere Dosimetry Tool.

90Y-microsphere radioembolization has become a well-established treatment option for liver malignancies and is one of the first U.S. Food and Drug Administration-approved unsealed radionuclide brachytherapy devices to incorporate dosimetry-based treatment planning. Several different mathematical models are used to calculate the patient-specific prescribed activity of 90Y, namely, body surface area (SIR-Spheres only), MIRD single compartment, and MIRD dual compartment (partition). Under the auspices of the MIRDsoft initiative to develop community dosimetry software and tools, the body surface area, MIRD single-compartment, MIRD dual-compartment, and MIRD multicompartment models have been integrated into a MIRDy90 software worksheet. The worksheet was built in MS Excel to estimate and compare prescribed activities calculated via these respective models. The MIRDy90 software was validated against available tools for calculating 90Y prescribed activity. The results of MIRDy90 calculations were compared with those obtained from vendor and community-developed tools, and the calculations agreed well. The MIRDy90 worksheet was developed to provide a vetted tool to better evaluate patient-specific prescribed activities calculated via different models, as well as model influences with respect to varying input parameters. MIRDy90 allows users to interact and visualize the results of various parameter combinations. Variables, equations, and calculations are described in the MIRDy90 documentation and articulated in the MIRDy90 worksheet. The worksheet is distributed as a free tool to build expertise within the medical physics community and create a vetted standard for model and variable management.

Post-process updating of model parameters to approximate thermal errors of machine tools operating in different configurations

Machine tool (MT) thermal errors induced by external and internal heat sources (e.g. changing environment, friction and power losses in MT components) are an important element in machined workpiece inaccuracies. In the past few decades, indirect software compensation techniques have been used to address thermal errors on account of their economic and ecological aspects. Many slightly different approaches are described in the literature. If thermal error models are properly identified based on experiments with sufficiently varying input parameters, they usually work within similar calibration and verification conditions. As the sensory equipment of machines increases, models can be adapted to regions with higher thermo-mechanical system nonlinearity and inhomogeneity through the introduction of deformation feedback from direct measurements into model structures. However, adaptive functionalities require discrete interruptions of the MT's work cycle, which threaten the integrity of the machined surface and complicate the model structure and thus also implementation and industrial deployment possibilities. In this research, a novel approach of automatic post-process adaptation suitable for repetitive manufacturing, which takes advantage of transfer functions (TF), is proposed for thermal error reduction. The method respects basic heat transfer mechanisms in the MT structure by combining linear parametric models, requires a minimum of additional gauges, and feedback from direct measurements is obtained only during technological pauses in the production process. Post-process adaptation is solved retrospectively by updating model parameters using the heuristic method of genetic algorithms (GA). First, the approach was tuned on a case study of a heavy-duty milling machine with a horizontal headstock for a configuration with an exchangeable spindle head and a configuration with a change in the position of the headstock in the workspace, both of which were different from the initial model's calibration range. Subsequently, the developed approach was applied to repeated production on a medium-sized milling centre. Another goal of the paper is to emphasize the need for quality input information for modelling efforts and the industrial applicability of scientific results.

Performance analysis of optimized differential power processing converter with switched inductor using enhanced fuzzy logic control for solar photovoltaic systems

In the current landscape of renewable energy systems, optimizing the performance of power electronic converters is crucial for ensuring reliability. Within the realm of direct current (DC)–DC power converter systems, the differential power processing (DPP) converter holds promise. However, realizing its full potential requires meticulous system design, especially in response to varying solar irradiation levels. Poor design can result in performance degradation, leading to system damage and voltage instability due to sudden irradiation fluctuations. To address these challenges, this study investigates the performance optimization of a DPP converter enhanced with a modified switched inductor, tailored for solar photovoltaic applications. To overcome traditional control strategy limitations, we propose an innovative enhanced fuzzy logic controller (E-FLC). This controller’s strength lies in its dynamic adaptability, achieved through variable duty cycle control, input parameters, membership functions, and output responses. This paper emphasizes methodological precision, particularly in applying the E-FLC to the modified switched inductor. The use of this controller significantly improves both steady-state and transient response performance compared to traditional switched inductors. It rigorously analyzes the responses of the DPP converter under steady-state and transient conditions, with and without the modified switched inductor. This analytical approach sheds light on a critical yet often overlooked aspect of photovoltaic systems. The core innovation driving this research is the adoption of the E-FLC, which outperforms the commonly used proportional–integral (PI) controller in steady-state performance and transient response characteristics. This paper goes beyond conventional converter optimization studies by introducing a holistic approach encompassing system dynamics, control strategy innovation, and performance evaluation. The proposed E-FLC represents a methodological breakthrough and a substantial improvement in converter efficiency and reliability. As renewable energy continues to reshape the global energy landscape, this research sets a new standard for harnessing the true capabilities of power electronic converters in solar photovoltaic systems.

A Rigid-Flexible Coupling Dynamic Model for Robotic Manta with Flexible Pectoral Fins

The manta ray, exemplifying an agile swimming mode identified as the median and paired fin (MPF) mode, inspired the development of underwater robots. Robotic manta typically comprises a central rigid body and flexible pectoral fins. Flexible fins provide excellent maneuverability. However, due to the complexity of material mechanics and hydrodynamics, its dynamics are rarely studied, which is crucial for the advanced control of robotic manta (such as trajectory tracking, obstacle avoidance, etc.). In this paper, we develop a multibody dynamic model for our novel manta robot by introducing a pseudo-rigid body (PRB) model to consider passive deformation in the spanwise direction of the pectoral fins while avoiding intricate modeling. In addressing the rigid-flexible coupling dynamics between flexible fins and the actuation mechanism, we employ a sequential coupling technique commonly used in fluid-structure interaction (FSI) problems. Numerical examples are provided to validate the MPF mode and demonstrate the effectiveness of the dynamic model. We show that our model performs well in the rigid-flexible coupling analysis of the manta robot. In addition to the straight-swimming scenario, we elucidate the viability of tailoring turning gaits through systematic variations in input parameters. Moreover, compared with finite element and CFD methods, the PRB method has high computational efficiency in rigid-flexible coupling problems. Its potential for real-time computation opens up possibilities for future model-based control.

Optimizing and hyper-tuning machine learning models for the water absorption of eggshell and glass-based cementitious composite.

Cementitious composites' performance degrades in extreme conditions, making it more important to enhance its resilience. To further the adaptability of eco-friendly construction, waste materials are increasingly being repurposed. Cementitious composites deteriorate in both direct and indirect ways due to the facilitation of hostile ion transport by water. The effects of using eggshell and glass powder as partial substitutes for cement and sand in mortar on the water-absorption capacity were investigated using machine learning (ML) modeling techniques such as Gene Expression Programming (GEP) and Multi Expression Programming (MEP). To further assess the importance of inputs, sensitivity analysis and interaction research were carried out. The water absorption property of cementitious composites was precisely estimated by the generated ML models. It was noted that the MEP model, with an R2 of 0.90, and the GEP model, with an R2 of 0.88, accurately predicted results. The sensitivity analysis revealed that the absorption capacity of the mortar was most affected by the presence of eggshell powder, sand, and glass powder. GEP and MEP model's significance lies in the fact that they offer one-of-a-kind mathematical formulas that can be applied to the prediction of features in another database. The mathematical models resulting from this study can help scientists and engineers rapidly assess, enhance, and rationalize mixture proportioning. The built models can theoretically compute the water absorption of cement mortar made from eggshell powder and glass powder based on varied input parameters, resulting in cost and time savings.

A model based material removal simulation for vacuum suction blasting of composites

Automated scarfing of carbon reinforced plastic (CFRP) layers is on its way to support commercial aircraft repair. In the industry, still, the manual scarfing operation is the qualified method. However, automated techniques as milling, laser removal and water jet cutting are in development and showed already good results. Another promising method is vacuum suction blasting (VSB) that was until now in particular used for the roughening of surfaces before adhesive bonding. To find the right adjustment for the parameters many experiments would be necessary accordingly to different CFRP parts with changing layer thicknesses. Simulation is a way to avoid this and to predict the removal result and the settings for the machining parameters. The VSB model uses a pixel method dividing the simulated part surface into smaller volume elements. Experimental data of VSB static blasting spots are the basic for the source matrix. The simulation feeds the matrix with the blasted depth and removal volume for different blasting times. A shifting of columns of the source matrix in the blasting movement direction simulates the movement of the blasting nozzle on the work piece surface. With this, the model can also predict the nozzle feed to remove exactly one complete layer for each scarfing step. In addition, it visualizes the seamless overlapping distance between two blasted tracks. With further adjustments, the model will predict the dynamic removal for varying input parameters such as negative pressure and nozzle distance or blasting agent.

Machine learning for underground gas storage with cushion CO2 using data from reservoir simulation

Underground natural gas storage (UNGS) is a means to store energy temporarily for later recovery and use. In such storage operations, carbon dioxide (CO2) can be injected as cushion gas to improve the operating efficiency of the working gas and then be permanently stored in the same reservoir. A potential obstacle for widespread use of this technology is that the mixing of the different gases can lead to undesired CO2 production. Herein, we use a two-component flow model to simulate injection and withdrawal periods of methane (CH4) in idealized reservoirs containing CO2. First, we simulate cases with a single well for both CH4 injection and production. From 1200 simulations with systematic variation of reservoir temperature, porosity, permeability, height, and injection time, we find that the reservoir height and permeability have the most significant impact on the production time until the well stream reaches 1% mole fraction of CO2. In another set of simulations, we investigate the impact of well spacing in seasonal gas storage scenarios with separate wells for CH4 injection and production, while CO2 injection occurs from a third well. Based on the simulated data we construct artificial neural networks (ANNs) that describe the relations between the varied input parameters and the production time of CH4, well-block mole fraction and pressure. We conclude that trained and validated ANN models are useful tools to optimize important parameters for UNGS operations, including well positioning, with the aim at maximizing the amounts of delivered gas.

Life cycle energy-carbon-water footprint assessment of an existing coal power plant retrofitted with calcium looping (CaL) based CCS system

The current study uses a detailed process based LCA model to evaluate the life cycle energy, carbon and water footprint of an existing 660 MWe supercritical coal powerplant in India retrofitted with the calcium looping based CCS system. LCA of the base case is followed by a sensitivity analysis to analyze effect of variation of different input parameters on the output performance metrics such as life cycle energy input, EROI, life cycle GHG emissions and life cycle blue water consumption. Further, the performance of calcium looping is compared with other mature CCS technologies such as MEA and oxycombustion, which are also retrofitted to the same existing coal power plant. Results show that, as compared to the base power plant, the life cycle energy input and life cycle blue water consumption of the integrated system using calcium looping increases by around 31 and 36% respectively, whereas EROI and life cycle GHG emissions decrease by 33–45% and 79–83% respectively. Sensitivity analysis results show that EROI has the largest variation range and change in rail transportation distance of coal leads to maximum variation in life cycle energy input, EROI and life cycle GHG emissions. Comparison of the three CCS technologies clearly shows that the integrated system using calcium looping has the lowest increase in life cycle energy input and life cycle blue water consumption and the lowest decrease in EROI. Thus, in addition to efficiently reducing CO2 emissions and enhancing power generation capacity of existing coal powerplants, use of calcium looping is also favourable from a life cycle sustainability perspective.

Recent Advances in Machining of Composite Materials by Electrical Discharge Machine

Composites are being used since decades and imparting excellent properties comparatively. It may be used in numerous industries because of its light weight and specific strength. Machinability of these materials is a concerned aspect. Conventional and The composites have been machined using unconventional machining techniques. Conventional methods are less suitable than non-conventional quoting the best surface finish and ability to machine complex parts. This article investigates the suitability of thermo-electric process for the machining of composites for higher surface quality and material removal. It includes the study of machining by die sinking, wire cut, powder mixed electric discharge machine in different matrix based composites along with the variation of reinforcement. Electric Discharge Machining (EDM) finds its suitability in machining of different metal matrix composites (MMC) more than the Polymer Matrix Composite (PMC) and Ceramic Matrix Composites (CMC). Variation in input parameters listed as Pulse duration, Voltage, Peak Current and Polarity is studied to obtain the optimum resulting parameters as Material Removal Rate (MRR), Surface Roughness (SR), Electrode Wear Rate (EWR) and Kerf Width. Material removal in PMC is 16% more in parallel fibre direction than with perpendicular. Low electrical conductance and high hardness of CMCs limits the use of EDM while natural ceramics are found more suitable for machining. Gap voltage, pulse on time and current are found most crucial in machining MMCs while quantifying material removal and surface roughness.

A Hydro-Powered Climate-Neutral Pump: Full Cycle Simulation and Performance Evaluation

This paper presents a parametric study of the multistorey hydro-powered pump, known as ‘Bunyip’, which has demonstrated significant potential in contributing to rural regions. The study is aimed at understanding the underlying physics of the system and ways to enhance its hydraulic performance. A transient three-dimensional model using the commercial Computational Fluid Dynamics (CFD) tool Ansys-Fluent is utilized to gain insights into its fundamental flow mechanics, operational efficiency, standard capacity, and relative delivery. The investigation involves an initial assessment of performance for three Bunyip devices based on manufacturing data. A parametric analysis is conducted for the dataset generated through meticulous application and numerical modelling. The CFD results are validated against experimental data. Three main design configurations are considered, and 58 sets of varied input parameters are examined. The best design configuration is evaluated against five cases of conventional hydro-power pump systems. The results indicate that a smaller diameter of the pressure chamber and a higher supply head lead to higher pressure, achieving a target head of 3 m with 15% efficiency and a flowrate of 11.82 L/min.