Effects Of Parameter Variations Research Articles

Effect of Parameter Variation in the BMJ Scheme on the Simulation of MJO Propagation and Structure

Effect of Parameter Variation in the BMJ Scheme on the Simulation of MJO Propagation and Structure

Three dimensional time-variation simulator for water flooding reservoir based on “effective water flux”

Long-term water flooding leads to changes in pore throat structure, resulting in alterations in macroscopic reservoir petrophysical parameters. However, commercial numerical simulation software does not have this capability. Ignoring variations in physical parameters during the formulation of development plans and numerical simulations can lead to significant prediction errors, which severely impacts oil field recovery. This paper, based on an analysis of effective flow rate and waterflood intensity, proposes a new erosion degree characterization parameter: Effective water flux, to represent the time-varying patterns of physical parameters. It is embedded into a black oil model to develop a time-variation simulator, whose accuracy and stability in both black oil and time-variation models are validated through comparison with the commercial numerical simulation software CMG. The study further explores the effects of different parameter variations on the development process. It was found that increases in permeability and oil viscosity exacerbate heterogeneity and reduce displacement efficiency, while decreases in residual oil saturation and water phase permeability under residual oil saturation enhance water flooding efficiency. In complex models, the effects of variations in different parameters intertwine, collectively influencing development outcomes. This paper advances the development of time-variation numerical simulation technology.

Effect of design and surgical parameters variations in mobile-bearing versus fixed-bearing unicompartmental knee arthroplasty: A finite element analysis.

Unicompartmental knee arthroplasty (UKAs) are available in the market as fixed- and mobile-bearing (FB and MB) and can be characterised by a different set of design parameters in terms of geometries, materials and surgical approaches, with overall good clinical outcomes. However, clear biomechanical evidence concerning the consequences of variations of these features on knee biomechanics is still lacking; therefore, the present study aims to perform a sensitivity analysis to see which outcomes are affected by these variations. For both MB-UKA and FB-UKA, five design and surgical parameters were defined (bearing insert thickness, tibial component material, implant components friction coefficient, antero-posterior slope angle and level of tibial bone resection). Two control models were defined based on standard configurations for both implants. Finite element analysis was chosen to perform this study, and different parameter combinations (216 models in total) were implemented and tested at both 0° and 90° of flexion, using a previously validated finite element knee model. The results were then evaluated in terms of bone and polyethylene Von Mises stress and tibio-femoral contact area. Bearing thickness, tibial bone cut and slope angle were found to be the most sensitive parameters for both types of UKAs. Specifically, changes in these parameters in the FB-UKA appeared to induce more significant variations in the polyethylene insert (both in terms of polyethylene stress and contact area), while in the MB-UKA, these changes influenced bone stress distribution more. Surgical parameters returned to have a more significant influence than material and friction variations; furthermore, the outcomes most affected by parameter variations were the insert-related ones for FB-UKA while for the MB-UKA were the ones regarding tibial bone stresses. Not Applicable.

Switching from Cigarettes to Heated Tobacco Products in Japan-Potential Impact on Health Outcomes and Associated Health Care Costs.

Japan's rising health expenditure, driven by an aging population, coincides with growing demands for increased spending. Reducing smoking-related costs could alleviate the burden on the health care system. Despite efforts to promote smoking cessation, success has been limited, indicating a need for strategies beyond cessation. Using a status quo simulation based on hospital resource data from the Japanese Ministry of Health, Labor, and Welfare, we examine the impact of heated tobacco products (HTPs) on the prevalence of four smoking-attributable diseases (chronic obstructive pulmonary disease, ischemic heart disease, stroke, and lung cancer) and the related direct health care costs. The baseline scenario assumes a 50% switch from combustible cigarettes to HTPs, with a 70% risk reduction. A sensitivity analysis was conducted to assess the effects of parameter variations. If 50% of smokers replaced combustible tobacco products with HTPs, 12 million patients could be averted equivalent to JPY 454 billion in health care savings. Prefectures located in the north and south of Japan would benefit the most. Considering the heterogeneous prevalence rates, a one-size-fits-all tobacco control approach is ineffective. Japan should prioritize cost-efficient measures that promote public health and economic benefits. Encouraging smokers to switch to reduced-risk products, raising awareness of health risks, and adopting a harm-based taxation model can drive positive change. Public-private partnerships can further enhance harm reduction efforts. With a combination of tax reforms, revised regulations, collaborations, and ongoing research, Japan can create a more effective and comprehensive approach to tobacco control.

Effects of Aquatic Physicochemical Parameters Variation on the Phytoplankton Abundance and Diversity in the Babon River, (Semarang, Central Java, Indonesia)

The Babon River is an integral component of Semarang City's drainage system, which is heavily impacted by the influx of waste, leading to elevated water temperatures caused by global warming and decreased pH levels due to acid rain. The two main environmental factors that affect phytoplankton growth and productivity are temperature and nutrient availability. Nutrients can change the balance of the natural food webs of aquatic ecosystems. Meanwhile, metals are toxic and can accumulate in biota tissues. This study examined how nutrients, pH, temperature, and heavy metals in water affected the diversity and abundance of phytoplankton. Water samples and phytoplankton were collected from 7 observation stations representing the upstream, middle, and downstream rivers. Observations at each station were made 3 times in the months of April-May, June-July, and August-September. Temperature and pH were measured in situ with the HORIBA Water Checker. Analysis of nitrate, phosphate, and heavy metals was carried out with an AAS. The results showed that the dominant phytoplankton in the Babon Rivers consisted of Bacillariophyceae, Chlorophyceae, and Cyanophyceae. There is no diversity against all independent variables (temperature, pH, nitrate, phosphate, Cd, Total Cr, Pb concentrations) and dependent variables (number of genera, abundance, and diversity index of phytoplankton) due to the sampling station's location. However, sampling time caused diversity in nitrate, phosphate, Cd, Total Cr, and Pb concentrations, the sampling location effect was more dominant than sampling time, so the results showed that all independent variables did not contribute significantly to the dependent variables.

Dynamics of autonomous and nonautonomous Leslie–Gower model for the impacts of fear and its carry‐over effects including predator harvesting

Recently, certain biological field experiments and research findings have revealed that predators not only reduce prey populations through direct predation (lethal effect) but also indirectly affect the growth rate of prey by inducing fear; these nonlethal effects can be carried over seasons or generations. In this paper, we proposed an autonomous and a nonautonomous Leslie–Gower model incorporating some biological factors such as fear and its carry‐over effects, prey refuge, and nonlinear predator harvesting. In the autonomous model, first, we examined the well‐posedness, positivity solutions, and their boundedness. Also, it is shown that new equilibrium points emerge and disappear with the change in intrinsic growth rate of predator. Further, we compute and analyzed the local and global stability at the interior equilibrium points. Furthermore, Hopf bifurcation and direction and stability of limit cycle at interior equilibrium points are established. Moreover, the sensitivity analysis of the biological parameters is carried out numerically with two statistical methods via Latin hypercube sampling and partial rank correlation coefficients. It was found that at the small values of fear factor, carry‐over effect, and refuge behavior of prey parameters, the autonomous system exhibits oscillatory behavior, whereas for small values of prey birth rate and harvesting effort, the system remains stable. However, when intrinsic growth rate of predator increases from a low value to a higher value, the system shows transition from stability to instability and back to stability. We also explore the effects of seasonal variations in biological parameters in the nonautonomous model. Additionally, we investigate the theoretical existence of positive periodic solutions using the continuation theorem. The influences of seasonal variations in some parameters such as prey's birth rate, fear, and its carry‐over effects; refuge behavior of prey; intrinsic growth rate of predator; and harvesting effort are then numerically investigated. The results reveal the emergence of positive periodic solutions and bursting patterns in the seasonally forced model.

Satellite observed oceanographic drivers of Mobulidae fisheries catch in the Southeast Indian Ocean

Indonesian coastal waters include several marine megafauna biodiversity hotspots. Several fish populations of ecological and socio-economic importance, such as elasmobranchs (sharks and rays), have experienced rapid decline due to unsustainable human activities, primarily overfishing. Small-scale fisheries (SSF) are currently exempt from governmental fisheries management measures despite contributing a significant proportion of a total catch. The Generalised Additive Models were used to investigate the effect of variations in oceanographic parameters of the Teluk Penyu fishing ground, south of central Java, on the magnitude of Mobulidae (Mobula spp.) catch based on its landings data over ten years (2009–2018) from one of Indonesia's largest ports, Cilacap, Central Java, Indonesia. Mobulidae catch from Teluk Penyu fishing ground was generally higher from June to November when the water exhibited relatively high sea surface salinity (sal >34.1 ‰), chlorophyll (0.32–0.45 mg/m3), and nitrate (nit >0.0045 mg NO3/m3), water speed (>0.29 m/s) and eddy kinetic energy (>0.04 m3/s2) levels, and relatively low sea surface temperature (<28 °C), oxygen (<0.182 mg O2/m3) and sea surface height (<0.9 m) levels than the other months of the year. This study reveals that satellite Earth Observation (EO) data provided a preliminary relationship between oceanographic conditions and the amount of catch for developing more effective management and conservation measures for endangered species like Mobulidae. Utilizing EO data may also be applied to help inform much-needed ecosystem-based management measures, including habitat protection and bycatch reduction for conserving endangered Mobulidae species in the Southeast Indian Ocean. The in-situ onboard ocean observation and temporal species-specific catch data will greatly complement the current work.

Optimizing dye-sensitized solar cell efficiency through TiO2/CuS doping: effects of internal parameter variations

Dye-sensitized solar cells (DSSC) have attracted significant research interest due to their semi-transparency, ease of fabrication, cost-effectiveness, and environmental friendliness. This research focuses on enhancing dye-sensitized solar cells efficiency by doping titanium dioxide with copper sulfide and varying internal parameters such as concentration, thickness, and temperature. The primary issue addressed is the low electron mobility of titanium dioxide, which limits its performance as a photoanode. Using simulation methods, this study analyzed dye-sensitized solar cells performance under different doping conditions. The results showed that the highest efficiency of 8.18 % was achieved at a TiO2/CuS concentration of 0.3 %. The optimal photoanode thickness was approximately 3 μm, yielding an efficiency of 8.33 %. Temperature variations at room temperature (275 K, 300 K, and 325 K) resulted in efficiency values of 13.94 %, 15.06 %, and 16.18 %, respectively. These findings indicate that targeted doping and precise control of internal parameters can significantly enhance the performance of titanium dioxide-based dye-sensitized solar cells. The improved efficiency is attributed to enhanced electron mobility and better structural and morphological characteristics of the doped titanium dioxide photoanode. This research provides valuable insights into developing more efficient and sustainable dye-sensitized solar cells. The practical implications of these results are significant for advancing dye-sensitized solar cells as a viable alternative to conventional solar cells, contributing to the global effort to address the energy crisis by providing a cost-effective and environmentally friendly energy source.

Uncertainty quantification of structural blade parameters for the aeroelastic damping of wind turbines: a code-to-code comparison

Abstract. Uncertainty quantification (UQ) is a well-established category of methods to estimate the effect of parameter variations on a quantity of interest based on a solid mathematical foundation. In the wind energy field most UQ studies focus on the sensitivity of turbine loads. This article presents a framework, wrapped around a modern Python UQ library, to analyze the impact of uncertain turbine properties on aeroelastic stability. The UQ methodology applies a polynomial chaos expansion surrogate model. A comparison is made between different wind turbine simulation tools on the engineering model level (alaska/Wind, Bladed, HAWC2/HAWCStab2, and Simpack). Two case studies are used to demonstrate the effectiveness of the method to analyze the sensitivity of the aeroelastic damping of an unstable turbine mode to variations of structural blade cross-section parameters. The code-to-code comparison shows good agreement between the simulation tools for the reference model, but also significant differences in the sensitivities.

Economic framework for green shipping corridors: Evaluating cost-effective transition from fossil fuels towards hydrogen

Global warming's major cause is the emission of greenhouse-effect gases (GHG), especially carbon dioxide (CO2) whose main source is the combustion of fossil fuels. Fossil fuels serve as the primary energy source in many industries, including shipping, which is the focus of this study. One of the measures proposed to tackle GHG emissions is the development of green shipping corridors - carbon-free shipping routes that require the transition to alternative fuels, which are gaining competitiveness. One of the reasons for that is carbon pricing, which taxes CO2 emissions. However, the lack of consensus on the most cost-advantageous alternative fuel in the long run results in the delay of the implementation of green shipping corridors.To make it more accessible for stakeholders to conduct an economic analysis of the various options, a framework to determine and minimize the costs of transitioning from fossil fuels to any alternative fuel is proposed, over the period of one voyage, considering the lost opportunity cost, the deployment cost of bunkering vessels at the necessary call ports, the cost of converting the vessel, the car-bon emissions tax cost, and the fuel cost. This will allow stakeholders to choose the most economical alternative fuel, accelerating the development of green shipping corridor initiatives. To validate the effectiveness of the framework, it was applied in a case study involving a shipowner seeking to transition from heavy fuel oil (HFO) to Ammonia, Hydrogen, Liquefied Natural Gas (LNG), or Methanol. This study faced limitations due to the unknown costs of installing bunkering vessels for Ammonia and Hydrogen. However, it evaluates the cost-effectiveness of alternative fuels, providing insights into their short-term economic viability. The results showed that Hydrogen is the most cost-advantageous fuel until a deployment cost per bunkering vessel of 1,990,285$ for a sailing speed of 22 knots and 2,190,171$ for a sailing speed of 18 knots is reached, after which LNG becomes the most economical option regardless of variations in the carbon tax. Moreover, a sensitivity analysis was conducted to determine the effects of variations in parameters, such as carbon tax, fuel prices and vessel conversion costs in the total cost of each fuel option. Results highlighted that even though HFO remains the most economical fuel option, even when considering a high increase in carbon tax, the cost gap between HFO and alternative fuels narrows significantly with the increase in carbon tax. Furthermore, the sailing speed impacts the fuels’ competitiveness, as the cost difference between HFO and alternative fuels decreases at higher speeds.

Optimization of Plasma-Propelled Drone Performance Parameters

Recently, the world’s first plasma-propelled drone was successfully flown, demonstrating that plasma propulsion technology is suitable for drone flight. The research on plasma propulsion drones has sparked a surge of interest. This study utilized a proxy model and the NSGA-II multi-objective genetic algorithm to optimize the geometric parameters based on staggered thrusters that affect the performance of electroaerodynamics (EAD) thrusters used for solid-state plasma aircraft. This can help address key issues, such as the thrust density and the thrust-to-power ratio of solid-state plasma aircraft, promoting the widespread application of plasma propulsion drones. An appropriate sample set was established using Latin hypercube sampling, and the thrust and current data were collected using a customized experimental setup. The proxy model employed a genetically optimized Bayesian regularization backpropagation neural network, which was trained to predict the effects of variations in the geometric parameters of the electrode assembly on the performance parameters of the plasma aircraft. Based on this information, the maximum achievable value for a given performance parameter and its corresponding geometric parameters were determined, showing a significant increase compared to the sample data. Finally, the optimal parameter combination was determined by using the NSGA-II multi-objective genetic algorithm and the Analytic Hierarchy Process. These findings can serve as a basis for future researchers in the design of EAD thrusters, helping them produce plasma propulsion drones that better meet specific requirements.

Evaluating energy harvesting UAV-NOMA network with random user pairing in the finite blocklength regime

This study proposes an integrated system that combines energy harvesting (EH) enabled unmanned aerial vehicles (UAVs) with non-orthogonal multiple access (NOMA) to enhance communication system performance within a cellular network. Addressing the limitations of existing analyses that often assume an infinite blocklength scenario, we explore EH-enabled UAV-NOMA systems within a cellular framework under a finite blocklength (FBL) scenario. The study investigates the complex interactions and advantages resulting from the integration of EH, NOMA, and UAV technologies, aiming to assess whether EH can sustain communication within this framework. The network model considers base stations (BSs), UAVs, and terrestrial devices distributed with independent Poisson point processes (PPPs) over a large area. In this network, BSs employ NOMA to serve cell center devices directly, while cell edge devices, which are nor in direct contact with BS, are served via simultaneous wireless information and power transfer (SWIPT) enabled UAVs. The study derives metrics including joint harvesting and decoding probability for a randomly selected UAV, coverage probability (CP) for cell devices, and end-to-end block error rate (BLER) probabilities for typical device pairs. The findings demonstrate that the proposed scheme effectively supplies all the necessary transmit power for communication purposes through EH, achieving reasonable reliability. Additionally, the study highlights the importance of considering a combination of blocklengths from different phases to achieve optimal performance, rather than solely relying on an increment in blocklength. Finally, the effects of parameter variations on network performance are examined.

Revealing the Impact of the Combination of Parameters on SVM Performance in COVID-19 Classification

Non-linear SVM functions to modify the kernel in the SVM. Each kernel function in linear and non-linear SVMs has several parameters that are used in the classification process. SVM is a method that has advantages in classification, but there are still obstacles in selecting optimal parameters. This research investigates the effect of parameter variations on SVM classification performance on the COVID-19 dataset, using linear, RBF, Sigmoid and polynomial kernels. The analysis shows that the polynomial kernel is superior with the highest performance compared to other kernels. The highest accuracy of 77.57% was achieved with a combination of C values ??of 0.75 and Gamma of 0.75, and an F1-Score value of 76.67% indicating an optimal balance between precision and recall. The performance stability produced by the polynomial kernel provides advantages in classifying the COVID-19 dataset, with more controlled fluctuations compared to other kernels. The interaction between the C and Gamma parameters shows that a Gamma value of 0.75 consistently provides good results, while adjusting the C parameter shows more controlled performance variations. This confirms that appropriate Gamma parameter settings are key in improving the accuracy and consistency of SVM model predictions in this case.

Sustainable nitrogen fixation by bubble discharge plasma: Performance optimization and mechanism

Sustainable nitrogen fixation driven by renewable energy sources under mild conditions has been widely sought to replace the industrial Haber-Bosch process. The fixation of nitrogen in the form of NOx− and NH4+ into aqueous solutions using electricity-driven gas–liquid discharge plasma is considered a promising prescription. In this paper, a scalable bubble discharge excited by nanosecond pulse power is employed for nitrogen fixation in the liquid phase. The nitrogen fixation performance and the mechanisms are analyzed by varying the power supply parameters, working gas flow rate and composition. The results show that an increase in voltage and frequency can result in an enhanced NO3− yield. Increases in the gas flow rate can result in inadequate activation of the working gas, which together with more inefficient mass transfer efficiencies can reduce the yield. The addition of O2 effectively elevates NO3− production while simultaneously inhibiting NH4+ production. The addition of H2O vapor increases the production of OH and H, thereby promoting the generation of reactive nitrogen and enhancing the yield of nitrogen fixation. However, the excessive addition of O2 and H2O vapor results in negative effect on the yield of nitrogen fixation, due to the significant weakening of the discharge intensity. The optimal nitrogen fixation yield was up to 16.5 μmol/min, while the optimal energy consumption was approximately 21.3 MJ/mol in this study. Finally, the mechanism related to nitrogen fixation is discussed through the optical emission spectral (OES) information in conjunction with the simulation of energy loss paths in the plasma by BOLSIG + . The work advances knowledge of the effect of parameter variations on nitrogen fixation by gas–liquid discharge for higher yield and energy production.

Numerical study on formation characteristics of particle-crystal mixed fouling in the heat exchanger tube

A mixed fouling prediction model is developed to investigate the problem of heat exchanger fouling in cooling water systems, which considers the particle-crystal mixed fouling deposition and evolution processes. As the fouling thickness exceeds the fluid cell height, the fluid cell is converted into a porous media cell to represent the fluid flow in the fouling layer. The mixed fouling layer growth and evolution are simulated using the porous media model and a user-defined program. The fouling resistance obtained from the model agrees well with the experimental data in the literature. Additionally, the fouling thickness distribution on heat transfer surfaces is predicted. The differences between mixed fouling and single fouling are compared. Then the effect of fluid parameter variations on fouling characteristics is investigated within the heat exchange channel. The results show that fouling accumulates at the end of the pipe, exhibiting a distribution trend of being thin at the front and thick at the back. Moreover, the risk of fouling formation significantly increases with increasing inlet temperature, concentration and heat flux, while high inlet velocity leads to small fouling resistance. The asymptotic value of fouling resistance decreases by 59.1% when the inlet velocity increases from 0.25 to 0.65 m/s.

A study on production of hydrogen using Al scrap feedstock.

A sustainable future, concerning the energy transformation of a country, heavily relies on the availability of energy resources, particularly renewables such as solar, wind, hydropower, and clean hydrogen. Among these, hydrogen is the most promising energy source due to its high calorific value, ranging between 120 and 140MJ/kg. It has the potential to lead the market in various industries such as power generation, steel, chemical, petrochemical, and automotive. Significant research has been going on in hydrogen production technologies to reduce costs and improve competitiveness with fossil fuels. One such potential approach includes the use of metal-water reactions, which offer unique opportunities for producing clean hydrogen and other valuable byproducts. However, the quantity of hydrogen produced varies depending on the metal feedstock, type of electrolyte, and the activator or catalyst, used in combination with water. This latest work discusses recent progress on hydrogen production and the effects of variations in different parameters on the process, with a focus on aluminum (Al)-water reactions. Investigations have been conducted and reported on the effect of various activators with different concentrations, the quantity of aluminum scrap feedstock, and the volume of the electrolyte on the kinetics of the metal-water reactions and hydrogen production. Sodium hydroxide (NaOH) was observed to be more effective than potassium hydroxide (KOH) in promoting metal-water reactions. These activator-assisted metal-water reactions help produce clean hydrogen, along with other value-added products such as hydroxides. This work clearly sheds light on the potential utilization of industrial aluminum scrap as feedstock for producing clean hydrogen.

The Role of Overflow on the Mechanical Propert ies of 3D Printed PLA

3D printing is a technology used on an ever-increasing scale, which makes it easier to obtain parts with complex geometry. The printing process is very complex because, in addition to the variables introduced by the various materials that are used, there is a multitude of process parameters: printing direction, layer thickness, infill level, filament feed rate, printing temperature, printing bed temperature, etc. Each process parameter influences the mechanical properties of the 3D-printed structure, which is why it is necessary to define the range of possible values where the effect is maximum. In this paper it was studied the effect of process parameters variation on the roughness and mechanical properties of the 3D-printed samples. Using a commercially PLA filament (produced by Prussia), we made six sets of 3D-printed samples, using six different overflow (OF) values: 90%, 95%, 100%, 105%, 110%, 115%. The test samples (realized according to ISO 572-2) were subjected to tensile tests on an Instron 3382 machine, and the results were interpreted comparatively. It has been observed that there are variations of the mechanical properties, dependent on the chosen values of the overflow and, in addition, this process parameter has an important role for the achieving the desired structure.

Chaos detection and control of a fractional piecewise-smooth system with nonlinear damping

Chaotic response is a robust effect in natural systems, and it is usually unfavorable for applications owing to uncertainty. In this paper, we propose several control strategies to stabilize the chaotic rhythm of a fractional piecewise-smooth oscillator. First, the Melnikov analysis is applied to the system, and the critical condition for the occurrence of homoclinic chaos is scrupulously established. Then, by applying appropriate control mechanisms, including delayed feedback control and periodic excitations, to the system, we can eliminate the zeros in the original Melnikov function, which serve as sufficient criteria for chaos suppression. Numerical simulations further demonstrate the accuracy of the theoretical results and the validity of the control schemes. Finally, the effects of parameter variations on the efficiency of control strategies are investigated. Note that we use the complex Simpson formula to calculate the complicated Melnikov functions presented in this paper. The current work may open a new innovative path to detect and control the chaotic dynamics of fractional non-smooth models.

An extended Goodwin model with endogenous technical change and labor supply

This paper extends the Goodwin model of distributive cycles by incorporating the simultaneous endogeneity of technical change and labor supply within a classical-Marxian framework. It reinterprets induced innovation, suggesting that firms optimize mechanization to maximize cost reduction, obtaining a micro-founded relationship between mechanization and the wage share. Additionally, it assumes a positive relationship between labor supply and the employment rate. The resulting three-dimensional dynamical system includes wage share, employment rate, and capital-output ratio as state variables. The Hopf bifurcation theorem reveals the emergence of limit cycles as the employment rate's effect on labor productivity (reserve-army-creation effect) approximates a critical value from below. Numerical simulations for 10 OECD countries illustrate the cyclical nature of the model and its consistency with empirical patterns. Furthermore, a sensitivity analysis explores the effect of parameters variations, emphasizing the social dimensions of productivity and labor supply as critical factors defining the stability of distributive cycles.

Influence of process variations on clinch joint characteristics considering the effect of the nominal tool design

Focusing on upcoming challenges in lightweight design, such as increasing emission targets or novel multimaterial connections, versatile applicable and environmentally friendly production technologies are crucial. In this context, mechanical joining technology clinching offers a fast and energy-efficient procedure for assembling sheet metals, being a proper alternative to established joining methods, such as spot welding. However, the design of clinch points is a challenge, which is partly supported by numerical or data-based approaches for optimal tool dimensions assuring proper joint characteristics. While this is usually done for an ideal environment, real joining processes are characterized by multiple inevitably varying parameters, e.g. of the material, which have a significant impact on the quality of clinch points. Therefore, this contribution addresses the current gap by analyzing the effect of parameter variations or uncertainties on the resulting joint characteristics and studying the impact of the nominal tool design. Thus, an efficient meta-model-based variation simulation procedure is proposed and used for analyzing the effect of different tool design configurations and variation scenarios. Based on the results, it was found that varying process parameters have a strong impact on the resulting joint characteristics, whereby the effect significantly depends on the nominal tool design. This reveals the potential for a robust tool design and implies that the nominal tool design and the tolerancing of parameters should be done simultaneously for a reliable virtual joining point design without extensive iterations and physical tests.