Variations In Input Parameters Research Articles

Virtual tryout planning in automotive industry based on simulation metamodels

Deep drawn sheet metal parts are increasingly designed to the feasibility limit, thus achieving a robust manufacturing is often challenging. The fluctuation of process and material properties often lead to robustness problems. Therefore, numerical simulations are used to detect the critical regions. To enhance the agreement with the real process conditions, the material data are acquired through a variety of experiments. Furthermore, the force distribution is taken into account. The simulation metamodel contains the virtual knowledge of a particular forming process, which is determined based on a series of finite element simulations with variable input parameters. Based on the metamodels, virtual process windows can be displayed for different configurations. This helps to improve the operating point as well as to adjust process settings in case the process becomes unstable. Furthermore, the time of tool tryout can be shortened due to transfer of the virtual knowledge contained in the metamodels on the optimisation of the drawbeads. This allows the tool manufacturer to focus on the essential, to save time and to recognize complex relationships.

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This paper presents the transport of volatile organic compounds (VOC) through contaminated subsoil to groundwater using one dimensional vadose zone transport model, VLEACH. The model is applied using field specific data for a typical Toluene at Fuel pumping station and Transformer dumping yard. Laboratory column leaching studies were conducted to assess the mass flow rate of Toluene flux through subsoil and compared with VLEACH model output. The research also focused on the sensitivity of the model output with the varying input parameters, one at a time, across its maximum range of values identified from the study area. The observed Toluene fluxes are not consistent with the experimentally input concentrations and may not be valid due to its lower density of 0.865 g/ml. Hence, liquid phase transport of Toluene for experimental column leaching studies is not suitable. The sensitivity analysis results reveals that, model output is a function of fractional organic carbon content as well as water content.

Local thermal sensation modeling-a review on the necessity and availability of local clothing properties and local metabolic heat production.

Local thermal sensation modeling gained importance due to developments in personalized and locally applied heating and cooling systems in office environments. The accuracy of these models depends on skin temperature prediction by thermophysiological models, which in turn rely on accurate environmental and personal input data. Environmental parameters are measured or prescribed, but personal factors such as clothing properties and metabolic rates have to be estimated. Data for estimating the overall values of clothing properties and metabolic rates are available in several papers and standards. However, local values are more difficult to retrieve. For local clothing, this study revealed that full and consistent data sets are not available in the published literature for typical office clothing sets. Furthermore, the values for local heat production were not verified for characteristic office activities, but were adapted empirically. Further analyses showed that variations in input parameters can lead to local skin temperature differences (∆Tskin,loc =0.4-4.4°C). These differences can affect the local sensation output, where ∆Tskin,loc =1°C is approximately one step on a 9-point thermal sensation scale. In conclusion, future research should include a systematic study of local clothing properties and the development of feasible methods for measuring and validating local heat production.

A Gaussian process–based approach to cope with uncertainty in structural health monitoring

Structural health monitoring is widely applied in industrial sectors as it reduces costs associated with maintenance intervals and manual inspections of damage in sensitive structures, while enhancing their operation safety. A major concern and current challenge in developing “robust” structural health monitoring systems, however, is the impact of uncertainty in the input training parameters on the accuracy and reliability of predictions. The aim of this article is to adapt an advanced statistical pattern recognition technique capable of considering variations in input parameters and arriving at a new structural health monitoring system more immune to the effect of uncertainty. Gaussian processes have been implemented to predict the state of damage in a typical composite airfoil structure. Different covariance functions were evaluated during the training stage of structural health monitoring. Results through a case study showed a remarkable capability of the Gaussian process–based approach to deal with uncertainty in the pattern recognition problem in structural health monitoring of a multi-layer composite airfoil structure. To illustrate robustness advantage of the approach as compared to conventional neural network models, the damage size and location prediction accuracy of the Gaussian process structural health monitoring has been compared to multi-layer perceptron neural networks. Some practical insights and limitations of the approach have also been outlined.

Intermodal soliton interaction in nearly degenerate modes of a multimode fiber

We numerically investigate the interaction of two temporal solitons propagating in the two degenerate or nearly degenerate modes of a multimode fiber. Similar to the case of single-mode fibers, the solitons are found to attract or repel each other depending on their relative phase, even though they belong to two different modes of the fiber. However, unlike the single-mode case, each soliton transfers some of its power to the other mode through intermodal four-wave mixing. Our results show that, in spite of this intermodal power transfer, each soliton keeps propagating as a bimodal soliton and interacts with the other bimodal soliton as if they were propagating inside a single-mode fiber. Indeed, the total power in the two modes evolves similar to the case of a single-mode fiber. We study the impact of varying input parameters such as the relative phase, amplitude, and spacing of the two input pulses used to excite the fundamental solitons and point out differences introduced by the intermodal nature of the nonlinear effects.

Cost-Effectiveness of Deep Brain Stimulation for Advanced Parkinson’s Disease in the United States

Deep brain stimulation (DBS), which uses an implantable device to modulate brain activity, is clinically superior to medical therapy for treating advanced Parkinson's disease (PD). We studied the cost-effectiveness of DBS in conjunction with medical therapy compared to best medical therapy (BMT) alone, using the latest clinical and cost data for the U.S. healthcare system. We used a decision-analytic state-transition (Markov) model to project PD progression and associated costs for the two treatment strategies. We estimated the discounted incremental cost-effectiveness ratio (ICER) in U.S. dollars per quality-adjusted life-year (QALY) from the Medicare payer perspective, considering a ten-year horizon, and evaluated the robustness of our projections through extensive deterministic sensitivity analyses. Over ten years, DBS treatment led to discounted total costs of $130,510 compared to $91,026 for BMT and added 1.69 QALYs more than BMT, resulting in an ICER of $23,404 per QALY. This ICER was relatively insensitive to variations in input parameters, with neurostimulator replacement, costs for DBS implantation, and costs for treatment of disease-related falls having the greatest effects. Across all investigated scenarios, including a five-year horizon, ICERs remained under $50,000 per QALY. Longer follow-up periods and younger treatment age were associated with greater cost-effectiveness. DBS is a cost-effective treatment strategy for advanced PD in the U.S. healthcare system across a wide range of assumptions. DBS yields substantial improvements in health-related quality of life at a value profile that compares favorably to other well-accepted therapies.

Evaluating the Use of Global Sensitivity Analysis in Dynamic MFA

SummaryDynamic material flow analysis (MFA) provides information about material usage over time and consequent changes in material stocks and flows. In order to understand the effect of limited data quality and model assumptions on MFA results, the use of sensitivity analysis methods in dynamic MFA studies has been on the increase. So far, sensitivity analysis in dynamic MFA has been conducted by means of a one‐at‐a‐time method, which tests parameter perturbations individually and observes the outcomes on output. In contrast to that, variance‐based global sensitivity analysis decomposes the variance of the model output into fractions caused by the uncertainty or variability of input parameters. The present study investigates interaction and time‐delay effects of uncertain parameters on the output of an archetypal input‐driven dynamic material flow model using variance‐based global sensitivity analysis. The results show that determining the main (first‐order) effects of parameter variations is often sufficient in dynamic MFA because substantial effects attributed to the simultaneous variation of several parameters (higher‐order effects) do not appear for classical setups of dynamic material flow models. For models with time‐varying parameters, time‐delay effects of parameter variation on model outputs need to be considered, potentially boosting the computational cost of global sensitivity analysis. Finally, the implications of exploring the sensitivities of model outputs with respect to parameter variations in the archetypical model are used to derive model‐ and goal‐specific recommendations on choosing appropriate sensitivity analysis methods in dynamic MFA.

Performance investigation of a counter-flow heat pump driven liquid desiccant dehumidification system

Liquid desiccant dehumidification is an energy-efficient approach for humid air handling process. Increasing attention has been paid to heat pump driven liquid desiccant (HPLD) systems in China. The current research focuses on a counter-flow HPLD system. The configuration of this HPLD system is introduced and theoretical models of key components are analyzed. Based on the simulation model, operating performances with varying input parameters are obtained. Effects of input NTUm, NTUevap and required ωsa are drawn from the simulated results. NTUm of the packed tower is a key parameter, which affects Qfa/Qe indicating the heat recovery performance and COPhp indicating the energy performance of heat pump. As NTUm is not sufficient, heat-cold offset resulted from solutions circulating between dehumidifier and regenerator severely affects the system performance. Then adding a solution heat exchanger is regarded as an appropriate approach to improve the performance. Besides, adopting a multi-stage heat pump cycle helps to improve the match properties between solution and refrigerant. It's treated as an approach to improve the energy efficiency of this counter-flow HPLD system to a certain extent. The present study is expected to be beneficial to design an optimized HPLD system.

On deterministic-stochastic time domain study of dipole antenna for GPR applications

A deterministic-stochastic transient study of Ground Penetrating Radar (GPR) dipole antenna radiating in a presence of a two-media configuration is carried out in the paper. A deterministic direct time domain formulation is based on the corresponding space-time Hallen integral equation. The numerical solution is carried out via the improved space-time variant of the Galerkin-Bubnov Indirect Boundary Element Method (GB-IBEM). The Stochastic-Collocation (SC) method is then applied to determine accurate confidence intervals due to the random variations of GPR input parameters. Once obtaining the current along the dipole antenna, it is possible to calculate other parameters of interest for GPR dipole antenna behavior, such as the field reflected from the interface of two media, or the field transmitted into a lower half-space. Some illustrative numerical results for the transient current along the dipole antenna and transient electric field transmitted into the lower half-space are given.

Forecasting the medical workforce: a stochastic agent-based simulation approach.

Starting in the 50s, healthcare workforce planning became a major concern for researchers and policy makers, since an imbalance of health professionals may create a serious insufficiency in the health system, and eventually lead to avoidable patient deaths. As such, methodologies and techniques have evolved significantly throughout the years, and simulation, in particular system dynamics, has been used broadly. However, tools such as stochastic agent-based simulation offer additional advantages for conducting forecasts, making it straightforward to incorporate microeconomic foundations and behavior rules into the agents. Surprisingly, we found no application of agent-based simulation to healthcare workforce planning above the hospital level. In this paper we develop a stochastic agent-based simulation model to forecast the supply of physicians and apply it to the Portuguese physician workforce. Moreover, we study the effect of variability in key input parameters using Monte Carlo simulation, concluding that small deviations in emigration or dropout rates may originate disparate forecasts. We also present different scenarios reflecting opposing policy directions and quantify their effect using the model. Finally, we perform an analysis of the impact of existing demographic projections on the demand for healthcare services. Results suggest that despite a declining population there may not be enough physicians to deliver all the care an ageing population may require. Such conclusion challenges anecdotal evidence of a surplus of physicians, supported mainly by the observation that Portugal has more physicians than the EU average.

Optimization and Analysis of Laser Beam Machining Parameters for Al7075-TiB2 In-situ Composite

The paper focuses on laser beam machining (LBM) of In-situ synthesized Al7075-TiB2 metal matrix composite. Optimization and influence of laser machining process parameters on surface roughness, volumetric material removal rate (VMRR) and dimensional accuracy of composites were studied. Al7075-TiB2 metal matrix composite was synthesized by in-situ reaction technique using stir casting process. Taguchi's L9 orthogonal array was used to design experimental trials. Standoff distance (SOD) (0.3 - 0.5mm), Cutting Speed (1000 - 1200 m/hr) and Gas pressure (0.5 - 0.7 bar) were considered as variable input parameters at three different levels, while power and nozzle diameter were maintained constant with air as assisting gas. Optimized process parameters for surface roughness, volumetric material removal rate (VMRR) and dimensional accuracy were calculated by generating the main effects plot for signal noise ratio (S/N ratio) for surface roughness, VMRR and dimensional error using Minitab software (version 16). The Significant of standoff distance (SOD), cutting speed and gas pressure on surface roughness, volumetric material removal rate (VMRR) and dimensional error were calculated using analysis of variance (ANOVA) method. Results indicate that, for surface roughness, cutting speed (56.38%) is most significant parameter followed by standoff distance (41.03%) and gas pressure (2.6%). For volumetric material removal (VMRR), gas pressure (42.32%) is most significant parameter followed by cutting speed (33.60%) and standoff distance (24.06%). For dimensional error, Standoff distance (53.34%) is most significant parameter followed by cutting speed (34.12%) and gas pressure (12.53%). Further, verification experiments were carried out to confirm performance of optimized process parameters.

Effect of cutout on stochastic natural frequency of composite curved panels

The present computational study investigates on stochastic natural frequency analyses of laminated composite curved panels with cutout based on support vector regression (SVR) model. The SVR based uncertainty quantification (UQ) algorithm in conjunction with Latin hypercube sampling is developed to achieve computational efficiency. The convergence of the present algorithm for laminated composite curved panels with cutout is validated with original finite element (FE) analysis along with traditional Monte Carlo simulation (MCS). The variations of input parameters (both individual and combined cases) are studied to portray their relative effect on the output quantity of interest. The performance of the SVR based uncertainty quantification is found to be satisfactory in the domain of input variables in dealing low and high dimensional spaces. The layer-wise variability of geometric and material properties are included considering the effect of twist angle, cutout sizes and geometries (such as cylindrical, spherical, hyperbolic paraboloid and plate). The sensitivities of input parameters in terms of coefficient of variation are enumerated to project the relative importance of different random inputs on natural frequencies. Subsequently, the noise induced effects on SVR based computational algorithm are presented to map the inevitable variability in practical field of applications.

Numerical study and optimization of the cross-flow Maisotsenko cycle indirect evaporative air cooler

Presented article describes the optimization process of the cross-flow Maisotsenko cycle indirect evaporative air cooler. The optimization was performed on the basis of three carefully selected individual quality criteria. The optimization was performed for five influence factors (inlet temperature and relative humidity, primary air mass flow rate, working to primary air ratio and relative length of the initial part). The optimization was performed with two methods: single-parameter and multi-parameter compromise method. The single parameter optimization lead to the unsatisfactory results, due to the different trends shown by the quality criteria under identical variation of input parameters. The multi-parameter optimization was based on analysis of the Harrington function of desirability. The results of optimization allowed establishing optimal range of operational and geometrical conditions for the presented exchanger and establishing the optimal climate conditions for its effective operation.

Coupled code analysis of uncertainty and sensitivity of Kalinin-3 benchmark

Abstract An uncertainty and sensitivity analysis is performed for the OECD/NEA coolant transient Benchmark (K-3) on measured data at Kalinin-3 Nuclear Power Plant (NPP). A switch off of one main coolant pump (MCP) at nominal reactor power is calculated using a coupled thermohydraulic and neutron-kinetic ATHLET-PARCS code. The objectives are to study uncertainty of total reactor power and to identify the main sources of reactor power uncertainty. The GRS uncertainty and sensitivity software package XSUSA is applied to propagate uncertainties in nuclear data libraries to the full core coupled transient calculations. A set of most important thermal-hydraulic parameters of the primary circuit is identified and a total of 23 thermohydraulic parameters are statistically varied using GRS code SUSA. The ATHLET model contains also a balance-of-plant (BOP) model which is simulated using ATHLET GCSM module. In particular the operation of the main steam generator regulators is modelled in detail. A set of 200 varied coupled ATHLET-PARCS calculations is analyzed. The results obtained show a clustering effect in the behavior of global reactor parameters. It is found that the GCSM system together with varied input parameters strongly influence the overall nuclear power plant behavior and can even lead to a new scenario. Possible reasons of the clustering effect are discussed in the paper. This work is a step forward in establishing a “best-estimate calculations in combination with performing uncertainty analysis” methodology for coupled full core calculations.

Comparison of Field Measurements to Methane Emissions Models at a New Landfill.

Estimates of methane emissions from landfills rely primarily on models due to both technical and economic limitations. While models are easy to implement, there is uncertainty due to the use of parameters that are difficult to validate. The objective of this research was to compare modeled emissions using several greenhouse gas (GHG) emissions reporting protocols including: (1) Intergovernmental Panel on Climate Change (IPCC); (2) U.S. Environmental Protection Agency Greenhouse Gas Reporting Program (EPA GHGRP); (3) California Air Resources Board (CARB); and (4) Solid Waste Industry for Climate Solutions (SWICS), with measured emissions data collected over three calendar years from a young landfill with no gas collection system. By working with whole landfill measurements of fugitive methane emissions and methane oxidation, the collection efficiency could be set to zero, thus eliminating one source of parameter uncertainty. The models consistently overestimated annual methane emissions by a factor ranging from 4-31. Varying input parameters over reasonable ranges reduced this range to 1.3-8. Waste age at the studied landfill was less than four years and the results suggest the need for measurements at additional landfills to evaluate the accuracy of the tested models to young landfills.

Process Windows for Sheet Metal Parts based on Metamodels

Achieving robust production of deep drawn sheet metal parts is challenging. The fluctuations of process and material properties often lead to robustness problems. Numerical simulations are used to validate the feasibility and to detect critical regions of a part. To enhance the consistency with the real process conditions, the measured material data and the force distribution are taken into account. The simulation metamodel contains the virtual knowledge of a particular forming process, which is determined based on a series of finite element simulations with variable input parameters. Based on the metamodels, process windows can be evaluated for different parameter configurations. This helps improving the operating point search, to adjust process settings if the process becomes unstable and to visualize the influence of arbitrary parameters on the process window.

Economic impact of combined torrefaction and pelletization processes on forestry biomass supply

AbstractThe cost of supplying wood biomass from forestry operations in remote areas has been an obstacle to expansion of forest‐based bioenergy in much of the western United States. Economies of scale in the production of liquid fuels from lignocellulosic biomass feedstocks favor large centralized biorefineries. Increasing transportation efficiency through torrefaction and pelletization at distributed satellite facilities may serve as a means to expand the utilization of forestry residuals in biofuel production. To investigate this potential, a mixed‐integer linear program was developed to optimize the feedstock supply chain design with and without distributed pretreatment. The model uses techno‐economic assessment of scale‐dependent biomass pretreatment processes from existing literature and multimodal biomass transportation cost evaluations derived from a spatially explicit network analysis as input. In addition, the sensitivity of the optimal system configuration was determined for variations of key input parameters including the production scale of pretreatment facilities, road and rail transportation costs, and feedstock procurement costs. Torrefaction and densification were found to reduce transportation costs by $0.84 per GJ and overall delivered costs by $0.24 per GJ, representing 14.5% and 5.2% cost reductions compared to feedstock collection without pretreatment. Significant uncertainties remain in terms of the costs associated with deploying torrefaction equipment at the scales modeled, but the level of potential cost savings suggests further analysis and development of these alternatives.

Concept of educational renewable energy laboratory integrating wind, solar and biodiesel energies

This paper presents the concept of novel educational renewable energy laboratory. The proposed laboratory objective is to allow students to learn all aspects of renewable energies and take hands-on equipment's for better understanding of clean energy technologies. This laboratory includes a hybrid power system (HPS) integrating wind energy conversion system (WECS), photovoltaic (PV) panel, biodiesel generator, maximum power point tracking (MPPT) controllers and storage battery. In this paper, the whole HPS dimensioning is performed and the three renewable energy source systems are simulated and analyzed separately. The PV panels are examined under solar radiation and cell temperature variation conditions. The solar power functioning is verified and studied through a direct connection to battery and resistive load. The WECS is also analyzed through the same connection under constant wind's speed and variable wind's speed. Also, the biodiesel generator (BDG) using permanent magnet synchronous machine was simulated and analyzed. The three energy sources were coupled and analyzed under input parameters variations. The laboratory physical environment is described and the virtual platform is presented. The e-learning concept and the laboratory learning topics are also developed.

Process optimization of single screw extruder for development of Nylon 6-Al-Al2O3 alternative FDM filament

Purpose The purpose of this study is to investigate the process parameters of a single-screw extruder for development of Nylon6-Al-Al2O3-based alternative fused deposition modeling process (FDM) feedstock filament (in lieu of commercial acrylonitrile butadiene styrene filament). The effect of major screw extruder parameters on the tensile strength of fabricated filaments has also been analyzed. Design/methodology/approach The Taguchi experimental log has been designed for investigating the significance of input parameters of screw extruders (such as mean barrel temperature, die temperature, screw speed, material composition and speed of take up unit) on the tensile strength of fabricated filaments. The suitability of alternative material as an FDM filament has been verified by rheological investigations. The tensile strength of an alternative feedstock filament has been investigated experimentally according to the ASTM-638 standard. The analysis was performed by the analysis of variance (ANOVA) method with the help of MINITAB 17 software. The stiffness of the FDM printed parts with nine different feedstock filaments (prepared by selecting nine different combinations of analytical parameters) was determined by dynamic mechanical analysis (DMA). Findings The tensile strength of the feedstock filament was significantly affected by the variation of major input parameters during the processing of alternative material on a single-screw extruder. The ANOVA shows that two process parameters (namely, material composition and die temperature) were significant at the 5 per cent level (“F” value 41 and 21.96, respectively) and remaining two (mean barrel temperature and screw speed) were insignificant at the 5 per cent level. Further, a linear regression model has been developed to predict the tensile strength of the alternative feedstock FDM filament. The results highlight that a deviation of <1 per cent was observed (in the tensile strength of nine sets of experimental runs) as compared to the predicted values of the regression model. In addition to above, the DMA result also indicates that the filament fabricated with optimum combination of parameters has highest stiffness and is more suitable for the FDM system. Research limitations/implications During the processing of alternative material in a single-screw extruder and FDM system, the increase of filler contents adversely affects the contact surfaces. Practical implications The FDM parts with customized properties (viz., thermal and tribological) can be fabricated with alternative feedstock filament material. Originality/value The potential to consider alternative filament material for FDM system includes rapid manufacturing of functional parts, tailor-made grinding tools for dentists and rapid tooling of metal matrix composites having complex geometry.

Multiscale imaging, modeling, and principal component analysis of gas transport in shale reservoirs

Characterization and upscaling of gas transport in shales is challenging because of the multiple spatial scales. Rock characterization using high-resolution imaging (e.g., X-ray computed tomography [CT] and scanning electron microscope [SEM]) captures fundamental geometrical and transport properties, but the obtained information is usually highly localized and contains significant uncertainties. An effective upscaling method is thus needed to propagate the pore-scale information across multiple spatial scales. A modified dual-porosity model was proposed to study multiscale gas transport in shales. The model consists of two domains, a kerogen domain, and an inorganic matrix. Within kerogen, gas transport is dominated by molecular diffusion and nonlinear adsorption and desorption. Within inorganic matrix, gas transport is dominated by viscous flow driven by a pressure gradient. A mass-exchange-rate coefficient is used to describe gas transport between kerogen and inorganic matrix. The modified dual-porosity model was used to perform history matching of a pressure-pulse-decay experiment in the laboratory. The long tail of the pressure decline curve was well-captured by the model, suggesting that it accounted for both fast and slow transport mechanisms. Sensitivity analysis was conducted to study the impact of input variation on model output. We found that the impact of the transport processes within the slower domain (kerogen) depends primarily on the transport rate within the faster domain (inorganic matrix). The principal component analysis (PCA) method was applied to study the continuous movement of the upstream pressure decline curve resulting from input parameter variation. This study is the first to apply the PCA method in analysis of pressure decline curve in reservoir engineering. We found that increased convective transport rate within inorganic matrix expedited upstream pressure decline; conversely, increased mass-exchange rate and desorption-rate coefficients slowed down upstream pressure decline in the short term, but expedited it in the long term, when convective transport within inorganic matrix was fast. We also conducted primary recovery simulations to study the impacts of adsorbed gas ratio and Klinkenberg effect on upstream pressure decline, recovery factor, and recovery rate. We found that a higher adsorbed gas ratio led to a longer recovery rate curve. This confirms that a higher adsorbed gas ratio increases the longevity of a gas-producing well. Furthermore, Klinkenberg effect significantly enhanced initial recovery rate while lowering recovery rate at later times. Therefore, it is critical to accurately evaluate adsorbed gas ratio because it determines the shape and longevity of production curves; when reservoir pressure is relatively low, Klinkenberg effect might affect both early- and later-time production and thus cannot be simply ignored.