Single Set Of Input Parameters Research Articles

Validation of the extended COCOSYS cable fire model based on a generic FLASH-CAT approach

Fire simulations and analytical validation approaches have gained more and more relevance, in particular with respect to fire safety assessment of operating nuclear power plants (NPPs). For providing an added value to the regulatory authorities and the technical safety experts authorized by them suitability and applicability of the analytical tools applied such as fire simulation codes need be known and demonstrated.The COCOSYS (Containment Code System) code developed by Gesellschaft für Anlagen- und Reaktorsicherheit (GRS) gGmbH contains i.e. models for simulating cable as well as liquid fuel fires. In NPPs, cables represent a non-negligible fire load as initial fire source or contributor to fire spreading.To overcome some generic deficiencies of the existing so-called simple cable fire model in COCOSYS the model has been improved based on a more generic FLASH-CAT approach. The extended model uses a time dependent mass loss rate (MLR) density profile, can consider composite materials to regard the increase of the heat of combustion during the fire development and is able to consider under-ventilated conditions.The model was validated on several experiments performed in open atmosphere and confined environment by the French Institut de Radioprotection et de Sûreté Nucléaire (IRSN) and conducted within the PRISME 2 and PRISME 3 Projects, launched by the Organisation for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA). One main objective is to provide one single set of input parameters for each cable type, which can be used under different ventilation conditions. The paper presents capabilities and remaining limitations of the new model.

Evaluation of global sensitivity analysis methods for computational structural mechanics problems

Abstract The curse of dimensionality confounds the comprehensive evaluation of computational structural mechanics problems. Adequately capturing complex material behavior and interacting physics phenomenon in models can lead to long run times and memory requirements resulting in the need for substantial computational resources to analyze one scenario for a single set of input parameters. The computational requirements are then compounded when considering the number and range of input parameters spanning material properties, loading, boundary conditions, and model geometry that must be evaluated to characterize behavior, identify dominant parameters, perform uncertainty quantification, and optimize performance. To reduce model dimensionality, global sensitivity analysis (GSA) enables the identification of dominant input parameters for a specific structural performance output. However, many distinct types of GSA methods are available, presenting a challenge when selecting the optimal approach for a specific problem. While substantial documentation is available in the literature providing details on the methodology and derivation of GSA methods, application-based case studies focus on fields such as finance, chemistry, and environmental science. To inform the selection and implementation of a GSA method for structural mechanics problems for a nonexpert user, this article investigates five of the most widespread GSA methods with commonly used structural mechanics methods and models of varying dimensionality and complexity. It is concluded that all methods can identify the most dominant parameters, although with significantly different computational costs and quantitative capabilities. Therefore, method selection is dependent on computational resources, information required from the GSA, and available data.

Modeling the Conductivity and Diffusion Permeability of a Track-Etched Membrane Taking into Account a Loose Layer.

The microheterogeneous model makes it possible to describe the main transport properties of ion-exchange membranes using a single set of input parameters. This paper describes an adaptation of the microheterogeneous model for describing the electrical conductivity and diffusion permeability of a track-etched membrane (TEM). Usually, the transport parameters of TEMs are evaluated assuming that ion transfer occurs through the solution filling the membrane pores, which are cylindrical and oriented normally to the membrane surface. The version of the microheterogeneous model developed in this paper takes into account the presence of a loose layer, which forms as an intermediate layer between the pore solution and the membrane bulk material during track etching. It is assumed that this layer can be considered as a "gel phase" in the framework of the microheterogeneous model due to the fixed hydroxyl and carboxyl groups, which imparts ion exchange properties to the loose layer. The qualitative and quantitative agreement between the calculated and experimental concentration dependencies of the conductivity and diffusion permeability is discussed. The role of the model input parameters is described in relation to the structural features of the membrane. In particular, the inclination of the pores relative to the surface and their narrowing in the middle part of the membrane can be important for their properties.

Thermal preference prediction based on occupants’ adaptive behavior in indoor environments- A study of an air-conditioned multi-occupancy office in China

Exploring new thermal preference prediction models or methods to precisely analyze occupants’ unconscious feedback on the thermal environment without disturbing them is essential for increased building efficiency, comfort and productivity. In this study, we propose a novel method for developing a comfort model that uses occupant self-adjusting behavior to predict thermal preference. The model development draws from field data including thermal self-adaptive behaviors, environmental parameters, and thermal preferences collected from 34 occupants in a single multi-occupancy room in a research office in a university in south-east China, and it employs four machine learning algorithms including Support Vector Machine, Random Forest, K-Nearest Neighbor, and Decision Tree. The results indicate that there is a close relationship between thermal preference and thermal self-adaptive behavior. The highest prediction accuracy of thermal preference using a single set of input parameters is 0.81, obtained using self-adaptive behaviors. When the input parameters increase to four sets or more, the highest median accuracy of thermal preference prediction (0.85) does not change significantly. When it is difficult to obtain personal information (gender, height, and weight) and data on clothing thermal resistance, it is recommended to use a combination of three sets of parameters (self-adaptive behavior, indoor temperature and relative humidity (T/RH), and outdoor T/RH) or a combination of four sets of parameters (self-adaptive behavior, indoor T/RH, outdoor T/RH, and globe temperature) as the input parameters of the thermal preference prediction model based on Random Forest, which results in an expected prediction accuracy of 0.84 and 0.85, respectively.

A dynamic prediction model for time‐to‐peak

AbstractA simplified empirical equation is developed for widespread prediction of dynamic catchment response time. This model allows for time‐to‐peak prediction to evolve from static, lumped models, thereby providing a single value for any storm within a given catchment, using a single set of input parameters, that can be applied to a dynamic model, thus accounting for the variability between storm sizes and catchment moisture conditions. These dynamic prediction methods are translated to North America for the first time. This allows the concepts and prediction methods for catchment response time prediction previously established for the United Kingdom (UK), to be translated to a simple empirical equation for use in North America, through the use of selected study areas in Canada and the United States. This reconfigured model is statistically successful in both the UK and North America and allows for a straightforward implementation of dynamic time‐to‐peak prediction. Further, the reconfigured model introduces the use of a runoff coefficient (Rc) to encompass historical catchment wetness, increasing the ease of incorporating antecedent moisture condition into predictions.

Multi-Objective Optimization of Turning Process during Machining of AISI 1025 on CNC machine Using Multi-objective particle swarm optimization

Parametric optimization of the transformation process is a task of improving multiple objectives. In general, no single set of input parameters can provide the best hardness of the metal and the best finish of the surface at the same time. While when using the genetic algorithm proved that it is possible to obtain more than one goal of the function at the same time. In the current research the use of Multi-objective particle swarm optimization (MOPSO) to determine the best hardness of the metal and less roughness of the surface operator at the same time. A mathematical model was also developed to predict the hardness of the metal and surface roughness using Taguchi technology depending on the input variables (cutting speed, cutting depth and feeding rate). Experiments were conducted on a CNC machine in turning carbon steel AISI 1025 using carbide cutting tool. It was found that the most influential factor on the surface roughness is the feeding rate, while the most important effect on the hardness is the cutting speed.

Use of a discrete electromagnetic model for simulating Aquarius L-band active/passive observations and soil moisture retrieval

In this paper, the discrete electromagnetic model developed at the Tor Vergata University of Rome (hereafter, TV-DEM) is used to simulate both active and passive L-band signals using a single set of input parameters. The simulations are compared to Aquarius observations collected from a view angle of 28.7° over the Maqu study area located near the north-eastern edge of the Tibetan Plateau. The TV-DEM parameters litter biomass, litter moisture factor, plant moisture and standard deviation of height variations are calibrated for Aquarius observations collected during the 2012 and 2013 warm seasons. The calibrated parameters are used to reproduce the brightness temperature (Tb) and backscattering coefficient (σo) for the 2014 and 2015 warm season as an independent assessment.The results show that the calibrated TV-DEM simulations capture Aquarius observations reasonably well with coefficients of determination (R2's) varying from 0.75 to 0.86 for the Tb's and from 0.36 to 0.68 (−) for the σo's depending on the polarization and adopted matchup set (e.g. calibration or validation). The simulations, however, systematically overestimate the H polarized Aquarius observation and underestimate the V polarized observations. The calibrated TV-DEM is also utilized for soil moisture retrieval from three combinations of Aquarius data, including a set of both active and passive microwave observations. The obtained error metrics indicate that the soil moisture estimates from the active/passive data have the smallest bias (−0.008m3m−3) and the lowest unbiased root mean squared difference (0.021m3m−3), while soil moisture retrieved using a calibrated τ (optical depth)-ω (single scattering albedo) model based algorithm provided the best R2. These results warrant further investigation of the synergistic use of active/passive data for soil moisture retrieval as well as the use of complex physically radiative transfer model as part of well-established algorithms.

A model for non-Newtonian flow in porous media at different flow regimes

Polymeric liquids are of great practical importance for porous media flow as they can be used to improve the sweep of water in the reservoir and therefore improve the recovery of oil. Due to the non-Newtonian behavior of these liquids, they are extremely challenging to model. In this paper, we present a model that is capable of describing the most commonly observed flow regimes in porous media: (i) Newtonian, (ii) Shear thinning, (iii) Shear thickening, and (iv) Mechanical degradation. The novel feature of our model is that the time constants for the shear thinning and shear thickening behavior are related to variations in reservoir properties and conditions, thus making it possible to translate lab results to larger scale without introducing new fitting parameters. Furthermore, we present a way to estimate polymer mechanical degradation in porous media. In our model, the polymer degradation rate is linked to the effective pore radius (using a Kozeny-Carman type equation), shear stress, and polymer molecular weight, M w . The degradation results in a lower M w , while the polymer volumetric concentration is unaffected. The model is applied to a series of laboratory core flood experiments conducted with partially hydrolyzed polyacrylamide, HPAM, of different initial M w ranging from 5 to 20 MDa in seawater, and core permeability varied from 137 to 2019 mD. The flow rate is varied approximately three orders of magnitude and covers the shear thinning, shear thickening, and degradation flow regimes. We show that our model is able to reproduce experimental rate-dependent flow resistance, as well as viscosity of effluent samples. An important aspect supporting the use of the model as a predictive tool is that all the simulations with a given brine have made use of a single set of input parameters to describe the observed shear thickening and degradation behavior. Simulation of a second experimental series using low salinity brine required a separate set of input parameters for the shear thickening and shear degradation. The onset of shear thickening was not affected while shear thickening was reduced and degradation appeared to be slower.

Cohesive element formulation for z-pin delamination bridging in fibre reinforced laminates

Z-pins are an effective method of reinforcing laminated composite materials for resisting the propagation of delamination. In this paper, a novel numerical method combines the classical cohesive finite element (FE) method with a semi-analytical z-pin crack bridging model.Special purpose cohesive elements, in which the generalized traction-displacement characteristics are provided by the semi-analytical model z-pin bridging map, are implemented in macro-scale FE models. This cohesive element offers the flexibility to employ two cohesive laws concurrently for prediction of delamination propagation, for both the pinned and unpinned behaviour. Its efficacy is evaluated by the simulation of double cantilever beam (DCB), mixed-mode bend (MMB), and pure mode II End-Loaded Split (ELS) fracture tests at 2% z-pin areal density. The numerical results in terms of load-deflection predictions agree well with experiments. The different simulations were all performed using a single set of input parameters derived from single z-pin tests with no fitting factors.

Simultaneous Precipitation and Recrystallization during Hot Deformation of Ti, Nb and V Microalloyed Steel

Recrystallization is a major means for controlling the grain size of steel during hot deformation. Usually, small grain sizes deliver superior mechanical properties. To aid the grain size controlling effect of recrystallization, small precipitates of carbo-nitride particles can be utilized to hinder the movement of grain boundaries. Interestingly, these particles are not only effective during grain growth, but also during recrystallization. In the present work, a recently developed state-parameter based model is introduced that is capable of describing both, the individual processes of static recrystallization, dynamic and static recovery and precipitation as well as the mutual interaction of these mechanisms in the course of elevated temperature processing. The evolution of state parameters within the model is discussed and the simulation results are compared to experimental information. Within our approach, a vast amount of experimental data for microalloyed steel is reproduced on basis of a single set of input parameters

Note: An iterative algorithm to improve colloidal particle locating.

Confocal microscopy of colloids combined with digital image processing has become a powerful tool in soft matter physics and materials science. Together, these techniques enable locating and tracking of more than half a million individual colloidal particles at once. However, despite improvements in locating algorithms that improve position accuracy, it remains challenging to locate all particles in a densely packed, three dimensional colloid without erroneously identifying the same particle more than once. We present a simple iterative algorithm that mitigates both the "missed particle" and "double counting" problems while simultaneously reducing sensitivity to the specific choice of input parameters. It is also useful for analyzing images with spatially varying brightness in which a single set of input parameters is not appropriate for all particles. The algorithm is easy to implement and compatible with existing particle locating software.

Stochastic analysis of rock fall dynamics on quarry slopes

In order to study rock fall dynamics, a research project conducted by the Vienna University of Technology and the Austrian Central Labour Inspectorate (Federal Ministry of Labour, Social Affairs and Consumer Protection), 589 full-scale drop tests were carried out in different quarries, recording key parameters of the rock fall trajectories. Similar drop tests were conducted by a team from the University of British Columbia and the B.C. Ministry of Transportation and Highways at a quarry site in British Columbia, Canada. In this paper, detailed results of 277 tests at three sites in Austria and 34 tests at the site in Canada are described. The tests involved a total of 311 boulders ranging from 0.18 to 1.8m in diameter and from 0.009 to 8.1Mg in mass. The geology of the different sites included strong rock belonging to igneous, metamorphic and volcanic types. The results of the tests are compared to computer analyses conducted with a new dynamic model, using a single set of parameters for all the sites. In general, reasonable stochastic approximations of the rock fall trajectories have been obtained in all dimensions, including runout, bounce heights and velocities. The approximations are compared to the measured data in terms of median, 95% and maximum values. The results of the comparisons indicate that approximate first-order predictions, using a single set of input parameters, are possible and can be used to aid practical hazard and risk assessment.

A calculator for Roche lobe properties

Abstract Background The Roche lobe geometry is important to understand and study the properties of the mass-losing component in a semi-detached binary system. However it is not easy to calculate accurately, and existing tables usually do not include the parameters of the binary system under study, nor do they allow for non-synchronous rotation. Results A calculator for properties of the Roche lobe is presented in two formats. An easy-to-use Java version has a graphic interface, and a Fortran 90 version has a command line interface. The Fortran version allows for easy modifications by the user. Both versions have two basic output options: one provides values of a set of various quantities (such as the Lagrange points along the binary axis, and area and volume of the Roche lobe); the second provides $R(\theta,\phi)$ R ( θ , ϕ ) , the distance from the stellar center to the stellar surface for any specified polar angle. A single set of input parameters can be entered directly or a large set of input parameters can be specified in a text file. The calculator includes the options to have non-synchronous rotation of the star, or to have the star underfill its Roche lobe. It can be used to calculate Roche lobe properties for the case of elliptical orbits, with some restrictions. Conclusions We present a convenient software tool for quickly and accurately calculating Roche lobe properties for mass ratio in the range 0.01 to 100, for Roche lobe fill-out factor in the range 0.1 to 1.0, and for dimensionless rotation rate of the star in the range 0.1 to 2.0. This will allow anyone working with a binary star system to obtain the Roche lobe or stellar surface geometry for their system.

Development of a radiation track structure clustering algorithm for the prediction of DNA DSB yields and radiation induced cell death in Eukaryotic cells

The preliminary framework of a combined radiobiological model is developed and calibrated in the current work. The model simulates the production of individual cells forming a tumour, the spatial distribution of individual ionization events (using Geant4-DNA) and the stochastic biochemical repair of DNA double strand breaks (DSBs) leading to the prediction of survival or death of individual cells.In the current work, we expand upon a previously developed tumour generation and irradiation model to include a stochastic ionization damage clustering and DNA lesion repair model. The Geant4 code enabled the positions of each ionization event in the cells to be simulated and recorded for analysis. An algorithm was developed to cluster the ionization events in each cell into simple and complex double strand breaks. The two lesion kinetic (TLK) model was then adapted to predict DSB repair kinetics and the resultant cell survival curve. The parameters in the cell survival model were then calibrated using experimental cell survival data of V79 cells after low energy proton irradiation. A monolayer of V79 cells was simulated using the tumour generation code developed previously. The cells were then irradiated by protons with mean energies of 0.76 MeV and 1.9 MeV using a customized version of Geant4.By replicating the experimental parameters of a low energy proton irradiation experiment and calibrating the model with two sets of data, the model is now capable of predicting V79 cell survival after low energy (<2 MeV) proton irradiation for a custom set of input parameters. The novelty of this model is the realistic cellular geometry which can be irradiated using Geant4-DNA and the method in which the double strand breaks are predicted from clustering the spatial distribution of ionisation events. Unlike the original TLK model which calculates a tumour average cell survival probability, the cell survival probability is calculated for each cell in the geometric tumour model developed in the current work. This model uses fundamental measurable microscopic quantities such as genome length rather than macroscopic radiobiological quantities such as alpha/beta ratios. This means that the model can be theoretically used under a wide range of conditions with a single set of input parameters once calibrated for a given cell line.

Combined use of active and passive microwave satellite data to constrain a discrete scattering model

Emissivity and backscattering have different sensitivities to soil and vegetation parameters and provide complementary information on the observed scene. For this reason, the main objective of this study was to investigate the potential of the combined use of active and passive microwave signatures to improve the understanding of emission and scattering mechanisms over land. The discrete scattering model developed by the Tor Vergata University, driven by a single set of input parameters, was used to simulate both emission and backscattering. The model accuracy was assessed by comparing modelled emissivity, multiplied by the effective temperature, with C-band AQUA AMSR-E observations and modelled backscattering coefficient with Metop ASCAT observations. The investigation was carried out using datasets collected over the Maqu area, which is located in the north-eastern part of the Tibetan Plateau and is covered by homogeneous grassland with a thin litter layer. In situ soil moisture measurements and MODIS Terra/Aqua 8-days LAI products served as model inputs, whereas the most sensitive other parameters, such as surface roughness and litter and vegetation parameters, were calibrated to obtain the best match between modelled outputs and both ASCAT and AMSR-E 2009 observations.With the calibrated parameters, measured soil moisture and MODIS LAI as input, the model correctly simulated the temporal variations of ASCAT backscattering coefficient, with R2 of 0.9 and rmse of 0.5dB. The results obtained in the passive case strongly depended on the adopted effective temperature. The best match with satellite observations was found when the surface temperature was obtained from Ka-band AMSR-E data, leading to R2 of 0.8 and rmse of 6K for H polarisation data and R2 of 0.5 and rmse of 6K for V polarisation data.The validation carried out using the 2010 data, without any further refinement of the calibrated parameters, showed a slightly worse agreement between modelled and observed signatures.The results showed that the combined use of active and passive microwave signatures contributed to an unambiguous interpretation of emission and scattering processes over the study area, it allowed us to impose constraints to non-available inputs of the Tor Vergata model and it led to correct simulations of the satellite signatures. These findings are important to better understand the potential of the synergistic use of radiometer and radar data, like those to be provided by NASA's SMAP.

Membrane Permeation of Testosterone from Either Solutions, Particle Dispersions, or Particle-Stabilized Emulsions

We derive a unified model that accounts for the variation in extent and rate of membrane permeation by a permeating species with the type of donor compartment formulation (aqueous and oil solutions, particle dispersions, and oil-in-water and water-in-oil emulsions stabilized by particles) initially containing the permeant. The model is also applicable to either closed-loop or open-flow configurations of the receiver compartment of the permeation cell. Predictions of the model are compared with measured extents and rates of permeation of testosterone across an 80 μm thick polydimethylsiloxane (PDMS) membrane from donor compartments initially containing testosterone dissolved in either aqueous or isopropylmyristate (IPM) solutions, aqueous or IPM dispersions of silica nanoparticles or IPM-in-water or water-in-IPM emulsions stabilized by silica nanoparticles. Using a single set of input parameters, the model successfully accounts for the wide variations in permeation behavior observed for the different donor formulation types with either closed-loop or open flow configurations of the permeation cell receiver compartment.

The kinetics of bainite and martensite formation in steels during cooling

The kinetics of bainite and martensite formation in steel 41Cr4 has been determined using dilatometry for a range of cooling rates. It is demonstrated that all fraction curves can be adequately described with a previously proposed model using a single set of input parameters. This validation shows that the model can give accurate predictions of the CCT diagram of bainitic steels provided the model can be tested against a single experimental fraction curve to determine the model parameters for the given composition.

A model for the analysis of rapid landslide motion across three-dimensional terrain

A new numerical model for the dynamic analysis of rapid flow slides, debris flows, and avalanches has been developed. The model is an extension of an earlier algorithm and is implemented using a numerical method adapted from smoothed particle hydrodynamics. Its features include (i) the ability to simulate flow across complex three-dimensional terrain; (ii) the ability to allow nonhydrostatic and anisotropic internal stress distributions, coupled with strain changes through frictional relationships; (iii) the ability to simulate material entrainment; (iv) a choice of different rheological kernels, including frictional, plastic, viscous, Bingham, and Voellmy; (v) a meshless solution, which eliminates problems with mesh distortion during long displacements; and (vi) highly efficient and simple operation. The model has been tested by analysing a series of laboratory flume experiments with granular materials, both on straight and curved paths. The model is capable of accurately predicting the margins of various curving flows using a single set of input parameters. A preliminary analysis of a real rock avalanche case history is also included.Key words: landslides, debris flows, rock avalanches, runout analysis, dynamic modelling, numerical methods.

Electromigration saturation in a simple interconnect tree

Electromigration-saturation experiments have been carried out on passivated “L”-shaped interconnects which had three contacts through W-filled vias at the ends and at the corners of the L’s. Currents of different magnitudes and directions were independently applied in the two limbs of the L’s. Variations in the steady-state electrical resistances were observed, and attributed to a variation in the location of the voids along the lines. Variations in the rate of approach to the steady state were also observed, and are attributed to variations in void shapes. The effects of electromigration in L-shaped interconnects were simulated using an electromigration simulator based on the one-dimensional Korhonen et al. [J. Appl. Phys. 73, 3790 (1993)] model for electromigration, modified to include the effects of junctions. It is shown that, using a single set of input parameters and information about void shapes, the simulation can be used to make accurate worst-case predictions of the evolving and the steady state values of electromigration-induced resistance changes in these simple L-shaped interconnect trees.

Modeling radiation-induced segregation in austenitic Fe–Cr–Ni alloys

To better predict radiation-induced segregation (RIS) in austenitic Fe–Cr–Ni alloys, a model is developed which uses pair interaction energies, ordering energies and local atomic configuration to calculate diffusion parameters. The model allows a single set of input parameters to describe a range of bulk alloy compositions. Comparisons of RIS measurements in austenitic Fe–Cr–Ni alloys with calculations using an inverse Kirkendall model have shown that composition specific diffusion parameters must be used to accurately predict segregation behavior, thus limiting the model's predictive capability when no prior segregation measurements exist. This paper describes the improved model and compares model calculations to segregation data from seven alloys irradiated at temperatures between 200 and 600°C and to doses from 0.1 to 3.0 dpa. Results show that the dose, temperature and composition dependence of the measured segregation data can be modeled using a single set of input parameters with no error greater than 5 at.%, but usually less than 2 at.%.