Different Sets Of Input Parameters Research Articles

Development of “Parameter space screening”-based single-wavelength anomalous diffraction phasing and structure determination pipeline

In this paper, we present a highly efficient structure determination pipeline software suite (X2DF) that is based on the “Parameter space screening” method, by combining the popular crystallographic structure determination programs and high-performance parallel computing. The phasing method employed in X2DF is based on the single-wavelength anomalous diffraction (SAD) theory. In the X2DF, the choice of crystallographic software, the input parameters to this software and the results display layout, are all parameters which users can select and screen automatically. Users may submit multiple structure determination jobs each time, and each job uses a slightly different set of input parameters or programs. Upon completion, the results of the calculation performed can be displayed, harvested, and analyzed by using the graphical user interface (GUI) of the system. We have applied the X2DF successfully to many cases including the cases that the structure solutions fail to be yielded by using manual approaches.

Design optimization of hydraulic fracturing

The approach to the problem of hydraulic fracturing design optimization is proposed which combines three modules: a fracture geometry module, an oil production module and an economic module. The approach is based on the genetic algorithm — the universal multivariate and multiobjective optimization method. The optimization process involves subsequent determination of the fracture geometry, calculation of the oil production depending on the number of fractures, and description of the investment–return cash flows. Iterated by the genetic algorithm for different set of input parameters, this approach allows determination of the optimal design of a multiply fractured well for a given set of geological and economical information. The results show that the proposed method is plausible and can be useful for achieving projected objectives.

Influence of Measurement and Prediction Uncertainties on Range Estimation for Electric Vehicles

Knowledge of the remaining driving range is crucial for drivers of battery electric vehicles (BEVs), because maximum range and charging infrastructure are limited. This and the fact that current implementations of range displays are deemed unreliable leads to range anxiety and ultimately less acceptance of BEVs. Every range estimation algorithm depends on various uncertain inputs and hence, its result is also uncertain. A more reliable range estimation could be achieved by assessing uncertainties of the underlying predictions and measurements and their influence on the calculation result. This paper presents a method for accurately calculating the probability of reaching the destination of a given route. For this, uncertainty of future velocity and battery state is analyzed using the example of an actual test vehicle. The error propagation through a physical model of the powertrain is evaluated by means of Monte-Carlo method and Taylor series expansion. Range uncertainty is investigated for different sets of input parameters, yielding a necessary safety margin between 12% and 23% battery charge. It is also shown that uncertainty more than doubles over lifetime of the vehicle and that neglecting this uncertainty can lead to unpredictable stranding for disadvantageous initial conditions. Knowledge of range uncertainty can reduce the necessary safety margin and avoid stranding of vehicles. In addition, displaying the uncertainty information to the driver alleviates range anxiety of drivers and consequently, acceptance of electric vehicles can be raised in society.

An adaptive semi-Lagrangian advection model for transport of volcanic emissions in the atmosphere

Abstract. The dispersion of volcanic emissions in the Earth atmosphere is of interest for climate research, air traffic control and human wellbeing. Current volcanic emission dispersion models rely on fixed-grid structures that often are not able to resolve the fine filamented structure of volcanic emissions being transported in the atmosphere. Here we extend an existing adaptive semi-Lagrangian advection model for volcanic emissions including the sedimentation of volcanic ash. The advection of volcanic emissions is driven by a precalculated wind field. For evaluation of the model, the explosive eruption of Mount Pinatubo in June 1991 is chosen, which was one of the largest eruptions in the 20th century. We compare our simulations of the climactic eruption on 15 June 1991 to satellite data of the Pinatubo ash cloud and evaluate different sets of input parameters. We could reproduce the general advection of the Pinatubo ash cloud and, owing to the adaptive mesh, simulations could be performed at a high local resolution while minimizing computational cost. Differences to the observed ash cloud are attributed to uncertainties in the input parameters and the course of Typhoon Yunya, which is probably not completely resolved in the wind data used to drive the model. The best results were achieved for simulations with multiple ash particle sizes.

PASSIOT: A Pareto-optimal multi-objective optimization approach for synthesis of analog circuits using Sobol’ indices-based engine

This paper presents a new approach for multi-objective optimization synthesis of analog circuits based on computing Sobol’ indices for vectors of input variable parameters ζ of analog circuits, PASSIOT. By adopting Sobol’ sensitivity analysis, the tool quantifies the amount of variance that each parameter (or a set of parameters) contributes to the uncertainty of model outputs, which are represented in the objective functions of the circuit. Assessing different higher order sensitivities effects of different sets of input parameters on model outputs contributes to: quantifying parameters having the highest impact on outputs uncertainties, traversing output design space efficiently, and converging to a higher circuit performance in a reasonable runtime compared to other approaches. PASSIOT adopts corner-driven Pareto-Optimal solution model for efficient multi-objective optimization of analog circuits parameters, accounting for on-chip performance variations. Experimental results are simulated on two well-known operational amplifier topologies, the Folded-Cascode amplifier and Miller Compensated Two-Stage amplifier. PASSIOT is simulated using real optimization functions and is proven to be competitive in terms of runtime and solution quality.

An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method

AbstractIn this paper, we estimated using the machine learning methodology the main wetting branch of the soil water retention curve based on the knowledge of the main drying branch and other, optional, basic soil characteristics (particle size distribution, bulk density, organic matter content, or soil specific surface). The support vector machine algorithm was used for the models' development. The data needed by this algorithm for model training and validation consisted of 104 different undisturbed soil core samples collected from the topsoil layer (A horizon) of different soil profiles in Poland. The main wetting and drying branches of SWRC, as well as other basic soil physical characteristics, were determined for all soil samples. Models relying on different sets of input parameters were developed and validated. The analysis showed that taking into account other input parameters (i.e., particle size distribution, bulk density, organic matter content, or soil specific surface) than information about the drying branch of the SWRC has essentially no impact on the models' estimations. Developed models are validated and compared with well‐known models that can be used for the same purpose, such as the Mualem (1977) (M77) and Kool and Parker (1987) (KP87) models. The developed models estimate the main wetting SWRC branch with estimation errors (RMSE = 0.018 m3/m3) that are significantly lower than those for the M77 (RMSE = 0.025 m3/m3) or KP87 (RMSE = 0. 047 m3/m3) models.

Seismotectonics Considered Artificial Neural Network Earthquake Prediction in Northeast Seismic Region of China

It is well known that earthquakes are a regional event, strongly controlled by local geological structures and circumstances. Reducing the research area can reduce the influence of other irrelevant seismotectonics. A new sub regiondividing scheme, considering the seismotectonics influence, was applied for the artificial neural network (ANN) earthquake prediction model in the northeast seismic region of China (NSRC). The improved set of input parameters and prediction time duration are also discussed in this work. The new dividing scheme improved the prediction accuracy for different prediction time frames. Three different research regions were analyzed as an earthquake data source for the ANN model under different prediction time duration frames. The results show: (1) dividing the research region into smaller subregions can improve the prediction accuracies in NSRC, (2) larger research regions need shorter prediction durations to obtain better performance, (3) different areas have different sets of input parameters in NSRC, and (4) the dividing scheme, considering the seismotectonics frame of the region, yields better results.

A MULTI-OBJECTIVE INPUT-OUTPUT MODEL TO ASSESS E4 IMPACTS OF BUILDING RETROFITTING MEASURES TO IMPROVE ENERGY EFFICIENCY

This paper develops a bottom-up approach in the scope of a multi-objective linear programming model (MOLP) based on Input-Output (I-O) analysis to account for investment options aimed at improving the thermal properties of building envelope (e.g., the insulation of external walls and roof, and the replacement of window frames and window glazing). This methodological framework aims at assessing the trade-offs between the overall employment, GDP and energy savings associated with the building sector (residential, private services and public services). Distinct impacts, namely on direct and indirect employment generation, environment (CO2 emissions), energy security supply (energy imports and renewable energy production) and other relevant economic indicators are also analysed. Different sets of input parameters for the economic context and the environmental impacts have been defined as interval coefficients to account for uncertainty. Robust solutions are then obtained by considering the minimisation of the worst possible deviation of the interval objective functions to the corresponding interval ideal solutions.

Artificial Neural Network based Prediction of Pressure Drop in Heat Exchangers

Objectives: The aim of the present work is to predict the value of pressure drop for different inlet-outlet configurations of an air cooled cross flow heat exchanger using artificial neural network. Methods: Configuration and mass flow rate are given as inputs. The pressure drop is calculated based on numerical simulations for various mass flow rates and different configurations. The numerical results are validated with experiments. This numerical data obtained for 4 different flow rates for each of the 24 configurations is used in training a back propagation based neural network to predict the pressure drop. Results: In the present work Levenberg-Marquardt algorithm is used to train the network. Two inputs are specified in the present study. Mass flow rate and type of configuration are assigned in the input layers and the output is the pressure drop across the heat exchanger. The number of hidden layers is fixed as 10 after a series of trial and error. The data are taken from the simulations and are fed to the network. 80% of data is taken for training and 20% are taken for validation and testing. Using the feed forward perception network the input is propagated and the error in the output is back propagated to modify the weight. Sigmoid transfer function is used as the activation function for the hidden layer and is represented in Equation 3. It can be concluded that the neural network is able to predict the pressure drop for varying input parameters. The neural network gave a mean regression coefficient to be 0.97. The regression plot for the training, validation, testing data has been shown in Figure 6. Conclusion: As the regression value approaches 1, it may be concluded that ANN is capable of predicting the results for different set of input parameters within the training range.

Simulating the behavior of volatiles belonging to the C–O–H–S system in silicate melts under magmatic conditions with the software D-Compress

Modeling magmatic degassing, or how the volatile distribution between gas and melt changes at pressure varies, is a complex task that involves a large number of thermodynamical relationships and that requires dedicated software. This article presents the software D-Compress, which computes the gas and melt volatile composition of five element sets in magmatic systems (O–H, S–O–H, C–S–O–H, C–S–O–H–Fe, and C–O–H). It has been calibrated so as to simulate the volatiles coexisting with three common types of silicate melts (basalt, phonolite, and rhyolite). Operational temperatures depend on melt composition and range from 790 to 1400°C. A specificity of D-Compress is the calculation of volatile composition as pressure varies along a (de)compression path between atmospheric and 3000bars. This software was prepared so as to maximize versatility by proposing different sets of input parameters. In particular, whenever new solubility laws on specific melt compositions are available, the model parameters can be easily tuned to run the code on that composition. Parameter gaps were minimized by including sets of chemical species for which calibration data were available over a wide range of pressure, temperature, and melt composition. A brief description of the model rationale is followed by the presentation of the software capabilities. Examples of use are then presented with outputs comparisons between D-Compress and other currently available thermodynamical models. The compiled software and the source code are available as electronic supplementary materials.

Physical and Topological Constraints on Growth in Human Brain Networks

Noninvasive MRI technology enables the mapping of white matter connectivity between cortical regions in the human brain. Graph theoretic analysis has shown the organization of this structural brain network facilitates certain functional properties, such as robust and efficient transfer of information between brain regions. What are the generative mechanisms of such a network? To address this, we analyze a broad range of synthetic networks produced by simple growth rules, and we compare these networks both qualitatively and quantitatively to the brain. We consider a growth model in which a set of three parameters govern the tendency of nodes to form connections with one another. These generated networks are analyzed in terms of direct measures of connectivity, such as the length and number of network connections, as well as higher-order graph metrics, such as hierarchy and clustering, that relate to efficient information transfer and resistance to random errors. We find that different sets of input parameters can greatly affect the final structure of the network, with only some subsets of parameter values creating brain-like networks. This shows that very similar growth rules can produce a diverse set of network structures. In ongoing work, we are investigating the role of known biophysical constraints, such as bilateral symmetry and wiring cost, on network growth. This study lends insight into the biophysical mechanisms that govern the development of human brain networks and ultimately shape robust and efficient network function.

Prediction of Yarn Strength Utilization in Cotton Woven Fabrics using Artificial Neural Network

The paper presents an endeavor to predict the percentage yarn strength utilization (% SU) in cotton woven fabrics using artificial neural network approach. Fabrics in plain, 2/2 twill, 3/1 twill and 4-end broken twill weaves having three pick densities and three weft counts in each weave have been considered. Different artificial neural network models, with different set of input parameters, have been explored. It has been found that % SU can be predicted fairly accurately by only five fabric parameters, namely the number of load bearing and transverse yarns per unit length, the yarn crimp % in the load bearing and transverse directions and the float length of the weave. Trend analysis of the artificial neural network model has also been carried out to see how the various parameters affect the % SU. The results indicate that while an increase in the number of load bearing or transverse yarns increases the % SU, an increase in the float length and the crimp % in the yarns have a detrimental effect.

Poisson-Boltzmann versus Size-Modified Poisson-Boltzmann Electrostatics Applied to Lipid Bilayers.

Mean-field methods, such as the Poisson–Boltzmann equation (PBE), are often used to calculate the electrostatic properties of molecular systems. In the past two decades, an enhancement of the PBE, the size-modified Poisson–Boltzmann equation (SMPBE), has been reported. Here, the PBE and the SMPBE are reevaluated for realistic molecular systems, namely, lipid bilayers, under eight different sets of input parameters. The SMPBE appears to reproduce the molecular dynamics simulation results better than the PBE only under specific parameter sets, but in general, it performs no better than the Stern layer correction of the PBE. These results emphasize the need for careful discussions of the accuracy of mean-field calculations on realistic systems with respect to the choice of parameters and call for reconsideration of the cost-efficiency and the significance of the current SMPBE formulation.

Modelling and optimisation of machining parameters for composite pipes using artificial neural network and genetic algorithm

An integrated approach to determine the influence of cutting parameters on carbon fiber reinforced polymer (CFRP) pipe using Taguchi’s Design of experiments (DOE), artificial neural network (ANN) and optimisation with genetic algorithm (GA) is followed when carrying out turning operation on a bidirectional carbon fabric reinforced bisphenol pipe using a carbide tool at different set of input parameters like feed, speed and depth of cut. Due to the anisotropic nature of composites and inhomogeneity, optimisation of machining parameters for composites is very difficult and largely dependent on wide variety of combinations among various factors such as fiber properties, orientation, resin properties, curing conditions, etc. For successful application of these composites in different fields, knowledge on machining the composites is necessary. Taguchi’s orthogonal array is used for DOE and the output under consideration is forces acting on cutting tool. Genetic algorithm is used to optimize the machining parameters to yield minimum cutting force. ANN with back propagation neural network algorithm has been adopted to model the machining operation. The results were verified using confirmation tests which indicate that the experimental values and observed values are almost equal with minimum error and ANN with GA could be successfully implemented for modelling and optimizing the machining parameters for bidirectional CFRP composite pipes.

Equivalent Static Analysis of Piled Bridge Abutments Affected by Earthquake-Induced Liquefaction

Deformation of bridge approach embankments caused by earthquake-induced liquefaction in the underlying soils can impose large force and displacement demands on the piled abutments. An equivalent static analysis (ESA) procedure that accounts for the beneficial coupling of the embankment displacements with the mobilized bridge/pile restraining forces is evaluated against the results of three centrifuge tests involving three nonpiled embankments and three piled embankments. The centrifuge tests, which showed that the restraining forces from the different pile configurations reduced the embankment displacements by 20–80%, relative to those of the nonpiled embankments, are briefly summarized. The ESA procedure is described and applied to the centrifuge tests using different sets of input parameters to isolate the questions of how well liquefaction-induced displacements are estimated for embankments without piles, and how well the relative pile pinning effects are estimated. The results of nonlinear deformation analyses (NDAs) of the same centrifuge tests are compared with those from the ESAs and are used to illustrate important differences in their capabilities. The relative merits of the ESAs and NDAs for evaluating the performance of pile abutments affected by liquefaction are discussed.

The PLATO Simulator: modelling of high-precision high-cadence space-based imaging

Many aspects of the design trade-off of a space-based instrument and its performance can best be tackled through simulations of the expected observations. The complex interplay of various noise sources in the course of the observations make such simulations an indispensable part of the assessment and design study of any space-based mission. We present a formalism to model and simulate photometric time series of CCD images by including models of the CCD and its electronics, the telescope optics, the stellar field, the jitter movements of the spacecraft, and all important natural noise sources. This formalism has been implemented in a versatile end-to-end simulation software tool, called PLATO Simulator, specifically designed for the PLATO space mission to be operated from L2, but easily adaptable to similar types of missions. We provide a detailed description of several noise sources and discuss their properties, in connection with the optical design, the allowable level of jitter, the quantum efficiency of the detectors, etc. The expected overall noise budget of generated light curves is computed as a function of the stellar magnitude, for different sets of input parameters describing the instrument properties. The simulator is offered to the scientific community for future use.

WE-D-BRE-07: Variance-Based Sensitivity Analysis to Quantify the Impact of Biological Uncertainties in Particle Therapy

Purpose: In particle therapy, treatment planning and evaluation are frequently based on biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2). In the context of the linear-quadratic model, these quantities depend on biological parameters (α, β) for ions as well as for the reference radiation and on the dose per fraction. The needed biological parameters as well as their dependency on ion species and ion energy typically are subject to large (relative) uncertainties of up to 20–40% or even more. Therefore it is necessary to estimate the resulting uncertainties in e.g. RBE or EQD2 caused by the uncertainties of the relevant input parameters. Methods: We use a variance-based sensitivity analysis (SA) approach, in which uncertainties in input parameters are modeled by random number distributions. The evaluated function is executed 10{sup 4} to 10{sup 6} times, each run with a different set of input parameters, randomly varied according to their assigned distribution. The sensitivity S is a variance-based ranking (from S = 0, no impact, to S = 1, only influential part) of the impact of input uncertainties. The SA approach is implemented for carbon ion treatment plans on 3D patient data,more » providing information about variations (and their origin) in RBE and EQD2. Results: The quantification enables 3D sensitivity maps, showing dependencies of RBE and EQD2 on different input uncertainties. The high number of runs allows displaying the interplay between different input uncertainties. The SA identifies input parameter combinations which result in extreme deviations of the result and the input parameter for which an uncertainty reduction is the most rewarding. Conclusion: The presented variance-based SA provides advantageous properties in terms of visualization and quantification of (biological) uncertainties and their impact. The method is very flexible, model independent, and enables a broad assessment of uncertainties. Supported by DFG grant WI 3745/1-1 and DFG cluster of excellence: Munich-Centre for Advanced Photonics.« less

Assessment of Cyclic Resistance Ratio (CRR) in Silty Sands Using Artificial Neural Networks

In this study, a backpropagation neural network algorithm was developed in order to predict the liquefaction cyclic resistance ratio (CRR) of sand-silt mixtures. A database, consisting of sufficient published data of laboratory cyclic triaxial, torsional shear and simple shear tests results, was collected and utilized in the ANN model. Several ANN models were developed with different sets of input parameters in order to determine the model with best performance and preciseness. It has been illustrated that the proposed ANN model can predict the measured CRR of the different data set which was not incorporated in the developing phase of the model with the good degree of accuracy. The subsequent sensitivity analysis was performed to compare the effect of each parameter in the model with the laboratory test results. At the end, the participation or relative importance of each parameter in the ANN model was obtained.

On the parameterization of vibronic Hamiltonians for molecular aggregates using absorption line-shapes as an input

Absorption line-shapes of molecular aggregates are often calculated using a simple form for a vibronic Hamiltonian. Parameters which enter into the model are usually taken from measured spectra. Here, we address the question in how far different sets of input parameters used to calculate the spectra lead to similar spectral features. Therefore, we first present analytical expressions which illustrate the relation between the various parameters such as Huang-Rhys factors, electronic coupling elements, and aggregate size. Numerical calculations show that identical peak ratios and spectra can be obtained for different sets of parameters. This illustrates the basic difficulties in extracting reliable molecular information from a comparison of calculated and measured spectra.

Parameter determination for device simulations of thin film silicon solar cells by inverse modeling based on temperature and spectrally dependent measurements

Opto-electronic device simulation of thin-film silicon solar cells requires a set of many input parameters for the electronic properties of amorphous and microcrystalline silicon. In this paper, a systematic determination of these is performed by inverse modeling and particle swarm optimization with experimental and simulated current-voltage characteristics of amorphous and microcrystalline silicon pin diodes, both in the dark and under illumination. The parameters are validated by additional experiments and simulations of the temperature-dependent dark current, of spectrally selective illumination, and of the external quantum efficiency of the amorphous and microcrystalline silicon diode, respectively. The validation of the input parameters is complemented by simulation of a tandem solar cell with the amorphous and the microcrystalline silicon input parameters. Simulation and experiment (spectrally dependent current-voltage characteristics and external quantum efficiency) of such a tandem solar cell show good agreement. Sensitivity studies on different sets of input parameters for the inverse modeling process are also presented.