Unique Set Of Parameters Research Articles

Determination and analysis of the constitutive parameters of temperature-dependent dislocation-density-based crystal plasticity models

Physics-based crystal plasticity models rely on certain statistical assumptions about the collective behavior of dislocation populations on one slip system and their interactions with the dislocations on the other slip systems. One main advantage of using such physics-based constitutive dislocation models in crystal plasticity kinematic frameworks is their suitability for predicting the mechanical behavior of polycrystals over a wide range of deformation temperatures and strain rates with the same physics-based parameter set. In this study, the ability of a widely used temperature-dependent dislocation-density-based crystal plasticity formulation to reproduce experimental results, with a main focus on the yield stress behavior, is investigated. First, the material parameters are identified from experimental macroscopic stress–strain curves using a computationally efficient optimization methodology that uses a genetic algorithm along with the response surface methodology. For this purpose, a systematic set of compression tests on interstitial free (IF) steel samples is performed at various temperatures and strain rates. Next, the influence of the individual parameters on the observed behavior is analyzed. Based on mutual interactions between various parameters, the ability to find a unique parameter set is discussed. This allows identifying shortcomings of the constitutive law and sketch ideas for possible improvements. Particular attention is directed toward identifying possibly redundant material parameters, narrowing the acceptable range of material parameters based on physical criteria, and modifying the crystal plasticity formulation numerically for high-temperature use.

Review and Recalculation of Growth and Nucleation Kinetics for Calcite, Vaterite and Amorphous Calcium Carbonate

The precipitation of calcium carbonate is well studied in many fields of research and industry. Despite the fact that, or perhaps because of the fact that, it is well studied in many fields, different approaches have been used to describe the kinetics of the precipitation process. The aim of this study was to collect and compare the data available in the literature and find a consistent method to describe the kinetics of growth and nucleation of the various polymorphs of calcium carbonate. Inventory of the available data showed that a significant number of the literature sources were incomplete in providing the required information to recalculate the kinetic constants. Using a unified method, we obtained a unique set of parameters to describe the kinetics for growth for calcite, vaterite and amorphous calcium carbonate (ACC) and nucleation for vaterite and ACC. Recalculation of the kinetic constants demonstrated that calcite confirmed there are two growth mechanisms within one polymorph, namely pure spiral growth and spiral growth mixed with surface nucleation. The spiral growth does not show second-order growth, which is typically attributed to it. Re-evaluation of the available nucleation data confirmed the suggested existence of a second pure ACC polymorph with a solubility product between 10−5.87 and 10−5.51 mol2 kgw−2.

Content and Distribution of Macroelements, Microelements, and Rare-Earth Elements in Different Tomato Varieties as a Promising Tool for Monitoring the Distinction between the Integral and Organic Systems of Production in Zeleni hit—Official Enza and Vitalis Trial and Breeding Station

The identification of agricultural food production systems has gained importance in order to protect both human health and the environment. The importance of organic production system of agriculture which involves the application of natural processes and substances, and limits or completely eliminates the use of synthesized means is emphasized. Knowledge of the mineral composition in tomato samples can be used as a potent tool in the identification of chemical markers as potential indicators of the farming system. A set of tomato samples taken from two factorial randomized trials were comprehended eight different varieties, belonging to four tomato types: large—BEEF and CLUSTER, and mini and midi—CHERRY and PLUM tomatoes, cultivated under two different farming systems: integral (IPM) and organic (O) were characterized based on the composition of the minerals. A total of 44 elements were quantified. To establish criteria for the classification of the samples and confirm a unique set of parameters of variation among the types of production, sophisticated chemometric techniques were used. The results indicate that the accumulation of elements varies between 8 tomato varieties and 2 different growing systems. The contents of Al, Mn, As, Pb, and some of the rare-earth elements (REEs) are able to distinguish between production types. Examination of different hybrids, which belong to different types in two production systems: organic and integral within Zeleni hit (official Enza and Vitalis trial and breeding station), was done with the aim of reaching a methodology of diversification, ie complete traceability of organic production, and to contribute to distinguishing types of agricultural systems and enhancing the possibility of acquiring a valuable authenticity factor about the type of agricultural production system employed for the cultivation of tomatoes.

High-Performance Biomimetic Water Channel: The Constructive Interplay of Interaction Parameters and Hydrophilic Doping Levels.

In this study, we introduce a superfast biomimetic water channel mimicking the hydrophobicity scales of the Aquaporin (AQP) pore lining. Molecular dynamics simulation is used to scrutinize the impact of hydrophilic doping level in the nanotube and the water-wall interaction strength on water permeability. In the designed biomimetic channel, the constructive interplay of Lennard-Jones (LJ) ε parameters and hydrophilic doping levels increased the possibility of ultrafast water transport. Moreover, a unique set of LJ parameters is discovered for each biomimetic channel with different hydrophilic doping levels, enhancing water permeation. Inside high-performance biomimetic channels, water distribution surprisingly implies a varying pore geometry that narrows down in the middle, mimicking the pattern obtained from GplF pore analysis, evoking the narrow pore induced by the aromatic/arginine selectivity filter. This exciting accordance occurred as a result of tailoring specific hydrophilic arrays within the hydrophobic channel backbone by mimicking the AQP pore interior. The main takeaway of hydrophilic doping arrays implanted within the hydrophobic nanotube is to break the large barrier in the water-wall vdW energy profile into multiple reduced ones to increase water conduction. Consequently, the "water jumping" phenomenon in the middle of the biomimetic channel occurs under specific circumstances. The biomimetic channel with the highest value of water permeability of about 13.67 ± 0.66 × 10-13 cm3·s-1 exhibits the best mechanism for artificial water channels (AWCs), serving superfast water transport considering the low entrance barrier and weak water-wall interaction.

Formalization of tasks generation for complex of interactive web-tests on math

The approach to the formalization of the generation of problem situations applicable to the development of tutoring programs consisting of many tasks is considered. The main errors arising during the software-based generation of parameters are specified. Mathematical modeling of parametrical generation algorithms examined by examples of tasks that make up complex tests on mathematics for secondary schools. The parametric generation method proposed in the article allows getting the large quantitative variations in task problem situations. Thereby, every learner will get a personal unique set of tasks. The structure and functionality of web-tests complex consisting of tasks generated via the proposed method are described. The subject of research in the article is the process of computer training in mathematics. The goal is to develop a method for task generation for mathematical disciplines. Tasks. Research and analysis of the set of mathematical problems. Parameterization of each task and development method and algorithms for automated generation parameters with the determination of incorrect combinations of parameters or problem situations that have no solution. Estimation of borders of admissible for approximate answers. Evaluation of the user solution of a single task and a sequence of tasks. The general objective of the work is to make the software product consisting of a sequence of mathematics tasks. The software should have an extended user interface for the graphical presentation of various problem situations in various mathematical topics. The program must be accessible via the Internet. The following results were obtained: developed methods and algorithms of task generation, which provide correct problem situations and unique parameter sets for each user; described the program complex structure and developed the software system of mathematical web-tests provides two levels of difficulty. Conclusion. The scientific novelty lies in the development of the method of task generation for interactive web tests on the mathematics and its computer implementation with the possibility of graphical representation of tasks and checking of tasks correctness.

Rock-physics machine learning toolkit for joint litho-fluid facies classification and compaction modeling

Correct lithofacies interpretation sourced from wireline log data is an essential source of prior information for joint seismic inversion for facies and impedances, among other applications. However, this information is difficult to interpret or extract manually due to the multivariate and high dimensionality of wireline logs. Facies inference is also challenging for traditional clustering-based approaches because pervasive compaction trends affect a number of petrophysical measurements simultaneously. Another common pitfall in automated clustering approaches is the inability to account for underlying diagenetic processes that correlate with depth. Here, we address these challenges by introducing a rock-physics machine learning toolkit for joint litho-fluid facies classification. The litho-fluid types are inferred from the borehole data within the objective framework of a maximum-likelihood approach for latent facies variables and rock-physics model parameters, explicitly accounting for compaction and depth effects. The inference boils down to an expectation-maximization (EM) algorithm with strong spatial coupling. Each litho-fluid type is associated with an instance of a particular rock-physics model with a unique set of fitting parameters, constrained to a physically reasonable range. These fitting parameters in turn are inferred using bound-constrained optimization as part of the EM algorithm. Outputs produced by the toolkit can be used directly to specify the necessary prior information for seismic inversion, including per-facies rock-physics models and facies proportions. We present an example application of the tool to real borehole data from the North West Shelf of Australia to illustrate the method and discuss its characteristic features in depth.

A multiphase irreversible-compaction model for granular-porous materials

An Eulerian, hyperbolic, multiphase-flow model for dynamic and irreversible compaction of porous materials is constructed. A reversible model for elastic, compressible, porous material is derived. Classical homogenization results are obtained. The irreversible model is then derived in accordance with the following basic principles. First, the entropy inequality is satisfied by the model. Second, the stress coming from the elastic energy decreases in time (the material behaves as Maxwell-type materials). The irreversible model admits an equilibrium state corresponding to a Gurson-type limit which varies with the porosity. The sound velocity at the yield limit is smaller than that of the reversible model. Such an embedded model structure ensures a thermodynamically correct formulation of the porous-material model. The usual model used in the detonation community is recovered. The model is then validated on quasi-static loading–unloading experiments with HMX. The ability of the model to capture strong shock propagation in porous material as well as its ability to deal with interface between a fluid and a porous material is demonstrated and validated on Hugoniot curve of aluminium with various porosities for a unique set of empirical parameters.

Quantifying and simulating carbon and nitrogen mineralization from diverse exogenous organic matters

AbstractThe potential contributions of exogenous organic matters (EOMs) to soil organic C and mineral N supply depend on their C and N mineralization, which can be assessed in laboratory incubations. Such incubations are essential to calibrate decomposition models, because not all EOMs can be tested in the field. However, EOM incubations are resource‐intensive. Therefore, easily measurable EOM characteristics that can be useful to predict EOM behaviour are needed. We quantified C and N mineralization during the incubation of 663 EOMs from five groups (animal manures, composts, sewage sludges, digestates and others). This represents one of the largest and diversified set of EOM incubations. The C and N mineralization varied widely between and within EOM subgroups. We simulated C and N mineralization with a simple generic decomposition model. Three calibration methods were compared. Individual EOM calibration of the model yielded good model performances, while the use of a unique parameter set per EOM subgroup decreased the model performance, and the use of two EOM characteristics to estimate model parameters gave an intermediate model performance (average RMSE‐C values of 32, 99 and 65 mg C g−1 added C and average RMSE‐N values of 50, 126 and 110 mg N g−1 added N, respectively). Because of the EOM variability, individual EOM calibration based on incubation remains the recommended method for predicting most accurately the C and N mineralization of EOMs. However, the two alternative calibration methods are sufficient for the simulation of EOMs without incubation data to obtain reasonable model performances.

A Unified Theory for 3D Gear and Thread Metrology

We propose a unified theory for the metrological treatment of helical machine elements such as cylindrical and conical gears, worms, and screw threads. The main idea is to introduce a universal 3D geometry model for threaded components that provides for distinct parameterization using a unique set of geometry parameters and that offers and a functional description of the transverse profile. Using modern 3D coordinate measuring technology, a holistic evaluation algorithm yields the actual geometry as the result of a high dimensional best-fit procedure and form deviations as corresponding residuals. All determinants and evaluation parameters can then be calculated from the set of actual geometry parameters. By applying certain constraints to the model to be fitted, the novel method can be reduced to the established 2D methods and hence meets demands for the comparison of the two procedures. The results of the novel approach have proven to be very stable and they enable the evaluation of areal measurements with no loss of information.

Global sensitivity and uncertainty analysis of a sugarcane model considering the trash blanket effect

The deterministic approach in crop modeling simplifies uncertainty present in the environment using a unique parameter set. In practice, this uncertainty is seen in the variability of data collected in a field experiment. One way to exploit this uncertainty is to use the stochastic approach, by inserting the range of plausible variability into the simulation’s parameters and inputs. This study aims to evaluate the ability of a process-based crop model to simulate the uncertainty of a sugarcane field. We employed the recently updated version of SAMUCA model to simulate the sugarcane growth and development in a 4-year field experiment, where the crop was grown under the effect of green cane trash blanket (GCTB) and bare soil (Bare). To analyze the effect of genotype and soil variability on output variables, a stochastic approach was applied to the corresponding parameters of the SAMUCA model. A global sensitivity analysis was utilized to prioritize and identify the most important parameters to explain the model uncertainty. Then, the uncertainty was analyzed in three different ways: uncertainty analysis only for genotype parameters (UG), uncertainty analysis only for soil parameters (US) and the analysis of both soil and genotype parameters (UGS). We quantified the variability of the stochastic simulation by the ratio between the average of the standard deviation of the simulations and the average of the standard deviation of the observed data. The variability observed in the field is not fully explained by the hydraulic parameters of the soil, possibly due to irrigation and good rainfall distribution in the area. Furthermore, the variability in US simulations were higher for GCTB than in Bare treatment, suggesting that the GCTB has a larger influence in SAMUCA’s variability than for the hydraulic parameters in the conditions of this study. The UG and UGS had the same capacity to quantify the variability present in the environment for the treatments Bare and GCTB. In this case, sensitivity to soil parameters can simply be ignored and genotype parameters can be chosen as the only source of variability for practical applications. Our suggestion for future work is to explore environments without irrigation, different amounts of GCTB and other soil parameters present in the model.

Topologically driven three-spin chiral exchange interactions treated from first principles

The mechanism behind the three-spin chiral interaction (TCI) included in the extended Heisenberg Hamiltonian and represented by an expression worked out recently (Phys.\ Rev.\ B, {\bf 101}, 174401 (2020)) is discussed. It is stressed that this approach provides a unique set of the multispin exchange parameters which are independent of each other either due to their different order of perturbation or due to different symmetry. This ensures in particular the specific properties of the TCI that were demonstrated previously via fully relativistic first principles calculations, and that result from the common influence of several issues not explicitly seen from the expression for the TCI parameters. Therefore, an interpretation of the TCI is suggested, showing explicitly its dependence on the relativistic spin-orbit coupling and on the topological orbital susceptibility (TOS). This is based on an expression for the TOS that is worked out on the same footing as the expression for the TCI. Using first-principles calculations we demonstrate in addition numerically the common topological properties of the TCI and TOS. To demonstrate the role of the relativistic spin-orbit coupling (SOC) for the TCI, a so-called topological' spin susceptibility (TSS) is introduced. This quantity characterizes the SOC induced spin magnetic moment on the atom in the presence of non-collinear magnetic structure, giving a connection between the TOS and TCI. Numerical results again support our conclusions.

A new framework to determine general multimodal soil water characteristic curves

A soil water characteristic curve (SWCC) model named as discrete-continuous multimodal van Genuchten model with a convenient parameter calibration method is developed to describe the relationship between soil suction and the water content of a soil with complex pore structure. The modality number N of the SWCC in the proposed model can be any positive integer (the so-called multimodal or N-modal SWCC). A unique set of parameters is determined by combining curve fitting and a graphical method based on the shape features of the SWCC in the log s–log Se plane. In addition, a modality number reduction method is proposed to minimize the number of parameters and simplify the form of SWCC function. The proposed model is validated using a set of bimodal and trimodal SWCC measurements from different soils, which yield a strong consistency between the fitted curves and the measured SWCC data. The uniqueness in the set of parameters provides the possibility to further improve the proposed model by correlating the parameters to soil properties and state parameters.

Data driven modelling and simulation of wetland dynamics

ABSTRACT A wetland is an ecosystem, which is inundated by water. Several biogeochemical processes related to wetlands place it into an important environmental system class. This study employs for the first time, a data-driven methodology, specifically one based on system identification, to develop a nonlinear-autoregressive-exogenous-input model structure corresponding to the tropical wetland dynamics, as an alternative to the classical topographic modelling approaches. Altogether 94 wetland sites from Amazon, Africa, and Asia constitute the model structure development. The topographical variations among the wetlands entail a unique set of model parameters for each wetland site, which is achieved by using the approximate Bayesian computation sequential Monte Carlo method. The performance of the single model structure is subsequently evaluated by simulating all the sample wetland sites. The obtained wetland model structure in this study can be used to parameterise any tropical wetland site as well as simulate their dynamical characteristics.

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

We introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based on coarse-grained atomistic simulation data of mechanical deformation and flow processes. The proposed framework utilizes a hypothesized coarse-graining methodology with manifold learning and surrogate-based optimization techniques. Coarse-grained high-dimensional data describing quantities of interest of the multiscale models are projected onto a nonlinear manifold whose geometric and topological structure is exploited for measuring behavioral discrepancies in the form of manifold distances. A surrogate model is constructed using Gaussian process regression to identify a mapping between stochastic parameters and distances. Derivative-free optimization is employed to adaptively identify a unique set of parameters of the upper-scale model capable of rapidly reproducing the system’s behavior while maintaining consistency with coarse-grained atomic-level simulations. The proposed method is applied to learn the parameters of the shear transformation zone (STZ) theory of plasticity that describes plastic deformation in amorphous solids as well as coarse-graining parameters needed to translate between atomistic and continuum representations. We show that the methodology is able to successfully link coarse-grained microscale simulations to macroscale observables and achieve a high-level of parity between the models across scales.

Deriving physical and unique bimodal soil Kosugi hydraulic parameters from inverse modelling

Hydraulic parameters define the water retention, θ(ψ), and the unsaturated hydraulic conductivity, K(θ), functions. These functions are usually obtained by fitting experimental data using inverse modelling. The drawback of inverting the hydraulic parameters is that they suffer from non-uniqueness and the optimal hydraulic parameters may not be physical. To reduce the non-uniqueness, it is necessary to invert the hydraulic parameters simultaneously from observations of θ(ψ) and K(θ), and ensure the measurements cover the full range of θ from saturated to oven dry. The challenge of using bimodal θ(ψ) and K(θ) compared to unimodal functions is that it requires double the number of parameters, one set for the matrix and another set for the macropore domain. The objective of this paper is to address this shortcoming by deriving a procedure to reduce the number of parameters to be optimized to obtain a unique physical set of bimodal soil Kosugi hydraulic parameters from inverse modelling. To achieve this, we (1) derive residual volumetric soil water content from the Kosugi standard deviation parameter of the soil matrix, (2) derive macropore hydraulic parameters from the water pressure head threshold between macropore and matrix flow, and (3) dynamically constrain the Kosugi hydraulic parameters of the soil matrix. The procedure successfully reduces the number of optimized hydraulic parameters and dynamically constrains the hydraulic parameters without compromising the fit of the θ(ψ) and K(θ) functions, and the derived hydraulic parameters are more physical. The robustness of the methodology is demonstrated by deriving the hydraulic parameters exclusively from θ(ψ) and Ks data, enabling satisfactory prediction of K(θ) even when no additional K(θ) data are available.

In vivo parameter identification in arteries considering multiple levels of smooth muscle activity

In this paper an existing in vivo parameter identification method for arteries is extended to account for smooth muscle activity. Within this method a continuum-mechanical model, whose parameters relate to the mechanical properties of the artery, is fit to clinical data by solving a minimization problem. Including smooth muscle activity in the model increases the number of parameters. This may lead to overparameterization, implying that several parameter combinations solve the minimization problem equally well and it is therefore not possible to determine which set of parameters represents the mechanical properties of the artery best. To prevent overparameterization the model is fit to clinical data measured at different levels of smooth muscle activity. Three conditions are considered for the human abdominal aorta: basal during rest; constricted, induced by lower-body negative pressure; and dilated, induced by physical exercise. By fitting the model to these three arterial conditions simultaneously a unique set of model parameters is identified and the model prediction agrees well with the clinical data.

MODELING TIME-DEPENDENT DEFORMATIONS OF CONCRETE MEMBERS SUBJECTED TO SYMMETRIC AND ASYMMETRIC DRYING

The time-dependent behavior of concrete members with uniform cross-section subjected to symmetric drying can be predicted with one of many models anchored in the design codes. Since these models use the cross-sectional approach, only the average quantities can be obtained. More complex phenomena can be investigated by means of a coupled hygro-mechanical finite element analysis. Owing to insufficient or missing experimental data, the input parameters for the moisture transport model are often tuned to match the development of axial shrinkage or moisture loss measured on small-scale laboratory specimens. In this paper it is assessed whether this approach leads to a unique set of parameters of the Bažant-Najjar model for moisture diffusion. Additionally, for the selected sets of parameters the analysis is repeated with modified size and/or boundary conditions leading to asymmetric drying and the results are discussed.

Undrained cyclic response of a dense saturated sand with various grain sizes and contents of nonplastic fines: Experimental analysis and constitutive modeling

The densification can increase the liquefaction resistance of sands. However, in most engineering practice, sands are not pure and contain a significant amount of fines with particle diameter smaller than 0.075 mm. Thus, it is necessary to quantitatively evaluate the influence of fines on the undrained cyclic behavior of dense sandy soils. In this study, a series of undrained cyclic triaxial tests were conducted on the dense saturated sand with various mean grain sizes and contents of nonplastic fines. A unified constitutive framework is modified to improve the capability of simulating the cyclic mobility behavior of sandy soils. The results indicate that the effect of the mean grain size on the cyclic stress ratio required to cause 5% double-amplitude axial strain for sandy soils depends on the amount of nonplastic fines added. Moreover, a good correlation between the cyclic resistance ratio and the initial value of the equivalent granular state parameter is obtained for various fines contents and mean grain sizes of nonplastic fines. The influence of the fines content and mean grain size of nonplastic fines on the liquefaction resistance of sandy soils can be reasonably characterized by the constitutive framework using a set of unique parameters. Additionally, the capability of the constitutive framework for describing the influence of the fines content on the cyclic instability behavior of sandy soils is also evaluated against the experimental data in the literature.

The historical case of Paricutin volcano (Michoacán, México): challenges of simulating lava flows on a gentle slope during a long-lasting eruption

Simulating lava flows on a gentle slope is complex since they can propagate in a wide range of directions. It is an even greater challenge to define lava flow trajectories when an eruption lasts over several years and flows cool down, changing the surrounding topography. In this study, we test Q-LavHA, an open source plug-in that simulates lava flow inundation calculating its probability, and using Paricutin’s eruption (1943–1952) in central Mexico as a case study. We have appropriately calibrated the Q-LavHA plug-in for the Paricutin case study, which provides insights on how to better model lava flows in gentle terrain. From this work, we observe that each phase is characterized by a unique set of parameters requiring a careful calibration and that low-relief topographies require special consideration. Our findings could be useful for real-time hazard evaluation in future volcanic scenarios in the Michoacán–Guanajuato volcanic field and elsewhere, where new monogenetic eruptions similar to Paricutin can be expected.

Low-dimensional offshore wave input for extreme event quantification

In offshore engineering design, nonlinear wave models are often used to propagate stochastic waves from an input boundary to the location of an offshore structure. Each wave realization is typically characterized by a high-dimensional input time-series, and a reliable determination of the extreme events is associated with substantial computational effort. As the sea depth decreases, extreme events become more difficult to evaluate. We here construct a low-dimensional characterization of the candidate input time series to circumvent the search for extreme wave events in a high-dimensional input probability space. Each wave input is represented by a unique low-dimensional set of parameters for which standard surrogate approximations, such as Gaussian processes, can estimate the short-term exceedance probability efficiently and accurately. We demonstrate the advantages of the new approach with a simple shallow-water wave model based on the Korteweg–de Vries equation for which we can provide an accurate reference solution based on the simple Monte Carlo method. We furthermore apply the method to a fully nonlinear wave model for wave propagation over a sloping seabed. The results demonstrate that the Gaussian process can learn accurately the tail of the heavy-tailed distribution of the maximum wave crest elevation based on only $$1.7\%$$ of the required Monte Carlo evaluations.