Space Variables Research Articles

Machine learning inspired workflow to revise field development plan under uncertainty

We present an efficient and risk-informed closed-loop field development (CLFD) workflow for recurrently revising the field development plan (FDP) using the accrued information. To make the process practical, we integrated multiple concepts of machine learning, an intelligent selection process to discard the worst FDP options and a growing set of representative reservoir models. These concepts were combined and used with a cluster-based learning and evolution optimizer to efficiently explore the search space of decision variables. Unlike previous studies, we also added the execution time of the CLFD workflow and worked with more realistic timelines to confirm the utility of a CLFD workflow. To appreciate the importance of data assimilation and new well-logs in a CLFD workflow, we carried out researches at rigorous conditions without a reduction in uncertainty attributes. The proposed CLFD workflow was implemented on a benchmark analogous to a giant field with extensively time-consuming simulation models. The results underscore that an ensemble with as few as 100 scenarios was sufficient to gauge the geological uncertainty, despite working with a giant field with highly heterogeneous characteristics. It is demonstrated that the CLFD workflow can improve the efficiency by over 85% compared to the previously validated workflow. Finally, we present some acute insights and problems related to data assimilation for the practical application of a CLFD workflow.

System for automated design of resonant inverters

In the MATLAB environment, with the help of author's code, a programming environment is developed in which the input parameters are entered, the type and structure of the inverter is selected, and the values of the circuit elements are automatically calculated. Finally, for the purpose of verification, the operation of the designed inverter is simulated. From the resulting simulation, the overall behavior of the state space variables is seen.

Robust Bayesian optimization for flexibility analysis of expensive simulation-based models with rigorous uncertainty bounds

The performance of emerging biochemical systems rests on their potential to adapt to uncertainties quickly and accurately. Flexibility analysis is a quantitative framework for determining if a system can maintain safe and feasible operation despite uncertainty. Most available methods assume access to equation-oriented models, which can be difficult to obtain in practice. In this paper, we propose a sequential black-box flexibility analysis method, BoFlex, that overcomes this challenge by constructing probabilistic surrogate models over the joint space of uncertain and recourse variables. BoFlex is based on a special alternating confidence bound procedure, which we show finitely converges to a correct solution under mild assumptions on the unknown functions. We also establish a rigorous upper bound on the convergence rate in terms of the maximum information gain of the surrogate model. The advantages of BoFlex are demonstrated on several case studies including a heat exchanger network and a bubble column reactor.

Conditional aggregation operators defined by the Choquet integral and the Sugeno integral with respect to general fractal measures

Conditional aggregation operators are defined by the Choquet integral and the Sugeno integral with respect to a monotone set function that assesses positive measure of the conditioning set. General Hausdorff and packing measures are introduced and examples of infinite s-sets with positive and finite generalized Hausdorff and packing measures are constructed and their fractal dimensions are compared. Coherent upper conditional previsions on the linear space of all Choquet integrable random variables are defined by the Choquet integral with respect to the general Hausdorff and packing measures when the conditioning event has positive and finite generalized Hausdorff and packing measures in its respective fractal dimensions. Conditional aggregation operators are defined by the Sugeno integral with respect to general Hausdorff and packing measures on the class of all Sugeno integrable random variables. Actually, the general Hausdorff and packing dimensions are proven to be the Sugeno integral with respect to the Lebesgue measure of the general Hausdorff and packing measures respectively.

Investigating the controls on architecture and facies distribution of a carbonate ramp to shelf system: Insights from stratigraphic forward modeling of the Jurassic Smackover Formation, Gulf of Mexico

To develop a better understanding of the controls on the stratigraphic architecture of carbonate systems, it is necessary to quantify the impact of the physical and chemical variables responsible. The growth, distribution, and nature of carbonate factories are dependent on various space and time-dependent variables, such as paleotopography, accommodation, sediment supply, energy and slope of the system, and water chemistry.The Upper Jurassic Smackover Formation is a heterogeneous hydrocarbon source rock and reservoir in the semi-restricted Conecuh Embayment of southwestern Alabama. It consists of a lower transgressive unit composed of microbialite and mudstone/wackestone, and an upper regressive mixed carbonate-siliciclastic unit composed of oolitic packstone-grainstone, microbialite, bioturbated to laminated carbonate mudstone, and shale. The physiographic setting of the Conecuh Embayment, along with the variability in the carbonate factory types, eustatic sea level fluctuations, and sea-water circulation presents an exceptional site for evaluating the controls on carbonate platform/shelf margin to slope architecture.Stratigraphic forward modeling using DionisosFlow, calibrated with a geologic cross-section built from well log and core data, was carried out to test the impact of antecedent topography, accommodation, and carbonate factory type on the facies distribution and architecture of the carbonate shelf. Results of our study indicate that geometries in the transgressive Smackover shelf are controlled by accommodation and rate of carbonate production, while limited accommodation controlled the stratigraphic architecture of the regressive system.

Quantitative detection of zearalenone in wheat using intervals selection coupled to near-infrared spectroscopy

Wheat is a globally cultivated cereal crop. However, it is susceptible to Fusarium head blight, which leads to the production of zearalenone (ZEN), significantly impacting the value of wheat. This paper proposed a novel technique for quantitatively detecting ZEN in wheat using near-infrared spectroscopy. The absorbance of wheat flour samples with varying degrees of mildew were collected using a spectral detection system based on the spectrometer. And this paper introduced two methods for feature intervals selection, interval combination optimization (ICO) and interval variable iterative space shrinkage approach (iVISSA), combined with partial least squares (PLS) and support vector machine (SVM) established a regression model based on feature intervals selection for quantitatively detecting ZEN in wheat, and then compared the performance of them. The research results showed that the SVM model outperformed the PLS model in terms of detection performance. Additionally, the SVM model detecting the level of ZEN in wheat based on the feature intervals selected by ICO achieved the best generalization performance, with a root mean square error of prediction (RMSEP) of 31.4148 μg·kg−1, a coefficient of determination of prediction (RP2) of 0.9434, and a relative percent deviation (RPD) of 4.2768. These results demonstrated that the SVM regression model, established by using ICO for selecting the near-infrared spectral intervals, can accurately detect ZEN in wheat. Near-infrared spectroscopy combined with appropriate data analysis methods, which can be used as a simple and fast tool for quantitative detection of mycotoxins of wheat and other grains.

A high‐order discontinuous Galerkin approach for physics‐based thermospheric modeling

AbstractThe accurate prediction of aerodynamic drag on satellites orbiting in the upper atmosphere is critical to the operational success of modern space technologies, such as satellite‐based communication or navigation systems, which have become increasingly popular in the last few years due to the deployment of constellations of satellites in low‐Earth orbit. As a result, physics‐based models of the ionosphere and thermosphere have emerged as a necessary tool for the prediction of atmospheric outputs under highly variable space weather conditions. This paper proposes a high‐fidelity approach for physics‐based space weather modeling based on the solution of the Navier–Stokes equations using a high‐order discontinuous Galerkin method, combined with a matrix‐free strategy suitable for high‐performance computing on GPU architectures. The approach consists of a thermospheric model that describes a chemically frozen neutral atmosphere in nonhydrostatic equilibrium driven by the external excitation of the Sun. A novel set of variables is considered to treat the low densities present in the upper atmosphere and to accommodate the wide range of scales present in the problem. At the same time, and unlike most existing approaches, radial and angular directions are treated in a nonsegregated approach. The study presents a set of numerical examples that demonstrate the accuracy of the approximation and validate the current approach against observational data along a satellite orbit, including estimates of established empirical and physics‐based models of the ionosphere‐thermosphere system. Finally, a one‐dimensional radial derivation of the physics‐based model is presented and utilized for conducting a parametric study of the main thermal quantities under various solar conditions.

Effects of marine climate variability on the relative abundance of Lutjanus purpureus (POEY, 1866) on the Amazon Continental Shelf

AbstractThe aim of this study was to analyze the effects of climate and marine variability on the catches of Lutjanus purpureus in three sectors of the Amazon Continental Shelf. Remote sensing data were compared with landings (CPUE) between 1997 and 2007 and analyzed for partial influence obtained through the Generalized Additive Model. Additionally, significant variables were analyzed through Wavelet Cross‐Spectrum, and periods of high correlation between variables in space and time were identified. The results indicated a high coherence between catch per unit effort (CPUE) and environmental variables on an interannual scale, suggesting that Red Snapper fishing is mainly influenced by the seasonal effects of oceanographic variables. These results suggest that fluctuations in Red Snapper catches between 1997 and 2007 may be the result of natural processes that occur in the Amazon Continental Shelf every year in synergy with environmental variables, which are also indirectly affected by the El Niño–Southern Oscillation (ENSO) pattern.

An efficient mixed-variable generation operator for integrated energy system configuration optimization

Energy consumption has skyrocketed as society has progressed, resulting in energy crises and environmental issues. New energy technologies are being developed to address these challenges, and technologies such as the cogeneration of electricity, heating, and cooling are being used to improve energy utilization. The configuration of an energy system has a critical impact on the economics of the system, its ability to supply energy, etc. This study establishes a multiobjective mixed-variable configuration optimization model for a comprehensive combined cooling, heating, and power energy system. The model considers equipment types (categorical variables), equipment quantities (integer variables), and critical parameters (real variables) as optimization variables, where economic performance, the proportion of renewable energy, supply shortage, and dependence on the external power grid are considered unoptimized objectives. This is a multiobjective and mixed-variable problem, and the combination of the two properties poses an excellent challenge for evolutionary algorithms in optimizing the model. Therefore, we propose an efficient generating operator based on a fully connected weighted neural network, called the FCWN operator. This operator overcomes the challenge of the discrete variable space and broken neighborhood relationships in mixed-variable problems. During the algorithm search process, search history information is used to update the fully connected weight network for distribution estimation of the variable space. Offspring are generated based on the fully connected network to improve the algorithm’s search efficiency. In the experimental section, we construct a model with 20 variables and conduct simulation-based configuration optimization for scenarios with 3, 5, and 8 available equipment types. The final obtained Pareto frontier solution set is evaluated using the hypervolume metric, a widely used multiobjective evaluation metric. The Wilcoxon rank sum test on the experimental results shows that the proposed algorithm has better results than other state-of-the-art algorithms at a 95% confidence level.

Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI.

Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery.

Characterizing linearizable QAPs by the level-1 reformulation-linearization technique

The quadratic assignment problem (QAP) is an extremely challenging NP-hard combinatorial optimization program. Due to its difficulty, a research emphasis has been to identify special cases that are polynomially solvable. Included within this emphasis are instances which are linearizable; that is, which can be rewritten as a linear assignment problem having the property that the objective function value is preserved at all feasible solutions. Various known sufficient conditions for identifying linearizable instances have been explained in terms of the continuous relaxation of a weakened version of the level-1 reformulation-linearization-technique (RLT) form that does not enforce nonnegativity on a subset of the variables. Also, conditions that are both necessary and sufficient have been given in terms of decompositions of the objective coefficients. The main contribution of this paper is the identification of a relationship between polyhedral theory and linearizability that promotes a novel, yet strikingly simple, necessary and sufficient condition for identifying linearizable instances; specifically, an instance of the QAP is linearizable if and only if the continuous relaxation of the same weakened RLT form is bounded. In addition to providing a novel perspective on the QAP being linearizable, a consequence of this study is that every linearizable instance has an optimal solution to the (polynomially-sized) continuous relaxation of the level-1 RLT form that is binary. The converse, however, is not true so that the continuous relaxation can yield binary optimal solutions to instances of the QAP that are not linearizable. Another consequence follows from our defining a maximal linearly independent set of equations in the lifted RLT variable space; we answer a recent open question that the theoretically best possible linearization-based bound cannot improve upon the level-1 RLT form.

Multisoliton complex systems with explicit superpotential interactions

We consider scalar field theories in dimensions with N fields which interact through a potential , which is defined in terms of an explicit superpotential W. We construct W for any N in terms of a known superpotential w for a single-scalar model, such as that for the sine-Gordon equation or the ϕ 4 model, leading to an expression for V which has multiple minima that supports solitons. Static solitons which minimize the total energy in each soliton sector appear as solutions of first-order Bogomolny equations, which have a gradient structure. These are identical in form to equations which arise in the context of synchronization phenomena in complex systems, with the space and time variables interchanged. The sine-Gordon superpotential, for example, leads to an explicit periodic superpotential W for N scalar fields, with associated Bogomolny equations that are equivalent to the well-known Kuramoto equations which describe the synchronization of identical phase oscillators on the unit circle. The known asymptotic properties of the Kuramoto system, for both positive and negative coupling constants, ensure that finite-energy solitons exist for any given set of intermediate values imposed at the origin. Besides the models derived from the sine-Gordon equation, we investigate ϕ 4 and ϕ 6 models with N scalar fields and show numerically that solitons again exist over a wide range of parameters. We also derive general properties of the elementary meson excitations of the system, in particular we show that meson-soliton bound states exist over a restricted range of mass parameters with respect to an exact solution of the ϕ 6 system for N = 3.

Transient responses of two temperature, fractional thermo-diffusive-elastic half space with temperature dependent properties

In scientific disciplines such as control engineering, astrophysics, geomechanics, nuclear physics, etc., thermoelastic diffusion plays a crucial role. When physical processes involve high temperature, considering two temperature theories and temperature-dependent material properties contribute to more rational results. The inclusion of fractional calculus in such studies provides results with significant accuracy. In this light, a model is formulated under two temperatures, fractional order Lord Shulman and Green Lindsay's theories of generalized thermo-diffusive-elasticity. The medium under consideration is a half space which is kept unstressed initially at a uniform temperature The material properties are considered to be a function of temperature. Governing equations and constitutive relations of the model are non-dimensionalized and simplified using potential functions. The field variables are determined analytically in the closed form in Laplace-Fourier transformed domain. Numerical computations are conducted with the assistance of MATLAB software. The study aims to observe the behavior of physical quantities for different values of two temperature, temperature-dependent, and fractional order parameters. Graphical representation is displayed with respect to space variables x and z. The results reveal that physical quantities express varying degrees of influence corresponding to different effects involved in the study.

The UTUH model: a time-dependent unified hardening constitutive model for unsaturated soils

AbstractAn advanced constitutive framework for unsaturated soils, the UTUH model, is proposed in this paper, which considers the joint effect of time, suction and overconsolidation within the framework of sub-loading surface plasticity. A reference line, namely the instantaneously normal compression line (INCLs) for unsaturated soils, is introduced from a conceptual framework drawn from constant rates of strain test results to determine creep time and overconsolidation states of unsaturated soils. Subsequently, an isotropic elasto-viscoplastic constitutive model for unsaturated soils is produced by combining viscous deformation with mechanical and hydraulic deformation through overconsolidation parameter. Net stress, suction and time are adopted as fundamental constitutive variables and time-dependent loading collapse yield surface is derived to characterize the relationship between yield stress, suction, and time. Then, an extension to a triaxial stress state is built in the space of mean effective stress, suction, deviator stress and time variable. The hardening of yield surface and sub-loading surface is controlled by viscoplastic volumetric strain and unified hardening parameter. The performance of the proposed UTUH model is addressed through four numerical studies, and the proposed model is validated against experimental data from the literature.

An elementary proof of the dual representation of Expected Shortfall

We provide an elementary proof of the dual representation of Expected Shortfall on the space of integrable random variables over a general probability space. Unlike the results in the extant literature, our proof only exploits basic properties of quantile functions and can thus be easily implemented in any graduate course on risk measures. As a byproduct, we obtain a new proof of the subadditivity of Expected Shortfall.

Insights in Hierarchical Clustering of Variables for Compositional Data

R-mode hierarchical clustering is a method for forming hierarchical groups of mutually exclusive subsets of variables. This R-mode cluster method identifies interrelationships between variables which are useful for variable selection and dimension reduction. Importantly, the method is based on metric elements defined on the sample space of variables. Consequently, hierarchical clustering of compositional parts should respect the particular geometry of the simplex. In this work, the connections between concepts such as distance, cluster representative, compositional biplot, and log-ratio basis are explored within the framework of the most popular R-mode agglomerative hierarchical clustering methods. The approach is illustrated in a paleoecological study to identify groups of species sharing similar behavior.

Superconvergence Analysis of Discontinuous Galerkin Methods for Systems of Second-Order Boundary Value Problems

In this paper, we present an innovative approach to solve a system of boundary value problems (BVPs), using the newly developed discontinuous Galerkin (DG) method, which eliminates the need for auxiliary variables. This work is the first in a series of papers on DG methods applied to partial differential equations (PDEs). By consecutively applying the DG method to each space variable of the PDE using the method of lines, we transform the problem into a system of ordinary differential equations (ODEs). We investigate the convergence criteria of the DG method on systems of ODEs and generalize the error analysis to PDEs. Our analysis demonstrates that the DG error’s leading term is determined by a combination of specific Jacobi polynomials in each element. Thus, we prove that DG solutions are superconvergent at the roots of these polynomials, with an order of convergence of O(hp+2).

Optimal deployment strategy of airships for near‐space communication system using multiobjective advanced evolutionary algorithm

SummaryThis paper presents a near‐space communication system (NSCS) using advanced deployment strategies to gain high throughput. The airships are deployed according to the user's location, assuming robust backbone network characteristics such as signal path loss, fading factor, routing efficiency, and safety issues among bi‐connected airships. Due to the independent flying nature of airships, it is very attractive to deploy them as aerial base stations and construct airborne networks to provide service for on‐ground users. However, it is quite challenging to optimally deploy multiple airships for on‐demand coverage while maintaining the connectivity among airships. A balance between the network parameters, i.e., capacity and coverage area, should be maintained for optimal deployment of the airships. We have derived the maximum throughput of NSCS, including system parameters, as a multiobjective optimization problem subjected to efficient routing protocol and safety constraints. A decomposition‐based advanced multiobjective evolutionary algorithm (AMOEA/D) is adopted to solve the deployment optimization problem. The proposed algorithm is motivated by the non‐dominated solutions that maintain population diversity over the variable space. Two designed test problems, that is, the L‐shaped hotspot problem and nine hotspot problems, are also investigated. Numerical results show that the proposed method improves the system performance compared with benchmark external archive‐guided MOEA/D (EGA‐MOEA/D) and non‐dominated sorted genetic algorithms (NSGA‐II) by 10.46% and 3.84%, respectively.

The turbulence models review for aerodynamic, thermal and structural problems in jet engine turbines

In the processes of their processing to obtain finished products, the internal structure and the properties of various scale levels of the material that depend on it change significantly, the prediction of which using empirical methods is extremely costly. For this reason, the development of processing technologies is unthinkable without mathematical models, the central element of which is constitutive relations. A promising approach for constructing the latter is the physical approach, which makes it possible to describe the evolution of the internal structure at various scale levels. As a rule, such models of materials are multilevel and represent a whole class of constitutive models. Of particular interest are elastoplastic models, since they allow calculations with sufficiently large time steps. Most models of this type are based on the Taylor - Bishop - Hill model. Models of this type have a drawback: uncertainty in the choice of a set of active slip systems. This uncertainty is due to the fact that the dislocation shear rates along slip systems are determined from a system of equations, the dimension of which may exceed the dimension of the space of independent variables being sought. In this paper, we study the influence of a random choice of sets of active slip systems in elastoplastic models of the Taylor - Bishop - Hill type on the results of mod-eling the elastoplastic deformation of polycrystals with a face-centered cubic lat-tice. The deviation in the simulation results was considered for a random choice of sets of active slip systems. For this purpose, special metrics were introduced to show the effect of uncertainty on the macrolevel stress tensor and misorientation angles between crystallites. It is shown that with increasing strains, a random choice of sets of active slip systems leads to an almost monotonous increase in differences in the calculation of the macrolevel stress tensor and misorientation angles between grains. This fact means that the problem of uncertainty in the choice of active slip systems inherent in these models affects the simulation re-sults. The indicated discrepancy between the simulation results evidences the need to develop an algorithm to eliminate the noted uncertainty, which is the sub-ject of the forthcoming publication of the authors.

Concavity principles for nonautonomous elliptic equations and applications

In the study of concavity properties of positive solutions to nonlinear elliptic partial differential equations the diffusion and the nonlinearity are typically independent of the space variable. In this paper we obtain new results aiming to get almost concavity results for a relevant class of anisotropic semilinear elliptic problems with spatially dependent source and diffusion.