Explicit Dependency Research Articles

Uncertainty Quantified Parametrically Homogenized Constitutive Models for Microstructure-Integrated Structural Simulations

This paper investigates the role of material microstructures in structural analysis and establishes the need for microstructure-integrated constitutive models in predicting structural response. A focus is on microstructure and temperature dependency of stresses and plastic strains in a structural panel under realistic loading conditions. Structural analysis is conducted using the recently developed uncertainty-quantified parametrically homogenized constitutive model (UQ-PHCM) for near-alpha Titanium alloys like Ti6242S. PHCMs exhibit explicit microstructural dependency and are developed from homogenization of crystal plasticity finite element simulation results with machine learning. Uncertainties due to model reduction, data sparsity and microstructural variability are accounted for in the model. Structural response with the UQ-PHCMs is compared to those predicted by isotropic elasticity and $${J}_2$$ plasticity models without explicit microstructure information. Parametric studies illustrate how different uncertainties in the UQ-PHCM framework propagate to the structural response variables. The results also show the relative contribution of different microstructural features to the propagated uncertainty in structural response variables. The studies establish the UQ-PHCM as an effective tool for reliable structural analysis with consequences in material design.

Semiclassical theory of resonant dissociative excitation of molecular ions by electron impact

We have developed a semiclassical approach to the description of resonant dissociative excitation of a molecular ion induced by collisions with plasma free electrons and accompanied by the non-adiabatic transitions between its different electronic terms. It is based on the quasistatic treatment of nuclear particles relative motion in a molecular ion combined with the approximation of a quasicontinuum for rovibrational states. We have derived the semianalytic expressions for the Boltzmann-averaged cross sections, , and rate constants, α de(T, T e), of this process. The resulting expressions for and α de(T, T e) with explicit dependencies on the electron energy, ɛ, the gas, T, and electron, T e, temperatures are valid when the thermal energy k B T exceeds the value of the lowest vibrational quantum, ℏω e, of a molecular ion. The theory developed is applied to studying the resonant dissociative excitation of homo-nuclear , and ions as well as the hetero-nuclear rare gas ions RgXe+ (Rg = Ne, Ar, and Kr) with significantly different values of the dissociation energy. The efficiencies of the electron-impact dissociative excitation process and dissociative recombination leading to the population of the Rydberg states are compared. Our results of calculations are in good agreement with the available experimental data.

A novel Omega-driven dynamic PANS model

A novel Omega (Ω) -driven dynamic partially-averaged Navier-Stokes (PANS) model is proposed in this paper. The ratio of the modeled-to-total turbulent kinetic energies fk is dynamically adjusted by the rigid vorticity ratio (the ratio of the rigid vorticity to the total vorticity), the key parameter of the Ω vortex identification method. Three classical flow cases with rotation and curvature are used to test the model. The results show that the turbulent viscosity is effectively adjusted by the new dynamic fk and the LES-like mode is activated, which can help the revelation of more turbulence information and improve the prediction accuracy. The new PANS model does not contain any explicit dependency on the grid size and enjoys good adaptability to the flow fields, and can be used for efficient engineering computations of the turbulent flows in the hydraulic machinery.

Scale-Resolving Simulation of a Propane-Fuelled Industrial Gas Turbine Combustor Using Finite-Rate Tabulated Chemistry

The scale-resolving simulation of a practical gas turbine combustor is performed using a partially premixed finite-rate chemistry combustion model. The combustion model assumes finite-rate chemistry by limiting the chemical reaction rate with flame speed. A comparison of the numerical results with the experimental temperature and species mole fraction clearly showed the superiority of the shear stress transport, K-omega, scale adaptive turbulence model (SSTKWSAS). The model outperforms large eddy simulation (LES) in the primary region of the combustor, probably for two reasons. First, the lower amount of mesh employed in the simulation for the industrial-size combustor does not fit the LES’s explicit mesh size dependency requirement, while it is sufficient for the SSTKWSAS simulation. Second, coupling the finite-rate chemistry method with the SSTKWSAS model provides a more reasonable rate of chemical reaction than that predicted by the fast chemistry method used in LES simulation. Other than comparing with the LES data available in the literature, the SSTKWSAS-predicted result is also compared comprehensively with that obtained from the model based on the unsteady Reynolds-averaged Navier–Stokes (URANS) simulation approach. The superiority of the SSTKWSAS model in resolving large eddies is highlighted. Overall, the present study emphasizes the effectiveness and efficiency of coupling a partially premixed combustion model with a scale-resolving simulation method in predicting a swirl-stabilized, multi-jets turbulent flame in a practical, complex gas turbine combustor configuration.

EXPRESS MODEL FOR WATER TREATMENT PROCESS CALCULATION

Purpose. The use of a physical experiment to study mass transfer processes in structures used in water supply and sewage systems requires considerable time and is very expensive. The aim of the work is to develop numerical models for a computational experiment to study the mass transfer process in sand traps. Methodology. For mathematical modeling of the mass transfer process in sand traps, the two-dimensional Navier-Stokes equations and the two-dimensional impurity mass transfer equation are used. For numerical integration of equations describing the motion of a viscous incompressible fluid, implicit difference splitting schemes are used. The unknown parameters at each step of the splitting were found by explicit dependencies. For the numerical integration of the two-dimensional mass transfer equation, an alternately triangular difference splitting scheme is used. Findings. To conduct a computational experiment, a specialized code was created on the basis of the constructed numerical model. The results of computational experiments on the study of mass transfer in sand traps with additional elements are presented. It was determined that water purification efficiency changes with installation of additional elements at the bottom of the sand trap. Originality. The constructed numerical models make it possible to quickly analyze and predict the efficiency of sand traps having a complex geometric shape. They also make it possible to take into account the flow hydrodynamics in the treatment plant. Practical value. The proposed numerical models can be used at the design stage of sewage treatment plants.

Inferring Implicit Rules by Learning Explicit and Hidden Item Dependency

Revealing complex relations between entities (e.g., items within or between transactions) is of great significance for business optimization, prediction, and decision making. Such relations include not only co-occurrence-based explicit relations but also nonco-occurrence-based implicit ones. Explicit relations have been substantially studied by rule mining-based approaches, including association rule mining and causal rule discovery. In contrast, implicit relations have received much less attention but could be more actionable. In this paper, we focus on the implicit relations between items which rarely or never co-occur while each of them co-occurs with other identical items (link items) with a high probability. A framework integrates both explicit and hidden item dependencies and a corresponding efficient algorithm IRRMiner captures such implicit relations with implicit rule inference. Experimental results show that IRRMiner not only infers implicit rules of various sizes consisting of both frequent and infrequent items effectively, it also runs at least four times faster than IARMiner, a typical indirect association rule mining algorithm which can only mine size-2 indirect association rules between frequent items. IRRMiner is applied to make recommendations and shows that the identified implicit rules can increase recommendation reliability.

Ratio of Loss and Storage Moduli Determines Restitution Coefficient in Low-Velocity Viscoelastic Impacts

Impact tests are an important tool to analyze dynamic material properties of viscoelastic media in technology and biology. In this context rigorous contact mechanical models of the collision problem are necessary to adequately interpret data from impact experiments. It is shown here theoretically, that the coefficient of restitution in these types of testing is mainly a function of one specific material property, namely the ratio between the loss and storage moduli of the viscoelastic probe at the characteristic time scale of the impact. Explicit dependencies of the restitution coefficient on factors like impact velocity, impactor shape, general material rheology and functional grading – beyond the fact that those may influence the impact duration and the dynamic modulus associated with it – are weak.

A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis

This paper proposes an active learning-based Gaussian process (AL-GP) metamodelling method to estimate the cumulative as well as complementary cumulative distribution function (CDF/CCDF) for forward uncertainty quantification (UQ) problems. Within the field of UQ, previous studies focused on developing AL-GP approaches for reliability (rare event probability) analysis of expensive black-box solvers. A naive iteration of these algorithms with respect to different CDF/CCDF threshold values would yield a discretized CDF/CCDF. However, this approach inevitably leads to a trade off between accuracy and computational efficiency since both depend (in opposite way) on the selected discretization. In this study, a specialized error measure and a learning function are developed such that the resulting AL-GP method is able to efficiently estimate the CDF/CCDF for a specified range of interest without an explicit dependency on discretization. Particularly, the proposed AL-GP method is able to simultaneously provide accurate CDF and CCDF estimation in their median-low probability regions. Three numerical examples are introduced to test and verify the proposed method.

Polyaxial strength criterion and closed-form solution for squeezing rock conditions

In this paper, a nonlinear strength criterion is proposed using the average of intermediate ( σ 2 ) and minor ( σ 3 ) principal stresses in place of σ 3 in Ramamurthy (1994)'s strength criterion. The proposed criterion has the main advantages of negligible variation of strength parameters with confining stress and ability to link with conventional strength parameters. Additionally, a new closed-form solution based on the proposed criterion is derived and validated for Chhibro Khodri tunnel. Further, analytical solutions including Singh's elastoplastic theory, Scussel's approach, and closed-form solutions based on conventional and modified Ramamurthy (2007) criteria are compared with the results of proposed approach. It is shown that the in situ squeezing pressure predictions made by the proposed approach are more accurate. Also, a parametric study of the present analytical solution is carried out, which displays explicit dependency of tunnel stability on internal support pressure and tunnel depth. The influence of tunnel geometry is observed to be dependent on the applied support pressure.

Electric-Field-Induced Connectivity Switching in Single-Molecule Junctions.

SummaryThe manipulation of molecule-electrode interaction is essential for the fabrication of molecular devices and determines the connectivity from electrodes to molecular components. Although the connectivity of molecular devices could be controlled by molecular design to place anchor groups in different positions of molecule backbones, the reversible switching of such connectivities remains challenging. Here, we develop an electric-field-induced strategy to switch the connectivity of single-molecule junctions reversibly, leading to the manipulation of different connectivities in the same molecular backbone. Our results offer a new concept of single-molecule manipulation and provide a feasible strategy to regulate molecule-electrode interaction.

Tÿcho

Many application areas for embedded systems, such as DSP, media coding, and image processing, are based on stream processing. Stream programs in these areas are often naturally described as graphs, where nodes are computational kernels that send data over the edges. This structure also exhibits large amounts of concurrency, because the kernels can execute independently as long as there are data to process on the edges. The explicit data dependencies also help making efficient sequential implementations of such programs, allowing programs to be more portable between platforms with various degrees of parallelism. The kernels can be expressed in many different ways; for example, as imperative programs with read and write statements for the communication or as a set of actions that can be performed and conditions for when these actions can be executed. Traditionally, there has been a tension between how the kernels are expressed and how efficiently they can be implemented. There are very efficient implementation techniques for stream programs with restricted expressiveness, such as synchronous dataflow. In this article, we present a framework for building stream program compilers that we call Tÿcho. At the core of this framework is a common kernel representation, based on a machine model for stream program kernels called actor machine , on which transformations and optimizations are performed. Both imperative and action-based kernels are translated to this common representation, making the same optimizations applicable to different kinds of kernels, and even across source language boundaries. An actor machine is described by the steps of execution that a kernel can take, and the conditions for taking them, together with a controller that decides how the conditions are tested and the steps are taken. We outline how kernels of an imperative process language and an action-based language are decomposed and translated to the common kernel representation, and we describe a simple backend that generates sequential C code from this representation. We present optimization heuristics of the decision process in the controller that we evaluate using a few dozen kernels from a video decoder with various degrees of complexity. We also present kernel fusion, by merging the controllers of actor machines, as a way of scheduling kernels on the same processor, which we compare to prior art.

Temporally multimode four-photon Hong-Ou-Mandel interference

The two-photon Hong-Ou-Mandel (HOM) interference is a pure quantum effect which indicates the degree of indistinguishability of photons. The four-photon HOM interference exhibits richer dynamics in comparison to the two-photon interference and simultaneously is more sensitive to the input photon states. We demonstrate theoretically and experimentally an explicit dependency of the four-photon interference to the number of temporal modes, created in the process of parametric down-conversion. Moreover, we exploit the splitting ratio of the beam splitter to manipulate the interference between bunching and antibunching. Our results reveal that the temporal mode structure (multimodeness) of the quantum states shapes many-particle interference.

Modeling Marked Temporal Point Process Using Multi-relation Structure RNN

Event sequences with marker and timing information are available in a wide range of domains, from machine log in automatic train supervision systems to information cascades in social networks. Given the historical event sequences, predicting what event will happen next and when it will happen can benefit many useful applications, such as maintenance service schedule for mass rapid transit trains and product advertising in social networks. Temporal point process (TPP) is one effective solution to solve the next event prediction problem due to its capability of capturing the temporal dependence among events. The recent recurrent temporal point process (RTPP) methods exploited recurrent neural network (RNN) to get rid of the parametric form assumption in the density functions of TPP. However, most existing RTPP methods focus only on the temporal dependence among events. In this work, we design a novel multi-relation structure RNN model with a hierarchical attention mechanism to capture not only the conventional temporal dependencies but also the explicit multi-relation topology dependencies. We then propose an RTPP algorithm whose density function conditioned on the event sequence embedding learned from our RNN model for cognitively predict the next event marker and time. The experiments show that our proposed MRS-RMTPP outperforms the state-of-the-art baselines in terms of both event marker prediction and event time prediction on three real-world datasets. The capability of capturing both ontology relation structure and temporal structure in the event sequences is of great importance for the next event marker and time prediction.

Ferroelectric phase transition and crystal asymmetry monitoring of SrTiO3 using quasi TE m,1,1 and quasi TM m,1,1 modes

Dielectric spectroscopy of a SrTiO3 single crystal over a broad range of microwave frequency using quasi TEm,1,1 and quasi TMm,1,1 modes reveals crystal asymmetry from typical measurement of Q-factor, transmission, or frequency characteristics in continuous cooling down to a few Kelvin. The properties of the modes due to the crystal asymmetry are validated by implementing a quasiharmonic phonon approximation. The observed ferroelectric phase transition temperature is around 51 K, and quantum-mechanical stabilization of the paraelectric phase arises below 5 K with very high permittivity. Also, an antiferrodistortive transition was indicated at 105 K. Landau’s theory of correlation length supports the observation of an extra-loss term so the transition may be identified near the Q-factor maxima or transmission maxima depending on the other loss terms present in the cavity. Thus, the ferroelectric phase transition with respect to temperature is identified when its extra-loss term causes a discontinuity or deviation in the derivative of the temperature characteristic near the minimum of total cavity loss (maximum Q-factor or maximum transmission temperature characteristic). This temperature is confirmed by transmission amplitude variation of quasi TE2,1,1 under 200 V dc electric field showing the existence of the soft-mode. These measurements support a typical polarization model and explicit temperature dependency of the soft-mode incorporating an imaginary frequency.

Mesoscale elements description of turbulent flow

• Description of turbulent flow as an ensemble of finite size, or mesoscale , Lagrangian elements is formulated. • Mesoscale element has no deterministic position but a probability of presence and a region of influence growing with time. • Consideration of finite size elements allows explicit account of molecular transport described through pairwise interactions. • The method applied to a mixing layer in grid turbulence yielded scalar statistics in quantitative agreement with experiments. This work proposes to describe a turbulent flow using a set of mesoscale elements, that is fluid elements of small, but finite, size the properties of which are representative of larger region surrounding them. Consideration of fluid elements of finite dimensions allows one to formulate a small scale mixing model with an explicit dependency on the molecular transport coefficients. The dimensions of a mesoscale element are determined from an evolution equation accounting for the molecular diffusion and the strain rate induced by small-scale turbulence while its position is determined by convection by large-scale velocity components. In addition to consideration of the fluid elements of a finite size, the second key new concept is the notion of radius of influence over which such an element contributes to the statistics of the flow; this radius grows with time. It is shown that the proposed method satisfies the mass conservation and normalisation of the probability density functions of scalar quantities . The proposed method is illustrated with simulations of thermal mixing layer in grid turbulence.

Serial‐fed linear frequency diverse arrays for obtaining direction of arrival (DOA) of frequency modulated continuous waveform (FMCW) sources with ambiguity reduction

Possibility of exploiting serial-fed linear arrays for obtaining the direction of arrival (DOA) of frequency modulated continuous waveform (FMCW) sources is discussed in this article. The angle of incident wave is assumed to be in the range, [−90°, 90°], with ambiguity reduction compared with the previous work. At first, the serial-fed arrays are assumed as a transmitter with triangular FMCW sources, composing modified frequency diverse arrays. An explicit angle dependency is obtained on the separations between the maximums of the produced spatial power pattern in a design procedure. The approach is generalized for modified sawtooth frequency modulated continuous waveform (SFMCW) sources, which are combinations of two SFMCW sources with two different slopes of frequency shifts. In addition, a measurement setup is implied assuming the serial-fed array as the receiver and an antenna with a known FMCW source and an unknown DOA as the transmitter. The DOA will be obtained from the separations between maxima of the received signals. Advantages of the proposed structure are the simplicity of antenna structure, ability of operation with narrowband antenna elements, and ambiguity reduction compared with the previous work.

Attention-based spatio-temporal dependence learning network

Multivariate time series (MTS) classification is a challenging problem due to the complex nature of data, especially for tasks with spatial dependencies such as three-dimensional (3D) skeleton sequences, traffic flow sequences, sign language sequences, and activity acceleration sequences. Existing methods do not fully exploit spatial features in these MTS, so the major challenge is how to effectively extract these spatial features and integrate them into an end-to-end network. Therefore, we propose the Spatio-Temporal Dependence Learning Network (STDL-Net), which is an attention-based network for MTS with explicit spatial dependencies. The STDL-Net first simultaneously extracts spatial dependencies and models short-term temporal dependencies via a sparse convolutional neural network (CNN) to obtain spatio-temporal dependence units (STDUs). Then the STDL-Net leverages a long short-term memory (LSTM) network with an attention mechanism to model long-term temporal dependencies and adaptively select the most discriminative STDUs for MTS classification tasks. In this way, the STDL-Net acts as an end-to-end model which captures discriminative sample-specific spatial features at each time step and models underlying multi-scale dynamics in the MTS data. Experiments on 12 MTS benchmark datasets with explicit spatial dependencies and 3 skeleton-based action recognition tasks are conducted. The proposed STDL-Net yields better performance than existing methods and the visualized analysis demonstrates the effectiveness of learning STDUs.

Should one use term proximity or multi-word terms for Arabic information retrieval?

Recently, several information retrieval (IR) models have been proposed in order to boost the retrieval performance using term dependencies. However, in the context of the Arabic language, most IR researchers have focused on the problem of stemming, which is highly challenging in this language. In this paper, we propose to explore whether term dependencies can help improve Arabic IR systems, and what are the best methods to use. To do so, we consider both explicit term dependencies based on multi-word terms (MWTs) that are extracted using syntactic patterns and statistical filters, as well as implicit ones based on the notion of cross-terms or term proximities. Our experiments, performed on standard TREC Arabic IR collections, show the importance of taking into account term dependencies for Arabic IR. To the best of our knowledge, this is the first study that provides complete extensions, and their comparison, of most standard IR models to deal with term dependencies in the Arabic language.

A New Continuous-Time Stability Perspective of Time-Delay Control: Introducing a State-Dependent Upper Bound Structure

In the literature of any time-delay control (TDC)-based methods, the boundedness of the error due to time-delay estimation (TDE) is crucial to prove the stability. However, the TDE error has been studied by discretizing the closed-loop system while neglecting the effect of discretization error; consequently, the TDE error is considered to be upper bounded by a constant. This letter proves that such constant upper bound is restrictive in nature due to the explicit involvement of system states in the TDE error. Thereby, without discretizing the closed-loop system, a new structure of the upper bound of TDE error is directly formulated in the continuous-time domain which has an explicit dependency on the system states. Via this formulation, this letter solves the long-standing problem for TDC of having consistent stability analysis and control design in continuous time. Based on the newly proposed structure of TDE error, an enhanced robust control law is formulated. The effectiveness of the proposed method is experimentally substantiated as compared to the conventional TDC using a multiple-degrees-of-freedom robot.

A Classical Sequent Calculus with Dependent Types

Dependent types are a key feature of the proof assistants based on the Curry-Howard isomorphism. It is well known that this correspondence can be extended to classical logic by enriching the language of proofs with control operators. However, they are known to misbehave in the presence of dependent types, unless dependencies are restricted to values. Moreover, while sequent calculi naturally support continuation-passing-style interpretations, there is no such presentation of a language with dependent types. The main achievement of this article is to give a sequent calculus presentation of a call-by-value language with a control operator and dependent types, and to justify its soundness through a continuation-passing-style translation. We start from the call-by-value version of the λμ˜μ -calculus. We design a minimal language with a value restriction and a type system that includes a list of explicit dependencies to maintain type safety. We then show how to relax the value restriction and introduce delimited continuations to directly prove the consistency by means of a continuation-passing-style translation. Finally, we relate our calculus to a similar system by Lepigre and present a methodology to transfer properties from this system to our own.