Recent Advances In Computational Techniques Research Articles

High-Entropy Alloys in Catalysis: Progress, Challenges, and Prospects.

High-entropy alloys (HEAs) have become pivotal materials in the field of catalysis, offering unique advantages due to their diverse elemental compositions and complex atomic structures. Recent advances in computational techniques, particularly density functional theory (DFT) and machine learning (ML), have significantly enhanced our understanding and design of HEAs for use in catalysis. These innovative atomistic simulations shed light on the properties of HEAs, enabling the discovery and optimization of catalysis materials for solid-solution structures. This Perspective discusses recent studies that illustrate the progress of HEAs in catalysis. It offers an overview of the properties, constraints, and prospects of HEAs, emphasizing their roles in catalysis to enhance catalytic activity and selectivity. The discussion underscores the capabilities of HEAs as multifunctional catalysts with stable structures. The presented insights aim to inspire future computational and experimental efforts to address the challenges in fine-tuning HEAs properties for improved catalytic performance.

A framework for modelling spatio-temporal trends in crop production using generalised additive models

Satellite imagery provides opportunities for inference of trends in crop production across space and time. However, the large size of these datasets has made application of statistical modelling approaches computationally difficult. Recent advances in computational techniques and infrastructure have allowed generalised additive models to be fitted to very large datasets. We propose a framework for inferring trends in crop production across space and time using generalised additive models which accounts for inter-annual trends (main effect of year), spatial distribution (main effect of space), crop ontogeny (main effect of month), inter-annual changes in seasonality (interaction between year and month), and inter-annual changes in spatial distribution (interaction between year and space). Application of the proposed model to farm scale, multi-field sites in the Ord River Irrigation Area, Western Australia, demonstrates that this approach is able to decompose variation into the aforementioned effects. Furthermore, comparison of grain production observations and estimates for the Western Australian Wheatbelt as ground-truth data showed agreement with inferences drawn from the proposed model, with prediction terms for the main effect of year positively correlated with estimated tonnes produced from 2013 to 2021 (p = 0.03). Finally, application to Madagascar, which has been experiencing a food crisis, revealed a decreasing trend in cropland Normalised Difference Vegetation Index (NDVI) from 2014 to 2021 of 2.5%, raising concerns about ongoing food security. The proposed modelling framework is adaptable to numerous agricultural research problems.

Defect modeling and control in structurally and compositionally complex materials.

Conventional computational approaches for modeling defects face difficulties when applied to complex materials, mainly due to the vast configurational space of defects. In this Perspective, we discuss the challenges in calculating defect properties in complex materials, review recent advances in computational techniques and showcase new mechanistic insights developed from these methods. We further discuss the remaining challenges in improving the accuracy and efficiency of defect modeling in complex materials, and provide an outlook on potential research directions.

Evaluation of machine learning methodologies using simple physics based conceptual models for flow in porous media

Machine learning (ML) techniques have drawn much attention in the engineering community due to recent advances in computational techniques and an enabling environment. However, often they are treated as black-box tools, which should be examined for their robustness and range of validity/applicability. This research presents an evaluation of their application to flow/transport in porous media, where exact solutions (obtained from physics-based models) are used to train ML algorithms to establish when and how these ML algorithms fail to predict the first order flow-physics. Exact solutions are used so as not to introduce artifacts from the numerical solutions.To test, validate, and predict the physics of flow in porous media using ML algorithms, one needs a reliable set of data that may not be readily available and/or the data might not be in suitable form (i.e. incomplete/missing reporting, metadata, or other relevant peripheral information). To overcome this, we first generated structured datasets for flow in porous media using simple representative building blocks of flow physics such as Buckley-Leverett, convection-dispersion equations, and viscous fingering. Then, the outcomes from those equations are fed into ML algorithms to examine their robustness and predictive strength of the key features, such as breakthrough time, and saturation and component profiles. In this research, we show that a physics-informed ML algorithm can capture the physical behavior and effects of various physical parameters (even when shocks and sharp gradients are present) on the features of the flow. Furthermore the ML approach can be utilized to solve inverse problems to estimate various physical parameters. In this study, we have focused on capturing the dominant flow physics on selected processes constituting the outcome of the physics of flow in porous media using ML algorithms. We expect that capturing the physics per selected processes can help solving more complex problems by providing correlative physical proxies.

2D nonlinear finite element analysis of reinforced concrete beams using total strain crack model

Concrete shows a highly non-linear pressure-sensitive constitutive response when it is strained more than its elastic limit. Recent advances in computational techniques have greatly aided the rapid implementation of non-linear material behaviour in finite element modelling for reinforced concrete structures. In this study, a finite element model is implemented in conjunction with non-linear stress–strain laws for concrete to capture the plastic damage of a reinforced concrete beam. Nonlinear finite element modelling of a simply supported reinforced concrete beam under static loads is performed using the DIANA10.4 software. The total strain crack model is used as it allows stress–strain behaviour as an input. The predictions of the base model were validated against the experimental work of Vecchio and Shim (2004), which is used as a classical benchmark for the validation of numerical models for concrete structures. Subsequently, various parametric studies are performed to understand the behaviour of beams. A parabolic curve was employed as a material model, for modelling the behaviour in compression. The influence of various total strain crack model parameters like the tension softening laws, crack type, and shear retention factors, for their ability to accurately predict the behaviour of reinforced concrete beams is examined. The results from the analyses are used to study the global load–displacement curve, contour maps of crack widths, and principal stresses.

Natural language processing as a technique for conducting text‐based research

AbstractResearch in discourse processing has provided us with a strong foundation for understanding the characteristics of text and discourse, as well as their influence on our processing and representation of texts. However, recent advances in computational techniques have allowed researchers to examine discourse processes in new ways. The purpose of the current paper is to build on prior work in this domain and describe how new methodologies that consider the multi‐dimensional nature of texts can serve as a complement to the existing literature. We focus on natural language processing (NLP) methodologies, in which computers calculate information about the linguistic and semantic properties of language data. We first provide a context for the origins of computational discourse analysis through the integration of research across computer science and psychology. We then provide an overview of different NLP methodologies and describe prior work that has leveraged these techniques to advance theoretical perspectives of discourse comprehension and production. Finally, we propose new areas of research that integrate these advances with traditional research methodologies in the field.

Investigation of internal flow pattern of a multiphase axial pump

Due to recent advances in computational techniques, single phase performances of pumps can be easily estimated using CFD. However, when the working fluid is a mixture of liquid and gas (multi-phase flow) computations may struggle to capture the more complex physical phenomena that occurs, and the precision of the prediction worsen as the gas volume fraction at pump inlet is increased. To provide reliable benchmark for the improvement of simulations, in this study we experimentally investigated the internal flow pattern of a multiphase pump using high speed camera and high frequency pressure measurements, so as to obtain the data of pressure distribution inside of impeller, or to obtain blade loading, for several gas concentration conditions. Unsteady phenomena were identified at low flow rate conditions, and an explanation of their nature has been attempted.

The Heston stochastic volatility model with piecewise constant parameters — efficient calibration and pricing of window barrier options

The Heston stochastic volatility model is a standard model for valuing financial derivatives, since it can be calibrated using semi-analytical formulas and captures the most basic structure of the market for financial derivatives with simple structure in time-direction. However, extending the model to the case of time-dependent parameters, which would allow for a parametrization of the market at multiple timepoints, proves more challenging. We present a simple and numerically efficient approach to the calibration of the Heston stochastic volatility model with piecewise constant parameters. We show that semi-analytical formulas can also be derived in this more complex case and combine them with recent advances in computational techniques for the Heston model. Our numerical scheme is based on the calculation of the characteristic function using Gauss-Kronrod quadrature with an additional control variate that stabilizes the numerical integrals. We use our method to calibrate the Heston model with piecewise constant parameters to the foreign exchange (FX) options market. Finally, we demonstrate improvements of the Heston model with piecewise constant parameters upon the standard Heston model in selected cases.

Slow crack growth, its modeling and crack-layer approach: A review

A review of empirical equations for slow crack growth under fatigue and creep conditions is presented. The crack propagation rate is commonly expressed as a function of stress intensity factor or energy release rate. A concept of crack driving force and crack stability analysis is employed to predict crack behavior under different loading conditions; the concept of crack growth resistance, such as an effective surface energy, is used. It is shown that for a stable crack propagation (the energy release rate is a decreasing function of crack length) crack growth equation results from the crack equilibrium condition, if the crack resistance is assumed to be constant. Experimental studies demonstrate that crack growth resistance is not constant, but changes with crack propagation. Formation of the so-called process zone appears to be responsible for the changes. Process zone (PZ) is a material reaction to stress concentration at crack tip and it is commonly observed under fatigue and creep conditions in most engineering materials. For an unstable crack growth (the energy release rate is an increasing function of crack length) the effect of PZ even more dramatic; for example, in a perfectly homogeneous elastic solid slow crack growth would be impossible without the stabilizing effect of PZ. A system of a crack and PZ is referred to as crack-layer (CL). CL driving forces that are thermodynamic forces associated with crack and PZ growth are introduced. CL growth equations are employed to explain the observed variations of crack growth resistance commonly known as R-curve behavior. The evolution of the process zone in a complex stress field is illustrated by an experiment demonstrating how process zone accelerates and decelerates crack growth. Modeling of the crack–process zone interaction is a challenging problem; recent advances in computational techniques make it solvable. The CL approach is illustrated on an example of engineering thermoplastics. Applications to lifetime prediction of structural components, as well as toughness and durability of materials are discussed.

New Development Environment for Modern Bioelectromagnetics Signal Processing Applications [Application Notes

Bioelectromagnetics is the study of the interaction of electromagnetic (EM) signals with biological systems. The most common area of investigation in bioelectromagnetics is the study of the interactions between EM signals and human tissue. With the recent advances in computational techniques, it has become easier to assess numerically the EM behavior within the exposed biological tissue model. There are extremely powerful computational techniques for solving bioelectromagnetic problems, and they have been employed successfully in the modeling of microwave applicators used in hyperthermia, in calculating EM signal absorption in human body models, and for the dosimetry of mobile handhelds. LabVIEW is a multithreaded programming language so that specific operations within a single application can be subdivided into individual threads, each of which can run in parallel. In other words, the block diagrams in LabVIEW are parallel programs.In this article, a LabVIEW development environment is designed to simulate a set of EM/biological tissue interactions. Two standard methods are presented to investigate the effect of EM radiation on the human tissue: the multilayered method and the finite-difference, time-domain (FDTD) method. The LabVIEW development environment provides a graphical user interface to the model that makes it easy to observe and modify the behavior of each element of the model.

Recent advances in computational techniques for MHD flow and application to fusion

Recent advances in computational techniques for MHD flows and application to fusion will be briefly reviewed, which includes different approaches for calculation of the Lorentz force with focus on the consistent and conservative scheme for the electrical potential formulation. The application of MHD solver on R&D of DCLL blanket and liquid metal divertor will be addressed with focus on the 3D flows in a blanket with FCI (flow channel insert).

Reduced order modeling of X-31 wind tunnel model aerodynamic loads

With recent advances in computational techniques, turbulent Navier–Stokes solvers are now capable of capturing the unsteady nonlinear aerodynamic behavior that leads to various static and dynamic instabilities of full aircraft. As such, the focus of stability and control (S&C) research has been to effectively incorporate Computational Fluid Dynamics (CFD) into the model development process using dynamic CFD solutions of complete aircraft configurations. This has lead to an innovative approach for modeling aircraft stability and control characteristics: (a) CFD simulations are performed using computational training maneuvers designed to excite the relevant flow physics encountered during actual missions, (b) a mathematical Reduced Order Model (ROM) is built of the aircraft response using system identification methods, (c) the model is validated by comparing CFD simulations against model predictions, and (d) predictions of all fight test points are made using the model to determine the expected behavior of the aircraft. This process would identify unexpected S&C issues early in the design process. This article describes the application of this innovative approach to a problem of the System Identification (SID) of a high performance fighter-type configuration, the X-31. The training maneuver used to produce the training data has been tested in the DNW low speed wind tunnel which enables directly comparing the predictive capabilities of the CFD and wind tunnel SID modeling.

ChemInform Abstract: Thermophysical Properties of Ionic Liquids

AbstractReview: 122 refs.

Special issue on recent advances in computational techniques for biomedical imaging

AbstractA total of 12 papers in the area of mathematical methods and computational techniques for biomedical imaging and image analysis are presented in this special issue. Copyright © 2009 John Wiley & Sons, Ltd.

Call for Papers: Special Issue on ‘Recent Advances in Computational Techniques for Biomedical Imaging’ Communications in Numerical Methods in Engineering (CNM)

Call for Papers: Special Issue on ‘<i>Recent Advances in Computational Techniques for Biomedical Imaging</i>’ <i>Communications in Numerical Methods in Engineering</i> (<i>CNM</i>)

Call for Papers: Special Issue on ‘Recent Advances in Computational Techniques for Biomedical Imaging’ Communications in Numerical Methods in Engineering (CNM)

Call for Papers: Special Issue on ‘<i>Recent Advances in Computational Techniques for Biomedical Imaging</i>’ <i>Communications in Numerical Methods in Engineering</i> (<i>CNM</i>)

Call for Papers: Special Issue on ‘Recent Advances in Computational Techniques for Biomedical Imaging’ Communications in Numerical Methods in Engineering (CNM)

Call for Papers: Special Issue on ‘<i>Recent Advances in Computational Techniques for Biomedical Imaging</i>’ <i>Communications in Numerical Methods in Engineering (CNM)</i>

Special Issue on ‘Recent Advances in Computational Techniques for Biomedical Imaging’ International Journal for Numerical Methods in Fluids (FLD)

Special Issue on ‘<i>Recent Advances in Computational Techniques for Biomedical Imaging</i>’ <i>International Journal for Numerical Methods in Fluids</i> (<i>FLD</i>)

Special Issue on ‘Recent Advances in Computational Techniques for Biomedical Imaging’ International Journal for Numerical Methods in Engineering (NME)

Special Issue on ‘<i>Recent Advances in Computational Techniques for Biomedical Imaging</i>’ <i>International Journal for Numerical Methods in Engineering (NME)</i>

Adapting the Optimal Job Search Model for Active Labour Market Policy

SummaryThe structural theoretical framework for the analysis of duration of unemployment has been the optimal job search model. Recent advances in computational techniques in Bayesian inference now facilitate the analysis of incomplete data sets and the recovery of structural model parameters. The paper uses these methods on a UK data set of the long-term unemployed to illustrate how the optimal job search model can be adapted to model the effects of an active labour market policy. Without such an adaptation our conclusion is that the simple optimal job search model may not fit empirical unemployment data and could thus lead to a misspecified econometric model and incorrect parameter estimates.