Computational Science – ICCS 2019

It might be easier for intelligent extraterrestrial civilizations to be found when they mark their position with a bright laser beacon. Given the possible distances involved, however, it is likely that weak signal detection techniques would still be required to identify even the brightest SETI Beacon. The Bootstrap Error-adjusted Single-sample Technique (BEST) is such a detection method. The BEST has been shown to outperform the more traditional Mahalanobis metric in analysis of SETI data from a Project Argus near infrared telescope. The BEST algorithm is used to identify unusual signals and returns a distance in asymmetric nonparametric multidimensional central 68% confidence intervals (equivalent to standard deviations for 1-D data that are normally distributed, or Mahalanobis distance units for normally distributed data of d dimensions). Calculation of the Mahalanobis metric requires matrix factorization and is order of d3. Furthermore, the accuracy and precision of the BEST metric are greater than the Mahalanobis metric in realistic data collection scenarios (many more wavelengths available then observations at those wavelengths). An extension of the BEST to examine multiple samples (subclusters of data) simultaneously is explored in this paper.

The graph computing is often used to analyze complex relationships in the interconnected world, and the strongly connected components (SCC) detection in digraphs is a basic problem in graph computing. As graph size increases, many parallel algorithms based on GPUs have been proposed to detect SCC. The state-of-the-art parallel algorithms of SCC detection can accelerate on various graphs, but there is still space for improvement in: (1) Multiple traversals are time-consuming when processing real-world graphs; (2) Pivot selection is less accurate or time-consuming. We proposed an SCC detection method with multi-partition that optimizes the algorithm process and achieves high performance. Unlike existing parallel algorithms, we select a pivot and traverse it forward, and then select a vice pivot and traverse the pivot and the vice pivot backwards simultaneously. After updating the state of each vertex, we can get multiple partitions to parallelly detect SCC. At different phases of our approach, we use a vertex with the largest degree product or a random vertex as the pivot to balance selection accuracy and efficiency. We also implement weakly connected component (WCC) detection and 2-SCC to optimize our algorithm. And the vertices marked by the WCC partition are selected as the pivot to reduce unnecessary operations. We conducted experiments on the NVIDIA K80 with real-world and synthetic graphs. The results show that the proposed algorithm achieves an average detection acceleration of 8.8 $$\times $$ and 21 $$\times $$ when compared with well-known algorithms, such as Tarjan’s algorithm and Barnat’s algorithm.

Associative classification refers to a class of algorithms that is very efficient in classification problems. Data in such domain are multidimensional, with data instances represented as points of a fixed-length attribute space, and are exploited from two large sets: training and testing datasets. Models, known as classifiers, are mined in the training set by class association rules and are used in eager and lazy strategies for labeling test data instances. Because test data instances are independent and evaluated by sophisticated and high costly computations, an expressive overlap among similar data instances may be introduced. To overcome such drawback, we propose a parallel and high-performance associative classification based on a lazy strategy, which partial computations of similar data instances are cached and shared efficiently. In this sense, a PageRank-driven similarity metric is introduced to reorder computations by affinity, improving frequent-demanded association rules memoization in typical cache strategies. The experiments results show that our similarity-based metric maximizes the reuse of rules cached and, consequently, improve application performance, with gains up to 60% in execution time and 40% higher cache hit rate, mainly in limited cache space conditions.

Most current clustering based anomaly detection methods use scoring schema and thresholds to classify anomalies. These methods are often tailored to target specific data sets with “known” number of clusters. The paper provides a streaming clustering and anomaly detection algorithm that does not require strict arbitrary thresholds on the anomaly scores or knowledge of the number of clusters while performing probabilistic anomaly detection and clustering simultaneously. This ensures that the cluster formation is not impacted by the presence of anomalous data, thereby leading to more reliable definition of “normal vs abnormal” behavior. The motivations behind developing the INCAD model [17] and the path that leads to the streaming model are discussed.

Porous media and conduit coupled systems are heavily used in a variety of areas. A coupled dual-porosity-Stokes model has been proposed to simulate the fluid flow in a dual-porosity media and conduits coupled system. In this paper, we propose an implementation of this multi-physics model. We solve the system with the automated high performance differential equation solving environment FEniCS. Tests of the convergence rate of our implementation in both 2D and 3D are conducted in this paper. We also give tests on performance and scalability of our implementation.

In today’s information environment there is an increasing reliance on online and social media in the acquisition, dissemination and consumption of news. Specifically, the utilization of social media platforms such as Facebook and Twitter has increased as a cutting edge medium for breaking news. On the other hand, the low cost, easy access and rapid propagation of news through social media makes the platform more sensitive to fake and anomalous reporting. The propagation of fake and anomalous news is not some benign exercise. The extensive spread of fake news has the potential to do serious and real damage to individuals and society. As a result, the detection of fake news in social media has become a vibrant and important field of research. In this paper, a novel application of machine learning approaches to the detection and classification of fake and anomalous data are considered. An initial clustering step with the K-Nearest Neighbor (KNN) algorithm is proposed before training the result with a Recurrent Neural Network (RNN). The results of a preliminary application of the KNN phase before the RNN phase produces a quantitative and measureable improvement in the detection of outliers, and as such is more effective in detecting anomalies or outliers against the test dataset of 2016 US Presidential Election predictions.

Data augmentation methods are often applied to prevent overfitting and improve generalization of deep neural network models. Recently proposed contextual augmentation augments labeled sentences by randomly replacing words with more varied substitutions predicted by language model. Bidirectional Encoder Representations from Transformers (BERT) demonstrates that a deep bidirectional language model is more powerful than either an unidirectional language model or the shallow concatenation of a forward and backward model. We propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. We retrofit BERT to conditional BERT by introducing a new conditional masked language model (The term “conditional masked language model” appeared once in original BERT paper, which indicates context-conditional, is equivalent to term “masked language model”. In our paper, “conditional masked language model” indicates we apply extra label-conditional constraint to the “masked language model”.) task. The well trained conditional BERT can be applied to enhance contextual augmentation. Experiments on six various different text classification tasks show that our method can be easily applied to both convolutional or recurrent neural networks classifier to obtain improvement.

The WADI project (Water-tightness Airborne Detection Implementation), integrated within the H2020 initiative, is developing an airborne water leak detection surveillance service, based on manned and unmanned aerial vehicles. This service aims to provide water utilities with adequate information on leaks in large water distribution infrastructures outside urban areas. Given the high cost associated with water infrastructure networks repairs, a reliability layer is necessary to improve the trustworthiness of the WADI leak identification procedure, based on complementary technologies for leak detection. Herein, a methodology based on the combined use of Sentinel remote sensing data and a water leak pathways model is presented, based on data-intensive computing. The resulting water leak detection reliability service, provided to the users through a web interface, targets prompt and cost-effective infrastructure repairs with the required degree of confidence on the detected leaks. The web platform allows for both data analysis and visualization of Sentinel images and relevant leak indicators at the sites selected by the user. The user can also provide aerial imagery inputs, to be processed together with Sentinel remote sensing data at the satellite acquisition dates identified by the user. The platform provides information about the detected leaks location and time evolution, and will be linked in the future with the outputs from water pathway models.

A Weak Constraint Gaussian Process (WCGP) model is presented to integrate noisy inputs into the classical Gaussian Process predictive distribution. This follows a Data Assimilation approach i.e. by considering information provided by observed values of a noisy input in a time window. Due to the increased number of states processed from real applications and the time complexity of GP algorithms, the problem mandates a solution in a high performance computing environment. In this paper, parallelism is explored by defining the parallel WCGP model based on domain decomposition. Both a mathematical formulation of the model and a parallel algorithm are provided. We prove that the parallel implementation preserves the accuracy of the sequential one. The algorithm’s scalability is further proved to be $$\mathcal {O}(p^2)$$ where p is the number of processors.

This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. We aim to address the following problems: (1) estimation of systematic model errors in output quantities of interest at future times and (2) identification of specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. To address these problems, we employ simple machine learning algorithms and perform numerical experiments with Weather Research and Forecasting (WRF) model and the results show a reduction of forecast errors by an order of magnitude.

Recently, some studies have suggested methods to combine variational probabilistic inference with Monte Carlo sampling. One promising approach is via local optimal transport. In this approach, a gradient steepest descent method based on local optimal transport principles is formulated to deterministically transform point samples from an intermediate density to a posterior density. The local mappings that transform the intermediate densities are embedded in a reproducing kernel Hilbert space (RKHS). This variational mapping method requires the evaluation of the log-posterior density gradient and therefore the adjoint of the observational operator. In this work, we evaluate nonlinear observational mappings in the variational mapping method using two approximations that avoid the adjoint, an ensemble based approximation in which the gradient is approximated by the sample cross-covariances between the state and observational spaces the so-called ensemble space and an RKHS approximation in which the observational mapping is embedded in an RKHS and the gradient is derived there. The approximations are evaluated for highly nonlinear observational operators and in a low-dimensional chaotic dynamical system. The RKHS approximation is shown to be highly successful and superior to the ensemble approximation for non-Gaussian posterior densities.

When the heat released by a flame is sufficiently in phase with the acoustic pressure, a self-excited thermoacoustic oscillation can arise. These nonlinear oscillations are one of the biggest challenges faced in the design of safe and reliable gas turbines and rocket motors [7]. In the worst-case scenario, uncontrolled thermoacoustic oscillations can shake an engine apart. Reduced-order thermoacoustic models, which are nonlinear and time-delayed, can only qualitatively predict thermoacoustic oscillations. To make reduced-order models quantitatively predictive, we develop a data assimilation framework for state estimation. We numerically estimate the most likely nonlinear state of a Galerkin-discretized time delayed model of a horizontal Rijke tube, which is a prototypical combustor. Data assimilation is an optimal blending of observations with previous system’s state estimates (background) to produce optimal initial conditions. A cost functional is defined to measure (i) the statistical distance between the model output and the measurements from experiments; and (ii) the distance between the model’s initial conditions and the background knowledge. Its minimum corresponds to the optimal state, which is computed by Lagrangian optimization with the aid of adjoint equations. We study the influence of the number of Galerkin modes, which are the natural acoustic modes of the duct, with which the model is discretized. We show that decomposing the measured pressure signal in a finite number of modes is an effective way to enhance state estimation, especially when nonlinear modal interactions occur during the assimilation window. This work represents the first application of data assimilation to nonlinear thermoacoustics, which opens up new possibilities for real-time calibration of reduced-order models with experimental measurements.

We discuss surrogate models based on machine learning as approximation to the solution of an ordinary differential equation. Neural networks and a multivariate linear regressor are assessed for this application. Both of them show a satisfactory performance for the considered case study of a damped perturbed harmonic oscillator. The interface of the surrogate model is designed to work similar to a solver of an ordinary differential equation, respectively a simulation unit. Computational demand and accuracy in terms of local and global error are discussed. Parameter studies are performed to discuss the sensitivity of the method and to tune the performance.

Methods from learning theory are used in the state space of linear dynamical systems in order to estimate the system matrices and some relevant quantities such as a the topological entropy.The approach is illustrated via a series of numerical examples.

We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve supervised learning tasks while ensuring that their predictions do not violate physical laws. This is achieved by introducing an additional loss function during the training of the ESNs, which penalizes non-physical predictions without the need of any additional training data. This approach is demonstrated on a chaotic Lorenz system, where the physics-informed ESNs improve the predictability horizon by about two Lyapunov times as compared to conventional ESNs. The proposed framework shows the potential of using machine learning combined with prior physical knowledge to improve the time-accurate prediction of chaotic dynamical systems.

Ensemble Kalman filter (EnKF) has proven successful in assimilating observations of large-scale dynamical systems, such as the atmosphere, into computer simulations for better predictability. Due to the fact that a limited-size ensemble of model states is used, sampling errors accumulate, and manifest themselves as long-range spurious correlations, leading to filter divergence. This effect is alleviated in practice by applying covariance localization. This work investigates the possibility of using machine learning algorithms to automatically tune the parameters of the covariance localization step of ensemble filters. Numerical experiments carried out with the Lorenz-96 model reveal the potential of the proposed machine learning approaches.

An atmospheric and marine computational forecasting system for Guanabara Bay (GB) was developed to support the Brazilian Sailing Teams in the 2016 Olympic and Paralympic Games. This system, operational since August 2014, is composed of the Weather Research and Forecasting (WRF) and the Regional Ocean Modeling System (ROMS) models, which are both executed daily, yielding 72-h prognostics. The WRF model uses the Global Forecast System (GFS) as the initial and boundary conditions, configured with a three nested-grid scheme. The ocean model is also configured using three nested grids, obtaining atmospheric fields from the implemented WRF and ocean forecasts from CMEMS and TPXO7.2 as tidal forcing. To evaluate the model performances, the atmospheric results were compared with data from two local airports, and the ocean model results were compared with data collected from an acoustic current profiler and tidal prediction series obtained from harmonic constants at four stations located in GB. According to the results, reasonable model performances were obtained in representing marine currents, sea surface heights and surface winds. The system could represent the most important local atmospheric and oceanic conditions, being suitable for nautical applications.

Rías Baixas is a coastal region located in northwestern Spain (Galicia), between Cape Fisterra and the Portugal-Spain border. Its rich natural resources, which are key for the welfare of the region, are highly vulnerable to natural and anthropogenic stress. In this study, the operational ocean forecasting system developed at the meteorological agency of the Galician government (MeteoGalicia) is presented focussing on the Rías Baixas region. This system includes four models providing daily output data: the hydrodynamic models ROMS and MOHID, the atmospheric model WRF and the hydrological model SWAT. Here, MOHID’s implementation for the Rías Baixas region is described and the model’s performance with respect to observations is shown for those locations where Current-Temperature-Depth (CTD) profiles are obtained weekly by the Technological Institute for the Monitoring of the Marine Environment in Galicia (INTECMAR). Although the hydrodynamical conditions of this region are complex, the model skillfully reproduces these CTDs. The model results and derived products are publicly available through MeteoGalicia’s web page and data server ( www.meteogalicia.gal ).

Coastal upwelling systems are very important from the socio-economic point of view due to their high productivity, but they are also vulnerable under changing climate. The impact of climate change on the Canary Current Upwelling System (CCUS) has been studied in recent years by different authors. However, these studies show contradictory results on the question whether coastal upwelling will be more intense or weak in the next decades. One of the reasons for this uncertainty is the low resolution of climate models, making it difficult to properly resolve coastal zone processes. To solve this issue, we propose the use of a high-resolution regional climate coupled model. In this work we evaluate the performance of the regional climate coupled model ROM (REMO-OASIS-MPIOM) in the influence zone of the CCUS as a first step towards a regional climate change scenario downscaling. The results were compared to the output of the global Max Planck Institute Earth System Model (MPI-ESM) showing a significant improvement.

Drifting Fish Aggregating Devices (dFADs) are small drifting platforms with an attached solar powered buoy that report their position with daily frequency via GPS. We use data of 9,440 drifting objects provided by a buoys manufacturing company, to test the predictions of surface current velocity provided by two of the main models: the NEMO model used by Copernicus Marine Environment Monitoring Service (CMEMS) and the HYCOM model used by the Global Ocean Forecast System (GOFS).

Coastal zones have always been preferential areas for human settlement, mostly due to their natural resources. However, human occupation poses complex problems and requires proper management tools. Numerical models rank among those tools and offer a way to evaluate and anticipate the impact of human pressures on the environment. This work describes the implementation of a hydrodynamic 3-dimensional computational model for the coastal zone in Sines, Portugal. This implementation is done with the MOHID model which uses a finite volume approach and an Arakawa-C staggered grid for spatial equation discretization and a semi-implicit ADI algorithm for time discretization. Sines coastal area is under significant pressure from human activities, and the model implementation targets the location of a fish aquaculture. Validation of the model was done comparing model results with in situ data observations. The comparison shows relatively small differences between model and observations, indicating a good simulation of the hydrodynamics of this system.

The Douro is one of the largest rivers of the Iberian Peninsula, representing the most important buoyancy source into the Atlantic Ocean on the northwestern Portuguese coast. The main goal of this study is to contribute to the knowledge of physical processes associated with the propagation of the Douro River plume. The general patterns of dispersion in the ocean and how the plume change hydrography and coastal circulation were evaluated, considering the main drivers involved: river discharge and wind. Coastal models were implemented to characterize the propagation of the plume, its dynamics, and its impact on coastal circulation. Different numerical scenarios of wind and river discharge were analyzed. The estuarine outflow is sufficient to generate a northward coastal current without wind under moderate-to-high river discharge conditions. Under easterly winds, the propagation pattern is similar to the no wind forcing, with a northward current speed increasing. A southward coastal current is generated only by strong westerly winds. Under upwelling-favorable (northerly) winds, the plume extends offshore with tilting towards the southwest. Southerly winds increase the velocity of the northward current, being the merging of the Douro and Minho estuarine plumes a likely consequence.

Understanding the impact of sea level rise on coastal areas is crucial as a large percentage of the population live on the coast. This study uses computational tools to examine how two major consequences of sea level rise: salt intrusion and an increase in water volume affect the hydrodynamics and flooding areas of a major estuary in the Iberian Peninsula. A 2D numerical model created with the software MOHID was used to simulate the Guadiana Estuary in different scenarios of sea level rise combined with different freshwater flow rates. An increase in salinity was found in response to an increase in mean sea level in low and intermediate freshwater flow rates. An increase in flooding areas around the estuary were also positively correlated with an increase in mean sea level.

The ecosystem function of local fisheries holds great societal importance in the coastal zone of Cartagena, Colombia, where coastal communities depend on artisanal fishing for their livelihood and health. These fishing resources have declined sharply in recent decades partly due to issues of coastal water pollution. Mitigation strategies to reduce pollution can be better evaluated with the support of numerical hydrodynamic models. To model the hydrodynamics and water quality in Cartagena Bay, significant consideration must be dedicated to the process of light attenuation, given its importance to the bay’s characteristics of strong vertical stratification, turbid surface water plumes, algal blooms and hypoxia. This study uses measurements of total suspended solids (TSS), turbidity, chlorophyll-a (Chla) and Secchi depth monitored in the bay monthly over a 2-year period to calculate and compare the short-wave light extinction coefficient (Kd) according to nine different equations. The MOHID-Water model was used to simulate the bay’s hydrodynamics and to compare the effect of three different Kd values on the model’s ability to reproduce temperature profiles observed in the field. Simulations using Kd values calculated by equations that included TSS as a variable produced better results than those of an equation that included Chla as a variable. Further research will focus on evaluating other Kd calculation methods and comparing these results with simulations of different seasons. This study contributes valuable knowledge for eutrophication modelling which would be beneficial to coastal zone management in Cartagena Bay.

The significant advances in high-performance computational resources have boosted the seamless evolution in ocean modelling techniques and numerical efficiency, giving rise to an inventory of operational ocean forecasting systems with ever-increasing complexity. The skill of the Iberia-Biscay-Ireland (IBI) regional ocean forecasting system, implemented within the frame of the Copernicus Marine Environment Monitoring Service (CMEMS), is routinely evaluated by means of the NARVAL (North Atlantic Regional VALidation) web-based toolbox. Multi-parameter comparisons against observational sources (encompassing both in situ end remote-sensing platforms) are regularly conducted along with model intercomparisons in the overlapping areas. Product quality indicators and skill metrics are automatically computed not only averaged over the entire IBI domain but also over specific sub-regions of particular interest in order to identify strengths and weaknesses of each model. The primary goal of this work is three-fold. Firstly, to provide a flavor of the basic functionalities of NARVAL software package in order to elucidate the accuracy of IBI near real time forecast components (physical, biogeochemical and waves); secondly, to showcase a number of the practical applications of NARVAL; finally, to present the future roadmap to build a new upgraded version of this software package, which will include the quality assessment of multi-year and interim products, the computation of long-term skill metrics or the evaluation of event-oriented multi-model intercomparison exercises. This synergistic approach, based on the integration of numerical models and multi-platform observational networks, should be useful to comprehensively characterize the highly dynamic sea states and the dominant modes of spatio-temporal variability.

This research proposes a modelling framework in which simulation and optimisation tools are used together in order to obtain optimal reservoir operation rules for the multi-objective Dautieng reservoir on the Saigon River (Vietnam), where downstream salinity control is the main objective. In this framework, hydrodynamic and salinity transport modelling of the Saigon River is performed using the MIKE 11 modelling system. In the first optimisation step this simulation model is coupled with the population simplex evolution (PSE) algorithm from the AUTOCAL optimisation utility (available as a part of MIKE 11) to estimate the discharge required to meet salinity standards at the downstream location of Hoa Phu pumping station for public water supply. In the second optimisation step, with the use of MATLAB optimisation toolbox, an elitist multi-objective genetic algorithm is coupled with a simple water balance model of the Dautieng reservoir to investigate how the optimised discharges obtained from the first optimisation step can be balanced with the other objectives of the reservoir.The results indicate that optimised releases improve the performance of the reservoir especially on controlling salinity at Hoa Phu pumping station. In addition, the study demonstrates that use of smaller time steps in optimisation gives a closer match between varying demands and releases.

In this paper we describe our endeavours to explore the role of unsupervised learning technology in profiling marine conditions. The characterization of the marine environment with hydrographic variables allows, for example, to make technical and health control of sea products. However, the continuous monitoring of the environment produces large amounts of data and, thus, new information technology tools are needed to support decision-making. We present here a first contribution to this area by building a tool able to represent and normalize hydrographic conditions, cluster them using unsupervised learning methods, and present the results to domain experts. The tool, which implements visualization methods adapted to the problem at hand, was developed under the supervision of specialists on monitoring marine environment in Galicia (Spain). This software solution is promising to early identify risk factors and to gain a better understanding of sea conditions.

Toxins from harmful algae and certain food pathogens (Escherichia coli and Norovirus) found in shellfish can cause significant health problems to the public and have a negative impact on the economy. For the most part, these outbreaks cannot be prevented but, with the right technology and know-how, they can be predicted. These Early Warning Systems (EWS) require reliable data from multiple sources: satellite imagery, in situ data and numerical tools. The data is processed and analyzed and a short-term forecast is produced. Computational science is at the heart of any EWS. Current models and forecast systems are becoming increasingly sophisticated as more is known about the dynamics of an outbreak. This paper discusses the need, main components and future challenges of EWS.

Here, we describe our efforts to create a multi-scale and multi-physics framework that can be retargeted across different disciplines. Currently we have implemented our approach in the astrophysical domain, for which we developed AMUSE ( github.com/amusecode/amuse ), and generalized this to the oceanographic and climate sciences, which led to the development of OMUSE ( bitbucket.org/omuse ). The objective of this paper is to document the design choices that led to the successful implementation of these frameworks as well as the future challenges in applying this approach to other domains.

We introduce a new parallelizable numerical multiscale method for advection-dominated problems as they often occur in engineering and geosciences. State of the art multiscale simulation methods work well in situations in which stationary and elliptic scenarios prevail but are prone to fail when the model involves dominant lower order terms which is common in applications. We suggest to overcome the associated difficulties through a reconstruction of subgrid variations into a modified basis by solving many independent (local) inverse problems that are constructed in a semi-Lagrangian step. Globally the method looks like a Eulerian method with multiscale stabilized basis. The method is extensible to other types of Galerkin methods, higher dimensions, nonlinear problems and can potentially work with real data. We provide examples inspired by tracer transport in climate systems in one and two dimensions and numerically compare our method to standard methods.

Computational models have been widely used to predict the efficacy of surgical interventions in response to Peripheral Occlusive Diseases. However, most of them lack a multiscale description of the development of the disease, which, in our hypothesis, is the key to develop an effective predictive model. Accordingly, in this work we present a multiscale computational framework that simulates the generation of atherosclerotic arterial occlusions. Starting from a healthy artery in homeostatic conditions, the perturbation of specific cellular and extracellular dynamics led to the development of the pathology, with the final output being a diseased artery. The presented model was developed on an idealized portion of a Superficial Femoral Artery (SFA), where an Agent-Based Model (ABM), locally replicating the plaque development, was coupled to Computational Fluid Dynamics (CFD) simulations that define the Wall Shear Stress (WSS) profile at the lumen interface. The ABM was qualitatively validated on histological images and a preliminary analysis on the coupling method was conducted. Once optimized the coupling method, the presented model can serve as a predictive platform to improve the outcome of surgical interventions such as angioplasty and stent deployment.

A mathematical model of the flow of a polyatomic gas containing a combination of the Navier-Stokes-Fourier model (NSF) and the model kinetic equation of polyatomic gases is presented. At the heart of the hybrid components is a unified physical model, as a result of which the NSF model is a strict first approximation of the model kinetic equation. The model allows calculations of flow fields in a wide range of Knudsen numbers ( $$ Kn $$ ), as well as fields containing regions of high dynamic nonequilibrium. The boundary conditions on a solid surface are set at the kinetic level, which allows, in particular, to formulate the boundary conditions on the surfaces absorbing or emitting gas. The hybrid model was tested. The example of the problem of the shock wave profile shows that up to Mach numbers $$ M \approx 2 $$ the combined model gives smooth solutions even in those cases where the sewing point is in a high gradient region. For the Couette flow, smooth solutions are obtained at $$ M = 5 $$ , $$ Kn = 0.2 $$ . A model effect was discovered: in the region of high nonequilibrium, there is an almost complete coincidence of the solutions of the kinetic region of the combined model and the “pure” kinetic solution.

In this paper, a parametric study has been conducted to evaluate the effects of frequency and duration of the short burst pulses during pulsed radiofrequency ablation (RFA) in treating chronic pain. Affecting the brain and nervous system, this disease remains one of the major challenges in neuroscience and clinical practice. A two-dimensional axisymmetric RFA model has been developed in which a single needle radiofrequency electrode has been inserted. A finite-element-based coupled thermo-electric analysis has been carried out utilizing the simplified Maxwell’s equations and the Pennes bioheat transfer equation to compute the electric field and temperature distributions within the computational domain. Comparative studies have been carried out between the continuous and pulsed RFA to highlight the significance of pulsed RFA in chronic pain treatment. The frequencies and durations of short burst RF pulses have been varied from 1 Hz to 10 Hz and from 10 ms to 50 ms, respectively. Such values are most commonly applied in clinical practices for mitigation of chronic pain. By reporting such critical input characteristics as temperature distributions for different frequencies and durations of the RF pulses, this computational study aims at providing the first-hand accurate quantitative information to the clinicians on possible consequences in those cases where these characteristics are varied during the pulsed RFA procedure. The results demonstrate that the efficacy of pulsed RFA is significantly dependent on the duration and frequency of the RF pulses.

Transient molecular-continuum coupled flow simulations often suffer from high thermal noise, created by fluctuating hydrodynamics within the molecular dynamics (MD) simulation. Multi-instance MD computations are an approach to extract smooth flow field quantities on rather short time scales, but they require a huge amount of computational resources. Filtering particle data using signal processing methods to reduce numerical noise can significantly reduce the number of instances necessary. This leads to improved stability and reduced computational cost in the molecular-continuum setting.We extend the Macro-Micro-Coupling tool (MaMiCo) – a software to couple arbitrary continuum and MD solvers – by a new parallel interface for universal MD data analytics and post-processing, especially for noise reduction. It is designed modularly and compatible with multi-instance sampling. We present a Proper Orthogonal Decomposition (POD) implementation of the interface, capable of massively parallel noise filtering. The resulting coupled simulation is validated using a three-dimensional Couette flow scenario. We quantify the denoising, conduct performance benchmarks and scaling tests on a supercomputing platform. We thus demonstrate that the new interface enables massively parallel data analytics and post-processing in conjunction with any MD solver coupled to MaMiCo.

Galerkin projection provides a formal means to project a differential equation onto a set of preselected basis functions. This may be done for the purpose of formulating a numerical method, as in the case of spectral methods, or formulation of a reduced-order model (ROM) for a complex system. Here, a new method is proposed in which the basis functions used in the projection process are determined from an asymptotic (perturbation) analysis. These asymptotic basis functions (ABF) are obtained from the governing equation itself; therefore, they contain physical information about the system and its dependence on parameters contained within the mathematical formulation. This is referred to as reduced-physics modeling (RPM) as the basis functions are obtained from a physical model-driven, rather than data-driven, technique. This new approach is tailor-made for modeling multiscale problems as the various scales, whether overlapping or distinct in time or space, are formally accounted for in the ABF. Regular- and singular-perturbation problems are used to illustrate that projection of the governing equations onto the ABF allows for determination of accurate approximate solutions for values of the “small” parameter that are much larger than possible with the asymptotic expansion alone and naturally accommodate multiscale problems in which large gradients occur in adjacent regions of the domain.

Multiscale simulations are an essential computational method in a range of research disciplines, and provide unprecedented levels of scientific insight at a tractable cost in terms of effort and compute resources. To provide this, we need such simulations to produce results that are both robust and actionable. The VECMA toolkit (VECMAtk), which is officially released in conjunction with the present paper, establishes a platform to achieve this by exposing patterns for verification, validation and uncertainty quantification (VVUQ). These patterns can be combined to capture complex scenarios, applied to applications in disparate domains, and used to run multiscale simulations on any desktop, cluster or supercomputing platform.

This paper presents a weak Galerkin (WG) finite element solver for Darcy flow and its implementation on the deal.II platform. The solver works for quadrilateral and hexahedral meshes in a unified way. It approximates pressure by Q-type degree $$k({\ge }0)$$ polynomials separately defined in element interiors and on edges/faces. Numerical velocity is obtained in the unmapped Raviart-Thomas space $$ RT_{[k]} $$ via postprocessing based on the novel concepts of discrete weak gradients. The solver is locally mass-conservative and produces continuous normal fluxes. The implementation in deal.II allows polynomial degrees up to 5. Numerical experiments show that our new WG solver performs better than the classical mixed finite element methods.

The velocity, coupling term in the flow and transport problems, is important in the accurate numerical simulation or in the posteriori error analysis for adaptive mesh refinement. We consider Enhanced Velocity Mixed Finite Element Method (EVMFEM) for the incompressible Darcy flow. In this paper, our aim is to study the improvement of velocity at interface to achieve the better approximation of velocity between subdomains. We propose the reconstruction of velocity at interface by using the post-processed pressure. Numerical results at the interface show improvement on convergence rate.

In this work we propose a new combination of finite element methods to solve incompressible miscible displacements in heterogeneous media formed by the coupling of the free-fluid with the porous medium employing the stabilized hybrid mixed finite element method developed and analyzed by Igreja and Loula in [10] and the classical Streamline Upwind Petrov-Galerkin (SUPG) method presented and analyzed by Brooks and Hughes in [2]. The hydrodynamic problem is governed by the Stokes and Darcy systems coupled by Beavers-Joseph-Saffman interface conditions. To approximate the Stokes-Darcy coupled system we apply the stabilized hybrid mixed method, characterized by the introduction of the Lagrange multiplier associated with the velocity field in both domains. This choice naturally imposes the Beavers-Joseph-Saffman interface conditions on the interface between Stokes and Darcy domains. Thus, the global system is assembled involving only the degrees of freedom associated with the multipliers and the variables of interest can be solved at the element level. Considering the velocity fields given by the hybrid method we adopted the SUPG method combined with an implicit finite difference scheme to solve the transport equation associated with miscible displacements. Numerical studies are presented to illustrate the flexibility and robustness of the hybrid formulation. To verify the efficiency of the combination of hybrid and SUPG methods, computer simulations are also presented for the recovery hydrological flow problems in heterogeneous porous media, such as continuous injection.

We consider the affect of capillary pressure on the Van der Waals fuid and on the Peng-Robinson fluid by minimizing total Helmholtz energy in given total volume, temperature, and total moles. We propose simple but conditionally energy stable numerical schemes, and we provide interesting numerical examples. We compare our numerical results with the prediction of Kelvin’s equation, indicating that Kelvin’s equation works well only when the temperature is not too low.

In this paper, the effects of different numerical integration schemes on the distributed Lagrange multiplier/fictitious domain (DLM/FD) method with body-unfitted meshes are studied for solving different types of interface problems: elliptic-, Stokes- and Stokes/elliptic-interface problems. Commonly-used numerical integration schemes, compound type formulas and a specific subgrid integration scheme are presented for the mixed finite element approximation and the comparison between them is illustrated in numerical experiments, showing that different numerical integration schemes have significant effects on approximation errors of the DLM/FD finite element method for different types of interface problems, especially for Stokes- and Stokes/elliptic-interface problems, and that the subgrid integration scheme always results in numerical solutions with the best accuracy.

In this paper we discuss an application of the double potential method for modelling flow of incompressible fluid. This method allows us to avoid the known difficulties in calculating pressure and overcome the instability of numerical solution. Also, the double potential method enables us to simplify the problem of boundary conditions setting. It arises when computing the incompressible fluid flow by the Navier-Stokes equations in the vector potential - velocity rotor variables. In the approach given, the final system of equations is approximated through applying the finite volume method. In this case, an exponential transformation of the flow terms is applied. A parallel program was developed by means of using MPI and OpenMP technologies for the purpose of the numerical method computer implementation. We used two tasks to test. One of them deals with the classical calculation of the fluid flow establishment in a long round pipe. The other one is connected with the flow calculation in the pipe that in the output region contains a separation into two symmetrical parts. To perform numerical simulation, we take into consideration the steady flow with Reynolds numbers of 50 and 100. The numerical results obtained are consistent with computational results received through using the ANSYS CFD package.

The formation of the bubbles in the liquid was examined numerically and results were successfully compared with the results provided by experiments. The study covered two different patterns defined by different Morton numbers or gas flow rates. The unsteady three dimensional calculations were carried out in code OpenFoam with the volume of fluid approach. Numerical results were in a good match to the experiments in respect to bubble shapes, diameters and Reynolds numbers. More accurate comparison was found for lower gas flow rate then for the higher one. The main reason can be that under higher gas flow rate, a complex flow behavior between gas bubbles and surrounding liquid flow is created which worsens the accuracy of calculations. The main important output of the study was a comparison of the bubble diameters in time. Especially for higher gas flow rates, bubbles are growing rapidly during its climbing. Nevertheless a satisfactory agreement was found between numerics and experiments.

A direct numerical simulation (DNS) code was developed for solving incompressible homogeneous isotropic turbulence with high Reynolds numbers in a periodic box using the Fourier spectral method. The code was parallelized using the Message Passing Interface and OpenMP with two-directional domain decomposition and optimized on the K computer. High resolution DNSs with up to $$12288^3$$ grid points were performed on the K computer using the code. Efficiencies of 3.84%, 3.14%, and 2.24% peak performance were obtained in double precision DNSs with $$6144^3$$ , $$8192^3$$ , and $$12288^3$$ grid points, respectively. In addition, a two-path alias-free procedure is proposed and clarified its effectiveness for some number of parallel processes.

This work presents Direct Numerical Simulation of mass transfer from buoyancy-driven bubbles rising in a wall-confined vertical channel, through a multiple markers level-set method. The Navier-Stokes equations and mass transfer equation are discretized using a finite volume method on a collocated unstructured mesh, whereas a multiple markers approach is used to avoid the numerical coalescence of bubbles. This approach is based on a mass conservative level-set method. Furthermore, unstructured flux-limiter schemes are used to discretize the convective term of momentum equation, level-set advection equations, and mass transfer equation, to improve the stability of the solver in bubbly flows with high Reynolds number and high-density ratio. The level-set model is used to research the effect of bubble-bubble and bubble-wall interactions on the mass transfer from a bubble swarm rising in a vertical channel with a circular cross-section.

A hybrid particle/mesh Vortex Method, called remeshed vortex method, is proposed in this work to simulate three-dimensional incompressible flows. After a validation study of the present method in the context of Direct Numerical Simulations, an anisotropic artificial viscosity model is proposed in this paper in order to handle multi-resolutions simulations in the context of vortex methods.

Multiphase fluid flow with complex compositions is an increasingly attractive research topic with more and more attentions paid on related engineering problems, including global warming and green house effect, oil recovery enhancement and subsurface water pollution treatment. Prior to study the flow behaviors and phase transitions in multi-component multiphase flow, the first effort should be focused on the accurate prediction of the total phase numbers existing in the fluid mixture, and then the phase equilibrium status can be determined. In this paper, a novel and fast prediction technique is proposed based on deep learning method. The training data is generated using a selected VT dynamic flash calculation scheme and the network constructions are deeply optimized on the activation functions. Compared to previous machine learning techniques proposed in literatures to accelerate vapor liquid phase equilibrium calculation, the total number of phases existing in the mixture is determined first and other phase equilibrium properteis will be estimated then, so that we do not need to ensure that the mixture is in two phase conditions any more. Our method could handle fluid mixtures with complex compositions, with 8 different components in our example and the original data is in a large amount. The analysis on prediction performance of different deep learning models with various neural networks using different activation functions can help future researches selecting the features to construct the neural network for similar engineering problems. Some conclusions and remarks are presented at the end to help readers catch our main contributions and insight the future related researches.

Enhanced geothermal systems (EGS) are the major way of the hot dry rock (HDR) exploitation. At present, the finite element method (FEM) is often used to simulate the thermal energy extraction process of the EGS. Satisfactory results can be obtained by this method to a certain extent. However, when many discrete fractures exist in the computational domain, a large number of unstructured grids must be used, which seriously affects the computational efficiency. To solve this challenge, based on the embedded discrete fracture model (EDFM), two sets of seepage and energy conservation equations are respectively used to describe the flow and heat transfer processes of the matrix and the fracture media. The main advantages of the proposed model are that the structured grids can be used to mesh the matrix, and there is no need to refine the mesh near the fracture. Compared with commercial software, COMSOL Multiphysics, the accuracy of the proposed model is verified. Subsequently, a specific example of geothermal exploitation is designed, and the spatial-temporal evolutions of pressure and temperature fields are analyzed.

Due to the simultaneous effects of different physical phenomena that occur on different length and time scales, modelling the fusion and heat affected microstructure of an Additive Manufacturing (AM) process requires more than intelligent meshing schemes to make simulations feasible. The purpose of this research was to develop an efficient high quality and high precision thermal model in wire + arc additive manufacturing process. To quantify the influence of the process parameters and materials on the entire welding process, a 3D transient non-linear finite element model to simulate multi-layer deposition of cast IN-738LC alloy onto SAE-AISI 1524 Carbon Steel Substrates was developed. Temperature-dependent physical properties and the effect of natural and forced convection were included in the model. A moving heat source was applied over the top surface of the specimen during a period of time that depends on the welding speed. The effect of multi-layer deposition on the prediction and validation of melting pool shape and thermal cycles was also investigated. The heat loss produced by convection and radiation in the AM layers surfaces were included into the finite element analysis. As the AM layers itself act as extended surfaces (fins), it was found that the heat extraction is quite significant. The developed thermal model is quite accurate to predict thermal cycles and weld zones profiles. A firm foundation for modelling thermal transport in wire + arc additive manufacturing process it was established.