Computation, Volume 10, Issue 3 (March 2022) – 13 articles

Quantum embedding is a divide and conquer strategy that aims at solving the electronic Schrödinger equation of sizeable molecules or extended systems. We establish in the present work a clearer and in-principle-exact connection between density matrix embedding theory (DMET) and density-functional theory (DFT) within the simple but nontrivial one-dimensional Hubbard model. For that purpose, we use our recent reformulation of single-impurity DMET as a Householder transformed density-matrix functional embedding theory (Ht-DMFET). On the basis of well-identified density-functional approximations, a self-consistent local potential functional embedding theory (LPFET) is formulated and implemented. Combining both LPFET and DMET numerical results with our formally exact density-functional embedding theory reveals that a single statically embedded impurity can in principle describe the density-driven Mott–Hubbard transition, provided that a complementary density-functional correlation potential (which is neglected in both DMET and LPFET) exhibits a derivative discontinuity (DD) at half filling. The extension of LPFET to multiple impurities (which would enable to circumvent the modeling of DDs) and its generalization to quantum chemical Hamiltonians are left for future work. Full article

This study aims at developing models in analyzing the results of proficiency testing (PT) schemes for a limited number of participants. The models can determine the best estimators of location and dispersion using unsatisfactory results as a criterion by combining: (a) robust and classical estimators; (b) kernel density plots; (c) Z-factors; (d) Monte Carlo simulations; (e) distributions derived from the addition of one or two contaminating distributions and one main Gaussian. The standards ISO 13258:2015, ISO 5725:2:1994, and EN ISO/IEC 17043:2010 are the basis of the analysis. The study describes an algorithm solving the optimization problem for (a) Gaussian, bimodal or trimodal distributions; (b) participating labs from 10 to 30; (c) fraction of the contaminating population up to 0.10; (d) variation coefficient of the main distribution equal to 2; (e) equal standard deviations of all the distributions, and provide figures with the optimal estimators. We also developed a generalized algorithm using kernel density plots and the previous algorithm, which is not subject to restrictions (b)–(e) and implemented in the results of a PT for the 28-day strength of cement with 12–13 participants. Optimal estimators’ figures and the generalized algorithm are helpful for a PT expert in choosing robust estimators. Full article

Full-potential linearized augmented plane wave (LAPW) and APW plus local orbital (APW+$lo$) codes differ widely in both their user interfaces and in capabilities for calculations and analysis beyond their common central task of all-electron solution of the Kohn–Sham equations. However, that common central task opens a possible route to performance enhancement, namely to offload the basic LAPW/APW+$lo$ algorithms to a library optimized purely for that purpose. To explore that opportunity, we have interfaced the Exciting-Plus (“EP”) LAPW/APW+$lo$ DFT code with the highly optimized SIRIUS multi-functional DFT package. This simplest realization of the separation of concerns approach yields substantial performance over the base EP code via additional task parallelism without significant change in the EP source code or user interface. We provide benchmarks of the interfaced code against the original EP using small bulk systems, and demonstrate performance on a spin-crossover molecule and magnetic molecule that are of size and complexity at the margins of the capability of the EP code itself. Full article

Maritime operations rely heavily on surveillance and require reliable and timely data that can inform decisions and planning. Critical information in such cases includes the exact location of objects in the water, such as vessels, persons, and others. Due to the unique characteristics of the maritime environment, the location of even inert objects changes through time, depending on the weather conditions, water currents, etc. Unmanned aerial vehicles (UAVs) can be used to support maritime operations by providing live video streams and images from the area of operations. Machine learning algorithms can be developed, trained, and used to automatically detect and track objects of specific types and characteristics. EFFECTOR is an EU-funded project, developing an Interoperability Framework for maritime surveillance. Within the project, we developed an embedded system that employs machine learning algorithms, allowing a UAV to autonomously detect objects in the water and keep track of their changing position through time. Using the on-board computation unit of the UAV, we ran and present the results of a series of comparative tests among possible architecture sizes and training datasets for the detection and tracking of objects in the maritime environment. We tested architectures based on their efficiency, accuracy, and speed. A combined solution for training the datasets is suggested, providing optimal efficiency and accuracy. Full article

This paper deals with the issues of computation of the nonlinear load harmonic currents in the presence of external distortions based on the real measurements with help of passive harmonic. Such values are necessary when modeling an equivalent nonlinear load as current sources in the presence of external distortions. The passive filter allows to separate external and internal distortions, which is necessary when determining the harmonic currents of a single consumer. The influence of various parameters on the computation of harmonic currents of a nonlinear load, such as the parameters of a passive harmonic filter, the impedance of the power supply transformer, the load parameters of the consumer, taking into account the harmonic generation from the grid side, and from the consumer side, is considered. It is shown that an external source of distortion has practically no effect on the error in estimating the harmonic current magnitudes of a nonlinear load. The obtained simulation results were confirmed experimentally on a laboratory test bench. Recommendations for the selection of passive harmonic filter parameters have been developed to minimize the error in determining the harmonic current magnitudes of a nonlinear load. Full article

The scope of this research is to develop a hyperstable adaptive control system of a direct current (DC) drive speed for effective load torque attenuation. The proposed speed controller is based on the model reference adaptive control framework and integrated into the conventional DC drive cascade control system. Its main features are as follows: (1) the boundedness of the control action signal, as well as the armature current control loop non-stationarities, are taken into consideration with the help of the reference model hedging technique; (2) its inputs include only measurable signals, thus there is no need to use any kind of state estimators; (3) it attenuates the disturbances, which are matched with its control action signal, particularly, the inertia moment non-stationarity and load torque. The asymptotic hyperstability of the obtained DC drive control system is proven with the help of Lyapunov’s theorems and Popov’s criterion. The numerical experiments corroborate the obtained results. They include the demonstration of disadvantages of the conventional cascade control system under conditions of the drive parameters’ non-stationarity and advantages of the proposed solution for different disturbance types and amplitudes. Full article

The interest for properties of clusters deposited on surfaces has grown in recent years. In this framework, the Density Functional based Tight Binding (DFTB) method appears as a promising tool due to its ability to treat extended systems at the quantum level with a low computational cost. We report the implementation of periodic boundary conditions for DFTB within the deMonNano code with k-points formalism and corrections for intermolecular interactions. The quality of DFTB results is evaluated by comparison with dispersion-corrected DFT calculations. Optimized lattice properties for a graphene sheet and graphite bulk are in agreement with reference data. The deposition of both benzene monomer and dimers on graphene are investigated and the observed trends are similar at the DFT and DFTB levels. Moreover, interaction energies are of similar orders of magnitude for these two levels of calculation. This study has evidenced the high stability of a structure made of two benzene molecules deposited close to each other on the graphene sheet. This work demonstrates the ability of the new implementation to investigate surface-deposited molecular clusters properties. Full article

This paper presents a regularization framework that aims to improve the fidelity of Tikhonov inverse solutions. At the heart of the framework is the data-informed regularization idea that only data-uninformed parameters need to be regularized, while the data-informed parameters, on which data and forward model are integrated, should remain untouched. We propose to employ the active subspace method to determine the data-informativeness of a parameter. The resulting framework is thus called a data-informed (DI) active subspace (DIAS) regularization. Four proposed DIAS variants are rigorously analyzed, shown to be robust with the regularization parameter and capable of avoiding polluting solution features informed by the data. They are thus well suited for problems with small or reasonably small noise corruptions in the data. Furthermore, the DIAS approaches can effectively reuse any Tikhonov regularization codes/libraries. Though they are readily applicable for nonlinear inverse problems, we focus on linear problems in this paper in order to gain insights into the framework. Various numerical results for linear inverse problems are presented to verify theoretical findings and to demonstrate advantages of the DIAS framework over the Tikhonov, truncated SVD, and the TSVD-based DI approaches. Full article

Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, a widely used approach is based on the Pareto front. In this document, a proposal is made for the analysis of the optimal front for multi-objective optimization problems using clustering techniques. With this approach, an alternative is sought for further use and improvement of multi-objective optimization algorithms considering solutions and clusters found. To carry out the clustering, the methods k-means and fuzzy c-means are employed, in such a way that there are two alternatives to generate the possible clusters. Regarding the results, it is observed that both clustering algorithms perform an adequate separation of the optimal Pareto continuous fronts; for discontinuous fronts, k-means and fuzzy c-means obtain results that complement each other (there is no superior algorithm). In terms of processing time, k-means presents less execution time than fuzzy c-means. Full article

Early prediction of mortality and risk of deterioration in COVID-19 patients can reduce mortality and increase the opportunity for better and more timely treatment. In the current study, the DL model and explainable artificial intelligence (EAI) were combined to identify the impact of certain attributes on the prediction of mortality and ventilatory support in COVID-19 patients. Nevertheless, the DL model does not suffer from the curse of dimensionality, but in order to identify significant attributes, the EAI feature importance method was used. The DL model produced significant results; however, it lacks interpretability. The study was performed using COVID-19-hospitalized patients in King Abdulaziz Medical City, Riyadh. The dataset contains the patients’ demographic information, laboratory investigations, and chest X-ray (CXR) findings. The dataset used suffers from an imbalance; therefore, balanced accuracy, sensitivity, specificity, Youden index, and AUC measures were used to investigate the effectiveness of the proposed model. Furthermore, the experiments were conducted using original and SMOTE (over and under sampled) datasets. The proposed model outperforms the baseline study, with a balanced accuracy of 0.98 and an AUC of 0.998 for predicting mortality using the full-feature set. Meanwhile, for predicting ventilator support a highest balanced accuracy of 0.979 and an AUC of 0.981 was achieved. The proposed explainable prediction model will assist doctors in the early prediction of COVID-19 patients that are at risk of mortality or ventilatory support and improve the management of hospital resources. Full article

Ensuring citizens’ safety and security has been identified as the number one priority for city authorities when it comes to the use of smart city technologies. Automatic understanding of the scene, and the associated provision of situational awareness for emergency situations, are able to efficiently contribute to such domains. In this study, a Video Analytics Edge Computing (VAEC) system is presented that performs real-time enhanced situation awareness for person detection in a video surveillance manner that is also able to share geolocated person detection alerts and other accompanied crucial information. The VAEC system adopts state-of-the-art object detection and tracking algorithms, and it is integrated with the proposed Distribute Edge Computing Internet of Things (DECIoT) platform. The aforementioned alerts and information are able to be shared, though the DECIoT, to smart city platforms utilizing proper middleware. To verify the utility and functionality of the VAEC system, extended experiments were performed (i) in several light conditions, (ii) using several camera sensors, and (iii) in several use cases, such as installed in fixed position of a building or mounted to a car. The results highlight the potential of VAEC system to be exploited by decision-makers or city authorities, providing enhanced situational awareness. Full article

The main purpose of this work is to simulate the flow of air and solid particles over a wildfire and to investigate the single and multiphase flow over the surface of a custom-designed wing with an Eppler-420 airfoil including an appendant custom-designed blended winglet. The wing is the result of a conceptual and preliminary design of a small-scale unmanned aerial vehicle (UAV) designed to assist in firefighting. The fire embers will be simulated in the Ansys Fluent commercial code as solid particles injected in the continuous phase, in an Euler–Lagrange approach. Primarily studied were the response of the model in air and air–solid flows, as well as the impact on aerodynamic efficiency due to the existence of the second phase. Moreover, the effects of unstructured, structured and mosaic poly-hexcore meshes are investigated and compared. The computational fluid dynamics (CFD) simulations, were implemented using a pressure-based solver, spatial discretization was conducted with a second-order upwind scheme, and the k-omega SST (k-ω SST) turbulence model was applied. Meanwhile, the two-phase flow was simulated using the Discrete Phase Model with reflect boundary condition on the surface of the wing and two-way coupling between continuous and discrete phase. To validate the results, experiments were conducted in a subsonic wind tunnel using a 3D printed model of the wing. The results show good agreement between simulations and experiments, with the structured mesh coming closer to reality, followed by the mosaic and unstructured meshes, respectively. Finally, a reduction in the aerodynamic efficiency of the wing section is observed, due to the presence of solid particles. Full article

The correlation between computational fluid dynamics (CFD) and experimental fluid dynamics (EFD) is crucial for the behavior prediction of aerodynamic bodies. This paper’s objective is twofold: (1) to develop a method that approaches commercial CFD codes and their link with EFD in a more efficient way, using a downscaled model, and (2) to investigate the effect of rain on the aerodynamic behavior of a wing. More specifically, we investigate the one-phase and two-phase flow over a typical wing section NACA 641-212 airfoil, in the commercial code Ansys Fluent. Two computational models were developed; the first model represents the original dimensions of the wing, while the second is downscaled to 23% of the original. The response of the models in air and air–water flow were primarily studied, as well as the impact on aerodynamic efficiency due to the existence of the second phase. For the computational fluid dynamics simulations, a pressure-based solver with a second-order upwind scheme for the spatial discretization and the Spalart–Allmaras (SA) turbulence model were utilized. Meanwhile, for the two-phase flow of air–water, the discrete phase model (DPM) with wall–film boundary conditions on the surface of the wing and two-way coupling between continuous and discrete phase was considered. The second phase was simulated as water droplets injected in the continuous phase, in a Euler–Lagrange approach. The experimental model was constructed in accordance with the downscaled model and tested in a subsonic wind tunnel, using 3D printing technology which reduced the experiment expenses. The presence of water in two-phase flow was proven to deteriorate the aerodynamic factors of the wing compared to one-phase flow, as expected. The three-stage comparison of CFD and EFD results showed a very good convergence, in both single and two-phase flow. This can lead to the conclusion that a rapid and low-cost study for the estimation of the aerodynamic performance of objects with high accuracy is feasible with the suggested method. Full article