Limited Computational Cost Research Articles

Rotorcraft comprehensive code assessment for blade–vortex interaction conditions

The scope of this paper is the presentation of the computational methodologies applied in the comprehensive code for rotorcraft developed in the last years at Roma Tre University, along with the assessment of its prediction capabilities focused on flight conditions characterized by strong blade–vortex interactions. Boundary element method approaches are applied for both potential aerodynamics and aeroacoustics solutions, whereas a harmonic-balance/modal approach is used to integrate the rotor aeroelastic equations. The validation campaign of the comprehensive code has been carried out against the well-known HART II database, which is the outcome of a joint multi-national effort aimed at performing wind tunnel measurements of loads, blade deflection, wake shape and noise concerning a four-bladed model rotor in low-speed descent flight. Comparisons with numerical simulations available in the literature for the same test cases are also presented. It is shown that, with limited computational cost, the results provided by the Roma Tre aero-acousto-elastic solver are in good agreement with the experimental data, with a level of accuracy that is in line with the state-of-the-art predictions. The influence of the vortex core modeling on aerodynamic predictions and the influence of the inclusion of the fuselage shielding effect on aeroacoustic predictions are discussed.

Modeling yield properties of compacted powder using a multi-particle finite element model with cohesive contacts

The multi- particle finite element method (MPFEM) was used to simulate the yield properties of compacted powders with cohesive contacts. A suitable representative volume element (RVE) of monodisperse, spherical, deformable particles was created to be implemented into a commercially available finite element code. New efficient periodic boundary conditions were proposed to compute representative properties of the volume at limited computational costs. A contact model was introduced, which includes repulsive forces, friction forces and cohesion forces. As a result, the proposed model is capable of considering tensile strength in a MPFEM setting, which was not attainable in related published work.We present extensive parameter studies to demonstrate the performance of the proposed RVE and to find the optimal balance between accuracy and computational speed. The minimum mesh fineness and the minimum number of particles in the RVE were determined during convergence studies. The employed explicit integration scheme was enhanced by means of mass scaling. The optimized model was used to predict the strength of compacted powders. A simple analytical expression was fitted to the numerical predictions to describe the uniaxial tensile strength and the uniaxial compression strength as a function of the powders' relative density and the cohesion strength of the contacts. A general form of a yield surface was proposed to describe the yield properties for generic load cases, which can be applied to different relative densities and cohesion strengths. As a result, we showed that the yield surfaces grow with increasing relative density, while they change their shape with increasing cohesion strength. The obtained yield surface results in the Drucker-Prager/Cap model in case of low cohesion, whereas it has an elliptical shape in case of high cohesion. The proposed analytical form of the yield surface is capable of describing both cases.

A faster algorithm for the calculation of the fast spectral correlation

Abstract One of the main aims of second order cyclostationary (CS2) analysis is the estimation of the full spectral correlation, allowing the identification of different CS2 components in a signal and their characterisation in terms of both spectral frequency f and cyclic frequency α . Unfortunately, traditional estimators of the full spectral correlation (e.g. averaged cyclic periodogram) are highly computationally expensive and hence their application has been quite limited. On the other hand, fast envelope-based CS2 indicators (e.g. cyclic modulation spectrum, CMS) are bound by a cyclic-spectral form of the uncertainty principle, which limits the extent of the cyclic frequency axis α max at approximately the value chosen for the spectral frequency axis resolution Δ f . A recent work has however introduced a ground-breaking approach resulting in a fast algorithm for the calculation of the spectral correlation. This approach is based on the calculation of a series of CMS-like quantities, each scanning a different cyclic-frequency band, given a certain spectral frequency resolution. The superposition of all these quantities allows covering a larger α -band breaking the constraint between maximum cyclic frequency α max and spectral frequency axis resolution Δ f , at a limited computational cost. In this paper a new algorithm for the calculation of the same fast spectral correlation is introduced, resulting in a further computational efficiency gain, and a simplification of the computational procedure.

Analysis of Multiple Binary Responses Using a Threshold Model

Several discrete responses, such as health status, reproduction performance and meat quality, are routinely collected for several livestock species. These traits are often of binary or discrete nature. Genetic evaluation for these traits is frequently conducted using a single-trait threshold model, or they are considered continuous responses either in univariate or in multivariate context. Implementation of threshold models in the presence of several binary responses or a mixture of binary and continuous responses is far from simple. The complexity of such implementation is primarily due to the incomplete randomness of the residual (co)variance matrix. In the current study, a multiple binary trait simulation was carried out in order to implement and validate a new procedure for dealing with the consequences of the restrictions imposed to the residual variance using threshold models. Using three and eight binary responses, the proposed method was able to estimate all unknown parameters without any noticeable bias. In fact, for simulated residual correlations ranging from −0.8 to 0.8, the resulting HPD 95% intervals included the true values in all cases. The proposed procedure involved limited additional computational cost and is straightforward to implement independent of the number of binary responses involved in the analysis. Monitoring of the convergence of the procedure must be conducted at the identifiable scale, and special care must be placed on the selection of the prior of the non-identifiable model. The latter could have serious consequences on the final results due to potential truncation of the parameter space.

Microscale poroelastic metamodel for efficient mesoscale bone remodelling simulations

Bone functional tissue adaptation is a multiaspect physiological process driven by interrelated mechanical and biological stimuli which requires the combined activity of osteoclasts and osteoblasts. In previous work, the authors developed a phenomenological mesoscale structural modelling approach capable of predicting internal structure of the femur based on daily activity loading, which relied on the iterative update of the cross-sectional areas of truss and shell elements representative of trabecular and cortical bones, respectively. The objective of this study was to introduce trabecular reorientation in the phenomenological model at limited computational cost. To this aim, a metamodel derived from poroelastic microscale continuum simulations was used to predict the functional adaptation of a simplified proximal structural femur model. Clear smooth trabecular tracts are predicted to form in the regions corresponding to the main trabecular groups identified in literature, at minimal computational cost.

Local residual similarity for image re-ranking

Similarity measurement is an essential component in image retrieval systems. While previous work is focused on generic distance estimation, this paper investigates the problem of similarity estimation within a local neighborhood defined in the original feature space. Specifically, our method is characterized in two aspects, i.e., “local” and “residual”. First of all, we focus on a subset of the top-ranked relevant images to a query, with which anchors are discovered by methods such as averaging or clustering. The anchors are then subtracted from the neighborhood features, resulting in residual representations. The proposed Local Residual Similarity (LRS) homogenizes the feature distances within the local neighborhood. Effective and efficient image re-ranking is achieved by calculating LRS between the query and the top-ranked images. The method constrains that relevant images should appear similar in both original and local residual feature space. We evaluate the proposed method on two image retrieval benchmarks with global CNN representations, demonstrating a consistent improvement on performance with very limited extra computational cost.

Industrial Wastes Risk Ranking with TOPSIS, Multi Criteria Decision Making Method

Today, various types of industrial waste are produced in different industries to meet human demands. Growth in quantity as well as complication in quality of these wastes are followed by the advance of technology. Management of such wastes need a proper identification and comprehensive understanding of the risk, emerging after the harmful characteristics of the wastes and negatively affect the human and environment health. Wastes risk ranking systems, in this regard, links between the industrial wastes indices and mathematical method/algorithm, being able at estimation of the risk level as well as comparison between the wastes of an industrial unit based on the risk level. Complexity of the method, high computational costs and lack of proper description of waste using selected indices in former studies has led to the proposal of an applicable and flexible method. In this study, the “TOPSIS Multi-Criteria Decision-Making (MCDM) method” was developed in order for ranking the risk of various industrial wastes. Totally, a number of 9 subsidiary indices on the human health and 11 subsidiary indices on the environment health was identified and employed. Finally, the proposed waste risk ranking system was used for ranking 9 types of identified industrial waste in three industrial section. Results show that the “TOPSIS MCDM”, due to the lack of complexities in method and limited computational costs, is an efficient and appropriate method for ranking industrial wastes.

Numerical modeling of the flow over wind turbine airfoils by means of Spalart–Allmaras local correlation based transition model

The aerodynamics of the blade sections constitute the core problem in the design of new-generation wind turbines. Aerodynamic theories for blade design suffer from the unavailability of aerodynamic coefficients for the airfoils involved in the blade. The aim of this work was therefore to develop an efficient and accurate tool for computing the flow parameters, reducing the need for complex and costly wind tunnel tests.Our approach is based on Computational Fluid Dynamics (CFD) and consists in the adoption of a laminar-to-turbulent transition model. For the flow regimes involved in wind energy applications, the fully turbulent or laminar flow model fails completely in the prediction of aerodynamic performance. We consequently propose a Reynolds Averaged Navier Stokes (RANS)-based approach capable of modeling transitional flows with a limited computational cost. This is a crucial aspect because standard RANS models assume a fully turbulent regime.Our proposed approach couples the well-known γ−Reθ,t˜ technique with the Spalart-Allmaras turbulence model. The effectiveness, efficiency and robustness of the computational method introduced here were tested by computing the flow over several wind turbine airfoils.

Simulation-based exploration of high-dimensional system models for identifying unexpected events

Mathematical numerical models are increasingly employed to simulate system behavior and identify sequences of events or configurations of the system’s design and operational parameters that can lead the system to extreme conditions (Critical Region, CR). However, when a numerical model is: i) computationally expensive, ii) high-dimensional, and iii) complex, these tasks become challenging.In this paper, we propose an adaptive framework for efficiently tackling this problem: i) a dimensionality reduction technique is employed for identifying the factors and variables that most affect the system behavior; ii) a meta-model is sequentially trained to replace the computationally expensive model with a computationally cheap one; iii) an adaptive exploration algorithm based on Markov Chain Monte Carlo is introduced for exploring the system state-space using the meta-model; iv) clustering and other techniques for the visualization of high dimensional data (e.g., parallel coordinates plot) are employed to summarize the retrieved information.The method is employed to explore a power network model involving 20 inputs. The CRs are properly identified with a limited computational cost, compared to another exploration technique of literature (i.e., Latin Hypercube Sampling).

Examples of computational approaches for elliptic, possibly multiscale PDEs with random inputs

We overview a series of recent works addressing numerical simulations of partial differential equations in the presence of some elements of randomness. The specific equations manipulated are linear elliptic, and arise in the context of multiscale problems, but the purpose is more general. On a set of prototypical situations, we investigate two critical issues present in many settings: variance reduction techniques to obtain sufficiently accurate results at a limited computational cost when solving PDEs with random coefficients, and finite element techniques that are sufficiently flexible to carry over to geometries with random fluctuations. Some elements of theoretical analysis and numerical analysis are briefly mentioned. Numerical experiments, although simple, provide convincing evidence of the efficiency of the approaches.

From regulatory life tables to stochastic mortality projections: The exponential decline model

Often in actuarial practice, mortality projections are obtained by letting age-specific death rates decline exponentially at their own rate. Many life tables used for annuity pricing are built in this way. The present paper adopts this point of view and proposes a simple and powerful mortality projection model in line with this elementary approach, based on the recently studied mortality improvement rates. Two main applications are considered. First, as most reference life tables produced by regulators are deterministic by nature, they can be made stochastic by superposing random departures from the assumed age-specific trend, with a volatility calibrated on market or portfolio data. This allows the actuary to account for the systematic longevity risk in solvency calculations. Second, the model can be fitted to historical data and used to produce longevity forecasts. A number of conservative and tractable approximations are derived to provide the actuary with reasonably accurate approximations for various relevant quantities, available at limited computational cost. Besides applications to stochastic mortality projection models, we also derive useful properties involving supermodular, directionally convex and stop-loss orders.

Using an atmospheric turbulence model for the stochastic model of geodetic VLBI data analysis

Space-geodetic techniques at radio wavelength, such as global navigation satellite systems and very long baseline interferometry (VLBI), suffer from refractivity of the Earth’s atmosphere. These highly dynamic processes, particularly refractivity variations in the neutral atmosphere, contribute considerably to the error budget of these space-geodetic techniques. Here, microscale fluctuations in refractivity lead to elevation-dependent uncertainties and induce physical correlations between the observations. However, up to now such correlations are not considered routinely in the stochastic model of space-geodetic observations, which leads to very optimistic standard deviations of the derived target parameters, such as Earth orientation parameters and station positions. In this study, the standard stochastic model of VLBI observations, which only includes, almost exclusively, the uncertainties from the VLBI correlation process, is now augmented by a variance–covariance matrix derived from an atmospheric turbulence model. Thus, atmospheric refractivity fluctuations in space and time can be quantified. One of the main objectives is to realize a suitable stochastic model of VLBI observations in an operational way. In order to validate the new approach, the turbulence model is applied to several VLBI observation campaigns consisting of different network geometries leading the path for the next-generation VLBI campaigns. It is shown that the stochastic model of VLBI observations can be improved by using high-frequency atmospheric variations and, thus, refining the stochastic model leads to far more realistic standard deviations of the target parameters. The baseline length repeatabilities as a general measure of accuracy of baseline length determinations improve for the turbulence-based solution. Further, this method is well suited for routine VLBI data analysis with limited computational costs.

Going beyond the vertical approximation with time‐dependent density functional theory

Since two decades, time‐dependent density functional theory (TD‐DFT) has been in the limelight due to its noteworthy efficiency. Indeed, in many cases, TD‐DFT provides accurate excited‐state properties for a relatively limited computational cost. Recently, there has been a rapidly growing interest in applications of TD‐DFT (partly) exploring the excited‐state potential energy surfaces. In this review, we summarize such TD‐DFT investigations going beyond the vertical approximation and devoted to spectroscopic applications, with a focus on both 0–0 energies and vibrationally resolved absorption and emission spectra. We show how these quantities can be computed, considering various models for the latter, and illustrate their advantages compared to vertical estimates in terms of comparisons with experimental data. WIREs Comput Mol Sci 2016, 6:460–486. doi: 10.1002/wcms.1260This article is categorized under: Electronic Structure Theory > Density Functional Theory Theoretical and Physical Chemistry > Spectroscopy

Finite-volume versus streaming-based lattice Boltzmann algorithm for fluid-dynamics simulations: A one-to-one accuracy and performance study.

A finite-volume (FV) discretization method for the lattice Boltzmann (LB) equation, which combines high accuracy with limited computational cost is presented. In order to assess the performance of the FV method we carry out a systematic comparison, focused on accuracy and computational performances, with the standard streaming lattice Boltzmann equation algorithm. In particular we aim at clarifying whether and in which conditions the proposed algorithm, and more generally any FV algorithm, can be taken as the method of choice in fluid-dynamics LB simulations. For this reason the comparative analysis is further extended to the case of realistic flows, in particular thermally driven flows in turbulent conditions. We report the successful simulation of high-Rayleigh number convective flow performed by a lattice Boltzmann FV-based algorithm with wall grid refinement.

Vehicle Lateral Motion Control with Performance and Safety Guarantees

This paper explores the use of Model Predictive Control (MPC) techniques to solve vehicle lateral motion control problem on highway scenarios. In particular, the problem of autonomously driving a vehicle along a desired path is formulated, where safety constraints and performance levels must be guaranteed for all possible road curvatures within a compact set. Safety constraints are translated into a maximum lateral deviation and orientation error w.r.t. a desired path, while performance requirements are formulated in terms of bounded lateral acceleration and velocity. Preliminary simulation results show that the designed controller is capable of delivering acceptable performance at the cost of limited online computational costs.

Removing the Spectral Leakage in Time-Domain Based Near-Field Scanning Measurements

This paper describes the application of a new and easy to implement algorithm to EMI near-field scanning measurement results obtained with a time-domain-based measurement system. The algorithm aims to reduce the effect of spectral leakage on amplitude and phase of the measured field components. The proposed algorithm significantly increases the accuracy of the measured electromagnetic near field with a limited extra computational cost. The versatility and effectiveness of the proposed algorithm is shown on both simulated and measured data. It is shown that for situations with one single frequency component or with several well-separated frequency components, the algorithm is as performant as the application of a flat top window and outperforms other types of windows. However, as soon as the frequency components approach each other implying their spectral leakage patterns overlap, the proposed algorithm outperforms the application of a flat top window.

Mapping the Protein Fold Universe Using the CamTube Force Field in Molecular Dynamics Simulations.

It has been recently shown that the coarse-graining of the structures of polypeptide chains as self-avoiding tubes can provide an effective representation of the conformational space of proteins. In order to fully exploit the opportunities offered by such a ‘tube model’ approach, we present here a strategy to combine it with molecular dynamics simulations. This strategy is based on the incorporation of the ‘CamTube’ force field into the Gromacs molecular dynamics package. By considering the case of a 60-residue polyvaline chain, we show that CamTube molecular dynamics simulations can comprehensively explore the conformational space of proteins. We obtain this result by a 20 μs metadynamics simulation of the polyvaline chain that recapitulates the currently known protein fold universe. We further show that, if residue-specific interaction potentials are added to the CamTube force field, it is possible to fold a protein into a topology close to that of its native state. These results illustrate how the CamTube force field can be used to explore efficiently the universe of protein folds with good accuracy and very limited computational cost.

Resolving flows around black holes: numerical technique and applications

Black holes are believed to be one of the key ingredients of galaxy formation models, but it has been notoriously challenging to simulate them due to the very complex physics and large dynamical range of spatial scales involved. Here we address a significant shortcoming of a Bondi-Hoyle-like prescription commonly invoked to estimate black hole accretion in cosmological hydrodynamic simulations of galaxy formation, namely that the Bondi-Hoyle radius is frequently unresolved. We describe and implement a novel super-Lagrangian refinement scheme to increase, adaptively and 'on the fly', the mass and spatial resolution in targeted regions around the accreting black holes at limited computational cost. While our refinement scheme is generically applicable and flexible, for the purpose of this paper we select the smallest resolvable scales to match black holes' instantaneous Bondi radii, thus effectively resolving Bondi-Hoyle-like accretion in full galaxy formation simulations. This permits us to not only estimate gas properties close to the Bondi radius much more accurately, but also allows us to improve black hole accretion and feedback implementations. We thus devise a more generic feedback model where accretion and feedback depend on the geometry of the local gas distribution and where mass, energy and momentum loading are followed simultaneously. We present a series of tests of our refinement and feedback methods and apply them to models of isolated disc galaxies. Our simulations demonstrate that resolving gas properties in the vicinity of black holes is necessary to follow black hole accretion and feedback with a higher level of realism and that doing so allows us to incorporate important physical processes so far neglected in cosmological simulations.

Parameters affecting laterally loaded piles in frozen soils by an efficient sensitivity analysis method

In cold regions, accurately simulating the nonlinear responses of frozen soil–structure interaction (SSI) systems is of significant importance for the seismic safety assessment. This paper presents a finite element (FE) simulation of nonlinear lateral responses of a real reinforced-concrete filled steel pipe pile embedded in frozen ground at an outdoor test site in Fairbanks, Alaska. A pressure-independent multi-yield surface J2 plasticity model is used to simulate the frozen soil behavior, while frame element with fiber section and nonlinear steel/concrete model are used for the pile. The FE analysis results agree well with the experiment results. Furthermore, the response sensitivities to various material parameters are computed by using an efficient and accurate gradient computation method, i.e., direct differentiation method (DDM), with limited additional computational cost. Based on DDM, the relative importance of material parameters on system responses is studied when the system is subjected to varying lateral deflections, respectively (corresponding to different levels of lateral loads such as earthquakes). Stiffness-related parameters are dominant when the system is subject to small deflections, while strength-related or post-yield parameters become dominant for large deflections. In addition, global responses are slightly more sensitive to material parameters of pile than the parameters of frozen soil. The response sensitivity to the unfrozen soil below the frozen ground crust is almost negligible. Finally, the response sensitivities to the soil parameters are observed to decrease when the distance of the soil from the pile increases. The sensitivity analysis methodology presented in this paper can be adapted to other frozen soil–pile interaction cases and the study results provide valuable insight for engineering practice.

A new boundary treatment method in smoothed particle hydrodynamics

As the smoothed particle hydrodynamics (SPH) is a truly Lagrangian meshfree method, the implementation of solid boundary condition has been one of the key problems that hinder SPH from applying to lots of engineering problems. In order to treat the boundary conditions efficiently, based on the boundary-fluid interaction principles, a new boundary treatment method is proposed. In this method, the solid boundary is repreflented implicitly by several layers of dummy particles along the boundary line. During the simulation, the dummy particles are treated as an extension of the fluid phase. The densities of dummy particles are kept constant, and the pressures and velocities are interpolated from the nearby fluid particles at each time step. Dummy particles can be involved in the calculation of the continuity equation conditionally and exert influences on the density/pressure field of the fluid phase. Then, for the fluid particles that approach the solid boundary, local pressure gradients are used to repreflent the dummy-fluid particle pair’s interaction strength and act as the boundary force term implicitly, which is tuned to be repulsive only and normal to the boundary. Thus, large pressure gradients mean strong boundary-fluid interaction strength, and the boundary force from the dummy particles should also be large enough to preflent the fluid particles from penetrating the solid boundary; and on the contrary, small pressure gradients mean weak boundary-fluid interaction strength and the boundary force becomes soft and little disturbs the flow field. Results of numerical tests demonstrate that, compared with the existing boundary treatment methods, the new method is in better accordance with the physical principles of the fluid-boundary interaction, and is able to treat arbitrary solid boundaries with limited modeling and computational costs. With the help of this new boundary treatment method, the stable flow field, well-ordered particle distribution, smooth velocity and pressure fields could be obtained. Theoretically, this new boundary treatment method could be directly used in three-dimensional multi-phase problems. Further tests are planned to be carried out; meanwhile, expanding the new boundary treatment method to rigid-fluid interaction problems is also a work in the future.