Numerical Prediction Method Research Articles

The Promotion of Mechanical Properties by Bone Ingrowth in Additive-Manufactured Titanium Scaffolds.

Although the initial mechanical properties of additive-manufactured (AM) metal scaffolds have been thoroughly studied and have become a cornerstone in the design of porous orthopaedic implants, the potential promotion of the mechanical properties of the scaffolds by bone ingrowth has barely been studied. In this study, the promotion of bone ingrowth on the mechanical properties of AM titanium alloy scaffolds was investigated through in vivo experiments and numerical simulation. On one hand, the osseointegration characteristics of scaffolds with architectures of body-centred cubic (BCC) and diamond were compared through animal experiments in which the mechanical properties of both scaffolds were not enhanced by the four-week implantation. On the other hand, the influences of the type and morphology of bone tissue in the BCC scaffolds on its mechanical properties were investigated by the finite element model of osseointegrated scaffolds, which was calibrated by the results of biomechanical testing. Significant promotion of the mechanical properties of AM metal scaffolds was only found when cortical bone filled the pores in the scaffolds. This paper provides a numerical prediction method to investigate the effect of bone ingrowth on the mechanical properties of AM porous implants, which might be valuable for the design of porous implants.

Development of a wind power ramp forecasting system via meteorological pattern analysis

AbstractRamp phenomena caused by abrupt changes in wind speed may confound the stable operation of correlated electrical power supply systems, yet accurate numerical predictions are challenging, as the wind is affected by complex interactions between large‐scale weather patterns and local geographical conditions. Further, optimal numerical weather prediction (NWP) methods and physics schemes vary as a function of weather patterns. The present study proposed a new real‐time wind power ramp forecast framework based on the flexible selection of optimal NWP models, which were derived via principal component analysis (PCA). The novelty of this analysis lies in that statistical methods were employed for NWP optimization, compared with their more conventional use during an NWP postprocessing. Here, a weather pattern was classified by PCA using outcomes from the global‐scale prediction models, and the optimum regional NWP system settings were acquired according to the weather patterns for further wind field dynamical downscaling. The performance of the developed prediction system was verified with wind power at wind turbine hub‐heights for three areas in eastern Japan, and the Critical Success Index (CSI) indicated an improvement of prediction accuracy over benchmark predictions by ≤0.184 for ramp‐up events and ≤0.127 for ramp‐down events (both observed in Tohoku area). Higher CSI values were consistently seen in three wind farm areas, indicative of the improvement in detection probability for actual ramp events compared with benchmark.

Prediction and validation of diffusive uptake rates for indoor volatile organic compounds in axial passive samplers

The diffusive uptake rate is essential for using passive samplers to measure indoor volatile organic compounds (VOCs). The traditional theoretical model of passive samplers requires available regression formulas of uptake rates and physicochemical properties of adsorbents to predict the uptake rate. However, it is difficult to obtain the uptake rates of different VOCs under different sampling periods, and it is also difficult to obtain the physical parameters of adsorbents accurately and effectively. This study provides a reliable numerical prediction method of diffusive uptake rates of VOCs. The modeling was based on the standard automated thermal desorption (ATD) tubes packed with Tenax TA and the mass transfer process during adsorption. The experimental determinations of toluene uptake rate are carried out to verify the prediction model. Diffusive uptake rates of typical indoor VOCs are obtained from the literature to calibrate the key apparent parameters in the model by statistical regression fitting. The predicted model can provide the VOC diffusive uptake rates under different sampling duration with an average deviation of less than 5%. This study can provide the basis for fast and accurate prediction of diffusive uptake rates for various VOC pollutants in built environments.

Adaptive step-size numerical vibration correlation technique for buckling prediction of thin-walled shells under axial compression and thermal loads

PurposeThe purpose of this paper is to propose a numerical prediction method of buckling loads for shell structures under axial compression and thermal loads based on vibration correlation technique (VCT).Design/methodology/approachVCT is a non-destructive test method, and the numerical realization of its experimental process can become a promising buckling load prediction method, namely numerical VCT (NVCT). First, the derivation of the VCT formula for thin-walled structures under combined axial compression and thermal loads is presented. Then, on the basis of typical NVCT, an adaptive step-size NVCT (AS-NVCT) calculation scheme based on an adaptive increment control strategy is proposed. Finally, according to the independence of repeated frequency analysis, a concurrent computing framework of AS-NVCT is established to improve efficiency.FindingsFour analytical examples and one optimization example for imperfect conical-cylindrical shells are carried out. The buckling prediction results for AS-NVCT agree well with the test results, and the efficiency is significantly higher than that of typical numerical buckling methods.Originality/valueThe derivation of the VCT formula for thin-walled shells provides a theoretical basis for NVCT. The adaptive incremental control strategy realizes the adaptive adjustment of the loading step size and the maximum applied load of NVCT with Python script, thus establishing AS-NVCT.

Test analysis and numerical simulation of noise from forage crushers

To identify the exact noise source and accurately predict the coupled noise from forage crushers, the multipoint synchronous measurement method based on sensor arrays is used, and the collected data are analyzed using auto-spectrum and wavelet analysis. The vibration transmission characteristics of the unbalanced rotor, the airflow–material coupling flow field, and the shell are calculated and analyzed based on the analysis results. The acoustic-solid coupling method is then used to numerically predict the coupled noise of the forage crusher's air noise and mechanical noise. The primary source of noise in forage crushers is aerodynamic noise. Mechanical noise from unbalanced rotor excitation, as well as material and mechanical structure collisions, play important roles in coupled noise distribution. The noise energy around the excitation fundamental frequency generated by the rotor hammer disturbing the airflow accounts for 58.32% of the total energy, the noise energy around the third harmonic frequency accounts for 9.15%, the noise energy caused by the unbalanced rotor excitation accounts for 7.05%, and the noise energy around the second harmonic frequency accounts for 4.34%. The overall change trends of the simulated sound pressure levels (SPLs) are similar to those of the measured values, and the maximum SPLs of the measured and simulated results appear at the same location near the inlet. The total SPLs of the measured values are slightly higher than those of the simulated values, and the maximum difference is 1.6 dB(A). The numerical prediction method for the coupled noise of forage crushers is feasible, and the prediction results are credible. This study provides the basis for the low-noise design of forage crushers.

Improved RRT Algorithm for AUV Target Search in Unknown 3D Environment

Due to the complexity of the marine environment, underwater target search and interception is one of the biggest problems faced by an autonomous underwater vehicle (AUV). At present, there is quite a lot of research in terms of the two-dimensional environment. This paper proposes an improved rapidly exploring random trees (RRT) algorithm to solve the problem of target search and interception in an unknown three-dimensional (3D) environment. The RRT algorithm is combined with rolling planning and node screening to realize path planning in an unknown environment, and then the improved RRT algorithm is applied to the search and interception process in a 3D environment. Combined with the search decision function and the three-point numerical differential prediction method, the RRT algorithm can search for and effectively intercept the target. Numerical simulations in various situations show the superior performance, in terms of time and accuracy, of the proposed approach.

Performance comparison analysis for different single-zone natural ventilation building indoor temperature prediction method combined thermal mass

Natural ventilation design strategy was an important means to effectively reduce energy consumption and improve indoor air quality. The indoor temperature prediction methods could be well described the natural ventilation effect in the early design stage. Building thermal mass was the main influence factor of indoor temperature prediction of nature ventilation building through the literature review. The current simulation methods were difficult to achieve quick and accurate prediction coupled with building thermal mass. Therefore, most of the prediction methods were numerical solutions methods or prediction methods based on historical data. In order to obtain a feasible method for predicting the indoor temperature of natural ventilation building, this study put forward a simplified method based on the existing prediction methods. This study was verified by the experimental platforms with light and middle building thermal mass. The results showed that the maximum errors were about 1.5 °C between the measured values and simulated values.

On the Nonlinear Response of a Tri-SWACH Vessel in Incident Waves

Abstract In this article, the nonlinear motion response of a Trimaran with SWATH-type center hull vessel (TriSWACH) is investigated and compared with the linear response using a hybrid method combining a regression analysis of the viscous damping generated by the hulls with a nonlinear potential flow simulation of rigid body motions. The TriSWACH is a hybrid trimaran hull form, developed using the SWATH (small waterplane area twin hull) technology, from which a single small waterplane area center hull is retained. It is designed to keep the magnitude of its motion response below its equivalent monohull counterpart while also benefiting from large intact transverse stability provided by its side hulls. Since a significant portion of the total damping generated by the submerged hull girder is viscous in nature, an accurate prediction of the steady-state motion response near natural frequency is highly dependent upon the correct modeling of the boundary layer flow. The total damping coefficients obtained experimentally from motion response transfer functions are used to augment the potential flow solution and improve the numerical prediction method at large angles of inclination, characteristic of operation in rough weather near the peak frequency of the motion response. Introduction SWATH1-type vessels exhibit ship motions less pronounced than a monohull of the same displacement. It has been reported that SWATHs have the potential to significantly reduce the impact of harsh environmental conditions on ship operability (Kennell et al. 1985). Although the interest in SWATH technology in ship design continued over the past few decades, it was recognized that this hull form required modifications in order to accommodate high-speed applications. An example of a TriSWACH concept is shown in Fig. 1, with the main distinguishing features compared to the SWATH-type vessel being the presence of the side hulls and one single main hull.

COMPREHENSIVE NUMERICAL HYDRODYNAMIC CAMPAIGN TO EVALUATE FRIGATE CONCEPT HULLFORM

A hullform was developed for a new frigate based on the parent hullform of an existing design. The new hullform is able to provide higher payload and stability performance, consequently increase its adaptability capability. Initial bare hull resistance to the new hullform concept was estimated using CFD (Computational Fluid Dynamics). After the final fairing, a hydrodynamic campaign was defined to measure the hydrodynamic characteristics of the new concept. BAE Systems used, for the first time, numerical prediction methods to undertake a complete hydrodynamic campaign on a hullform. The campaign included Bare Hull Resistance, Propeller Nominal Wake Survey, Self- Propulsion, Appendage Alignment, Seakeeping and Manoeuvring. The current paper is focused on the Bare Hull Resistance Analysis. A validation was made against the available experimental results for the parent hullform. The validation showed a goodagreement. The validated numerical modelling was used in the new hullform evaluation. The results showed that despite the larger displacement, the new hullform hydrodynamic performance is similar to the parent hullform.

Methods of Linearization of Nonlinear Dynamic System

In This Research paper, described different linearization methods in terms of analytical and numerical simulation (Computer added linearized -FE) method and Digital image correlation method for prediction of loading analysis. Article can be helping for linearization in nonlinearity of systems, that what is using different methods likes Numerical (approximation), Analytical (Exact) and Experimental. Where, analytical is difficult to calculate in computers so it uses numerical methods. That is nearer to exact values so we can determine behaviour of dynamical systems. Numerical simulation is static with different time, so analysis is easy for its. Numerical methods work on algorithms, so it is easy to evaluate for calculating in computers. For future aspects image correlation method is more helpful to determine behaviour of nonlinear dynamic system which will discretize system with different displacement and FEM analyses to dynamically nonlinear system.

Cross-Domain Remaining Useful Life Prediction Based on Adversarial Training

Remaining useful life prediction can assess the time to failure of degradation systems. Currently, numerous neural network-based prediction methods have been proposed by researchers. However, most of the work contains an implicit prerequisite: the network training and testing data have the same operating conditions. To solve this problem, an adversarial discriminative domain adaption prediction method based on adversarial training is proposed to improve the accuracy of cross-domain prediction under different working conditions. First, an LSTM feature extraction network is constructed to mine the source domain data and the target domain data for deep feature representation. Subsequently, the parameters of the target domain feature extraction network are adjusted based on the idea of adversarial training to achieve domain invariant feature mining. The proposed scheme is experimented on a publicly available dataset and achieves state-of-the-art prediction performance compared to recent unsupervised domain adaptation prediction methods.

A recursive-cluster based reduced order method for numerical prediction of effective properties of heterogeneous viscoelastic materials

A novel recursive-cluster based reduced order method is presented to predict the effective properties of heterogeneous viscoelastic materials. By virtue of a piecewise series expansion based variable separation, a temporally and spatially homogenization process is decoupled into a series of recursive elastic homogenization ones, which require only one-time clustering decomposition with one-time elastic heterogeneous analysis. Such that, the heavy computational burden caused by the direct numerical simulation over the whole domain of viscoelastic heterogeneous RUC can significantly be alleviated. The effective creep compliance or relaxation modulus is presented via a time varying average strain or average stress, and a piecewise adaptive technique is proposed to secure the temporal accuracy when step size varies. Numerical examples are provided to demonstrate the effectiveness and efficiency of proposed approach, and the predicted results agree well with those given by FEM/SBFEM based viscoelastic DNS. • Propose a recursive-cluster based reduced order method to evaluate effective properties of heterogeneous viscoelastic materials. • Decouple a temporally and spatially homogenization process into a series of recursive elastic homogenization ones. • Take only one-time clustering decomposition with one-time elastic heterogeneous analysis. • Execute a piecewise adaptive computing to gain the effective strains of clusters on RUC. • Circumvent the heavy computational burden caused by the high-fidelity viscoelastic DNS on RUC significantly.

Efficient non-ergodic ground-motion prediction for large datasets

An efficient numerical method for non-ergodic ground-motion inference and prediction is proposed that alleviates the large computational and memory requirements associated with the traditional approach based on Gaussian Processes described in Landwehr et al. (Bull Seismol Soc Am, https://doi.org/10.1785/0120160118, 2016). The method uses the latest developments in Gaussian Processes and Machine Learning from Wilson and Nickisch (in: International Conference on Machine Learning, pp 1775–1784, 2015) (SKI) and Gardner et al. (in: International Conference on Artificial Intelligence and Statistics, pp. 1407–1416, 2018) (SKIP) and uses sparse approximations combined with efficient matrix decompositions to accurately approximate the large covariance matrices involved in the calculations. This efficient method can be used for both inference of hyperparameters of the non-ergodic ground-motion models and for forward predictions of non-ergodic median ground-motion. The application to predictions are presented. For large datasets of 100,000 to 1,000,000 ground motion values, the proposed method increases the computation speed by factors of 100 to 1000, reducing run times from days to minutes. In addition, the memory requirements are reduced from hundreds of GB to a few GB only, which makes the development of non-ergodic ground-motion models practical using traditional desktop computers.

An improved numerical integration method for prediction of milling stability using the Lagrange-Simpson interpolation scheme

An improved numerical integration method for prediction of milling stability using the Lagrange-Simpson interpolation scheme

Link prediction for existing links in dynamic networks based on the attraction force

Link prediction has become an important technology which can be widely applied to many aspects of social network analysis. Numerous link prediction methods have been proposed in recent years, which can detect missing links in the current network and predict whether two disconnected nodes will be connected in the future. However, some existing links may be spurious links that should be removed from the current network, and some existing links will leave the network in the future because network changes over time. In this paper, a novel link prediction approach based on the attraction force between nodes in dynamic networks is proposed to detect spurious links at time t and predict whether existing links at time t will leave the network at time t + 1. At first, the attraction force between nodes is defined according to the law of gravitation, and a level is assigned to each node in the network at time t. Then, the connection probability of each pair of connected nodes is calculated according to the level of nodes and the attraction force between nodes. Next, the connection probability of all existing links is calculated to detect whether they were spurious links that should be removed from the network at time t. For an existing link, the lower the connection probability, the more likely it is to be a spurious link. Furthermore, the changes of network from time t to t + 1 is considered to build a virtual network and the connection probability of all existing links in the virtual network is calculated to predict whether they will leave the network at time t + 1. Experimental results on some real-world networks show that the proposed approach can achieve acceptable prediction accuracy and can be effectively applied to the prediction of existing links in dynamic networks.

Fast and Accurate Prediction of Light Scattering from Plasmonic Nanoarrays in Multiple Directions.

The method of moments (MoM) is an efficient electromagnetic numerical method for the accurate prediction of light scattering from plasmonic nanostructures. In practice, the light-scattering properties in different incident directions are often concerning. However, traditional MoM generally resorts to the iterative method, which suffers from the problems of convergence rate and redundant computations for multiple incident excitations. Nanoarray structures will further aggravate these problems due to a large number of unknowns. In this article, an efficient numerical method based on MoM and a hierarchical matrix (H-matrix) algorithm is proposed to solve these problems. Numerical experiments demonstrate the efficiency and accuracy of the proposed method for the prediction of light scattering from plasmonic nanoarrays in multiple directions.

Crack propagation in articular cartilage under cyclic loading using cohesive finite element modeling

Severe joint injuries often involve cartilage defects that propagate after mechanical loading. The propagation of these lesions may contribute to the development of post-traumatic osteoarthritis (PTOA). However, the mechanisms behind their propagation remain unknown. Currently, no numerical predictive methods exist for estimating crack propagation in cartilage under cyclic loading, yet they would provide essential insights into crack growth in injured tissue after trauma. Here, we present a numerical approach to estimate crack propagation in articular cartilage under cyclic loading using a cohesive damage model. Four different material models for cartilage (hyperelastic, poro-hyperelastic, poro-hyper-viscoelastic, and fibril-reinforced poro-hyperelastic (FRPHE) with different collagen orientations) were implemented. Our numerical cohesive damage model was able to replicate the experimental crack length reported in the literature, showing greater crack length with an increasing number of loading cycles. Damage initiation stress (4.35–4.73 MPa) and fracture energy (0.97–1.55 N/mm) values obtained for the poro-hyperelastic, poro-hyper-viscoelastic, and parallel-FRPHE models were within the range of what has been reported previously. The crack growth predictions obtained by the FRPHE models showed the influence of anisotropy of the fibrillar matrix on the cartilage response. Our results indicate that our cohesive damage model could potentially be used to estimate the adverse conditions in injured soft tissue such as osteochondral lesions, menisci tears, or partial ligament ruptures under (ab)normal biomechanical scenarios.

Step-by-Step Numerical Prediction of Aerodynamic Noise Generated by High Speed Trains

In this paper, the unsteady flow around a high-speed train is numerically simulated by detached eddy simulation method (DES), and the far-field noise is predicted using the Ffowcs Williams-Hawkings (FW-H) acoustic model. The reliability of the numerical calculation is verified by wind tunnel experiments. The superposition relationship between the far-field radiated noise of the local aerodynamic noise sources of the high-speed train and the whole noise source is analyzed. Since the aerodynamic noise of high-speed trains is derived from its different components, a stepwise calculation method is proposed to predict the aerodynamic noise of high-speed trains. The results show that the local noise sources of high-speed trains and the whole noise source conform to the principle of sound source energy superposition. Using the head, middle and tail cars of the high-speed train as noise sources, different numerical models are established to obtain the far-field radiated noise of each aerodynamic noise source. The far-field total noise of high-speed trains is predicted using sound source superposition. A step-by-step calculation of each local aerodynamic noise source is used to obtain the superimposed value of the far-field noise. This is consistent with the far-field noise of the whole train model’s aerodynamic noise. The averaged sound pressure level of the far-field longitudinal noise measurement points differs by 1.92 dBA. The step-by-step numerical prediction method of aerodynamic noise of high-speed trains can provide a reference for the numerical prediction of aerodynamic noise generated by long marshalling high-speed trains.

Hybrid 4-Dimensional Trajectory Prediction Model, Based on the Reconstruction of Prediction Time Span for Aircraft en Route

This paper presents the results from a test of the performance of several general trajectory prediction methods and proposes a hybrid trajectory prediction model that aims to increase the safety of flights en route and improve airspace management capabilities by predicting the aircraft’s four-dimensional trajectory (4DT) more accurately. The automatic dependent surveillance-broadcast (ADS-B) data from 589 trajectories of cruising aircraft from the Guangzhou area were extracted for experiments. Numerous trajectory prediction methods, including velocity trend extrapolation, long short-term memory (LSTM), stateful-LSTM, back propagation (BP) neural network, a one-dimensional convolutional neural network (1D-ConvNet), Kalman filter, and flight plan interpolation were used for prediction experiments, and their performance at different time spans of prediction is obtained. By extracting the best methods using different time spans of prediction, a hybrid prediction model is proposed based on the reconstruction of these methods. For the data in this paper, the mean squared error (MSE) of the hybrid prediction model is significantly reduced compared to other methods in different time spans of prediction, which has great significance for future trajectory prediction in a structured airspace.

Numerical study on the influence of mist-air mixtures on axial compressor performance

To study the influence of mist-air mixtures on the performance of an axial-flow compressor, based on the Euler-Lagrange multiphase flow model coupled with compressor aerodynamic calculation, droplet motion, and wall film model, we establish a numerical method for compressor multiphase flow, which is used to study the performance change law of a low-pressure axial compressor with mist-air injection. The results indicate that the compressor inlet fogging will produce a wall film on the blade surface and the casing wall, resulting in a decrease in the total pressure coefficient and mechanical efficiency of the compressor under full operating conditions, and this decrease is more significant with the increase of spray water-air ratio and spray particle size. With the change of compressor mass flow, the wall film on the blade surface presents the following migration law: the thickness of wall film on the pressure surface and on the suction surface decreases and increases respectively with the increase of mass flow, and the specific distribution law of wall film on the blade surface does not change obviously. Meanwhile, a compressor inlet fogging test is carried out in this study to verify the accuracy of the multiphase flow numerical method for compressor performance prediction. The experimental results show that the numerical method can effectively and quantitatively analyze the effect of mist-air mixture injection on compressor performance, and can be further improved by more detailed experimental research to achieve a more accurate numerical method for compressor multiphase flow.