Reanalysis Method Research Articles

Dynamic Modal Reanalysis Using Flexibility Disassembly Perturbation Method

In structural optimization design and reliability analysis, it is necessary to repeatedly modify the structure and calculate the corresponding vibration modal parameters. It will require significant computational costs if a complete analysis is used to calculate the vibration modes of each modified structure. In order to improve computational efficiency, this work develops a dynamic modal reanalysis method based on flexibility disassembly perturbation, which utilizes the modal data of the original structure to quickly calculate the modal parameters of the modified structure. The core idea of the proposed method is to use the flexibility matrix decomposition formula to quickly calculate the flexibility matrix after each modification of the structure and then obtain a high-precision reduced eigenvalue problem for solving the vibration modes of the modified structure. Three numerical examples are used to validate the proposed modal reanalysis method. It has been shown that the proposed method has higher computational accuracy and efficiency than the existing modal reanalysis method, especially for high-rank and large modification scenarios.

An efficient online successive reanalysis method for dynamic topology optimization

In this study, an efficient reanalysis strategy for dynamic topology optimization is proposed. Compared with other related studies, an online successive dynamic reanalysis method and a Proper Orthogonal Decomposition (POD)-based approximate dynamic displacement strategy are integrated. In the dynamic reanalysis, the storage of the stiffness matrix decomposition can be avoided, and the reduced basis vectors should be successively updated according to the structural state in each iteration. Thus, the bottleneck of the combined approximation method for large-scale dynamic topology optimization can be overcome. Sequentially, POD is applied to obtain the approximate dynamic displacement, in which the Proper Orthogonal Mode (POM) of the displacement field is applied to obtain the approximate equivalent static loads of the Equivalent Static Load (ESL) method. Compared to the exact equivalent static loads at all the time intervals, the number of equivalent static loads is significantly reduced. Finally, the 2D and 3D test results indicate that the proposed method has remarkable speed-up effect under the premise of small relative error, support the strength of the proposed strategy.

The efficiency assessment of short-term maximum flood level forecast methodology in the upper and middle course of the Tsna river

A significant rise in water levels in the rivers during the spring flood and the release of rivers to the floodplain is the main danger in this period for nearby territories and people living there. This phenomenon can lead to flooding of large areas, significant economic losses, environmental damage, and threaten the health and life of local residents. Such negative consequences of high floods are typical for the Tambov region rivers, which indicates the need to develop an effective system for forecasting and preventing maximum flood levels. The climatic changes that have taken place over the past few decades, which are also reflected in the rivers’ water regime, show the need to modernize existing forecasting methodologies. In this paper, the authors have demonstrated the results of the effectiveness assessment of the existing methodology for short-term forecasting of the maximum flood level on the Tsna River at two gauging stations (“Kuzmino-Gat” and “Tambov”). Calculations were made using modern data in accordance with this methodology and a comparative analysis was carried out with the calculations of previous years. Based on this analysis, an assessment of the flood levels forecast methodology accuracy was given. According to the study results, it was found that the existing methodology for short-term forecasting of the maximum flood level on the Tsna River is largely ineffective nowadays with regard to modern conditions of spring flood runoff formation. In the analysis of the Kuzmino-Gat gauging station, the values of the maximum flood levels obtained by the reanalysis method according to the tested methodology using modern data showed significant deviations from the actual observed values. At the same time, for the operational forecast of maximum water levels at the Tambov gauging station, it is possible to use the correlation dependence with the observed water levels at the Kuzmino-Gat gauging station, as before. The correlation coefficient with modern data was 0.96. The authors have highlighted the main drawbacks of the existing methodology and made suggestions for improvement, in particular, what factors need to be analyzed in order to clarify the forecast.

Structural static reanalysis using flexibility disassembly perturbation and system reduction

Rapid static reanalysis technology is an important tool commonly used in structural optimization design and reliability analysis. The existing static reanalysis methods are not efficient enough in solving high-rank large modification problems. To overcome this drawback, this paper proposes a new method for structural static reanalysis based on flexibility disassembly perturbation and system reduction. Firstly, a generalized flexibility disassembly perturbation technique is developed to directly compute the flexibility matrix of the modified structure. Subsequently, a new system reduction method is proposed to quickly approximate the exact solution of the modified static displacement. Even for the global large modification problem, the proposed method only requires two basis vectors to obtain very accurate calculation results. Two numerical models of statically indeterminate structures are used to verify the effectiveness and progressiveness of the method. The results show that the proposed reanalysis method has higher computational accuracy than the existing combined approximation (CA) and preconditioned conjugate gradient (PCG) methods, especially for the high-rank large modification case. A steel reticulated shell structure is further used to illustrate the application of the proposed reanalysis method in structural reliability analysis. It can be found that the proposed method significantly saves the calculation time of structural reliability analysis.

Theoretical aspects and applications of goal‐oriented reanalysis methods

AbstractIn this contribution, a method for goal‐oriented reanalysis is presented. The process allows predicting the change in a quantity of interest due to structural modifications (design changes), for example, the shape, the topology, material properties, and so forth. The approach is based on a goal‐oriented method using primal and dual problems. Furthermore, the procedure is easy to implement in existing finite element programs because no derivatives with respect to the design variables are necessary. The proposed method can be used in classical applications with successive design steps, such as structural optimization, reliability, or structural damage analysis. The repeated structural analysis and calculating quantities of interest involve significant computational effort. Goal‐oriented reanalysis methods can reduce the overall computational cost in the numerical simulation.

The Distribution of Oceanographic Parameter Conditions in the Swordfish (Xiphias gladius) Fishing Ground in the Southern Indian Ocean off Java

The Southern Indian Ocean off Java is one of the potential locations for catching swordfish. One crucial factor in improving fishermen's productivity is the availability of information regarding the characteristics of waters related to potential fishing areas. This research aimed to determine the distribution conditions of several oceanographic parameters such as sea surface temperature, salinity, chlorophyll-a, and surface currents using remote sensing data at the swordfish (Xiphias gladius) fishing ground in the Southern Indian Ocean off Java. This research was conducted from April 27, 2022, to December 12, 2022, in the Indian Ocean. Data for several oceanographic parameters were obtained from the data.marine.copernicus.eu website from April 2022 to December 2022, and the coordinates of the swordfish (Xiphias gladius) fishing ground were obtained from KM Lulu Marina 08, which operates in WPPD 57. Data from data.marine.copernicus.eu are the result of reanalysis methods developed by the Copernicus Marine Service (CMS) using multisensor satellite images data such as MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS, and Sentinel 3A-OLCI. The spatial resolution of the data was standardized to 0.001 degrees using resampling techniques with B-Spline interpolation. The highest sea surface temperature (SST) at each fishing ground was recorded in April 2022 at the fishing ground ST 1, reaching 27.52°C. The lowest SST was observed in November 2022 at fishing ground ST 6, measuring 21.70°C. The lowest salinity values at each fishing ground were recorded in June 2022 at fishing ground ST 1, measuring 34.09 psu, while the highest salinity values were found in April 2022 at fishing ground ST 6, measuring 35.38 psu. The lowest chlorophyll-a concentration values at each fishing ground were recorded in December 2022 at fishing ground ST 6, measuring 0.062 mg/m3, while the highest concentration values were found in September 2022 at fishing ground ST 6, measuring 0.357 mg/m3. The lowest catch was recorded in September 2022, with only 2 fish caught, while the highest catch was recorded in November 2022, with a total of 42 fish caught. The optimal swordfish catch rate falls within the range of SST 23.47-24.74°C, salinity 34.45-34.78 psu, and chlorophyll-a concentration in the range of 0.079-0.124 mg/m3.

A multigrid assisted reanalysis method for accelerated heat transfer topology optimization

Heat sinks are widely applied in industry, and heat transfer topology optimization can improve their performance while saving materials. However, the prohibitive computational cost hinders its development. Therefore, the Multigrid Assisted Reanalysis (MGAR) method is proposed to address the issue. The Combined Approximate Reduced Order Model (CAROM) is employed to reduce the dimensionality of governing equations, and computational efficiency is significantly improved. In addition, CAROM should be dynamically updated to ensure the accuracy of solutions, thus constructing the CAROM also take up a major computational cost. For this purpose, the V-cycle multigrid is employed to construct basis vectors of CAROM instead of direct solvers. Since the MGAR involves almost no matrix decomposition, except for solving the reduced linear systems, the MGAR is equipped to handle large-scale problems due to the advantage of high computational efficiency and low memory requirements. The competence of the MGAR is verified by several cases including thermal compliance minimization and maximum temperature minimization.

Impact of land use and land cover change on land surface temperature over Lake Tana Basin

Isolation of climate and Land Use and Land Cover (LULC) changes and their compounded effect is complicated since the two influence each other through forcing-feedback cycles. This study investigates the changes in LULC and its contribution to the change in surface and air temperature in the Lake Tana Basin using data obtained from MODIS remote sensing satellites, ground observation and reanalysis model. Observation minus reanalysis (OMR) method and various analysis tools are used to estimate the impact of LULC changes on temperature. The analysis of LULC change indicates that an increase of Savanna and other type of forest cover by about 519 km2 from year 2001 to year 2020. Wetland increased by about 83.7 km2 during the said period. During the period 2001–2020, about 56.5% baren-land areas are being converted into wetland. The conversion of water bodies and baren land to wetland is likely due to the increase of water hyacinth on both the water body and nearby baren-land whereas the increase in Savanna is linked to an on-going eucalyptus tree plantation. The negative correlation between LST and NDVI (reaching a value of −0.60) is noticed over the Basin throughout the year on inter-annual time scale. An increase in vegetation cover such as Savanna and broad leaf forest coincides with the decreasing LST with an average rate of 0.08%/decade. Also, a reduction in temperature, derived from OMR, at an average cooling rate of about 0.87%/decade is observed in the study period. However, an increase in the LST change rate over built-up and grassland is about 0.03%/decade. Therefore, we conclude that the consistent associations of the increase in areal coverage of the some LULC types with lowest LSTs, 2m-temperature and temperature trend derived from NDVI-LST and LULC-LST associations as well as OMR temperature and LST are unambiguous signatures of the effect of LULC change on temperature over Lake Tana Basin, distinct from that arising due to climate change.

Regional climate change and possible causes over the Three Gorges Reservoir Area

The Three Gorges Project, the largest hydroelectric project in the world, has attracted widespread attention regarding its impact on regional climate. However, existing studies on the climate effects of the Three Gorges Project construction are not sufficient due to limited data accumulation. In this study, we analyzed the annual and seasonal trend changes in temperature, precipitation, and humidity over the Three Gorges Reservoir Area (TGRA) based on long-term meteorological stations data, remote sensing data, and reanalysis products. Observation minus reanalysis method (OMR) was used to reveal possible impacts of land cover changes on climate changes. Major results indicated that the TGRA experienced an overall warming trend for both annual and seasonal variations, with greater rising trends in the upstream. Except for autumn, the relative humidity of most regions mainly showed significant downward trends, indicating an overall drying trend in the TGRA. There was insignificant change in total precipitation and precipitable water vapor, with the largest variation observed during the summer. Although there were small differences among these datasets, their results of climate changes showed good consistency overall. In addition, the results of OMR indicated that land cover changes mainly had a warming and drying effect on the middle and upper reaches, and a cooling and moistening effect on the lower reaches of the TGRA. This may be due to the impact of land cover changes on the surface energy balance, thus affected temperature and humidity. The study has important reference value for understanding the climate changes in the TGRA and the climate effects brought about by large-scale engineering construction.

System reduction-based approximate reanalysis method for statically indeterminate structures with high-rank modification

Efficient structural reanalysis for high-rank modification plays an important role in engineering computations which require repeated evaluations of structural responses, such as structural optimization and probabilistic analysis. To improve the efficiency of engineering computations, a novel approximate static reanalysis method based on system reduction and iterative solution is proposed for statically indeterminate structures with high-rank modification. In this approach, a statically indeterminate structure is divided into the basis system and the additional components. Subsequently, the structural equilibrium equations are rewritten as an equation system with the stiffness matrix of the basis system and the pseudo forces derived from the additional elements. With the introduction of the spectral decomposition, a reduced equation system with the element forces of the additional elements as unknowns is established. The approximate solutions of the modified structure can then be obtained by solving the reduced equation system through a pre-conditioned iterative solution algorithm. The computational costs of the proposed method are compared with those of two other reanalysis methods, and numerical examples including static reanalysis and static nonlinear analysis are presented. The results demonstrate that the proposed method has excellent computational performance for both structures with homogeneous material and structures composed of functionally graded beams. Furthermore, the superiority of the proposed method suggests that the combination of system reduction and pre-conditioned iterative solution technology is an effective approach to develop high-performance reanalysis methods.

Selection Criterion of Reanalysis Methods for Plane Truss Optimization

Structural reanalysis methods have been proposed to improve the efficiency of structural analysis. However, the methods are typically only applicable to their specific type of structural modification. Since the optimization process often involves multiple types of modifications, it is necessary to establish a criterion for selecting the most suitable reanalysis method for each type of modification, aiming to accelerate the optimization process. In this study, the effects of different types of structural modifications are first analyzed. A qualitative correspondence is established between different types of structural modifications and the mainstream of the reanalysis methods. Secondly, the most suitable reanalysis method for different types of structural modifications is quantitatively analyzed from the aspects of selecting efficiency indicators and clarifying accuracy requirements. Finally, in conjunction with the Structural Topology and Shape Annealing (STSA) algorithm, a criterion for selecting reanalysis methods, which are applicable to the optimization process of plane trusses, is established. To verify the validity of the selection criterion, two types of numerical examples are conducted. The results show that the proposed criterion can effectively improve the efficiency of structural computations.

An electromagnetic scattering reanalysis method for partial geometry modifications

AbstractThis work proposes an effective and accurate method for solving the partial geometry modification problem of original scattering. The current and scattering characteristics after structure modification can be efficiently calculated by using the information of the initial impedance matrix. These modifications include adding geometry or moving and rotating different parts of geometry. Although the changes in each impedance matrix are local for partial modified geometries, the associated computational cost is high because the whole analysis is iterative. By combining the binomial series method and the reduced basis method, the proposed method can effectively evaluate the scattering field without solving the inverse impedance matrix of the modified geometry. The proposed algorithm has a computational cost of O(n2), which is much smaller than O(n3) of the traditional method of moments (MoM), where n is the number of unknowns. Through several examples, the traditional MoM and the proposed method are compared, and the efficiency and accuracy of the proposed method are proved.

Block-partitioned Rayleigh–Ritz method for efficient eigenpair reanalysis of large-scale finite element models

Abstract In this manuscript, we propose a new effective method for eigenpair reanalysis of large-scale finite element (FE) models. Our method utilizes the matrix block-partitioning algorithm in the Rayleigh–Ritz approach and expresses the Ritz basis matrix using thousands of block matrices of very small size. To avoid significant computational costs from the projection procedure, we derive a new formulation that uses tiny block computations instead of global matrix computations. Additionally, we present an algorithm that recognizes which blocks are changed in the modified FE model to achieve computational cost savings when computing new eigenpairs. Through selective updating for the recognized blocks, we can effectively construct the new Ritz basis matrix and the new reduced mass and stiffness matrices corresponding to the modified FE model. To demonstrate the performance of our proposed method, we solve several practical engineering problems and compare the results with those of the combined approximation method, the most well-known eigenpair reanalysis method, and ARPACK, an eigenvalue solver embedded in many numerical programs.

The rising impact of urbanization-caused CO2 emissions on terrestrial vegetation

Vegetation photosynthesis is largely determined by CO2 concentrations, which have significantly accumulated by urbanization. Thus, quantifying urbanization-caused CO2 concentrations’ impact on the terrestrial ecosystem, especially for vegetation photosynthesis (represented by GPP, NPP, NEP, and SIF), is vital. In this study, we firstly examined the effectiveness of the observation minus reanalysis (OMR) method in quantifying urbanization-caused CO2 concentrations and the effect of urbanization on CO2 accumulation from 2009 to 2018. Then, the effect of urbanization-caused CO2 on global vegetation GPP, NPP, NEP, and SIF was calculated and analyzed using terrestrial vegetation radiative transfer and prognostic models. Lastly, the impacts of urbanization-caused CO2 increase in different continents and vegetation types were analyzed, separately. The results demonstrated that: 1) Urbanization-caused CO2 concentrations accumulated for 3.93 ppm during 2009–2018. For carbon fluxes, urbanization-caused GPP, NPP and NEP accumulation increased 1.24 PgC, 0.64 PgC, 0.17 PgC, respectively, while urbanization-caused SIF accumulated 0.07 PWum−1sr−1. 2) From a continental perspective, South America accounted for the most significant urbanization caused GPP, NPP, NEP, and SIF accumulation, because the tropical rainforest climate dramatically assists photosynthesis in Amazon Rainforests. 3) Forests accounted for the most significant urbanization-caused GPP, NPP, NEP, and SIF accumulation (over 35% of global vegetation). 4) SIF is more sensitive to the autumn postponement of photosynthetic activities than GPP, NPP, and NEP. ΔOMR(GPP), ΔOMR(NPP), ΔOMR(NEP) showed the most significant urbanization impact in summertime, but the North Hemisphere’s photosynthesis accumulated dramatically in autumntime shown as rising ΔSIF_OMR, partly due to the delay of the end of the growing season which happens in most northern ecosystems. Thus, this study will be helpful for us to further understand the terrestrial carbon cycle dynamics and the potential for alleviating global climate change.

A Fast Calculation Method for Sensitivity Analysis Using Matrix Decomposition Technique

The sensitivity reanalysis technique is an important tool for selecting the search direction in structural optimization design. Based on the decomposition perturbation of the flexibility matrix, a fast and exact structural displacement sensitivity reanalysis method is proposed in this work. For this purpose, the direct formulas for computing the first-order and second-order sensitivities of structural displacements are derived. The algorithm can be applied to a variety of the modifications in optimal design, including the low-rank modifications, high-rank modifications, small modifications and large modifications. Two numerical examples are given to verify the effectiveness of the proposed approach. The results show that the presented algorithm is exact and effective. Compared with the existing two reanalysis methods, this method has obvious advantages in calculation accuracy and efficiency. This new algorithm is very useful for calculating displacement sensitivity in engineering problems such as structure optimization, model correction and defect detection.

An efficient topology optimization method based on adaptive reanalysis with projection reduction

An efficient topology optimization based on the adaptive auxiliary reduced model reanalysis (AARMR) method is proposed to improve computational efficiency and scale. In this method, a projection auxiliary reduced model (PARM) is integrated into the combined approximation reduced model (CARM) to reduce the dimension of the model in different aspects. First, the CARM restricts the solution space to avoid large matrix factorization. Second, the PARM is proposed to construct the CARM dynamically to save computational cost. Furthermore, the multi-grid conjugate gradient method is suggested to update PARM adaptively. Finally, several classic numerical examples are tested to show that the proposed method not only significantly improves computational efficiency, but also can solve large-scale problems that are difficult to solve by direct solvers due to the memory limitations.

Single-station meteor detection filtering using machine learning on MOROI data

ABSTRACT Nowadays, extensive data are collected in an automated regime. Combining this, with the increase in accessible computational power, led to large-scale implementations of machine learning (ML). This is also the case of meteor science, where object detection often requires tracking of a moving light source between frames, and the number of false positives can be up to an order of magnitude higher than true meteoric phenomena. While spatiotemporal coincidence of events recorded by close, multiple cameras can eliminate most of the false positives, single-station detections in some camera networks are currently discarded. In this paper, we explore a set of ML models aiming to find an optimal method for re-analysis of this single-station observations, in order to identify and extract real meteors. A set of 15 ML models were trained on features extracted from the meteor movement. Upon testing, we found a top accuracy score of 98,2 per cent, and a recall (i.e. percentage of meteors correctly classified) score of 96 per cent for the best performing models. When combined with the spatiotemporal coincidence of the detection, the recall increases to 99.92 per cent. These 15 ML techniques were selected according to their ability classify tabular data, hence the bundle can be applied to other studies. The same goes for the computed features, which are independent on the camera configuration, thus, the process can be scaled and applied to other networks. These methods are to be implemented to re-analyze the events recorded by the larger, FRIPON network.

Structural reanalysis method for local modifications based on system reduction and iterative solution

In this paper, a novel static reanalysis method based on system reduction and iterative solution is proposed for structures with local modifications of physical properties. In this approach, the equilibrium equations of a modified structure is expressed by the original stiffness matrix and the pseudo forces introduced under the consideration of the effects of structural modifications. Based on the relationship between the element deformation displacements and element nodal displacements, the reduced equation system of the modified structure is reconstructed according to the distribution of modified elements. The iterative solution algorithm is employed to solve the reduced equation system of the modified structure, and the simplified pre-conditioning operation is proposed to significantly improve the solution efficiency. In addition, the structural nonlinear analysis method based on the proposed reanalysis method is developed for static nonlinear analysis of the structures with material nonlinearity. As indicated by the numerical investigation, the proposed method realizes the integration of system reduction and iterative solution algorithm, achieving high computational efficiency and satisfactory solution accuracy in the cases of both high-rank and low-rank local modifications of physical properties. Furthermore, the introduction of the proposed reanalysis method significantly improves the performance of solution algorithms for structural static nonlinear analysis.

A reanalysis-based multi-fidelity (RBMF) surrogate framework for efficient structural optimization

In recent years, research on multi-fidelity (MF) surrogate modeling, which integrates high-fidelity (HF) and low-fidelity (LF) models, has been conducted to improve efficiency in structural optimization. However, even the latest well-developed MF surrogate models inevitably require a certain number of samples to maintain the fidelity of the surrogate models. To overcome this issue, in this paper, a reanalysis-based multi-fidelity (RBMF) surrogate framework, which combines the MF surrogate modeling and a structural reanalysis method, is developed to reduce the computational cost for each sample, not the number of samples. The core idea of the developed framework is to approximately obtain a large number of samples based on a small number of exactly calculated data as prior knowledge. Each sub-strategy for RBMF surrogate framework is developed to maximize the performances of the reanalysis method. Specifically, a reanalysis sample classification method, reanalysis sample infilling method, and local convergence criteria are proposed to effectively reflect the characteristics of the reanalysis method. Therefore, the coupling between MF surrogate modeling and reanalysis method is strengthened. Finally, two numerical examples show that the developed framework outperforms the conventional one.

Spatio-temporal variations of vegetation cover and its influence on surface air temperature change over the Yellow River Basin, China

Abstract Northwest China has experienced dramatic changes in vegetation cover over the past few decades with the Yellow River Basin (YRB) being the most representative area. As the major climate-sensitive area in China, vegetation cover change is one reason for its impact on surface air temperature (SAT). This study uses the observation minus reanalysis (OMR) method to reveal the spatio-temporal variations of vegetation cover and its impact on SAT change over non-urban areas of the YRB from 1982 to 2015. The Global Inventory Modeling and Mapping Studies dataset, SAT derived from meteorological stations, and European Reanalysis (ERA)-interim reanalysis temperature data were used to analyze the relationship between normalized difference vegetation index (NDVI) and temperature variation caused by vegetation change. The NDVI trend (SlopeNDVI) of the entire YRB reached 1.11×10−2 decade−1, which indicated the recovery of vegetation in general. The impact of variation in vegetation conditions on SAT change during 1982–2015 was estimated to be 0.037 °C decade−1, which contributed 7.62% to the temperature change. The mean annual NDVI (MNDVI) and SlopeNDVI in the YRB were significantly negatively correlated (P<0.001) with OMR temperature variation. A negative correlation was exhibited in semi-arid and semi-humid regions, whereas a positive correlation was found in the arid region. The observed changes in vegetation and SAT in the YRB support the theory of the impact of vegetation variation on SAT in China.