Load Curve Research Articles

Under-frequency load shedding setting method of new energy sending-end system based on improved IMQ-GWO algorithm

Aiming at the problems of low qualification rate, poor stability, and insufficient timeliness of the existing under-frequency load shedding implementation scheme at the new energy sending end, this paper proposes an under-frequency load shedding setting method for the new energy sending end system based on the improved IMQ-GWO algorithm. Firstly, considering the fluctuation of the trend of the line of the sending end system relative to the load curve of the whole network, the index system of the implementation scheme based on the under-frequency load shedding is established. Secondly, the objective function is established for the index system of the under-frequency load shedding scheme, and the under-frequency load shedding setting model is established considering the static constraints and transient constraints of the system. Then, the grey wolf optimization algorithm (GWO) is updated by using the characteristics of the IMQ function. At the same time, the individual position is adaptively updated based on the Sigmoid exponential function to better search and optimize the solution space of the problem. Finally, based on the modified IEEE39 node, the empirical analysis is carried out, and the improved IMQ-GWO algorithm proposed in this paper is used to solve the under-frequency load shedding setting model. It is proved that the proposed method is superior to other methods in terms of execution qualification rate, stability, and application timeliness.

An elastoplastic constitutive model of lunar soil simulant considering shear dilatancy and softening characteristics

The study of the constitutive relationship of lunar soil is the key to a deep understanding of the mechanical properties of lunar soil and the subsequent construction of lunar bases. Previous models mostly focused on the strengthening behavior before the peak, while rarely reflected the post-peak softening and residual deformation stages during shear deformation. A new elastoplastic constitutive relation is derived with combining kinematic hardening model and initial shear stress, which effectively compensates for the shortcomings of existing constitutive models, and the validity of the model is verified by comparing with existed laboratory test results. The developed constitutive model not only effectively captures the shear dilatancy and softening characteristics of lunar soil simulant, but also requires fewer parameters to be easily determined by simple initial loading curves from direct shear tests, Furthermore, the influences of some key parameters on shear strength and softening behavior of lunar soil simulant can be easily obtained based on this constitutive model.

An online long-term load forecasting method: Hierarchical highway network based on crisscross feature collaboration

In order to ensure the efficient operation of the power system, accurate online load forecasting is necessary. Existing studies face challenges in addressing unconventional load changes in long-term load forecasting (LTLF) and are unsuitable for online forecasting due to their reliance on measured Forecast Day Data (FDD). The development of a model independent of prior knowledge is essential to improve the interpretability and applicability of load forecasting methods. In this study, a hybrid model based on Crisscross Feature Collaboration (CFC) and Hierarchical Highway Network (HHN) is introduced. During the horizontal feature learning, the HHN is employed to learn temporal information from annual load curves across multiple time scales, reducing time complexity in LTLF. Subsequently, the prediction results of various time scales are combined to approximate FDD, addressing data leakage problems. In the vertical feature matching phase, the similar day data are screened by the multi-modal information of FDD and the Toeplitz Inverse Covariance Based Clustering (TICC) method, enabling the model to be independent of prior knowledge. Finally, through the HHN, all similar day data are utilized to reconstruct the forecasting load curve. In LTLF, the Root Mean Square Error (RMSE) of the proposed model reduces by 94.35 %, 85.74 %, 84.02 %, 90.72 %, 85.17 % and 87.76 % respectively in different scenarios compared to the direct forecasting. This signifies the effectiveness of our model in predicting the unconventional load change and achieving accurate online LTLF.

Experimentally calibrated FE modelling of helical buckling of anisotropic slender shafts

Helical buckling is very common in slender structures and it is characterized by the development of helical deformation patterns along their length. This phenomenon is particularly acute in very slender drill-strings as associated high stresses decrease drilling efficiency and shorten life expectancy of expensive drilling assemblies. Hence, understanding and predicting buckling behaviour is of paramount importance. In this paper, we investigated buckling phenomena of slender flexible shafts in forms of stranded wire ropes mimicking drill-strings, by experimental and numerical means. From experiments we obtained the loading curves for the flexible shafts and developed governing buckling equations using dimensional analysis. Based on these equations, we calculated the effective material properties of the stranded ropes and carried out FE modelling and analysis in Abaqus. Finally, we developed a robust method to predict the helical buckling behaviour, which matches well with experiment results.

Collaborative strategy for electric vehicle charging scheduling and route planning

AbstractDue to varying energy demands and supply levels in different regions, the distribution of power load exhibits an imbalanced state. It contributes to increased power loss and poses a threat to the security constraints of the electrical grid. Simultaneously, the global energy transition has led to a continuous increase in the proportion of renewable energy integrated into the grid. Electric vehicles (EVs), serving as representative of renewable energy, further magnify this load imbalance with their charging requirements, which poses a significant challenge to the stable operation of the grid. Therefore, to ensure the smooth operation of the grid under the context of renewable energy integration, the authors investigate the coordinated strategies of EV charging scheduling and route planning. The authors first model the coupling of the transportation network with the smart grid as a cyber‐physical system. Subsequently, the authors simulate and analyse the daily charging load curve of the network, capturing the travel characteristics of EVs. Based on this, the authors research the EV charging scheduling in both individual and collective travel scenarios during peak and off‐peak hours. For the off‐peak travel period of EVs, a charging schedule strategy based on travel plans is proposed, which reduces the time cost of EV owners' travel. Furthermore, for the collective travel of a large number of EVs within the system, a multi‐EV charging scheduling strategy based on charging station load balancing is presented. This strategy effectively balances the load levels of various charging stations while reducing the overall system travel time. Ultimately, through experimental results, the authors demonstrate that by deploying appropriate charging scheduling strategies, EVs cease to be a burden on the grid and can be transformed into tools for balancing the loads across different regions.

A study on the application of diffuse axonal multi-axis general evaluation for brain injury assessment in small overlap barrier crash test

PurposeHead injury criterion (HIC) companied by a rotation-based metric was widely believed to be helpful for head injury prediction in road traffic accidents. Recently, the Euro-New Car Assessment Program utilized a newly developed metric called diffuse axonal multi-axis general evaluation (DAMAGE) to explain test device for human occupant restraint (THOR) head injury, which demonstrated excellent ability in capturing concussions and diffuse axonal injuries. However, there is still a lack of comprehensive understanding regarding the effectiveness of using DAMAGE for Hybrid Ⅲ 50th percentile male dummy (H50th) head injury assessment. The objective of this study is to determine whether the DAMAGE could capture the risk of H50th brain injury during small overlap barrier tests. MethodsTo achieve this objective, a total of 24 vehicle crash loading curves were collected as input data for the multi-body simulation. Two commercially available mathematical dynamic models, namely H50th and THOR, were utilized to investigate the differences in head injury response. Subsequently, a decision method known as simple additive weighting was employed to establish a comprehensive brain injury metric by incorporating the weighted HIC and either DAMAGE or brain injury criterion. Furthermore, 35 sets of vehicle crash test data were used to analyze these brain injury metrics. ResultsThe rotational displacement of the THOR head is significantly greater than that of the H50th head. The maximum linear and rotational head accelerations experienced by H50th and THOR models were (544.6 ± 341.7) m/s2, (2468.2 ± 1309.4) rad/s2 and (715.2 ± 332.8) m/s2, (3778.7 ± 1660.6) rad/s2, respectively. Under the same loading condition during small overlap barrier (SOB) tests, THOR exhibits a higher risk of head injury compared to the H50th model. It was observed that the overall head injury response during the small overlap left test condition is greater than that during the small overlap right test. Additionally, an equation was formulated to establish the necessary relationship between the DAMAGE values of THOR and H50th. ConclusionIf H50th rather than THOR is employed as an evaluation tool in SOB crash tests, newly designed vehicles are more likely to achieve superior performance scores. According to the current injury curve for DAMAGE and brain injury criterion, it is highly recommended that HIC along with DAMAGE was prioritized for brain injury assessment in SOB tests.

CROCuS, a Phase II Study Evaluating the Antiviral Activity, Clinical Outcomes, and Safety of Rilematovir in Children Aged ≥28 Days and ≤3 Years with Acute Respiratory Tract Infection Due to Respiratory Syncytial Virus.

Respiratory syncytial virus (RSV) causes significant morbidity and mortality in children aged ≤5 years and adults aged ≥60 years worldwide. Despite this, RSV-specific therapeutic options are limited. Rilematovir is an investigational, orally administered inhibitor of RSV fusion protein-mediated viral entry. To establish the antiviral activity, clinical outcomes, safety, and tolerability of rilematovir (low or high dose) in children aged ≥28days and ≤3years with RSV disease. CROCuS was a multicenter, international, double-blind, placebo-controlled, randomized, adaptive phase II study, wherein children aged ≥28 days and ≤3 years with confirmed RSV infection who were either hospitalized (Cohort 1) or treated as outpatients (Cohort 2) were randomized (1:1:1) to receive rilematovir (low or high dose) or placebo. Study treatment was administered daily as an oral suspension from days 1 to 7, with dosing based on weight and age groups. The primary objective was to establish antiviral activity of rilematovir by evaluating the area under the plasma concentration-time curve of RSV viral load in nasal secretions from baseline through day 5. Severity and duration of RSV signs and symptoms and the safety and tolerability of rilematovir were also assessed through day 28 (±3). In total, 246 patients were randomized, treated, and included in the safety analysis population (Cohort 1: 147; Cohort 2: 99). Of these, 231 were included in the intent-to-treat-infected analysis population (Cohort 1: 138; Cohort 2: 93). In both cohorts, demographics were generally similar across treatment groups. In both cohorts combined, the difference (95% confidence interval) in the mean area under the plasma concentration-time curve of RSV RNA viral load through day 5 was -1.25 (-2.672, 0.164) and -1.23 (-2.679, 0.227) log10 copies∙days/mL for the rilematovir low-dose group and the rilematovir high-dose group, respectively, when compared with placebo. The estimated Kaplan-Meier median (95% confidence interval) time to resolution of key RSV symptoms in the rilematovir low-dose, rilematovir high-dose, and placebo groups of Cohort 1 was 6.01 (4.24, 7.25), 5.82 (4.03, 8.18), and 7.05 (5.34, 8.97) days, respectively; in Cohort 2, estimates were 6.45 (4.81, 9.70), 6.26 (5.41, 7.84), and 5.85 (3.90, 8.27) days, respectively. A similar incidence of adverse events was reported in patients treated with rilematovir and placebo in Cohort 1 (rilematovir: 61.9%; placebo: 58.0%) and Cohort 2 (rilematovir: 50.8%; placebo: 47.1%), with most reported as grade 1 or 2 and none leading to study discontinuation. The study was terminated prematurely, as the sponsor made a non-safety-related strategic decision to discontinue rilematovir development prior to full recruitment of Cohort 2. Data from the combined cohort suggest that rilematovir has a small but favorable antiviral effect of indeterminate clinical relevance compared with placebo, as well as a favorable safety profile. Safe and effective therapeutic options for RSV in infants and young children remain an unmet need. EudraCT Number: 2016-003642-93; ClinicalTrials.gov Identifier: NCT03656510. First posted date: 4 September, 2018.

Peak shaving potential and its economic feasibility analysis of V2B mode

Electric vehicles (EVs) as mobile energy-storage devices improve the grid's ability to absorb renewable energy while reducing peak-to-valley load differences. With a focus on smoothing the load curve, this study investigates the peak shaving potential and its economic feasibility analysis of V2B mode. First, based on the virtual microgrid system established in this paper, a Monte Carlo method was used to simulate the driving, charging, and discharging processes of EVs. Second, the effects of the number of charging stations, battery capacity of EVs, and photovoltaic (PV) penetration rate on the peak shaving results were evaluated separately by using the standard deviation of daily loads. Third, the V2B peak shaving strategy and cost composition were explored considering the battery aging costs. Moreover, the economic feasibility and factors influencing the proposed strategy were analyzed. The results indicate that 1) V2B effectively transfers peak power loads between 08:00 h And 20:00 h To low-demand periods at night; 2) as the deployment ratio of charging station increases, the level of load fluctuation decreases significantly. When EV battery capacity is at or above 60 kW h, charging station deployment ratios of 0.43, 0.42, and 0.47 are required for saturated load smoothing in winter, transition period, and summer, respectively; 3) the PV penetration rate for maximizing the collaborative peak shaving capability of V2B and PV power is 30%–40 % and 4) reducing battery costs or extending battery life has the most significant effect on V2B power costs, followed by charging station costs. This study provides a theoretical basis for determining the economic feasibility of charging station planning and provides technical guidance for the rational scheduling of EVs and their friendly interaction with the power grid.

Experiments and design of a kirigami-based multi-stage energy absorption structure subjected to axial impact

As a typical energy-absorbing structure, the thin-walled structure dissipates impact energy through the formation of plastic hinges and material failure. The geometric configuration of the energy-absorbing structure deeply influences its response to impact loads. For a certain load curve required by collision scenarios, this study proposes a novel design method for approximating a continuous impact load curve with discretized ones. Based on kirigami structures, the conceptual framework integrates multi-module and multi-stage designs to regulate segmental stiffness through theoretical analysis, thereby achieving the desired curve approximation. The effectiveness of the proposed kirigami-based multi-stage energy absorption structure (KMS) is validated by drop-weight impact tests and numerical simulations. Findings demonstrate that the KMS effectively dissipates impact energy in a progressive manner, aligning with the desired load curve and inducing stable and orderly structural deformation under axial impact. Through appropriate parameter design, the deformation mode and the inducing effect of the kirigami structure can be managed, enabling the structure's adaptability to diverse collision scenarios.

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

With the continuous increase in global energy demand and growing environmental awareness, the utilization of renewable energy has become a worldwide consensus. In order to address the challenges posed by the intermittent and unpredictable nature of renewable energy in distributed power distribution networks, as well as to improve the economic and operational stability of distribution systems, this paper proposes the establishment of an active distribution network capable of accommodating renewable energy. The objective is to enhance the efficiency of new energy utilization. This study investigates optimal scheduling models for energy storage technologies and economic-operation dispatching techniques in distributed power distribution networks. Additionally, it develops a comprehensive demand response model, with real-time pricing and incentive policies aiming to minimize load peak–valley differentials. The control mechanism incorporates time-of-use pricing and integrates a chaos particle swarm algorithm for a holistic approach to solution finding. By coordinating and optimizing the control of distributed power sources, energy storage systems, and flexible loads, the active distribution network achieves minimal operational costs while meeting demand-side power requirements, striving to smooth out load curves as much as possible. Case studies demonstrate significant enhancements during off-peak periods, with an approximately 60% increase in the load power overall elevation of load factors during regular periods, as well as a reduction in grid loads during evening peak hours, with a maximum decrease of nearly 65 kW. This approach mitigates grid operational pressures and user expense, effectively enhancing the stability and economic efficiency in distribution network operations.

Residential load forecasting based on symplectic geometry mode decomposition and GRU neural network with attention mechanism

AbstractShort‐term residential load forecasting plays an increasingly important role in modern smart grids, with its main challenge being the high volatility and uncertainty of load curves. This article proposes a hybrid Symplectic Geometry Mode Decomposition‐Gated Recurrent Unit with Attention Mechanism (SGMD‐GRUAM) model for hourly residential load forecasting. First, SGMD is used to decompose the residential load and obtain a series of stable subsequences. Then, the Pearson correlation coefficient is used to select features related to each subsequence, such as weather factors. Next, a GRUAM prediction model is constructed for each subsequence. Finally, the final load prediction value is obtained by superimposing the previous component sequences and eliminating the noise sequence. The experiment uses the public dataset from UMass for a case study and compares it with benchmark models such as ARIMA and EEDM‐GRUAM. The experimental results show that the proposed SGMD‐GRUAM model has significant advantages in terms of prediction performance.

Mode I fracture propagation and post-peak behavior of sandstone: Insight from AE and DIC observation

Microcracks generated at the crack tip, particularly in quasi-brittle materials such as sandstone, give rise to a non-linear region known as the fracture process zone (FPZ), which cannot be analyzed using linear elastic fracture mechanics (LEFM). To gain insights into the nonlinear fracturing behavior of rock, this study conducted three-point bending experiments on sandstone beams with prefabricated notch. Stable mode-Ⅰ fracture propagation during the post-peak stage was achieved by the monotonic increment of crack mouth opening displacement (CMOD) measured by a clip gauge. The acoustic emission (AE) and digital image correlation (DIC) techniques were utilized to visually track the detailed crack propagation process from both micro and macro perspectives. Nonlinear fracture parameters, including FPZ length, were identified by AE and DIC measurements and compared with the theoretical calculation. A nonlinear fracture criterion relating applied load, equivalent crack length, and stress intensity factor were established, from which the theoretical CMOD-P curve at the post-peak stage were obtained and validated with experimental measurement from MTS machine. The results reveal that microcracks formulating the FPZ materialize in the pre-peak stage, with the majority emerging post-peak to facilitate the propagation of the traction-free crack and FPZ in the loading direction, as evidenced by AE locations and DIC displacement gradient. Throughout the crack propagation process, over 65 % of microcracks are estimated to be tensile mode based on both AF/RA and polarity analysis, aligning with anticipated outcomes from macroscopic three-point bending fracture tests. Energy dissipation primarily occurs within the FPZ to facilitate separation of crack faces, with the dissipated energy region advancing towards the beam boundary. By integrating AE source locations and DIC displacement gradient, the FPZ length of the 3-point-bending sandstone specimens is determined to be lp = 8.1 mm, slightly exceeding the theoretical FPZ lengths calculated by the Irwin model (lp = 7.0 mm) and the Dugdale-Barenblatt model (lp = 7.6 mm). Using a non-linear fracture criterion that incorporates the FPZ and employing the in-time equivalent crack length measured by AE and DIC as an intermediate variable, the theoretical applied load-crack length curve during the post-peak stage is calculated, showing close agreement with load cell measurements with a negligible error of approximately 2 %. Furthermore, the theoretical post-peak CMOD vs. load curve aligns with experimentally obtained results, demonstrating a non-linear crack propagation speed at a constant CMOD rate, wherein crack propagation speed decreases as crack length increases.

Bond behavior of CFRP reinforcement systems with concrete under static and fatigue loading

In this research, the single lap shear tests were used to investigate the bond behavior of carbon fiber reinforced polymer (CFRP) sheets bonded to concrete blocks under static and fatigue loads. The magnitude of the fatigue load was considered as a parameter that varied from a minimum load of 10% of Pu to a maximum load of 60%, 70% and 80% of the ultimate static load. For both loading scenarios, the applied load versus slips curves and the profiles of the interface zone strain were presented. In addition, comparisons, and analyses of the failure mode of samples subjected to static and fatigue loading are presented. The experimental results demonstrate that the loading level has a significant impact on the failure mode, fatigue life and stiffness degradation of CFRP-concrete joints during the fatigue loading.Finally, a prediction model for the fatigue life of the CFRP-concrete joint was developed as part of this investigation.

Short-Term Electric Load Forecasting Based on Signal Decomposition and Improved TCN Algorithm

In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid stability. Short-term load curves are characteristically coarse, revealing high-frequency data upon decomposition that exhibit pronounced non-linearity and significant noise, complicating efforts to enhance forecasting precision. To address these challenges, this study introduces an innovative model. This model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to bifurcate the original load data into low- and high-frequency components. For the smoother low-frequency data, a temporal convolutional network (TCN) is utilized, whereas the high-frequency components, which encapsulate detailed load history information yet suffer from a lower fitting accuracy, are processed using an enhanced soft thresholding TCN (SF-TCN) optimized with the slime mould algorithm (SMA). Experimental tests of this methodology on load forecasts for the forthcoming 24 h across all seasons have demonstrated its superior forecasting accuracy compared to that of non-decomposed models, such as support vector regression (SVR), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), TCN, Informer, and decomposed models, including CEEMDAN-TCN and CEEMDAN-TCN-SMA.

MAGE: Microgrids with Advanced Grid Efficiency through Battery-Aware EV management

Higher penetration of Electric Vehicles (EVs) yields a positive impact by curbing pollution. However, this rapid proliferation of EVs presents several challenges to the efficient management of EVs and the existing grid infrastructure due to the very high energy requirement of newly adopted EVs, which can also act as energy resources. This work delves into the problem of intelligent charging–discharging of EVs in a single offline microgrid framework, which can partially cater to the energy needs of other EVs (V2V, vehicle-to-vehicle), grid (V2G, vehicle-to-grid), building (V2B, vehicle-to-building), etc. This approach also preserves the battery’s health by limiting the number of charging–discharging cycles. Hence, a battery degradation-aware energy scheduler named MAGE (Microgrids with Advanced Grid Efficiency through Battery-Aware EV Management) is formulated to minimize the microgrid’s operational cost (MOC). The MOC is comprised of — the energy procurement cost, the cost of surplus energy sales, and penalties for unsatisfied demands. A formulation based on Mixed-Integer Quadratic Programming is proposed to ascertain an optimal solution. Heuristic algorithms are designed to identify a reasonable solution quickly. A comprehensive experimental analysis of our proposed approach exhibits its potential in optimizing MOC and ensures grid efficiency by reducing the peak load and fattening the load curve.

Two level neuro-functional forecaster: A novel dynamic hybridization for functional data forecasting

With the advancement of technology, time series data are automatically collected without human intervention. As the data collection process becomes effortless, the next change encountered is to identify the best method to forecast time series data with high accuracies. In this regard, hybrid approaches have gained much attention where the strengths of two approaches can be combined to lessen the weaknesses of each individual approach. When exploring the features of time series data, some depict repetitive patterns and also data can be observed at several levels. The repeating curves can be considered as the higher level, whereas each individual observation can be considered as the lower level. Thus, in order to handle the data in a more effective way, the series can be handled at two levels by giving prominence to the features of each level separately. This paper proposes a novel algorithm named two level neuro-functional forecaster, which is capable of handling data at two levels, hybridizing a statistical approach with an artificial intelligence approach, to gain high accuracy levels. In addition, as this approach handles data at two levels, data sparsity at a particular level can be accommodated at the other level. To apply this algorithm to a real world dataset, electricity demand data in Sri Lanka was considered where the series consisted of daily load curves with repetitive pattern across the days. The proposed hybrid algorithm, outperforms the two approaches when individually used, with a MAPE of 3.324% for a year.

A Stochastic Methodology for EV Fast-Charging Load Curve Estimation Considering the Highway Traffic and User Behavior

The theoretical impact of the electric vehicle (EV) market share growth has been widely discussed with regards to technical and socioeconomic aspects in recent years. However, the prospection of EV scenarios is a challenge, and the difficulty increases with the granularity of the study and the set of variables affected by user behavior and regional aspects. Moreover, the lack of a robust database to estimate fast-charging stations’ load curves, for example, affects the quality of planning, allocation, or grid impact studies. When this problem is evaluated on highways, the challenge increases due to the reduced number of trips related to the reduced number of charger units installed and the limited EVs range, which influence user anxiety. This paper presents a methodology to estimate the highway fast-charging station operation condition, considering regional and EV user aspects. The process is based in a block of traffic simulation, considering the traffic information and highway patterns composing the matrix solution model. Also, the output block estimates charging stations’ operational conditions, considering infrastructure scenarios and simulated traffic. A Monte Carlo simulation is presented to model entrance rates and charging times, considering the PDF of stochastic inputs. The results are shown for the aspects of load curve and queue length for one case study, and a sensibility study was conducted to evaluate the impact of model inputs.

A novel method to calculate the load vs. crack extension curve based on the load vs. load-line displacement curve

In order to obtain the crack growth resistance curve, a typical fracture test must measure the load vs. crack extension (F-Δa) curve. Apart from the basic measurement of the load vs. load-line displacement (F-vLL) curve, additional instruments or tedious test operations such as partial unloading are necessary to measure the amount of crack extension. This paper proposed a novel method of calculating the F-Δa curve directly from the F-vLL curve, in which no additional instrument or test operations are required. The proposed method can be easily applicable to these non-standard specimens. Several examples of C(T) and M(T) specimens are adopted to verify this method. Thus, this method is expected to simplify these available standards for measuring crack length.

Assessment of empirical methods for interpreting bidirectional pile load test results

There are many available methods to convert the O-cell pile load test results into the Equivalent Top Load (ETL) curve. That curve should be representative of the load-settlement curve measured during the equivalent conventional test. This paper assessed the available empirical methods to investigate their capability of reproducing the measured conventional test results. The assessment was performed by applying these methods to several well documented cases of O-cell pile load tests to find the corresponding ETL curves. These estimated curves were compared to reference curves produced from simulated equivalent conventional tests using the finite element method. The judgement was based on finding the error in the settlement at expected working loads and the ultimate carrying capacity of the pile estimated from the ETL curve. Throughout the assessment, it was noted that the different interpretation methods produce significant errors, indicating the need for a new method that minimizes the error values.

Preparation and Mechanical Properties of Core-Shell PS&CeO2 Composite Abrasive Particles.

Core-shell composite abrasive particles are a topic of great interest in surface finishing. It is important to explore the preparation technology and performance parameters associated with them. In this paper, a core-shell composite abrasive particle made of polystyrene and cerium oxide (PS&CeO2, CSPC), which is rigid on the outside and flexible on the inside, is proposed. The microstructure, physical phase characteristics, and mechanical properties of the inner core and composite abrasive particles are investigated. PS microspheres and CSPC composite abrasive particles with different structural features were prepared through a series of experiments, morphological observations, and physical and chemical characterization experiments. Their microstructures and physical phase properties were investigated. The indentation load curves of the PS microspheres and CSPC composite abrasive samples were measured by using an atomic force microscope. The analysis focused on the effects of various dimensional and structural parameters on the modulus of elasticity of both PS microspheres and CSPC composite abrasive particles. The analysis shows that the experimentally prepared PS microspheres have good dispersion, a smooth surface, and a uniform particle size distribution. The prepared CSPC composite abrasive particles are regular spheres with rough, rice-like surfaces, low modulus of elasticity, and overall nonrigid and soft elastic properties. The results of this paper can provide a guide for the preparation technology, performance regulation, and application of polymer microspheres and core-shell composite abrasive particles in CMP.