Model Coefficients Research Articles

Nonlinear geometrically exact dynamics of hyperelastic pipes conveying fluid: Comparative study of different hyperelastic models

This work presents a comparative analysis of the nonlinear dynamic responses of fluid-conveying pipes modelled by four different phenomenological hyperelastic constitutive models, namely the Mooney-Rivlin model, the Neo-Hookean model, the Yeoh model, and the Ogden model. Based on the strain energy density functions and the exact geometric relation, the geometrically exact dynamic models corresponding to different hyperelastic contitutive models are established. Then the Galerkin's truncation is adopted to transfer the obtained nonlinear integral-partial differential equations into the nonlinear ordinary differential equations. Subsequently, linear dynamic study and finite difference method are taken to explore the natural frequencies and stabilities and nonlinear dynamic responses of the pipe, respectively. And the fitting coefficients for various models obtained from Treloar's experiment data are utilized in the numerical calculation. It is found the Hopf bifurcation and the limit cycle oscillations in the post-flutter status are influenced by the choice of the hyperelastic models, particularly under large flow velocity. However, hyperelastic constitutive models do not exert a significantly extreme influence on the results, which can be attributed to the relatively small strains even though large deformations occur in the pipe. Mathematically, it is proven that the symmetry observed in the pipe's displacement response stems from the symmetry of its cross-section and the inextensibility assumption of the centerline, with hyperelasticity having no effect on this symmetry.

Investigation of thermal comfort and preferred temperatures among rural elderly in Weihai, China: Considering metabolic rate effects

As the population ages, the thermal environment indoors for the elderly has garnered extensive attention. However, current research on thermal comfort for the elderly is not comprehensive, particularly for rural elderly individuals who experience unique thermal perceptions and adaptation behaviors due to physical changes. Moreover, metabolic rates, which are often overlooked in thermal comfort studies for the elderly, play a crucial role. A year-long survey of thermal comfort was conducted in the rural areas of Weihai, a coastal city in China's cold region, collecting 203 questionnaires. Three different values of metabolic rates (recommended values, estimated values, and values calculated using Basal Metabolic Rate and Physical Activity Ratio) were analyzed. The results demonstrate that the Basal Metabolic Rate and Physical Activity Ratio method provides predictions of thermal sensation that more closely align with actual thermal sensations. The adaptive coefficient of the aPMV model was found to be −0.38 in colder environments and 0.272 in warmer ones. Additionally, using PMV regression, TSV regression, and Griffiths' method, we calculated the local elderly's comfort temperatures to be 18.9 °C (15.43–22.57 °C), 19.3 °C (15.16–22.75 °C), and 21.63 °C (21.34–23.49 °C) respectively. This study introduces a straightforward method for calculating metabolic rates in elderly individuals, offering new insights for research on thermal comfort in the elderly and expanding the database for thermal comfort studies. It lays a foundation for future design and standardization of housing for rural elderly populations.

The Mean Free Path of 13–64 MeV Protons Derived from Statistical Results of Solar Energetic Particle Events

A recent study by Wang et al. investigated gradual solar proton events with energies >10 MeV, as observed by STEREO-A, STEREO-B, and the Solar and Heliospheric Observatory spacecraft. For each event, the spacecraft with the best magnetic connection to the source region among the three spacecraft was identified, and energetic proton intensities observed by the spacecraft were analyzed through fitting. The fitting process produced two parameters, b and c, for four energy channels (13–16 MeV, 20–25 MeV, 32–40 MeV, and 40–64 MeV) in each event. Parameters b and c govern the rise and decay of particle intensities, respectively. Statistical analysis revealed a power-law correlation between b and c, expressed as c ∼ b −γ . In this study, in order to explain the relation between the two parameters, we investigate the model of particle diffusion coefficients in the interplanetary space. In our simulations, the radial mean free path is modeled as a power function of radial distance, successfully reproducing the b–c relation. Consequently, the observations demonstrate that the radial mean free path varies with radial distance in a power law. In future research, the model of diffusion coefficients holds promise in determining the mean free path of energetic protons.

Experimental evaluations of Berkeley thermal sensation and comfort models in electric vehicle cabin under cold outdoor conditions

As the automobile industry is transitioning toward electric vehicles, manufacturers have started implementing local warmers alongside cabin heating, ventilation, and air conditioning (HVAC) systems for effective thermal comfort management. However, optimal operating strategies need to be developed for integrating local warmers with HVAC systems. Although the Berkeley models comprising local/overall thermal sensation and comfort models offer insights in this regard, they lack follow-up assessments for occupants transitioning from very cold states. In this study, Berkeley models were evaluated using two sets of experimental data collected in a transient vehicle cabin under cold outdoor conditions: test (A) with cabin HVAC alone and test (B) with both HVAC and local warmers. The findings confirm the satisfactory performances of the Berkeley models for predicting overall sensation and comfort, with a maximum root mean-squared error (RMSE) of 0.15. The local comfort model performed poorly with the original coefficients across both datasets (maximum RMSE of 1.96). Therefore, the model coefficients were regressed for the dataset from test A and validated against the dataset from test B to achieve a maximum RMSE of 0.49. With these regressed coefficients, it was observed that moving toward a neutral overall state diminished the potential to maximize local comfort. Conversely, the local sensation model showed poor agreement (maximum RMSE of 1.9); we confirmed that accurate adaptive setpoint temperatures are a prerequisite for ensuring good predictions from the model. These findings are expected to contribute toward future efforts in using Berkeley models to formulate effective local warmer–HVAC operational strategies in electric vehicles.

A novel configuration optimization approach for IES considering exergy-degradation and non-energy costs of equipment

Improving the exergy utilization and economic performance is the core goal for the configuration optimization of the integrated energy system (IES). Achieving such a goal requires accurate evaluation of the exergy efficiency and economy. However, IES is a multisource heterogeneous and strongly coupled system. The development of reasonable exergy efficiency-economy characteristic evaluation indicators is a complex issue that limits its application in IES configuration. This article proposes a novel IES configuration optimization approach based on the concept of the equipment exergyeconomic cost coefficient. The exergyeconomic cost coefficient consists of the exergy-degradation and non-energy costs, which can quantitatively characterize the exergy efficiency and economy of the IES under a unified benchmark. A mechanism model for the equipment exergyeconomic cost coefficient, output of various equipment and total exergyeconomic cost is established. Furthermore, exergy flow attribute classification method is proposed to obtain the equipment exergyeconomic cost coefficient in the IES. The proposed method is verified in a real-world study case located in Yancheng, China. Compared to the conventional configuration method, the capacity of the renewable energy and combined heat and power generation (CHP) are enhanced, while the capacity of purchased electricity and electric boiler (EB) are reduced. It can obtain the minimum total exergyeconomic cost (TEC) for the IES. This study offers a new direction for the optimal configuration of IES.

Effect of modifiers on Cr (VI) adsorption performance of activated modified-hydrochars

Hexavalent chromium (Cr (VI)) is high toxic and solubility in aquatic systems, which has attracted severe environment issues. In this work, corn straw, as raw biomass material, was modified by citric acid, ZnCl2, NaOH, or (NH4)2Fe(SO4)2 via hydrothermal carbonization process, and further activated by KOH to obtain the activated modified-hydrochars. The influence of different modifiers on the Cr (VI) adsorption performance for activated modified-hydrochars was investigated by controlling adsorbent dosages, adsorption time and different Cr (VI) concentrations at pH of 3.0 under room temperature. Among these absorbents, the activated modified-hydrochar modified by ZnCl2 possessed the excellent microporous characteristics and a high specific surface area of 1532.904 m2 g-1, thus exhibiting the Cr (VI) removal rate of 99.6wt.%. Cr (VI) was reduced to Cr (III) by -OH of activated modified-hydrochars during the Cr (VI) adsorption experiments. Due to higher R2 (>0.99) by kinetics analysis, pseudo-second-order kinetics was confirmed and further the Cr (VI) removal for the activated modified-hydrochars was a chemical adsorption process. The regression coefficients (R2) of the Langmuir model for all activated modified-hydrochars were higher than those of Freundlich model, which meant that Langmuir monolayer chemisorption was more consistent with the Cr (VI) adsorption process in this work.

Rest2Task: Modeling task-specific components in resting-state functional connectivity and applications

The networks observed in the brain during resting-state activity are not entirely “task-free.” Instead, they hint at a hierarchical structure prepared for adaptive cognitive functions. Recent studies have increasingly demonstrated the potential of resting-state fMRI to predict local activations or global connectomes during task performance. However, uncertainties remain regarding the unique and shared task-specific components within resting-state brain networks, elucidating local activations and global connectome patterns. A coherent framework is also required to integrate these task-specific components to predict local activations and global connectome patterns. In this work, we introduce the Rest2Task model based on the partial least squares-based multivariate regression algorithm, which effectively integrates mappings from resting-state connectivity to local activations and global connectome patterns. By analyzing the coefficients of the regression model, we extracted task-specific resting-state components corresponding to brain local activation or global connectome of various tasks and applied them to the brain lateralization prediction and psychiatric disorders diagnostic. Our model effectively substitutes traditional whole-brain functional connectivity (FC) in predicting functional lateralization and diagnosing brain disorders. Our research represents the inaugural effort to quantify the contribution of patterns (components) within resting-state FC to different tasks, endowing these components with specific task-related contextual information. The task-specific resting-state components offer new insights into brain lateralization processing and disease diagnosis, potentially providing fresh perspectives on the adaptive transformation of brain networks in response to tasks.

Hydrothermal bio-oil yield and higher heating value of high moisture and lipid biomass: Machine learning modeling and feature response behavior analysis

The yield and higher heating value (HHV) of bio-oil products are significant performance parameters for the hydrothermal conversion of high-water and high-lipid biomass. Machine learning (ML) modeling prediction is a fast and convenient means of obtaining performance parameters. An informative dataset with 243 samples was prepared, and two highly adapted ML algorithms were used: Random Forest (RF) and Extreme Gradient Boosting Tree (XGBoost). It is interesting to note that the developed ML models demonstrated great prediction ability; for example, the regression coefficient (R2) of the XGBoost model for yield and HHV prediction was as high as 0.942 and 0.940, respectively. Furthermore, partial dependence plots (PDP) and SHapley Additive exPlanations (SHAP) interpretability methodologies were adopted to address the main contributions of the feature identification and response behavior analysis of the features. The results demonstrated that the biomass composition had the greatest effect on bio-oil yield, with fat contributing up to 40 %. In contrast, the elemental composition had the most significant effect on the HHV of bio-oil. Notably, hydrogen content affected the HHV of up to 4.5 units. The interaction response behavior showed that the interaction of the process parameters with feedstock properties was most common and significant. The information obtained from the response mechanism can be used to enhance the subsequent hydrothermal fuel preparation process for bio-oils.

Modeling electrical impedance in brain tissue with diffusion tensor imaging for functional neurosurgery applications

Objective.Decades ago, neurosurgeons used electrical impedance measurements in the brain for coarse intraoperative tissue differentiation. Over time, these techniques were largely replaced by more refined imaging and electrophysiological localization. Today, advanced methods of diffusion tensor imaging (DTI) and finite element method (FEM) modeling may permit non-invasive, high-resolution intracerebral impedance prediction. However, expectations for tissue-impedance relationships and experimentally verified parameters for impedance modeling in human brains are lacking. This study seeks to address this need.Approach.We used FEM to simulate high-resolution single- and dual-electrode impedance measurements along linear electrode trajectories through (1) canonical gray and white matter tissue models, and (2) selected anatomic structures within whole-brain patient DTI-based models. We then compared intraoperative impedance measurements taken at known locations along deep brain stimulation (DBS) surgical trajectories with model predictions to evaluate model accuracy and refine model parameters.Main results.In DTI-FEM models, single- and dual-electrode configurations performed similarly. While only dual-electrode configurations were sensitive to white matter fiber orientation, other influences on impedance, such as white matter density, enabled single-electrode impedance measurements to display significant spatial variation even within purely white matter structures. We compared 308 intraoperative single-electrode impedance measurements in five DBS patients to DTI-FEM predictions at one-to-one corresponding locations. After calibration of model coefficients to these data, predicted impedances reliably estimated intraoperative measurements in all patients (R=0.784±0.116,n=5). Through this study, we derived an updated value for the slope coefficient of the DTI conductance model published by Tuchet al,k=0.0649 S⋅smm-3 (originalk=0.844), for use specifically in humans at physiological frequencies.Significance.This is the first study to compare impedance estimates from imaging-based models of human brain tissue to experimental measurements at the same locationsin vivo. Accurate, non-invasive, imaging-based impedance prediction has numerous applications in functional neurosurgery, including tissue mapping, intraoperative electrode localization, and DBS.

Research on the dynamic characteristics and improvement path of urban sustainable development level: Based on gravitational model and scenario simulation method

The rapid expansion of cities has brought new challenges to sustainable development (SD). Therefore, how to balance between SD and the economic growth has become the key to regional development. With the aim of enhancing the research system of SD level, founded on the deep research of SD level from a new perspective of “input-output” and the dynamic evolution law of SD level, this paper has explored the path for cities to improve their level of SD to achieve regional coordinated development. By virtue of the Super-efficiency Slack-Based Measure model based on unexpected output (unexpected super efficiency SBM model), it has measured the SD level of 21 cities in Sichuan from 2010 to 2019. Using methods such as equilibrium coefficient, obstacle analysis and gravitational model, this paper has studied the spatiotemporal evolution law of SD level and constructed six scenarios to simulate the future trend of SD level. The results have indicated that the level of SD in Sichuan is showing a gradual decline with significant constraints. The focus of SD in Sichuan is gradually shifting from Chengdu to a city cluster centered around Chengdu, Ziyang, and Meishan. In an ideal development scenario, the level of SD has significantly improved. This study has revealed the current status and potential of SD in Sichuan. In the future, with more concentration on the role of core urban agglomerations, Sichuan should adjust policies to address input–output barriers to promote SD throughout the province.

Multicentre Validation of the RESECT-90 Prediction Model for 90-Day Mortality After Lung Resection

BackgroundThe RESECT-90 model was developed to predict 90-day mortality for patients undergoing lung resection but hasn't been externally validated. The aim of this study was to validate the RESECT-90 clinical prediction model using multicentre patient data from across the United Kingdom (UK). Materials and MethodsData from 12 UK thoracic surgery centers for patients undergoing lung resection between 2016 and 2020 with available 90-day mortality status were used to externally validate the RESECT-90 model. Measures of discrimination (area under the receiving operator characteristic curve [AUC]) and calibration (calibration slope, calibration intercept and flexible calibration plot) were assessed as measures of model performance. Model recalibration was also performed by updating the original model intercept and coefficients. ResultsA total of 12,241 patients were included. Overall 90-day mortality was 2.9% (n = 360). Acceptable model discrimination was demonstrated (AUC 0.74 [0.73, 0.75]). Calibration varied between centers with some evidence of overall model miscalibration (calibration slope 0.80 [0.66, 0.95] and calibration intercept 0.40 [0.29, 0.52]) despite acceptable appearances of the flexible calibration plot. The model was subsequently recalibrated, after which all measures of calibration indicated excellent performance. ConclusionsAfter external validation and recalibration using a large contemporary cohort of patients undergoing surgery in multiple geographical locations across the UK, the RESECT-90 model demonstrated satisfactory statistical performance for the prediction of 90-day mortality after lung resection. Whilst the recalibrated model will require ongoing validation, the results of this study suggest that routine use of the RESECT-90 model in UK thoracic surgery practice should be considered.

Korean-specific internal dosimetry data for radioiodine bioassay and evaluation of the dosimetric impact

Our earlier works focused on exploring the Korean iodine biokinetic model and calculating Korean-specific S values and dose coefficients. Building on this foundation, this study extended our efforts to establish an extensive dosimetry dataset for Korean-specific iodine bioassay. We calculated bioassay functions (e.g., thyroid retention and urinary excretion functions), dose-per-content-functions, and content for the specified doses based on the Korean biokinetic model and dose coefficients previously developed. Also, this study provides a comprehensive understanding of the practical impact of using Korean-specific dosimetry data. Compared to the ICRP reference data, the thyroid retention functions for Koreans were significantly lower (for I-131, peaks at 18% vs. 27%). The physical half-lives of radioiodine played a significant role in the differences of dose-per-content-function. For long-lived iodine isotopes such as I-125, the dose-per-content-functions were comparable to ICRP data, whereas for short-lived iodine isotopes such as I-131, the higher values were observed in the Korean data, indicating a greater dose estimation for a given iodine activity measured in the thyroid. Although we revealed the differences between Korean and ICRP reference data, the comprehensive understanding of Korean data suggests that using the Korean-specific data should be considered only when high doses are predicted.

Can liquid-liquid equilibria be predicted by the combination of a cubic equation of state and a [formula omitted] model not suitable for liquid-liquid equilibria?

In modern versions of cubic equations of state (EoS), the mixing rules for EoS parameters are derived from an activity coefficient model using either the Huron-Vidal or the Zero Reference Pressure (ZRP) approach. As it is a fact that Wilson's activity coefficient model cannot predict liquid-liquid equilibria (LLE), this article attempts to answer the question: if Wilson's model is coupled with a cubic EoS, is the resulting model capable of predicting LLE?This question is actually becoming increasingly important as recent EoS rely on such a coupling (e.g., the tc-PR EoS). We show that although Wilson's model is mathematically unable to predict instable liquid phases, this is not true for Wilson-EoS models (i.e., EoS incorporating Wilson's model). However, it is also shown that the capacity of Wilson-EoS to predict LLE depends not only on the approach chosen (Huron-Vidal or ZRP) but also on mixture characteristics (such as the ratio of covolumes, the ratio of critical attractive parameters, the binary interaction parameters etc.).

Phase Behavior of System Carbon Dioxide + Hydrogen Sulfide

The global phase equilibrium behavior of the CO2+H2S system is discussed in this work for temperatures ranging from 160 K up to the critical region. Solid phases of CO2 and H2S and corresponding equilibria have been modeled by considering either a 4th-order equation of state (also used for the fluid phases) or a solid fugacity model for the pure components combined with an activity coefficient model (both coupled with a reference equation of state for the fluid phases).Phase equilibrium calculations have been performed by the minimization of the Gibbs Free Energy of mixing and compared to existing literature data.The types of pressure-mole fraction phase diagrams that can be encountered in the low-temperature thermodynamic region have been described. The temperature and pressure ranges where the phase behavior of the system changes have been identified and a representative phase diagram is presented for each range.

Sulfur dioxide absorption by novel green solvents of deep eutectic solvents: Modeling screening

Sulfur dioxide (SO2), produced mainly from the combustion of coal, is the most important cause of acidic rain, skin diseases, and environmental issues. To overcome the environmental problems, SO2 must be captured on an industrial scale before it is released into the air. In chemical industries, organic solvents are used for partial absorption of SO2. However, those organic solvents have negative environmental effects. Thus, proposing environmentally friendly and green solvents for SO2 absorption is vital for industries. Recently, increased attention has been paid to capturing SO2 using Deep Eutectic Solvents (DESs) as the most recently introduced category of green solvents. This study performed a comprehensive screening study on the investigation of the performance of various simple and complicated models for SO2 solubilities in a wide range of different nature DESs. For this purpose, the most updated and largest SO2 solubility data bank in DESs involving 976 data points for 63 different nature DESs over wide temperature and pressure ranges has been gathered from open literature. For model screening, for the physical absorption models, the performances of SRK and CPA as the simple cubic and complicated sophisticated equations of state, NRTL and UNIQUAC as the well-known activity coefficient models, and for the chemical absorption models, RETM were investigated and compared. For physical absorption models, coupling an equation of state with the UNIQUAC activity coefficient model i.e. CPA-UNIQUAC, SRK-UNIQUAC, and also using simple SRK-SRK models led to the best performances. Compared to all investigated models, RETM as the chemical absorption model showed the best performance with the AARD% value of 12.95. This shows the importance of considering the chemical absorption mechanism for SO2 absorption by DESs. Finally, general guidelines for using different modeling approaches were proposed to be considered by the researchers.

Thermodynamic simulation of stepwise precipitation of NH4VO3 and NaHCO3 from carbonating Na3VO4 solution

Thermodynamic simulation was conducted to design a new process of stepwise precipitating NH4VO3 and NaHCO3 from regulating the CO2 carbonation of Na3VO4 solution. Firstly, a new V(V) speciation model for the aqueous solution containing vanadate and carbonate is established by using the Bromley−Zemaitis activity coefficient model. Subsequently, thermodynamic equilibrium calculations are conducted to clarify the behavior of vanadium, carbon, sodium, and impurity species in atmospheric or high-pressure carbonation. To ensure the purity and recovery of vanadium products, Na3VO4 solution is initially carbonated to the pH of 9.3−9.4, followed by precipitating NH4VO3 by adding (NH4)2CO3. After vanadium precipitation, the solution is deeply carbonated to the final pH of 7.3−7.5 to precipitate NaHCO3, and the remaining solution is recycled to dissolve Na3VO4 crystals. Finally, verification experiments demonstrate that 99.1% of vanadium and 91.4% of sodium in the solution are recovered in the form of NH4VO3 and NaHCO3, respectively.

Coupled simulation of percolation and evapotranspiration in semi-mobile and semi-fixed dunes

Accurately describing the important components of the water balance, such as precipitation (P), deep percolation (D), and evapotranspiration (ET) and their relationships is crucial for understanding the water cycle and eco-hydrological processes in arid and semi-arid regions. This study coupled the dual crop coefficient model (Dualkc) with the HYDRUS-1D model to simulate D and ET processes in the semi-mobile and semi-fixed dune ecosystem in China’s Horqin Sandy Land during the growing seasons from 2015 to 2022. The results indicated that the improved HYDRUS-1D model achieved high simulation accuracy at a daily scale, with the R2 of 0.83, RMSE of 0.392, NSE of 0.714, and b of 0.828, representing increases of 15.27%, 20.16%, 16.01%, and 5.6%, respectively, compared to the original model. ET and D constituted the primary water dissipation processes in semi-mobile and semi-fixed dunes, accounting for 97% to 99% of P during the growing season. The evaporative ratio (ET/P) ranged from 46.7% to 79.3%, averaging 65.67% over multiple years, and the deep percolation ratio (D/P) varied between 16.1% and 53%, averaging 31.02%. The distribution of precipitation events, soil water content, and vegetation conditions are critical factors affecting ET and D in arid and semi-arid dune ecosystems. This study is of great significance for a deep understanding of the eco-hydrological processes in the dune ecosystem, and it can provide important support in developing water management strategies in the desertification region.

Eddy viscosity enhanced temporal direct deconvolution model for temporal large-eddy simulation of turbulent channel flow

In this work, a novel eddy viscosity enhanced temporal direct deconvolution model (TDDM) is proposed for temporal large-eddy simulation (TLES) of turbulent channel flow at large filter widths. To improve the accuracy of the constant eddy viscosity (CEV) model, particularly in the near-wall region, a damping function is incorporated to refine its performance. Moreover, a spatial filtering strategy is introduced to reduce the aliasing errors associated with the computation of subfilter-scale (SFS) stress, thereby enhancing numerical stability. In the a posteriori study, the accuracy of the CEV model is assessed comprehensively by comparing the TLES results with corresponding temporally filtered direct numerical simulation data. The results demonstrate that the CEV-enhanced TDDM provides accurate predictions across various statistical properties of velocity, instantaneous flow structures, kinetic energy spectra, and SFS energy fluxes. The coefficient sensitivity analysis of the CEV model reveals that the model coefficient significantly influences low Reynolds number flows, while its impact on high Reynolds number flows is relatively small. TLES on coarse grids demonstrate that the CEV-enhanced TDDM exhibits strong robustness and accuracy at different grid resolutions. Additionally, the CEV-enhanced TDDM in high Reynolds number flows is stable and accurate at remarkably large filter widths.

Investigating the role of fibre-matrix interfacial degradation on the ageing process of carbon fibre-reinforced polymer under hydrothermal conditions

The aqueous environment can deteriorate the fibre-matrix interface of carbon fibre-reinforced polymer (CFRP), significantly impairing the non-fibre-dominated mechanical properties. Thus, this study aimed to quantitatively analyse the impact of interfacial degradation on the hydrothermal ageing mechanism and process of the CFRP. Firstly, entire water absorption process of the CFRP under hydrothermal conditions was divided into three stages according to experimental measurement of its water content. Based on this division of stages, a novel water diffusion model was established for the hydrothermally aged CFRP. To measure the mechanical degradation, tensile tests were conducted on unaged, aged, and redried neat epoxy and transversely positioned unidirectional (UD) CFRP specimens. It was found that the transverse tensile strength degradation of UD CFRP was irreversible due to the permanent interfacial debonding between the fibres and matrix, in contrast to the reversible ageing of the epoxy matrix. To further quantify the fibre-matrix interfacial ageing, physically-based models were established for the degraded interfacial strength of CFRP subjected to hydrothermal conditions. After the characterization of the modelling coefficients, the physically-based models can be employed to predict interfacial strength inside the aged CFRP for various ageing durations. The prediction error was only 4.57% for the transverse tensile strength of degraded UD CFRP with various ageing durations from the representative volume element (RVE) simulation with its interfacial strength provided by the physically-based models, validating the effectiveness of the proposed physically-based models for degraded fibre-matrix interface under various ageing conditions.

Research on turbulence model parameters for the prediction of waterjet propulsion pump hydrodynamic performance

Abstract The hydrodynamic performance of waterjet propulsion pumps has an important impact on improving the speed of waterjet propulsion ships. High-precision prediction of the hydrodynamic performance of water-jet propulsion pumps based on CFD is of great significance to improving the pump design quality. Based on the Reynolds-averaged Naiver-Stokes equation, this paper conducts a grid-independence verification on a water jet propulsion pump based on hydrodynamic test data, uses the Latin hypercube sampling method to generate SST k-ω turbulence model parameter space calculation samples, performs steady-state calculation on all samples, analyzes the sensitivity of changes in the parameters of the turbulence model to the prediction of the external characteristics of the waterjet propulsion pump, and finally obtains the influence of changes in the parameters of the turbulence model on the prediction of the external characteristics of the pump. Among them, the sensitivities of the three parameters β *, β1 and a1 are higher. The results show that the prediction accuracy of the external characteristics of the water jet propulsion pump can be effectively improved by correcting these three highly sensitive parameters, and the correction values of the three key parameters are obtained. The prediction error of the external characteristics of the water jet propulsion pump is reduced from 4.3% to less than 2.1%. A feedforward neural network is used to establish a mapping model between the three turbulence model parameters and the head coefficient and power coefficient of the waterjet propulsion pump in order to achieve rapid prediction. The genetic algorithm is applied to optimize the weights, thresholds and other coefficients in the neural network, thereby improving the prediction accuracy. Based on the optimized neural network, the genetic algorithm is used to optimize the three turbulence model parameters, and the optimized coefficients are obtained. The optimized turbulence model parameters are put into the hydrodynamic model for RANS simulation to verify the degree of optimization of the model coefficients by the genetic algorithm.