Function Model Research Articles

Regional differences and catch-up analysis of energy efficiency in China’s manufacturing industry under environmental constraints

For coordinated regional growth and the development of high-quality manufacturing, China must narrow its regional energy efficiency gap and catch up inter-regionally. This paper focuses on whether China’s inter-provincial manufacturing energy efficiency has technological diffusion and a catch-up effect and explores its possible influencing factors, which are important for narrowing the differences in China’s manufacturing energy efficiency and promoting the improvement of the overall level of efficiency. Between 2011 and 2020, 30 Chinese manufacturing industries will be evaluated using a non-radial distance function model under environmental conditions. By employing the Dagum Gini coefficient method, regional disparities were analyzed, with hyper-variable density and efficiency discrepancies between regions making a noteworthy contribution. This paper evaluated a catch-up effect by constructing a frontier productivity model that considered the influence of China’s manufacturing energy efficiency. Results show a general rise in energy efficiency, particularly in coastal regions, higher than inland ones. The Gini coefficient of energy efficiency in manufacturing experienced a slight increase; however, when comparing it to the regional efficiency frontier, the catch-up effect and technology diffusion effect of China’s provincial manufacturing energy efficiency become more pronounced when taking into account the national efficiency frontier; the sub-regional manufacturing energy efficiency catch-up effect has different performances; the catch-up and technology diffusion effect is more evident after controlling for Economic development, innovation levels, the environmental regulation, and the proportion of high-energy-consumption output value and other influencing factors.

MODELING COOKING OIL PRODUCTION WITH CRUDE PALM OIL (CPO) PRICE USING TRANSFER FUNCTION

Indonesia is the largest palm oil producer in the world. However, Indonesia is currently experiencing a significant scarcity of cooking oil for the basic needs of the community. One of the causes of this problem is the high price of crude palm oil (CPO). The price of CPO affects the production of cooking oil produced by the cooking oil industry. CPO prices are released in the form of daily prices in the market which form a time series pattern that affects the production of cooking oil which is also produced daily in the industry. To forecast cooking oil production from CPO prices can be done with a transfer function model. The stages of analysis are forming an ARIMA model, processing input time series whitening, and output time series whitening. Checking white noise by plotting ACF αt, ACF βt. Make a CCF graph and determine the order (r,s,b). Estimate the first parameter of the disturbance series (nt) and identify the disturbance series (nt) in the ARIMA model. Next, perform a diagnostic check of the transfer function model by checking the cross-correlation between Xt & Yt and checking the autocorrelation of Xt and Yt for model feasibility testing. Then make an ACF graph of the transfer function model's residuals to test the model's suitability. The final stage is to select the best model based on the smallest AIC value, ensuring the accuracy of the selected model using MAPE value, and making predictions. As a result, the model for CPO price is an input series variable, and cooking oil production is an output series variable. However, the MAPE result was 100%, indicating that the model is not very accurate for this data. Nevertheless, by ignoring the result and continuing with the forecasting, this model shows that the forecasted values for cooking oil based on CPO price have increased from the form of the model obtained for the time data from 2013 to 2022.

Comprehensive population pharmacokinetic modelling of sugemalimab, an anti-programmed death-ligand 1 (PD-L1) human monoclonal antibody, in patients with solid tumours or lymphomas across multiple Phase I-III studies.

The aim of this study was to develop a population pharmacokinetics model for sugemalimab, a monoclonal antibody that targets programmed death-ligand 1 (PD-L1), using data from Phase I-III trials and to assess clinical factors affecting sugemalimab exposure. A nonlinear mixed-effect modelling approach was employed to analyse pooled data from nine studies involving 1628 subjects to characterize the PopPK of sugemalimab. This investigation examined the influence of various covariates on sugemalimab pharmacokinetics (PK), encompassing demographics, baseline hepatic and renal function-related covariates, and others (including anti-drug antibody [ADA], combination treatment, Eastern Cooperative Oncology Group [ECOG] performance score, tumour burden and tumour type). Estimation accuracy and predictive ability of the final model were evaluated using various methods. The influence of covariates on sugemalimab exposure was assessed by simulation from the final model. A two-compartment model with first-order elimination and time-varying clearance effectively described the PK of sugemalimab. Covariate analyses revealed significant relationships between sugemalimab clearance and body weight, albumin, gender, ADA, tumour burden and tumour type. The statistically significant covariates on central volume were body weight, albumin, gender and tumour type. No significant relationships were found in the final model for age, race, alanine aminotransferase, aspartate aminotransferase, creatinine, total bilirubin, alkaline phosphatase, combination treatment, creatinine clearance, ECOG, renal function or hepatic function. All significant covariates demonstrated less than a 20% effect on sugemalimab exposure. The PopPK model adequately described the pharmacokinetic profile of sugemalimab with no clinically meaningful impact observed on its exposure across all covariates. Dose adjustment does not appear to be necessary.

Prestressed concrete slabs subjected to low-velocity impact: Theoretical analysis model and design method

Bidirectional bonded prestressed concrete (PS) structures are widely used in energy engineering structures due to their excellent impermeability. Owing to the particularity of the structure and the severity of damage consequences, the impact dynamic performance of bidirectional bonded PS structures has been widely concerned. Nonetheless, the existing researches focus on the test and finite element, and lacks the theoretical analysis model that can consider the whole process of load action, let alone the corresponding anti-impact design method. Therefore, based on the existing test and finite element results, the resistance function model of bidirectional bonded PS slab is proposed in this paper, and the anti-impact design method is formed. Firstly, the performance of specimens under low-velocity impact loading and quasi-static loading conditions is compared, and the effects of inertia force and material strain rate on the dynamic performance of specimens under low-velocity impact are further explored. Then, a resistance function theoretical analysis model that can consider the whole process of load action is proposed with a quadrilinear form, and the analytical expressions of resistance and stiffness at characteristic points are derived. Subsequently, the accuracy of the resistance function model is verified based on the existing test results. Furthermore, based on the resistance function model, the influence laws of compressive strength of concrete, prestressing degree, reinforcement ratio of reinforcement and slab thickness on slab resistance are analyzed. Finally, an anti-impact design method considering perforation and scabbing failure is proposed, and the feasibility of the design method is verified by impact test results.

The Characteristics of the Chemical Composition of PM2.5 during a Severe Haze Episode in Suzhou, China

During the past decade, the air quality has been greatly improved in China since the implementation of the “Clean Air Act”. However, haze events are still being reported in some regions of China, and the pollution mechanism remains unclear. In this study, we investigate the chemical characteristics of the pollution mechanism of the PM2.5 composition in Suzhou from October 18 to December 15, 2020. A notable declining trend in temperature was observed from 18 to 27 November, which indicates the seasonal transition from fall to the winter season. Four representative periods were identified based on meteorological parameters and the PM2.5 mass concentrations. The heavy pollution period had the typical characteristics of a relatively low temperature, a high relative humidity, and mass loadings of atmospheric pollutants; nitrate was the dominant contributor to the haze pollution during this period. The nitrate formation mechanism was driven by the planetary boundary layer dynamics. The potential source contribution function model (PSCF) showed that the major PM2.5 composition originated from the northwest direction of the sampling site. The aerosol liquid water content presented increasing trends with an increasing relative humidity. The pH was the highest during the heavy pollution period, which was influenced by the aerosol liquid water content and the mass loadings of NO3−, SO42−, NH4+, and Cl−. The comprehensive analysis in this paper could improve our understanding of the nitrate pollution mechanism and environmental effects in this region.

Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models

Latent Gaussian process (GP) models are flexible probabilistic non-parametric function models. Vecchia approximations are accurate approximations for GPs to overcome computational bottlenecks for large data, and the Laplace approximation is a fast method with asymptotic convergence guarantees to approximate marginal likelihoods and posterior predictive distributions for non-Gaussian likelihoods. Unfortunately, the computational complexity of combined Vecchia-Laplace approximations grows faster than linearly in the sample size when used in combination with direct solver methods such as the Cholesky decomposition. Computations with Vecchia-Laplace approximations can thus become prohibitively slow precisely when the approximations are usually the most accurate, i.e., on large data sets. In this article, we present iterative methods to overcome this drawback. Among other things, we introduce and analyze several preconditioners, derive new convergence results, and propose novel methods for accurately approximating predictive variances. We analyze our proposed methods theoretically and in experiments with simulated and real-world data. In particular, we obtain a speed-up of an order of magnitude compared to Cholesky-based calculations and a threefold increase in prediction accuracy in terms of the continuous ranked probability score compared to a state-of-the-art method on a large satellite data set. All methods are implemented in a free C++ software library with high-level Python and R packages. 4

Spatio-temporal Evolution of Land Use in Guangdong-Hong Kong-Macao Greater Bay Economic Area, 1985-2022 and Analysis of Influencing Factors

This research employs GIS and remote sensing technology, along with time-series Land cover data, to examine the evolving patterns of various land use categories (such as forest land, cultivated land, and impervious surfaces) within the Guangdong-Hong Kong-Macao Greater Bay Area. The results indicate that the predominant spatial configuration of land use in this region follows a sequence of "forest land-cultivated land-impervious land." Notably, the key transformation observed involves the conversion of cultivated land to impervious surfaces, resulting in a cumulative expansion of impervious areas by 3,346,985 hectares between 1985 and 2022. Moreover, the level of spatial integration among land use types in the Greater Bay Area is progressively standardizing, with notable advancements in scientific, technological, economic, and transportation sectors leading to a high degree of synergy. The system as a whole demonstrates effective coordination and operational efficiency. The methodology section outlines the data sources and analytical techniques employed, encompassing the utilization of the land use transfer matrix, individual land use degree assessment, and spatial coupling function model. Through these methodologies, the study identifies urban economic development as the primary driver behind the proliferation of impervious surfaces, offering valuable insights to inform regional development strategies and environmental governance policies.

Accurate Geometries of Large Molecules at DFT Cost by Semiexperimental and Coupled Cluster Templating Fragments.

Accurate geometries of small semirigid molecules in the gas phase are available thanks to high-resolution spectroscopy and accurate quantum chemical approaches. These results can be employed for validating cheaper low-level quantum chemical models or correcting the corresponding structures of large molecules. On these grounds, in this work, a large panel of semiexperimental equilibrium structures already available in the literature is used to confirm the average error (1 mÅ for bond lengths and 2 mrad for valence angles) of a version of the Pisa composite schemes (PCS2), which is applicable to molecules containing up to about 20 atoms. Then, the geometries of 30 additional medium-sized systems were optimized at the PCS2 level to cover a more balanced chemical space containing moieties poorly represented in SE compilations. The final database is available on a public domain Web site (https://www.skies-village.it/databases/) and can be employed for correcting structures of larger molecules obtained by hybrid or double-hybrid density functionals in the framework of the templating molecule approach. Several examples show that corrections based on the structures of building blocks taken from this database reduce the error of the B3LYP geometrical parameters of large molecules by nearly an order of magnitude without increasing the computational cost. Furthermore, the results of different density functional theory (DFT) or wave function (e.g., MP2) models can be improved in the same way by simply computing both the whole molecule and suitable building blocks at the chosen level. Then, whenever reference structures of some building blocks containing up to about 20 atoms are not available, they can be purposely optimized at the PCS2 level by employing reasonable computer resources. Therefore, a new DFT-cost tool is now available for the accurate characterization of large molecules by experiment-oriented scientists.

Probability density estimation of polynomial chaos and its application in structural reliability analysis

Polynomial chaos expansion (PCE) is a widely used approach for establishing the surrogate model of a time-consuming performance function for the convenience of uncertainty quantification of a stochastic structure. However, it remains difficult to calculate the probability density function (PDF) of the PCE accurately for general cases, though the PDF, as a complete representation of a random variable, is often required in some uncertainty problems. To address this problem, this paper proposes a semi-analytical method to compute the PDF of a PCE. This method derives the closed-form solutions of characteristic functions (CFs) of the first- and second-order PCEs, while an equivalent parabolization technique is proposed to provide the approximate solutions of CFs of higher-order PCEs. Then, the PDF of the PCE can be obtained by the Fourier transform of the resulting CF. Three numerical examples are investigated to demonstrate the accuracy, applicability, and efficiency of the proposed method for probability density estimation of PCE in structural reliability analysis.

Predicting and Analyzing Electric Bicycle Adoption to Enhance Urban Mobility in Belgrade Using ANN Models

The development of alternative environmentally friendly modes of transportation is becoming an increasingly promising solution in traffic-congested and polluted urban areas. E-bikes, as one of them, are recognized as an ecologically sustainable means of transportation that has significant potential to replace motorized modes of transportation that can improve urban mobility. Relying on artificial intelligence and considering an ecological approach when considering the acceptability of e-bikes by setting a direct question for users influences the development of an innovative way of understanding and evaluating the use of more sustainable modes of transportation. In this regard, this study aims to elucidate the main variables influencing the acceptability of e-bike use using artificial neural network (ANN) models—multilayer perceptron (MLP) and radial basis function (RBF). For training and testing the models, data from a random sample obtained through an online questionnaire, which was answered by 626 residents of Belgrade (Serbia), were used. A multilayer perceptron with nine and seven neurons in two hidden layers, respectively, hyperbolic tangent activation function in the hidden layer and identity function in the output layer, gave better results than the radial basis function model. With an accuracy of 89%, a precision of 83%, a recall of 79%, and an area under the receiver operating characteristic (ROC) curve of 0.927, the multilayer perceptron model recognized the influential variables in predicting acceptability. The results of the model indicate that the mileage traveled, the frequency of motorcycle use, the respondents’ awareness of the pollution in Belgrade, and the age of the respondents have the greatest influence on the acceptability of using e-bikes. In addition to majority acceptability (69.8%), the results obtained by the model can represent a useful basis for decision-makers when defining strategies for the development and application of e-bikes while reducing traffic congestion and environmental pollution in Belgrade.

A dual-standby power source configuration scheme for micro grid in Pelagic Island

Realizing clean energy supply is important for the sustainable development of pelagic island. The volatility and randomness features of renewable energy generation may reduce power supply reliability, thus backup units is necessary in micro-grid in pelagic island. This paper proposed a dual-standby configuration scheme based on Sequence Operation Theory to achieve reliable power standby. Firstly, characteristics of standby units, micro gas turbine and diesel generator, are analyzed. Next, the probability distribution function models of wind, photovoltaic and load demand in pelagic island are established, and the reliability index of power shortage probability is calculated by Sequence Operation Theory. Then, the two-stage dual-standby control scheme to make full use of the complementary standby units is introduced and object function to minimize equivalent annual cost on the premise of satisfying probabilistic reliability index is modeled. Hybrid integer particle swarm algorithm is used to solve the problem. Finally, the proposed model for dual-standby configuration is applied to a pelagic island. The simulation results show that the proposed method can meet the load demand in both daily and extreme weather and achieve superior economy.

Research on photovoltaic water pumping system based on valve opening model

Photovoltaic water pumping system (PVWPS) is an important way to use solar energy. In order to further improve the solar energy utilization rate of PVWPS, this study proposes a valve opening model, which solves the problem of more solar energy waste caused by fixed pipeline characteristics under different irradiation intensities. Firstly, the theoretical calculation formula of pipeline flow is deduced according to the balance relationship between photovoltaic energy supply and energy demand of water lifting pipeline in PVWPS, and its working principle is expounded. Finally, the function model of pipeline flow-valve opening-radiation intensity is derived from the cycle test of PVWPS, and the PVWPS with water lifting height of 10 m, 12 m and 14 m is verified by all-day test. Compared with the unregulated valve, the cumulative water extraction of the PVWPS with three water lifting heights after valve regulation is increased by 15.85 %, 18.06 % and 80.71 % respectively, indicating that the cumulative water extraction of the PVWPS can be effectively improved by adjusting the valve opening. This study provides a new idea for improving the utilization rate of solar energy in PVWPS.

Unveiling the Genetic Architecture of Semen Traits in Thai Native Roosters: A Comprehensive Analysis Using Random Regression and Spline Function Models.

Improving reproductive traits, particularly semen quality and quantity, is crucial for optimizing poultry production and addressing the current limitations in native chicken reproduction. The aim of this study was to develop a genetic model to estimate genetic parameters guiding the selection of individual Thai native roosters. Using data collected from 3475 records of 242 Thai native grandparent roosters aged 1-4 years, we evaluated semen traits (mass movement, semen volume, and sperm concentration) over 54 weeks. A random regression test-day model incorporating five covariance functions, including a linear spline function with four, five, six, and eight knots (SP4, SP5, SP6, and SP8) and second-order Legendre polynomial function (LG2), was used to estimate genetic parameters. The results showed that the SP8 model consistently outperformed the other models across all traits, with the lowest mean square error, highest coefficient of determination, and superior predictive ability. Heritability estimates for mass movement, semen volume, and sperm concentration ranged from 0.10 to 0.25, 0.22 to 0.25, and 0.11 to 0.24, respectively, indicating moderate genetic influence on these traits. Genetic correlations between semen volume and sperm concentration were highest in the SP8 model, highlighting a strong genetic association between these traits. The SP8 model also revealed a high genetic correlation between mass movement and semen volume, supporting the potential for selecting mass movement as a predictor of semen volume. In conclusion, this study highlights the effectiveness of random regression models with linear spline functions to evaluate the genetic parameters of semen traits in native Thai roosters. The SP8 model is a robust tool for breeders to enhance the reproductive performance of native Thai chickens, contributing to sustainable poultry production systems.

Striosomes control dopamine via dual pathways paralleling canonical basal ganglia circuits.

Balanced activity of canonical direct D1 and indirect D2 basal ganglia pathways is considered a core requirement for normal movement, and their imbalance is an etiologic factor in movement and neuropsychiatric disorders. We present evidence for a conceptually equivalent pair of direct D1 and indirect D2 pathways that arise from striatal projection neurons (SPNs) of the striosome compartment rather than from SPNs of the matrix, as do the canonical pathways. These striosomal D1 (S-D1) and D2 (S-D2) pathways target substantia nigra dopamine-containing neurons instead of basal ganglia motor output nuclei. They modulate movement with net effects opposite to those exerted by the canonical pathways: S-D1 is net inhibitory and S-D2 is net excitatory. The S-D1 and S-D2 circuits likely influence motivation for learning and action, complementing and reorienting canonical pathway modulation. A major conceptual reformulation of the classic direct-indirect pathway model of basal ganglia function is needed, as well as reconsideration of the effects of D2-targeting therapeutic drugs.

An adaptive learning strategy for surrogate modeling of high-dimensional functions - Application to unsteady hypersonic flows in chemical nonequilibrium

Many engineering applications rely on the evaluation of expensive, non-linear high-dimensional functions. In this paper, we propose the RONAALP algorithm (Reduced Order Nonlinear Approximation with Adaptive Learning Procedure) to incrementally learn, on-the-fly as the application progresses, a fast and accurate reduced-order surrogate model of a target function. First, a combination of nonlinear auto-encoder, community clustering, and radial basis function networks allows us to learn an efficient and compact surrogate model with limited training data. Secondly, an active learning procedure overcomes any extrapolation issues during the online stage by adapting the surrogate model with high-fidelity evaluations that fall outside its current validity range. This approach results in generalizable, fast, and accurate reduced-order models of high-dimensional functions. The method is demonstrated on three direct numerical simulations of hypersonic flows in chemical nonequilibrium. Accurate simulations of these flows rely on detailed thermochemical gas models that dramatically increase the cost of such calculations. Using RONAALP to learn a reduced-order thermodynamic model surrogate on-the-fly, the cost of such simulations was reduced by up to 75% while maintaining an error of less than 10% on relevant quantities of interest.

A scenario for heart failure during the filling phase

Heart failure (HF) is a life-threating cardiac disease that develops progressively for the reduced ability of the left ventricle (LV) to pump blood into the circulation during systole. HF can also develop in patients with a preserved systolic function, typically in presence of hypertrophic cardiomyopathy (HCM). This type of HF is sometimes termed as diastolic HF, but its biomechanical origin is still unclear. This study employs a physics-based analysis of both the LV and left atrium (LA) in selected HCM patients and matched healthy subjects using 3D echocardiography and demonstrates that alteration on the LV side (stiffening) reduces the elastic recovery of the LA. Moreover, the analysis of the forces exchanged between the two chambers demonstrates that they result unbalanced, keeping the LA in a sustained stretched condition that leads to dilation. This scenario clarifies the diastolic root of the dysfunction that may likely be the cause of the spiraling of events progressing toward failure of both LA emptying and LV filling. This deeply interdisciplinary study provides a physics-based basis for both physics/engineering modeling of heart function and to cardiologists for the design of clinical studies.

Neuropeptide and serotonin co-transmission sets the activity pattern in the C. elegans egg-laying circuit

Neurons typically release both a neurotransmitter and one or more neuropeptides, but how these signals are integrated within neural circuits to generate and tune behaviors remains poorly understood. We studied how the two hermaphrodite-specific neurons (HSNs) activate the egg-laying circuit of Caenorhabditis elegans by releasing both the neurotransmitter serotonin and NLP-3 neuropeptides. Egg laying occurs in a temporal pattern with approximately 2-min active phases, during which eggs are laid, separated by approximately 20-min inactive phases, during which no eggs are laid. To understand how serotonin and NLP-3 neuropeptides together help produce this behavior pattern, we identified the G-protein-coupled receptor neuropeptide receptor 36 (NPR-36) as an NLP-3 neuropeptide receptor using genetic and molecular experiments. We found that NPR-36 is expressed in, and promotes egg laying within, the egg-laying muscle cells, the same cells where two serotonin receptors also promote egg laying. During the active phase, when HSN activity is high, we found that serotonin and NLP-3 neuropeptides each have a different effect on the timing of egg laying. During the inactive phase, HSN activity is low, which may result in release of only serotonin, yet mutants lacking either serotonin or nlp-3 signaling have longer inactive phases. This suggests that NLP-3 peptide signaling may persist through the inactive phase to help serotonin signaling terminate the inactive phase. We propose a model for neural circuit function in which multiple signals with short- and long-lasting effects compete to generate and terminate persistent internal states, thus patterning a behavior over tens of minutes.

Suppression of aldehyde dehydrogenase 2 in kidney proximal tubules contributes to kidney fibrosis through Transforming Growth Factor-β signaling

Chronic kidney disease (CKD) is an increasingly prevalent disorder that poses a significant global health and socioeconomic burden. East Asian countries such as China, Taiwan, Japan, and South Korea have a higher incidence and prevalence of kidney failure when compared to Western nations, and the reasons for this discrepancy remain unclear. Aldehyde dehydrogenase 2 (ALDH2) is an essential detoxifying enzyme for exogenous and endogenous aldehyde metabolism in mitochondria. Inactivating mutations at E504K and E487K are found in 35-45% of East Asian populations and has been linked to a higher risk of various disorders, including cardiovascular diseases and cancer. However, little is known about the role of ALDH2 in CKD. Here, we characterized the expression pattern of ALDH2 in normal and CKD human and mouse kidneys and demonstrated that ALDH2 expression was significantly reduced, and that the protein level was inversely correlated with the degree of CKD and fibrosis. Further, we treated ALDH2*2 knock-in mice, a loss of ALDH2 function model, with aristolochic acid and found that these mice showed enhanced fibrosis. Moreover, ALDH2 deficiency was associated with kidney fibrosis involving epithelial cell differentiation process in vivo and in vitro. However, ALDH2 overexpression protected proximal tubule epithelial cells from transforming growth factor-β-induced dedifferentiation or partial epithelial-mesenchymal transdifferentiation in vitro. Thus, our findings yield important clinical information regarding the development and progression of CKD involving ALDH2, especially among East Asian populations.

Learning Chinese as a Second Language: Implications of the Character-Word Dual Function Model

Learning new words is fundamental in both first and second-language reading. There are, however, divided opinions on the best instructional approaches. Two widely used approaches across languages are whole-word focus and word-constituent focus. The appropriateness of each approach has varied historically, even within a single language (e.g., the debate between whole-word instruction and phonics in English). In teaching Chinese, both approaches are applied but to different learner groups. Whole-word instruction predominates in teaching Chinese as a second language (L2), while instruction for Chinese children focuses more on character-level mappings. It may seem reasonable in L2 Chinese instructions to focus on direct mappings between Chinese words and their L1 equivalent words. However, this raises a question: Is whole-word instruction the most efficient approach in L2 Chinese instruction? Based on an analysis of the Chinese writing system, we proposed a Character-Word Dual Function model of Chinese and tested its application of a dual-focus approach on both characters and words in L2 Chinese classroom instruction. Empirical findings support the advantage of this new approach compared to conventional whole-word instruction. We discuss the alignment between our findings and the Unified Computational Model and its implications for word instruction across languages.

Hemolysis analysis of centrifugal blood pump based on numerical simulation

Abstract The operation of blood pump will cause mechanical damage to red blood cells and lead to hemolysis. Based on this, CFD technology is used to simulate the three-dimensional flow field of centrifugal blood pump. According to the analysis of velocity field, pressure field and stress field; Based on the hemolysis power function model, the hemolysis performance of blood pump is predicted by Lagrange particle tracking method, and the impeller structure is adjusted to improve its flow performance and reduce the hemolysis index. The research results can provide a basis for the structural improvement and performance improvement of centrifugal heart pump.