Search results

This paper aims to predict the effect of combustor inlet area ratio (CIAR) on the lean blowout limit (LBO) of a swirl stabilized can-type micro gas turbine combustor having a thermal capacity of 3 kW.

The blowout limits of the combustor were predicted predominantly from numerical simulations by using the average exit gas temperature (AEGT) method. In this method, the blowout limit is determined from characteristics of the average exit gas temperature of the combustion products for varying equivalence. The CIAR value considered in this study ranges from 0.2 to 0.4 and combustor inlet velocities range from 1.70 to 6.80 m/s.

The LBO equivalence ratio decreases gradually with an increase in inlet velocity. On the other hand, the LBO equivalence ratio decreases significantly especially at low inlet velocities with a decrease in CIAR. These results were backed by experimental results for a case of CIAR equal to 0.2.

Gas turbine combustors are vulnerable to operate on lean equivalence ratios at cruise flight to avoid high thermal stresses. A flame blowout is the main issue faced in lean operations. Based on literature and studies, the combustor lean blowout performance significantly depends on the primary zone mass flow rate. By incorporating variable area snout in the combustor will alter the primary zone mass flow rates by which the combustor will experience extended lean blowout limit characteristics.

This is a first effort to predict the lean blowout performance on the variation of combustor inlet area ratio on gas turbine combustor. This would help to extend the flame stability region for the gas turbine combustor.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

The lean blowout (LBO) limit of the combustor is one of the important performance parameters for any gas turbine combustor design. This study aims to predict the LBO limits of an in-house designed swirl stabilized 3kW can-type micro gas turbine combustor.

The experimental prediction of LBO limits was performed on 3kW swirl stabilized combustor fueled with methane for the combustor inlet velocity ranging from 1.70 m/s to 6.80 m/s. The numerical prediction of LBO limits of combustor was performed on two-dimensional axisymmetric model. The blowout limits of combustor were predicted through calculated average exit gas temperature (AEGT) method and compared with experimental predictions.

The results show that the predicted LBO equivalence ratio decreases gradually with an increase in combustor inlet velocity.

This LBO limits predictions will use to fix the operating boundary conditions of 3kW can-type micro gas turbine combustor. This methodology will be used in design stage as well as in the testing stage of the combustor.

This is a first effort to predict the LBO limits on micro gas turbine combustor through AEGT method. The maximum uncertainty in LBO limit prediction with AEGT is 6 % in comparison with experimental results.

Despite the support of digital technology, there is a high degree of ambiguity and fluidity in the boundaries of digital products. This is because the addition of distributed innovation entities has an impact on the scope and scale of digital product innovation. Building upon the knowledge orchestration perspective, this study aims to construct a theoretical model, comprising distributed innovation, knowledge reorchestration and digital product innovation performance, and discuss the moderating roles of intellectual property protection and knowledge exchange activities.

Using a sample of 362 Chinese science and technology enterprises, the scholarship’s framework and hypotheses were tested using regression and bootstrap analysis.

The results confirm that distributed innovation positively enhances enterprises’ digital product innovation performance; knowledge reorchestration plays a partial mediating role in the linkage amongst distributed innovation and digital product innovation performance; and intellectual property protection and knowledge exchange activities negatively and positively moderate the mediating role of knowledge reorchestration amongst distributed innovation and digital product innovation performance, respectively.

This empirical scholarship explores the effect mechanism of intellectual property protection, knowledge exchange activities and knowledge reorchestration on the linkage amongst distributed innovation and digital product innovation performance. This paper expands the theoretical application of distributed innovation, knowledge orchestration and other related theories in the context of the digital economy and further provides a policymaking reference for the improvement of enterprises’ digital product innovations.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

The purpose of this paper is to construct a key factors selection approach for a class of small-sample multi-factor cross-sectional data analysis (SMCDA) problem, which is very common in productive practice and scientific research, such as coal-bed methane (CBM) content analysis, civil aircraft cost analysis, etc. Key factors selection is an important basic work for SMCDA problem; the proposed method is constructed to improve the accuracy and explanatory of the selected key factors.

Using grey system theory to solve SMCDA problem is more reasonable under few data and poor information. Therefore, this paper constructs a grey incidence analysis (GIA) model with rate of change to select the key factors of an SMCDA problem. The basic idea of the proposed method is to simulate time series by randomly sorting the selected samples, and to calculate the degree of grey incidence with rate of change by loop iterative algorithm, then to construct the degree matrix of grey incidence with rate of change, and finally by which, to utilise quantitative and qualitative analysis methods to select the key factors.

The experimental analysis of application cases demonstrates that the key factors of system’s characteristic can be successfully screened out by the proposed method, the results are consistent with actual conditions, and they have a clearer meaning and a better interpretability.

The method proposed in this paper could be utilised to select key factors for such a class of SMCDA problem, which has fewer observation samples (small-sample), which is influenced by a number of factors (multi-factor) and whose observation samples are placed randomly rather than by time (cross-sectional data). Taking the key influence factors of CBM content and the key driving factors of the vulnerability of agricultural drought in Henan as examples, the results proved the feasibility and superiority of this proposed method.

Most of the existing GIA models mainly focus on these classes of issues with time series data or panel data. However, few GIA models take SMCDA problem as the research object. In this paper, the authors develop the GIA model with rate of change according to the characteristics of SMCDA problem, and present some properties and application suggestions of the proposed method.

As an important measure to promote sustainable development, green finance has developed rapidly in recent years. In order to comprehensively analyze the positive and negative indicators of the influencing factors of green finance, this paper puts forward a grey relational method of spatial-temporal panel data from the perspective of the development trend of the object dimension indicators and the performance difference between the time dimension indicators.

From the different perspectives of object dimension and time dimension, the positive and negative indicators are standardized differently considering the reverse of indicators and characterizing factors. The grey absolute relational degree is used to define the matrix sequence. This method reflects the development trend of objects in time and the difference characteristics among objects, which comprehensively represents the correlation between the reference panel and the comparison panel.

The results show that: (1) The object dimension reflects the internal driving force of the development of green finance in each provincial administrative region and the time dimension reflects the relationship between regional differences of influencing factors and green finance. (2) From the object dimension, the influencing factors of green finance from high to low are economic development potential, economic development level, air temperature, policy support, green innovation and air quality. (3) From the time dimension, the influencing factors of green finance from high to low are green innovation, air quality, economic development potential, economic development level, policy support and air temperature.

The different standardized processing methods of positive and negative indicators proposed in this paper not only eliminate the sample dimension, but also study the grey relational degree among the indicator panels from different reference dimensions. The proposed model is applied to identify the influencing factors of green finance, which expands the practical application scope of the grey relational model. The research results can provide reference for relevant departments to better promote the development of green finance.

The purpose of this study is to explore the effects of aerospike and hemispherical aerodisks on flow characteristics and drag reduction in supersonic flow over a blunt body. Specifically, the study aims to analyze the impact of varying the length of the cylindrical rod in the aerospike (ranging from 0.5 to 2.0 times the diameter of the blunt body) and the diameter of the hemispherical disk (ranging from 0.25 to 0.75 times the blunt body diameter). CFD simulations were conducted at a supersonic Mach number of 2 and a Reynolds number of 2.79 × 10⁶.

ICEM CFD and ANSYS CFX solver were used to generate the three-dimensional flow along with its structures. The flow structure and drag coefficient were computed using Reynolds-averaged Navier–Stokes equation model. The drag reduction mechanism was also explained using the idea of dividing streamline and density contour. The performance of the aero spike length and the effect of aero disk size on the drag are investigated.

The separating shock is located in front of the blunt body, forming an effective conical shape that reduces the pressure drag acting on the blunt body. It was observed that extending the length of the spike beyond a specific critical point did not impact the flow field characteristics and had no further influence on the enhanced performance. The optimal combination of disk and spike length was determined, resulting in a substantial reduction in drag through the introduction of the aerospike and disk.

To predict the accurate results of drag and to reduce the simulation time, a hexa grid with finer mesh structure was adopted in the simulation.

The blunt nose structures are primarily employed in the design of rockets, missiles, and re-entry capsules to withstand higher aerodynamic loads and aerodynamic heating.

For the optimized size of the aero spike, aero disk is also optimized to use the benefits of both.

Thomson financial database reports a monthly consensus measure of analysts’ forecasts in the third week of every month, and firms’ earnings announcement dates are usually different from the last consensus calculation date. Thus, there is a gap between the last consensus calculation date and the earnings announcement date of firms. This study aims to address the question: “Do analysts issue forecasts that are slightly higher than the consensus number to increase the accuracy of their forecasts?”

This study is based on a sample of 91,172 quarterly earnings forecasts of various firms from 1990 to 2007 made between the last consensus calculation date and quarterly earnings announcement date. Descriptive statistics and statistical tests were used to analyze the data.

The findings propose that contrary to expectation, analysts’ forecasts between the last consensus calculation date and earnings announcement date are smaller than the consensus number. Also, the forecasts made between the last consensus and earnings announcement date is not as informative as forecasts made at other times as they could merely reflect the analysts’ herding behavior resulting from their career concerns.

This study provides a link between the literature that studies firms’ meet or beat analysts’ earnings phenomenon and analysts’ forecast decision-making context. This study also provides useful implications for the literature on the information content of analysts’ forecasts.

The purpose of this paper is to purify the wastewater in the garment industry.

The preparation of the calcium alginate (CA)/activated carbon (AC) composite membrane was achieved by vacuum freeze-drying and the cross-linking reaction between sodium alginate and CaCl₂. Effective parameters in the methylene blue (MB) adsorption such as temperature, dose, contact time and pH were discussed. The adsorption properties of the composite membrane were investigated by isotherm, kinetics and thermodynamic analysis. The adsorption equilibrium data were described by the adsorption isotherm Langmuir model and the Freundlich model. The pseudo-first-order, pseudo-second-order and intra-particle diffusion equations were selected to evaluate the kinetics. The thermodynamic study described that the adsorption reaction was spontaneous and exothermic.

The AC/CA membrane is an efficient and powerful adsorbent to remove MB in printing and dyeing wastewater, and provides a new idea for the selection of adsorption materials for industrial printing and dyeing wastewater.

The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials.

The paper contributes to its wider and convenientapplication in wastewater treatment.

Studies on the combination of CA and AC into adsorption membranes and for the removal of dyes from printing and dyeing wastewater have not been reported. A novel composite material is provided for treatment dyeing wastewater in garment production. The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials and contribute to its wider and convenient application in wastewater treatment.