Impact Of Uncertain Factors Research Articles

Dynamic reliability analysis of main shaft bearings in wind turbines

Tapered roller bearings (TRBs) are widely employed in large wind turbines as main shaft supports. The reliability of TRBs is directly related to the operational efficiency and safety of wind turbines. In this paper, a method for dynamic reliability analysis of TRBs used for supporting the main shafts of wind turbines is presented. First, a new quasi-static TRB model is established based on the meta-heuristic optimization algorithm. Then, the influence of preload, bearing clearance, and friction on the fatigue life of the TRB is investigated in detail. Moreover, considering the impact of uncertain factors, a dynamic reliability analysis of TRBs under fatigue load is conducted based on the composite limit state and adaptive double-loop Kriging theories. The effect of preload on the dynamic reliability of TRBs is assessed to guide the main shaft bearing assembly of wind turbines. Finally, a practical application example is provided to demonstrate the effectiveness and benefits of the proposed method.

A scenario-based two-stage stochastic optimization approach for multi-energy microgrids

With the increase in renewable energy penetration, the impact of uncertain factors on the efficient operation of multi-energy microgrids (MEMGs) is becoming more and more prominent. Considering the source-load uncertainties of MEMGs, a two-stage stochastic optimization approach based on scenario analysis is proposed in this paper. First, mixed distribution and conditional distribution were used to fit the forecast errors of wind power and multiple loads respectively, so as to provide basic data for scenario generation. Then, an improved K-means clustering algorithm based on relative entropy was used for scenario reduction. This algorithm ensured the scenario reduction speed while maintaining the probability distribution characteristics of the generated scenarios. Taking the day-ahead forecast and scenario analysis results of the sources and loads as inputs, a two-stage stochastic optimization model of MEMG based on random fluctuation stabilization was constructed. In the first stage, equipment outputs are formulated by deterministic optimization based on day-ahead forecasts. In the second stage, the forecast errors are regarded as fluctuations and combined with scenarized source and load variables, energy storage equipment are given priority to stabilize scenario fluctuations. At the same time, based on the conditional value at risk (CVaR), the outputs of energy supply and storage equipment can be flexibly adjusted under different risk preferences to realize the efficient operation of MEMG. The example simulation showed that the proposed stochastic optimization approach make better use of energy storage equipment, make scheduling plans according to different risk preferences to deal with uncertainty flexibly.

Multi-Objective Optimal Sizing of HRES under Multiple Scenarios with Undetermined Probability

In recent years, technologies for renewable energy utilization have been booming. Hybrid renewable energy systems (HRESs), integrating multiple energy sources to mitigate the unstable, unpredictable, and intermittent characteristics of a single renewable energy source, have become increasingly popular. However, due to the inherent intermittency and uncertainty of renewable energies, the impact of uncertain factors on the capacity optimization of HRESs needs to be considered. In the traditional scenario-based planning method, when dealing with uncertain factors, the probability corresponding to the scenario is difficult to determine. Furthermore, when applying the robust optimization method, it is difficult to fully use existing data to describe uncertain parameters in the form of intervals. To tackle these difficulties, this study proposes a probability undetermined scenario-based sizing model (PUSS model) for stand-alone HRES configuration optimization and a multi-objective evolutionary algorithm as the problem solver. The solution set obtained by the method covers multiple possible values of scenario probability combinations and can provide decision-makers with an overview of alternatives for HRES sizing under different power supply pressures. Based on the real environment data and load data of a certain place, the proposed model and algorithm are applied to sizing a typical HRES comprising wind generators, solar photovoltaic panels, energy-storage devices, and diesel generators. The experimental results show that the proposed PUSS method is both effective and efficient.

Research on multi-interval coupling optimization of ship main dimensions for minimum EEDI

In order to improve energy-saving effect of the YUKUN ship, minimize the value of the Energy Efficiency Design Index (EEDI), while considering the impact of uncertain factors. In this research, interval analysis method is used in uncertain optimization, and main dimensions of interval optimization research for EEDI are carried out. First, multi-island genetic algorithm (MIGA), particle swarm optimization (PSO) and adaptive simulated annealing algorithm (ASA) optimization methods are used for deterministic optimization, to analyze the influence of different optimization algorithms on the optimization results and to obtain the best deterministic optimization scheme. Second, considering uncertain factors—flow velocity VW, ship's wet surface area S and total resistance coefficient Ct—the interval analysis method is used to determine the main dimensions of the optimization problem and to analyze the influence of single interval and multi-interval coupling on the EEDI. Analysis shows that an increased number of uncertain factors will increase EEDI while decrease the energy saving effect, as will the coupling effect between uncertain factors. Final value of EEDI obtained by multi-interval coupling optimization, however, can be more realistic and guarantees the reliability of the scheme of the ship's main dimensions, and also be feasible as is still below the required baseline.

Design for Interface Stiffness of Mechanical Products Using Integrated Simulation and Optimization Under Uncertainty

Abstract Interface stiffness is an important factor influencing the performance of mechanical products. Uncertain factors affect the interface stiffness and stability in the process of product design, manufacture, and operation. How to reduce the impact of uncertain factors on the interface stiffness is a vital problem in interface design. In this paper, a robust optimal design method is proposed for mechanical interfaces considering uncertain factors, which combines the finite element simulation, experiment, and optimization to reduce the sensitivity of interface stiffness to uncertain factors. The proposed interface design method provides an effective way to improve the interface stiffness under uncertain conditions. In order to validate the proposed method, the bolted connection structure of a flange is applied as an example. The interface stiffness of the flange is selected as an optimization target, and the Gaussian process regression is used to construct a two-layer optimal model of the objective function for the design and uncertain parameters. When experimental and optimization results differ significantly, the Kalman filter is used to provide the feedback for the optimization results until the results meet requirements. The final results show that the optimized mechanical interface stiffness is increased by 15.5%, and the error between the optimized prediction and experimental results is within 1% after three times experimental validation and feedback adjustment.

Probabilistic Airport Traffic Demand Prediction Incorporating the Weather Factors

With the development of air transport industry in China, the congestion problem in the terminal areas of busy airports has become increasingly serious. In order to alleviate the increasingly frequent air traffic congestion, it is necessary to accurately and objectively predict traffic flow. Traditionally, most predicted methods are based on the number of aircrafts flight in the terminal area to obtain deterministic traffic flow data, without considering the impact of uncertain factors on the prediction results. Based on the uncertainty of demand, this paper uses a probability density prediction method based on quantile regression neural network and kernel density estimation, to analyse the variation of traffic flow at different quantiles according to the obtained continuous conditional quantile function. Predicting the probability density of traffic flow on a certain day, and then comparing the point prediction value corresponding to the peak value, which consider the weather factor and the conditional probability density prediction curve without considering the weather factor, it is concluded that considering the weather factor can make the traffic flow prediction more accurate.

Research on the dynamic impact of external uncertain factors on tourism

In recent years, a variety of uncertain factors have occurred frequently, such as international financial crisis, geographic conflicts, Sino-US trade disputes, and COVID-19, which have brought obvious unconventional fluctuations to China's tourism industry. By combing the uncertain events, this paper divides the uncertain factors into three categories. The first is Economic Policy Uncertainty (EPU), which refers to the uncertainty of future tourism development and unpredictable effects of tourism policy. The second is geopolitical risk (GPR), which refers to the risks related to armed conflicts or tensions between countries, which are more exogenous than economic and have a huge impact on inbound and outbound tourism. The third is financial stress (FS), which is concentrated to reflect the uncertainty of changes in the financial system to market, which is more likely to cause uncertain effects on the financial aspects of tourism companies' such as investment and cash flow. A comprehensive discussion of these three types of external uncertainties' impact mechanisms on tourism would help tourism companies to prevent and deal with risk events, and is significant for promoting the upgrade of supply-side transformation of the tourism industry. Based on the existing research, the documents provide good academic value about the impacts of uncertain factors on tourism, but only qualitatively or statically. Therefore, there is still a lack of dynamic research. To this end, We introduces a time-varying parameter vector autoregressive model (TVP-SVAR-SV), which extends the constant parameters of the classic SVAR to the stochastic volatility parameters. This model could capture the time-varying changes of variables caused by external shocks, including gradual changes or potential structural mutations, without the need to split the time series into sub-sequences, that makes it possible to study the characteristics of heteroscedasticity, clustering, asymmetry, and periodic effects of tourism variables. Therefore, based on the advantages of the TVP-SVAR-SV, we studies the impact of EPU, GPR and FS on tourism companies in different intervals or at specific points in time, which will help tourism companies better deal with challenges, seize opportunities, and maintain sustainable development.In summary, this paper exploits the financial data of tourism companies to analyze the dynamic impact of three uncertain factors, EPU, GPR, and FS on China's tourism companies. The results show that the economic policy uncertainty has the greatest impact on the scenic enterprises, especially the uncertainty brought by the SARS epidemic, which has a long-term significant negative effect;The geographical risk will have an obvious downward impact on the travel agency enterprises, which tends to increase in the near future, but has a certain positive impact on the scenic and hotel enterprises;The aggravation of financial pressure will bring strong adverse effects to scenic spots and travel agency enterprises, while the alleviation of financial pressure has a positive effect on the development of the two types of enterprises;After the outbreak of highly uncertain events, tourism enterprises will show obvious time-varying lag response, usually more than one year. The conclusion of this study is helpful to improve the understanding of the uncertain factors in the tourism industry, and also provides policy implications for how to deal with the complex and changeable external environment.

New models of supply chain network design by different decision criteria under hybrid uncertainties

Supply chain network design is to determine factories and distribution centers to produce and distribute products, in order to satisfy the demand of customers to some extent. Supply chain network design plays an important strategic role in supply chain management and faces kinds of uncertainties, such as uncertain demand and cost. In order to deal with the impact of uncertain factors, this paper considers the supply chain network design under hybrid uncertainties, namely, the objective randomness of retailers’ demand and the subjective uncertainties of operating costs. We construct three models to handle managers’ different needs. They are the expected cost minimization model that minimizes the expected total cost, the $$\beta$$ -cost minimization model that minimizes the $$\beta$$ -cost which means that the chance measure of the actual cost not exceeding the cost is not less than the confidence level $$\beta$$ , and the chance measure maximization model that maximizes the chance measure of the actual cost not exceeding the given cost. This paper then transforms them into deterministic classes by uncertainty theory, and several numerical examples are presented to verify the validity of the models and algorithm.

Cost-savings for biosimilars in the United States: a theoretical framework and budget impact case study application using filgrastim

ABSTRACTBackground: Biosimilars can directly reduce the cost of treating patients for whom a reference biologic is indicated by offering a highly similar, lower priced alternative. We examine factors related to biosimilar regulatory approval, uptake, pricing, and financing and the potential impact on drug expenditures in the U.S.Methods: We developed a framework to illustrate how key factors including regulatory policies, provider and patient perception, pricing, and payer policies impact biosimilar cost-savings. Further, we developed a budget impact cost model to estimate savings from filgrastim biosimilars under various scenarios. The model uses publicly available data on disease incidence, treatment patterns, market share, and drug prices to estimate the cost-savings over a 5-year time horizon.Results: We estimate five-year cost savings of $256 million, of which 18% ($47 million) are from reduced patient out-of-pocket costs, 34% ($86 million) are savings to commercial payers, and 48% ($123 million) are savings for Medicare. Additional scenarios demonstrate the impact of uncertain factors, including price, uptake, and financing policies.Conclusions: A variety or interrelated factors influence the development, uptake, and cost-savings for Biosimilars use in the U.S. The filgrastim case is a useful example that illustrates these factors and the potential magnitude of costs savings.

Probabilistic analysis of dynamic stability for milling process

Self-excited vibrations known as regenerative chatter arise in milling operations, causing excessive tool wear or breakage, along with poor surface finish on the machined workpiece. Consequently, it is critical to understand regenerative chatter and predict stability boundary for the milling process. Traditionally, stability analysis for milling processes is developed based on the assumption that parameters of the milling system are deterministic. However, in many cases, parameters of the milling system are uncertain. Successfully taking parameter uncertainty into account would increase reliability for modeling of chatter stability in milling. Considering impact of uncertain factors, this paper presents a practical method for probabilistic analysis of dynamic stability of the milling process. Dimension reduction method is applied to compute statistical moments of the limit state function. Saddlepoint approximation is employed to estimate probability density function, cumulative distribution function and, more specifically, reliability of the dynamic stability of the milling process. Finally, a discussion of the practical application of this method along with a detailed analysis to evaluate efficiency of the proposed method is presented.

A study on the impact of uncertain factors on information distortion in supply chains

Information distortion is a troublesome problem in supply-chain management. The most familiar form of information distortion is the ‘bullwhip effect’, which can be predicted according to parameters such as lead-times and forecasting coefficients. However, this paper focuses on the other form of information distortion that is caused by uncertain factors. This paper first defines the concept of the bullwhip effect, and proposes a new term ‘extended-bullwhip effect’, which is employed to describe information distortion besides the bullwhip effect. Then we quantify the negative impact of uncertain factors on information distortion for a simple two-stage supply chain consisting of a single retailer and a single manufacturer. In addition, the magnifications of demand variance arisen by the extended-bullwhip effect are exemplified through a numerical simulation. It is clarified that if the members of a supply chain share information, the ‘extended-bullwhip effect’ can be completely eliminated. Finally, four forms of information distortion are identified according to the quantitative analysis.

Breast cancer screening and cost‐effectiveness; Policy alternatives, quality of life considerations and the possible impact of uncertain factors

Mammographic screening for women aged 50-70 is effective in reducing breast cancer mortality, but the impact on quality of life and the attainable mortality reduction remain to be discussed. The consequences of expanding screening programmes to include women in other age groups are uncertain. We have predicted the effects and costs for 5 popular screening variants, differing in age group and screening interval, on the basis of our analysis of the Dutch screening trials and of the reported mortality reductions in other trials. We have also investigated the influence of a large number of uncertain factors. Screening for women aged 50 and over with a 2- or 3-year interval is very cost-effective and will result in reductions of respectively 16% or 10% in breast cancer mortality in a real population. Variation of most variables keeps the cost-effectiveness (CE) ratio limited to the range of US $3,000 to 5,000 per life-year gained. A 2- to 3-fold change in CE ratio would only occur if the extreme estimates of mortality reduction in the Swedish screening trials were applied. The impact on quality of life (QoL) is limited: for the 2-yearly screening policy for women aged 50-70, the cost per Quality-Adjusted Life-Year (QALY) gained is 4,050, whereas the cost per life-year gained is US $3,825. The CE ratio for 2-yearly screening of women aged 40-70 is 5,400, but the additional cost per additional life-year gained is US $35,000. It would be preferable by far to extend the screening programme to women over the age of 70 or to shorten the screening interval for women aged 50-70. Screening performances, the demand for mammograms outside screening and the possibility of a survival improvement irrespective of screening have a strong impact on QoL and CE.