Results Of Uncertainty Analysis Research Articles

Investigating the effects of spatial scales on social vulnerability index: A hybrid uncertainty and sensitivity analysis approach combined with remote sensing land cover data.

Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300-m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance-based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300-m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300-m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation.

Hydrochemical and microbial community characteristics and the sources of inorganic nitrogen in groundwater from different aquifers in Zhanjiang, Guangdong Province, China

Groundwater from different aquifers in the Zhanjiang area suffers from different degrees of nitrogen pollution, which poses a serious threat to the health of urban and rural residents as well as the surrounding aquatic ecological environment. However, neither the water chemistry and microbial community characteristics in different aquifer media nor the sources of inorganic nitrogen pollution have been extensively studied. This study integrated water quality parameters, dual isotopes (δ15N–NO3− and δ18O–NO3−), and 16S rRNA data to clarify the hydrochemical and microbial characteristics of loose rock pore water (LRPW), layered bedrock fissure water (LBFW), and volcanic rock pore fissure water (VRPFW) in the Zhanjiang area and to determine inorganic nitrogen pollution and sources. The results show that the hydrochemistry of groundwater in different aquifers is complex and diverse, which is mainly affected by rock weathering and atmospheric precipitation, and the cation exchange is strong. High NO3− concentration reduces the richness of the microbial community (VRPFW). There are a large number of bacteria related to nitrogen (N) cycle in groundwater and nitrification dominated the N transformation. A quarter of the samples exceeded the relevant inorganic nitrogen index limits specified in the drinking water standard for China. The NO3− content is highest in VRPFW and the NH4+ content is highest in shallow loose rock pore water (SLRPW). In general, NO3−/Cl−, dual isotope (δ15N–NO3− and δ18O–NO3−) data and MixSIAR quantitative results indicate manure and sewage (M&S) and soil organic nitrogen (SON) are the main sources of NO3−. In LRPW, as the depth increases, the contribution rate of M&S gradually decreases, and the contribution rate of SON gradually increases. The results of uncertainty analysis show that the UI90 values of SON and M&S are higher. This study provides a scientific basis for local relevant departments to address inorganic nitrogen pollution in groundwater.

Point and interval forecasting for wine prices: an approach based on artificial intelligence

PurposeWith the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.Design/methodology/approachThe proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.FindingsThe empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.Originality/valueThis study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

Analysis of polychlorinated biphenyls (PCBs) in edible oils using the QuEChERS/GC‐MS method: A health risk assessment study

Population growth has made it difficult to provide safe food; because various toxic substances such as polychlorinated biphenyls (PCBs) can contaminate food products such as edible oils which have very high-rate consumption worldwide. Aims of study are to determine the concentration of PCBs in edible oil samples and to evaluate the carcinogenic risk of PCBs in them among Iranian people by Monte Calo Simulation (MCS). After finding the location of high customer hyper market in Tehran, 42 samples of 7 various types of edible oils were collected; then PCBs content of them measured using the modified QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) extraction method and GC/MS technique. According to the findings the mean level of NDL-PCB in oil samples were ranged from 1.88 to 25.62 ng/g fat. Results of uncertainty analysis showed that among children the 95th percentile of Incremental Lifetime Cancer Risk (ILCR) were 7.80E-3, 5.37E-4, 5.37E-4, 2.00E-3, 1.59E-3, 13.9E-3 and 7.04E-4 for animal oil, corn oil, frying oil, olive oil, bran oil, sesame oil and sunflower oil, respectively. Also, the 95th percentile of ILCR among adults were 4.12E-2, 3.04E-3, 3.09E-3, 1.06E-2, 8.43E-3, 7.38E-3 and 3.74E-3 for animal oil, corn oil, frying oil, olive oil, bran oil, sesame oil and sunflower oil, respectively. The risk evaluation showed that in all edible oils the 95th percentile of simulated ILCR were more than 10−5, so it can be threatening health among both aging groups, although, the producers don't deliberately subject the users' lives to such danger, but high consumption rate and accumulation of PCBs in body tissues are contribute to increasing carcinogenic risk. Also, the 95th percentile of ILCR among adults were more than children, because the edible oil ingestion rate among adults was more than children.

High-performance self-compacting concrete with recycled coarse aggregate: Soft-computing analysis of compressive strength

The growth of cities and industrialization has led to an increase in demand for concrete, resulting in resource depletion and environmental issues. Sustainable alternatives such as using recycled concrete aggregate (RCA) and industrial waste have been proposed to meet construction material demands while adhering to building codes and promoting sustainability. However, compressive strength (CS) is a crucial property of concrete, and the design parameters have different effects on CS for various grades. Recently, researchers have focused on partially replacing natural coarse aggregate (NCA) with RCA in concrete to achieve sustainability goals. This study aims to examine the influence of design parameters (w/c: water-cement ratio, w/b: water-binder ratio, A/c: total aggregate-cement ratio, FA/CA: fine-coarse aggregate ratio, SP: superplasticizer, w/s: water-solid ratio and RCA%) on concrete CS and address controversies in the insights gained from pairwise comparisons using Pearson's correlation coefficient (PCC) analysis. Additionally, five techniques (M5P, RF, SVM, LR, and ANNs) were used to predict the CS of high-performance self-compacting concrete (HP-SCC) with RCA, and the results were compared with an ANNs-based model as was the commonly used one in literature. The approaches were assessed based on their accuracy measured using correlation coefficient (CC), mean absolute error (MAE), Root Mean Square Error (RMSE), Mean absolute percentage error (MAPE), Scatter index (SI), and comprehensive measure (COM) indicators. Accordingly, the analysis indicated that SVM-PUK-based model is the most appropriate and effective technique to predict the CS of HP-SCC for the given datasets, with CC = 0.894, 0.900, MAE = 1.721, 3.813, RMSE = 5.137, 6.306, and MAPE = 4.5%, 7.6% for the training and testing stages, respectively. The uncertainty analysis results were 21%, 20.7%, 19%, 22%, and 19% for M5P, RF, SVM, LR, and ANN-based models, respectively, whereby all of them were under threshold of 35%. Moreover, according to sensitivity analysis, w/c, w/b, and w/s variables influences the most on CS prediction, while the RCA(%) variable has the least impact.

Uncertainty and sensitivity analysis of the ASTEC simulations results of a MBLOCA scenario in a generic KONVOI plant using the FSTC tool

This paper presents the results of uncertainty and sensitivity (U&S) analysis of Medium Break Loss-of-Coolant (MBLOCA) severe accident (SA) scenario simulations performed with the ASTEC code with a generic input deck prepared for a KONVOI-1300 nuclear power plant (NPP). The analysis was done with the in-house Fast Source Term Calculation Tool (FSTC) (currently: KArlsruhe Tool for Uncertainty and Sensitivity Analysis (KATUSA)). Part of the presented here results – analysis of MBLOCA scenario simulations up to the basemat rupture - were already shown at the ERMSAR conference in 2022. Here an extension related to the analysis of shorter MBLOCA simulations (up to 6000 s after lower head vessel failure (LHVF)) and investigation of the influence from adding correlations between uncertain input parameters is given. The analysis allows to identify which uncertain input parameters influence the release of fission products (FPs) in the containment and environment at the different stages of SA progression.

A probabilistic study on hole cleaning optimization

Hole cleaning is considered as one of the most important drilling fluid functions. An efficient hole cleaning ensures a reliable well drilling practice with minimum troublesome problems. In this study, two main steps of hole cleaning, i.e., cuttings removal from under the bit and cuttings transport to the surface are discussed based on the drilling data of a shale formation. The traditional models for optimization of each step are presented. As the models require variety of input data, which are usually subjected to some extent of errors and uncertainties, the output of the model is also an uncertain parameter. Using Monte Carlo simulation, a simple probabilistic study was conducted to quantify the certainty level of the obtained results. Based on the result of this study, it is shown that for the proposed well, a good hole cleaning is expected. However, a more reliable decision for further hole cleaning optimization should be made considering the results of uncertainty analysis.

Uncertainty and sensitivity analysis of radionuclide migration through fractured granite aquifer

The radionuclide migration in the high-level radioactive waste (HLW) disposal is usually predicted by numerical simulations for risk analysis of radionuclide contamination in a large scale of time and space. However, the uncertainties in radionuclide migration models and their associated parameters significantly affect the simulation results. In the present study, we first selected certain parameters and output data as independent parameters and risk metrics and performed a series of radionuclide transport models at a research site in Northwestern China. The models considered radionuclide migration in the equivalent porous medium with the mechanism of nuclide decay in an arbitrary-length decay chain, adsorption, advection, diffusion, and dispersion. Then 3000 Monte Carlo (MC) simulations were performed to carry out a set of uncertainty and global sensitivity analysis by coupling an uncertainty quantification tool with a radionuclide migration simulator. The results indicated that both hydraulic gradient and hydraulic conductivity significantly influenced the risk metrics. Thus, it is critical to obtain hydraulic gradient and hydraulic conductivity data under the same economic conditions. We applied the multivariate adaptive regression spline (MARS) method to generate response surface models representing the relationships among independent parameters and risk metrics. Calculations of the risk metric distribution ranges revealed that the peak release doses would appear at 0.40 and 0.79 million years, and their values will be in the range of 4.7 × 10−7–1.93 × 10−6 Sv/a. Uncertainty and sensitivity analysis results of radionuclide contamination in the fractured granite upon which HLW is disposed can improve simulation and prediction accuracy for radionuclide migration.

Predicting crop yields using a new robust Bayesian averaging model based on multiple hybrid ANFIS and MLP models

Predicting crop yield is an important issue for farmers. Food security is important for decision-makers. The agriculture industry can more accurately supply human demand for food if the crop yield is predicted accurately. Tomato is one of the most important crops so that 160 million tonnes of tomatoes are produced annually around the world. In this study, tomato yield based on data of 40 cities of Iran country including annual average temperature (T), relative humidity (RH), effective rainfall (R), wind speed (WS), and Evapotranspiration (EV) for the period of 1968–2018 was predicted using a new Bayesian model averaging (BMA). The paper's main innovation is the use of the new BMA so that it allows the modellers to quantify the uncertainty of model parameters and inputs simultaneously. For this aim, first, the multiple Adaptive neuro-fuzzy interface system (ANFIS) and multi-layer perceptron (MLP) were used for predicting crop yield. To train the ANFIS and MLP model, a new algorithm, namely, multi verse optimization algorithm (MOA) was used. Also, the ability of MOA was benchmarked against the particle swarm optimization (PSO), and firefly algorithm (FFA). In the next level, the new BMA used the outputs of the ANFIS-MOA, MLP-MOA, ANFIS, FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP for predicting tomato yield in an ensemble framework. The five- input combination of RH, T, and R, WS, and EV gave the best result. The mean absolute error (MAE) of the BMA in the testing level was 20.12 (Ton/ha) while it was 24.12, 24.45, 24.67, 25.12, 29.12, 30.12, 31.12, and 33.45 for the ANFIS-MOA, MLP-MOA, ANFIS-FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP models. Regarding the results of uncertainty analysis, the uncertainty of BMA was lower than those of the ANFIS-MOA, MLP-MOA, ANFIS-FFA, MLP-FFA, ANFIS-PSO, MLP-PSO, ANFIS, and MLP models while the MLP model provided the highest uncertainty. The results of this study indicated that BMA using multiple MLP and ANFIS model was useful for predicting tomato yield.

Analysis of Uncertainties in Open Water Test of B-Series Propeller at Indonesian Hydrodynamic Laboratory

A propeller’s performance characteristics in open water typically refer to the variation of its thrust, torque, and efficiency with speed and revolution rate. This investigation provides a method for gaining a better understanding of the achievable accuracy. of the open water propeller test and presents an example of how to conduct a comprehensive uncertainty analysis of prediction models4 blades B-series model propeller, diameter 157.59 mm, pitch ratio 1.133 and Blade Area Ratio (BAR) 0.65 was carried out for this study. The methodologies presented using the ITTC recommended procedure to obtain individual measurement systems, measurement of individual variables, data reduction and experimental results. The bias limits for the individual measurement systems are estimated: propeller geometry, speed, thrust, torque, propeller rate of revolution, and temperature/density. The precision limits for single and multiple runs have been determined for the thrust coefficient (K T ) and torque coefficient (K Q ) for one advance coefficient (J), 5 sets of tests with 3-speed measurements in each set are conducted, totaling 15 test runs. The experimental test results have represented as relative numbers the bias for K T represents 98.1 % and the bias for K Q represents 99.4 % of the total uncertainty for the mean value of 15 tests. The uncertainty analysis results of the open water propeller were recommended in an acceptable range of the 95% level of confidence.

Source apportionment and risk assessment of heavy metals in urban soils from a central China city by using positive matrix factorization model coupled with Monte Carlo simulation

The concentrations of As, Cd, Co, Cu, Mn, Ni, Pb and Zn in 78 urban soil samples from Zhengzhou, Henan Province, China were determined in the study. The mean values of the eight heavy metals were lower than the natural background reference values of soils from Henan Province, with the exception of Cd and Pb. However, the peak values for six species of the elements exceed the corresponding reference values, indicating enrichment of these metals in urban soils in Zhengzhou City, especially for Cd and Pb. The spatial distribution patterns showed that levels of As, Co, Mn and Ni decreased gently from northwest to southeast without hot-pot area, whereas Cd, Cu, Pb and Zn were with significant spatial variability. PMF model coupled with Pearson’s correlation analysis was performed to apportion and quantify possible sources of the eight elements. Four Factors (natural source, industrial production, agricultural activities and traffic discharges) were distinguished and their respective contribution rates were 18.73%, 33.22%, 23.91% and 24.14%, respectively. Results of uncertainty analysis confirmed that the all four source Factors based on the PMF model were reliable. Furthermore, the probability distribution of health risk assessed by means of Monte Carlo simulation revealed that non-carcinogenic risks associated with heavy metals were acceptable for both adults and children, while the total carcinogenic risk values remained relatively high. Additionally, the children were more susceptible to health risks in comparison to adults, and the ingestion way was considered to be the major exposure pathway. The results of source-oriented health risk demonstrated that the agricultural source was the major contributor to health risks, which was characterized by As, Ni and Co.

An Evaluation of the Data-Driven Model for Bubble Maximum Diameter in Subcooled Boiling Flow Using Artificial Neural Networks

In the subcooled boiling flow under low-pressure conditions, bubble characteristic diameter is of great influence on the surface heat transfer coefficient. However, large errors are still found in calculations using traditional mechanistic models or empirical correlations, especially for wide experimental condition. In this paper, we propose a widely applicable data-driven model using artificial neural networks (ANN) to predict the bubble maximum diameter and investigate the effect of experimental conditions. After a series of analyses on structural parameters and input parameters, the ANN model is established and validated based on six available experimental databases. The result shows that the relative error is around 14%. Uncertainty analysis is carried out for the four experimental conditions and two structural conditions. The results show the measuring accuracy of pressure is one of the most sensitive parameters on the prediction of bubble maximum diameter in the subcooled boiling flow under 1.0 MPa, especially for the bubble sizes larger than 0.5 mm. According to the results of uncertainty analysis, a new correlation is proposed for coefficients C and φ, which are used to express the effect of pressure and fluid dynamic. The new correlation works well for all the experimental databases, and the error for bubble datasets of large size is also modified. Furthermore, another independent validation with a low relative error to 14% is provided to prove the accuracy of the new correlation.

Estimation and uncertainty analysis of groundwater quality parameters in a coastal aquifer under seawater intrusion: a comparative study of deep learning and classic machine learning methods.

Excessive withdrawal of groundwater for agricultural irrigation can cause seawater intrusion into coastal aquifers. Such a case will in turn results in deterioration of irrigation water quality. Determination of irrigation water quality with traditional methods is a time-consuming and costly process. However, machine learning algorithms can be useful tools for modeling and estimating groundwater quality used for irrigation water purposes. In this study, TDS, PS, SAR, and Cl parameters of groundwater were estimated with models based on EC and pH variables. For this purpose, prediction performances of two different deep learning methods (convolutional neural network (CNN) and deep neural network (DNN)) and two different classical machine learning (Random Forest (RF) and extreme gradient boosting (XGBoost)) methods were compared. In addition, predictive uncertainty of the models was determined by quantile regression (QR) analysis. Performance criteria and results of uncertainty analysis revealed that CNN (in testing phase, NSE = 0.95 for TDS, NSE = 0.96 for PS, NSE = 0.67 for SAR and NSE = 0.93 for CI) and DNN (in testing phase, NSE = 0.91 for TDS, NSE = 0.91 for PS, NSE = 0.57 for SAR and NSE = 0.94 for Cl) models had quite a close performance in estimation of TDS, PS, SAR, and Cl parameters and higher than the other two classical machine learning methods. As a result, the CNN model can be considered the best performing model in estimating all quality parameters due to the highest NSE and lowest RMSE values. In addition, the Taylor diagram showed that the values estimated using the CNN model had the highest correlation with the measured data. It was determined that the model with the lowest uncertainty based on the PICP statistics was DNN, followed by the CNN model. However, the CNN model has predicted outliers more accurately. Present findings proved that deep learning models could offer efficient tools for predicting irrigation water quality parameters.

Life cycle cost analysis of near zero energy buildings benefited from earth-sheltering

Using passive solutions to take advantage of ambient energy sources would be more credible by considering the economic aspects of the project. Compared to aboveground buildings, Earth-Sheltered buildings (ESBs) have better energy efficiency. However, they might not be economically justifiable due to the escalation in construction costs. This paper presents a comprehensive analytical framework to compare the ESB options in the early design stage by the life cycle costs, including the cost/benefits of becoming a Net Zero energy building. An under-construction earth-sheltered hotel has been simulated using DesignBuilder® software to validate the method. Simulations were carried out to estimate the changes in energy consumption and cooling/heating loads for different wall structures, architectural forms, and soil cover depths. To evaluate the sensitivity of the results, Latin hypercube sampling (LHS) simulation was used. The results show that reducing energy consumption by earth sheltering is significant. Also, the Net present value (NPV) is statistically significantly positive, and forms are meaningfully influential. According to the results, the highest NPV is obtained when the depth of the building is seven meters. The uncertainty analysis results indicate that electricity price, interest rate, cooling setpoint, ground reflectance, and equipment consumption play a key role in NPV.

Optimization design and experimental validation of a vortex-based suction cup for a climbing AUV

This paper presents a climbing AUV, which is able to work in both swimming and climbing modes. The key issue in the design of a climbing robot is the choice of a reliable adhesion technique. Due to its great performance as regards payload, motion speed, simple system kinematics and gripping over a wide range of surfaces, a vortex-based suction cup is adopted to provide adhesion effect for the climbing motion of the AUV. Since the vortex-based suction cup is the core component of the vehicle, this paper mainly focuses on the structural and operational optimization of the suction cup by both Computational Fluid Dynamics (CFD) simulations and experimental validation. The proposed CFD simulation method is firstly verified by uncertainty study. The uncertainty analysis results indicate that the proposed simulation model and settings are reliable as reasonable agreements are found when comparing the computed results from varied grid size, iteration number, and time step cases. The total numerical uncertainties of the CFD simulation method for adhesion force and torque prediction are 1.50% and 1.46% of the average simulated values, respectively. In the CFD simulations, we investigate the effects of some key structural and operational parameters on the adhesion performance of the suction cup, including the vane number of impeller, the shape of cup shell, the adhesion gap (distance between cup bottom and wall surface), and the rotation rate of impeller. It can be concluded that more rotation rate of impeller can generate more adhesion force and more torque; smaller adhesion gap can produce more adhesion force but less torque; cup wall with inclined angle can increase the adhesion force and decrease the torque; the adhesion performance of the suction cup is less sensitive to the vane number of the impeller. CFD simulation results provide the basic design guidelines for the development of suction cup prototype. In the experiments, a specially designed testing platform is built up to validate and compare the performance of a set of suction cup prototypes, varied by different impeller and cup shell combinations. We compare the CFD simulation and experiment results of a 10-vane suction cup at a range of rotation rate conditions. The reasons of the discrepancy between CFD simulation and experiment results are mainly due to the open-loop control of the drive motor and the underestimate of the rotation rate in the experiments. The experimental results indicate that the effects of the structural and operational parameters on the generated adhesion performance are very complicated, and the effect of the structural parameters on the adhesion performance is influenced by the operational parameters, while the rotation rate and the adhesion gap play the key roles.

Seasonal variation of dissolved bioaccessibility for potentially toxic elements in size-resolved PM: Impacts of bioaccessibility on inhalable risk and uncertainty

The health effects of potentially toxic elements (PTEs) in airborne particulate matter (PM) are strongly dependent on their size distribution and dissolution. This study examined PTEs within nine distinct sizes of PM in a Chinese megacity, with a focus on their deposited and dissolved bioaccessibility in the human pulmonary region. A Multiple Path Particle Dosimetry (MPPD) model was used to estimate the deposited bioaccessibility, and an in-vitro experiment with simulated lung fluid was conducted for dissolved bioaccessibility. During the non-heating season, the dissolved bioaccessible fraction (DBF) of As, Cd, Co, Cr, Mn, Pb and V were greater in fine PM (aerodynamics less than 2.1 μm) than in coarse PM (aerodynamics between 2.1 and 10 μm), and vice versa for Ni. With the increased demand of heating, the DBF of Pb and As decreased in fine particle sizes, probably due to the presence of oxide/silicate compounds from coal combustion. Inhalation health risks based on the bioaccessible concentrations of PTEs displayed the peaks in <0.43 μm and 2.1–3.3 μm particulate sizes. The non-cancer risk was at an acceptable level (95th percentiles of hazard index (HI) was 0.49), but the cancer risk exceeded the threshold value (95th percentiles of total incremental lifetime cancer risk (TCR) was 8.91 × 10−5). Based on the results of uncertainty analysis, except for the exposure frequency, the total concentrations and DBF of As and Cr in <0.43 μm particle size segment have a greater influence on the uncertainty of probabilistic risk.

Multiple objective optimization design of hybrid composite structures considering multiple-scale uncertainties

A multi-objective uncertainty optimization design methodology of hybrid composite structures considering multiple-scale uncertainties is presented in this paper. The hybrid composite structures comprise of multiple materials with different types and volume ratios of matrix and fibers. The objective is to maximize the first-order natural frequency and frequency gap with the cost constraint, the micro-scale material parameters of matrix and fibers are considered as uncertain but bounded variables, and the optimization variables are stacking sequence and material patches (material selection in every patch of composite structures). Firstly, the uncertainty propagation analysis is implemented based on representative volume element method, and the macro material uncertainties are quantified based on neural network model. Furthermore, the multiple objective robust optimization design function is constructed based on the uncertainty analysis results of first-order natural frequency, frequency gap, and the total cost. An adaptive nondominated sorting genetic algorithm II (NSGA-II) method is proposed to optimize the stacking sequence and material patches simultaneously. The engineering examples of 9-layer laminated plate and conical cylindrical shell show that the proposed methodology can make full use of hybrid composite structures to improve the vibration characteristics while maintaining the low material cost.

Wind farm layout optimization with uncertain wind condition

Due to the uncertainty of wind condition, the annual energy production (AEP) of a wind farm during operation time is varying from year to year and is difficult to be estimated. A risk management method based on conditional value-at-risk (CVaR) is introduced for increasing the robustness of the AEP under uncertain wind conditions. To implement the risk management in the wind farm layout optimization, uncertainty analysis method for the discretized wind distribution in the future is proposed, and the CVaR of the AEP as the objective function is calculated by the proposed normal approximation method with the uncertainty analysis results. The discrete probability distribution of future wind condition is denoted by the wind relative frequencies. The wind relative frequencies itself are treated as random variables. A Dirichlet-Multinomial distribution is applied to model the uncertainty of the wind relative frequencies in the future. The approximated normal distribution of AEP is established based on the probability distribution of the wind relative frequencies, and the CVaR can be calculated analytically based on the mean and variance of the normal distribution. The simulation verifies that the optimization method achieves a trade-off between maximizing the expected value of AEP with historical wind condition, and minimizing the potential variation of the AEP due to a changing wind condition in the future.

Analysis of Electricity and Natural Gas Security. A Case Study for Germany, France, Italy and Spain

This work aims to present an analysis of electricity and natural gas supply security in the four European Union countries with the highest level of consumption, namely Germany, France, Italy, and Spain. The goal of the study is to use and adapt a set of 22 indicators for estimating the level of energy security. The evaluation of an index called “Energy Security Level”, measuring the overall energy security, is performed for the period 2006–2018 in order to compare its evolution in each of the analyzed countries. The application of the proposed indicator system demonstrates which measures weakened or strengthened energy security in the past. The analysis of the energy security level demonstrates that Germany and France perform better than Italy and Spain in terms of energy security assurance. The main factors for these differences are the rich energy mix for Germany and the massive presence of nuclear power in France. These two elements make German and French energy systems more resilient than the Italian and Spanish ones. The results of sensitivity and uncertainty analysis demonstrate that the initial values of indicator weights have low impact on the uncertainty of energy security level estimations.

Uncertainty and sensitivity analysis of the QUENCH-08 experiment using the FSTC tool

This paper presents results of a preliminary uncertainty and sensitivity (U&S) analysis of the QUENCH-08 experiment using the Fast Source Term Calculation (FSTC) tool, which currently is under development at KIT/INR as a part of WAME project. WAME is devoted to the development of a methodology and a tool for fast and accurate source term (ST) calculation in case of severe accident at nuclear power plant (NPP). The FSTC tool is divided into 2 parts: the U&S analysis and ST prediction (using the MOCABA approach), respectively. The description of the MOCABA approach, which is implemented in FSTC tool, will be presented – together with the results of ST evaluation – in a subsequent paper. In this paper, the FSTC tool is applied in conjunction with the Accident Source Term Evaluation Code (ASTEC) (version 2.2_b) severe accident (SA) code to the U&S analysis of the QUENCH-08 experiment to demonstrate the functionality of the tool and to check the methodology’s suitability for the application to a nuclear power plant.