Monte Carlo Uncertainty Analysis Research Articles

Mix proportion design and life cycle assessment of ultra-high-performance lightweight concrete with hollow microspheres

Achieving high strength and low weight in cement-based composites remains a critical objective in material and structure engineering. This study presents the development of an ultra-high-performance lightweight concrete (UHPLC) composite with a density not exceeding 1950 kg/m³ and a compressive strength of at least 100 MPa. The UHPLC mix design was optimized using the central composite design (CCD) response surface methodology, and a life cycle assessment (LCA) was conducted for the first time on UHPLC from cradle to gate. The interactions between cement content, hollow microspheres, and their effects on workability, mechanical properties, and durability were analyzed. The results show that using the CCD response surface method for designing UHPLC mix proportions is feasible, allowing effective optimization for various objectives, such as balancing both strength and density, highlighting low density, or emphasizing ultra-high strength. the designed UHPLC exhibits mechanical and durability properties comparable to or better than UHPC. LCA results show higher energy consumption and environmental impact for UHPLC in the cradle-to-gate stage, mainly due to the microsphere production phase. Monte Carlo uncertainty analysis reveals that ADP, HTP, POCP, and EP are most sensitive to microsphere content, while GWP and AP are more affected by cement content. The optimized UHPLCs in this study can be selected depending on specific technical or environmental scenarios. Introducing HMs into the UHPC system for UHPLC production shows excellent performance, making it a promising material for advanced structural applications.

Enhancing Coordination Efficiency with Fuzzy Monte Carlo Uncertainty Analysis for Dual-Setting Directional Overcurrent Relays Amid Distributed Generation.

In the contemporary context of power network protection, acknowledging uncertainties in safeguarding recent power networks integrated with distributed generation (DG) is imperative to uphold the dependability, security, and efficiency of the grid amid the escalating integration of renewable energy sources and evolving operational conditions. This study delves into the optimization of relay settings within distribution networks, presenting a novel approach aimed at augmenting coordination while accounting for the dynamic presence of DG resources and the uncertainties inherent in their generation outputs and load consumption-factors previously overlooked in existing research. Departing from conventional methodologies, the study proposes a dual-setting characteristic for directional overcurrent relays (DOCRs). Initially, a meticulous modeling of a power network featuring distributed generation is undertaken, integrating Weibull probability functions for each resource to capture their probabilistic behavior. Subsequently, the second stage employs the fuzzy Monte Carlo method to address generation and consumption uncertainties. The optimization conundrum is addressed using the ant lion optimizer (ALO) algorithm in the MATLAB environment. This thorough analysis was conducted on IEEE 14-bus and IEEE 30-bus power distribution systems, showcasing a notable reduction in the total DOCR operating time compared to conventional characteristics. The proposed characteristic not only achieves resilient coordination across a spectrum of uncertainties in both distributed generation outputs and load consumption, but also strengthens the resilience of distribution networks overall.

DyUnS: Dynamic and uncertainty-aware task scheduling for multiprocessor embedded systems

In this paper, an uncertainty-aware task scheduling approach capable of dynamically applying on multiprocessor embedded systems called ”DyUnS” is presented. This method is based on a type-2 fuzzy inference system to consider all design challenges of multiprocessor embedded systems along with their unavoidable uncertainty caused by the differences in models and measurements. The proposed method employs a fuzzy inference system to approximate the appropriate assignment of the application’s tasks to processing cores based on a defined rank including the main design challenges of the system including execution time, temperature, power consumption, and reliability. Moreover, an uncertainty level is defined for various design challenges as the footprint of uncertainty during the scheduling process to tackle the existing inaccuracy between the static models and dynamic environment. Thus, the generated uncertainty-aware solution could be efficiently employed as a dynamic scheduling at runtime. To demonstrate the effectiveness of DyUnS in tolerating uncertainty, several experiments on various application graphs are performed and its effectually is compared to related studies. Based on these experiments, DyUnS jointly optimizes the main design parameters, and its generated solution could be employed dynamically without violating the system’s thresholds. Moreover, its average difference compared to Monte Carlo uncertainty analysis is about 0.2 for all design parameters in three levels of uncertainty.

Uncertainty analysis of the plasma impedance probe

A plasma impedance probe (PIP) is a type of in situ, radio frequency (RF) probe that is traditionally used to measure plasma properties (e.g., density) in low-density environments such as the Earth's ionosphere. We believe that PIPs are underrepresented in laboratory settings, in part because PIP operation and analysis have not been optimized for signal-to-noise ratio (SNR), reducing the probe's accuracy, upper density limit, and acquisition rate. This work presents our efforts in streamlining and simplifying the PIP design, circuit-based-model, calibration, and analysis for unmagnetized laboratory plasmas, in both continuous and pulsed PIP operation. The focus of this work is a Monte Carlo uncertainty analysis, which identifies operational and analysis procedures that improve SNR by multiple orders of magnitude. Additionally, this analysis provides evidence that the sheath resonance (and not the plasma frequency as previously believed) sets the PIP's upper density limit, which likely provides an additional method for extending the PIP's density limit.

Uncertainty Analysis of the Storage Efficiency Factor for CO2 Saline Resource Estimation

Carbon capture and sequestration (CCS) is a promising technology for reducing CO2 emissions to the atmosphere. It is critical to estimate the CO2 storage resource before deploying the CCS projects. The CO2 storage resource is limited by both the formation pore volume available to store CO2 and the maximum allowable pressure buildup for safe injection. In this study, we present a workflow for estimating the volume- and pressure-limited storage efficiency factor and quantifying the uncertainty in the estimates. Thirteen independent uncertain physical parameters characterizing the storage formation are considered in the Monte Carlo uncertainty analysis. The uncertain inputs contributing most to the overall uncertainty in the storage efficiency factor are identified. The estimation and uncertainty quantification workflow is demonstrated using a publicly available dataset developed for a prospective CO2 storage site. The statistical distributions of the storage efficiency factor for the primary storage formation and the secondary storage formation located in deeper depth are derived using the proposed workflow. The effective-to-total porosity contributes most to the overall uncertainty in the estimated storage efficiency factor at the study site, followed by the maximum allowable pressure buildup, the net-to-gross thickness ratio, the irreducible water saturation, and the permeability. While the significant uncertain input variables identified are tailored to the characteristics of the study site, the statistical methodology proposed can be generalized and applied to other storage sites. The influential uncertain inputs identified from the workflow can provide guidance on future data collection needs for uncertainty reduction, improving the confidence in the CO2 saline storage resource estimates.

Mirror-based multimodal straightforward method for impedance eduction using a zigzag array

The Mirror-based Multimodal StraightForward Method (MM-SFM) is proposed herein to realize accurate and reliable liner impedance eduction covering the wide frequency range of aeroengine fan noises. It adopts a novel zigzag array in the flow duct, and unfolds them by a coordinate transformation into a mapping diagonal array in an extended duct, which can increase the array transverse spacing Δz considerably. Then, it invokes the M-SFM incorporating the trust-region and extended Prony's algorithms, to decompose the three-dimensional multimodal aeroacoustic field and educe the impedance. A parametric study based on the Monte-Carlo uncertainty analysis is conducted through finite-element numerical experiments on a 51 × 51 mm2 flow duct, to survey the method performance under random perturbations. It indicates that, the educing accuracy and reliability are promoted by increasing Δz or the axial spacing Δx properly, and the decomposed-mode number M×N=10 is appropriate herein. In our studied cases, using the recommended array of Δz=25.5mm and Δx=22mm locating at the nodes of transverse mode m = 2, the MM-SFM generally produces accurate averaged educed impedances in a considerably extended frequency range from 100 Hz up to nearly 10 kHz, and has small educing uncertainties at most absorptive frequencies and thus a good reliability.

Life cycle analysis and economic evaluation of adsorptive removal of arsenic from groundwater using GAC and GAC-Fe adsorbents

The potential health effects associated with consumption of arsenic contaminated water have motivated chemical and energy intensive treatment processes. Granulated activated carbon (GAC) and iron impregnated granular activated carbon (GAC-Fe) are the two adsorbents which have shown significant removal of arsenic (As(V)). However, the synthesis of different adsorbents involves significant different materials and energy interactions which have different environmental and economic burdens. This study evaluates and compares the environmental impacts arising from treatment of arsenic containing water using GAC and GAC-Fe adsorbents, for the selection of more suitable one. The life cycle inventory pertaining to treatment through two adsorbents was prepared based on the data available in literature. The impacts were assessed utilizing ‘Sphera LCA for experts’ software with three midpoint methods i.e. CML 2001, TRACI 2.1 and ReCiPe 2016. Results show that chemically treated/impregnated adsorbents for arsenic contaminated water treatment not only have lesser environmental emissions but also reduces the treatment cost. The treatment with GAC-Fe has ∼30% lower impacts in GWP, AP, EP, MAETP, FAETP, ODP, ADP (E) and HTP categories than treatment with GAC, since increase in adsorbent's specific uptake in GAC-Fe reduces the amount of total adsorbent requirement. GAC-Fe based water treatment costs ∼23% less than with GAC. The main responsible parameters for emissions are synthesis of GAC from hard coal precursor, and electricity consumption in carbonization and drying operations. Apart from this, in case of GAC-Fe, additional impregnation and drying operation also contributes to emissions. The utilization of coke oven gas generated in adsorbent synthesis for energy credits has potentially reduced the emissions and operating cost. The less standard deviation (<10%) in Monte Carlo (MC) uncertainty analysis, indicated the reliability and robustness of LCA impacts. This study exemplified the environment friendliness of metal impregnated GAC based arsenic contaminated water treatment.

Estimation of risk to soil and human health during irrigation using ZnO nanoparticles-containing water

This work estimated ecological and human health risks for the exposure pathway of spinach irrigation with metal oxide nanoparticles- contaminated river water. Using literature studies, log-logistic plant uptake models were developed to predict metal uptake in plant parts. Reported or expected values of ZnO nanoparticles (NPs) in surface water were collected from published literature for realistic scenarios and used to estimate the risk to human health in terms of hazard quotient (HQ). Monte Carlo simulation and uncertainty analysis were conducted to assess the variability in the obtained results. The Hazard Quotient (HQ) values obtained at environmental concentrations of ZnO nanoparticles were all below 1 for all age groups. Although the 99th percentile value of HQ was found to be the highest (3.57 ×10−3) for the age group (1−6) years, the probability of HQ value being greater than unity was found to be null for all age groups. The 99th percentile values of contamination factor, potential ecological risk factor, and geo-accumulation index, computed as part of ecological risk assessment, were found to be 0.098, 0.098, and − 0.62, respectively, well within their respective contamination limits. Bio-concentration and translocation factors were also computed to quantify the internal uptake of the plant. The results implied that at the current levels of zinc oxide nanoparticles in surface water, no health risk would be exerted on humans upon spinach consumption under the assumptions used. Also, negligible chances of soil contamination were observed due to zinc accumulation after irrigation. The approach proposed in this work can be used to determine potential health and ecological risks when reusing metal oxide NP-contaminated water for irrigating edible crops. This especially gains significance in a future perspective due to a likely increase in nanoparticles' concentration in the environmental media.

Investigation of the Carbon Footprint of the Textile Industry: PES- and PP-Based Products with Monte Carlo Uncertainty Analysis

The Carbon Border Adjustment Mechanism was developed to ensure that industrial sectors operating outside the EU follow the same environmental standards and targets while competing with the EU’s carbon market. This mechanism aims to calculate the carbon footprint of goods and services imported into the EU and make carbon adjustments accordingly. The transition phase, starting in 2023, represents the period when the Carbon Border Adjustment Mechanism will be implemented. The completion of the transition phase is targeted for 2025. By this date, the effective implementation of this mechanism is aimed at demonstrating that countries outside of the EU comply with emissions regulations using Carbon at Border certificates. The textile industry’s products have a significant environmental impact throughout their life cycle, from the production of raw materials to the disposal of the finished product. Textile production, especially synthetic yarns, requires large amounts of energy, contributing to greenhouse gas emissions and climate change. In this study, a “cradle-to-customer plus waste” life cycle assessment (LCA) is conducted to evaluate the environmental impacts of two products in the textile sector. The Monte Carlo analysis method can be used to handle uncertainties in LCA calculations. It is a method for modeling uncertainties and statistically evaluating results. In this study, this method is preferred at the stage of determining uncertainties. The processes from chips to yarns are investigated for two synthetic yarns: polyester (PES) and polypropylene (PP). The carbon emissions of PP and PES used in textiles are calculated for the first time in this study using detailed modeling with LCAs and a real application. The main production operations are considered: (i) transport of raw materials and packaging material, (ii) energy consumption during the production process, (iii) transport of products, and (iv) end-of-life steps. When the actual data obtained from a company are analyzed, the carbon footprints (CFs) of the PES and PP are calculated to be 13.40 t CO2-eq (t PES)-1 and 6.42 t CO2-eq (t PP)-1, respectively. These data can be used as reference points for future studies and comparisons. According to the results obtained, when the energy consumption and raw material stages in the production of the PES and PP products are compared, it is seen that the CF of PP yarn is lower, and it is more environmentally friendly. These findings can be utilized to enhance government policies aimed at reducing greenhouse gas emissions and managing synthetic yarn production in Türkiye. Since PP and PES raw materials are predominantly used in synthetic yarns, this study’s objective is to quantify the carbon emissions associated with the utilization of these raw materials and provide guidance to companies engaged in their production.

Non-Probabilistic Uncertainty and Correlation Propagation Analysis Methods Based on Multidimensional Parallelepiped Model

In engineering practice, the uncertainty and correlation may coexist in the input parameters, as well as in the output responses. To address such cases, several methods are developed for the non-probabilistic uncertainty and correlation propagation analysis in this study. In the proposed methods, the multidimensional parallelepiped model (MPM) is introduced to quantify the uncertainty and correlation of input parameters. In the uncertainty propagation analysis, three methods are presented to calculate the interval bounds of output responses. Among the methods, the Monte Carlo uncertainty analysis method (MCUAM) is firstly presented as a reference method, and then the first-order perturbation method (FOPM) is employed to promote the computational efficiency, and the sub-parallelepiped perturbation method (SPPM) is further developed to handle the correlated parameters with large uncertainty. In the correlation propagation analysis, the Monte Carlo correlation analysis method (MCCAM) is proposed based on the MPM and Monte Carlo simulation, which aims to compute the correlation among different output responses. The uncertainty domains between any two responses can also be constructed by the MCCAM. The effectiveness of the proposed methods on dealing with the uncertainty and correlation propagation problems is demonstrated by three numerical examples.

Physical distancing versus testing with self-isolation for controlling an emerging epidemic

Two distinct strategies for controlling an emerging epidemic are physical distancing and regular testing with self-isolation. These strategies are especially important before effective vaccines or treatments become widely available. The testing strategy has been promoted frequently but used less often than physical distancing to mitigate COVID-19. We compared the performance of these strategies in an integrated epidemiological and economic model that includes a simple representation of transmission by “superspreading,” wherein a relatively small fraction of infected individuals cause a large share of infections. We examined the economic benefits of distancing and testing over a wide range of conditions, including variations in the transmissibility and lethality of the disease meant to encompass the most prominent variants of COVID-19 encountered so far. In a head-to-head comparison using our primary parameter values, both with and without superspreading and a declining marginal value of mortality risk reductions, an optimized testing strategy outperformed an optimized distancing strategy. In a Monte Carlo uncertainty analysis, an optimized policy that combined the two strategies performed better than either one alone in more than 25% of random parameter draws. Insofar as diagnostic tests are sensitive to viral loads, and individuals with high viral loads are more likely to contribute to superspreading events, superspreading enhances the relative performance of testing over distancing in our model. Both strategies performed best at moderate levels of transmissibility, somewhat lower than the transmissibility of the ancestral strain of SARS-CoV-2.

Environmental Footprints of the Catalytic Pyrolysis of Pine Needles through Integration of a Nickel-Decorated Chemically Modified Biochar Catalyst

The current study presents the life cycle analysis (LCA) of a biochar-catalyzed pyrolysis-based biorefinery system. Noncondensable gases (NCGs), produced as a coproduct, have been recycled back to the system, while biochar produced was employed for catalyst preparation, to visualize a biorefinery model. Results showed the considerable environmental impact of the process, in particular of the catalyst and energy sources utilized during the process. Matching up with the LCA metrics, both single parameter and Monte Carlo uncertainty analyses also revealed the high sensitivity of the obtained impacts for the impregnating chemical (i.e., ZnCl2, H3PO4, and NaOH) employed during catalyst preparation. Among different processes, the Ni/BC-H3PO4 catalyzed process with NCG recycling has emerged as the best possible case with the lowest environmental emissions (GWP ≈ 0.109 kg CO2 equivalent) and low cumulative energy demand (∼2.47 MJ) together with high process efficacy and productivity. Furthermore, sustaining process energy needs with varieties of different sources advocates for renewable sources (more specifically hydropower) over nonrenewable sources of energy. The study highlights the hotspots of the current technology, envisaging ways of reducing emissions through clean/renewable energy sources as well as utilizing less impact-causing intermediate materials, and thus, acting like a building stone in creating a base toward the vision of achieving net zero emissions.

Life Cycle Analysis and Operating Cost Assessment of a Carbon Negative Catalytic Pyrolysis Technique Using a Spent Aluminum Hydroxide Nanoparticle Adsorbent-Derived Catalyst: Insights into Coproduct Utilization and Sustainability

The current research is intended toward the life cycle analysis (LCA) and operating cost assessment of catalytic and noncatalytic pyrolysis. The study assessed the impact of waste material-based catalysts (spent aluminum hydroxide nanoparticle (AHNP) adsorbent-derived catalysts) on the process’ overall environmental load and operating cost. Incorporating the utilization of process coproducts through recycling both noncondensable gases (NCGs) and biochar in the pyrolysis process, as well as exporting biochar as a coal substitute, reduced the values of the environmental impact and operating cost. Results revealed that biochar market export as coal replacement (for earning coal credits) is comparatively more beneficial than its back-recycling in the pyrolysis process (for offsetting natural gas requirements) from the environmental perspective. However, the reverse is true from the economic viewpoint in terms of operating cost. Among different considered processes, nickel-doped AHNP (Ni/Al)-catalyzed pyrolysis with scenario 2 (wherein all the produced biochar was coal-credited and all the NCGs were back-recycled) portrayed the highest environmental benefits owing to the maximum negative emissions (∼0.031 kg CO2 equivalent GHG emission saving). Low standard deviations (<10%), as obtained through Monte Carlo uncertainty analysis, indicated toward the reliability and robustness of the obtained LCA impacts. The present study exemplified the renewability and environmental friendliness of the AHNP-catalyzed pyrolytic biofuel over conventional petroleum-derived diesel and gasoline fuels.

A geospatial modeling approach to quantifying the risk of exposure to environmental chemical mixtures via a common molecular target

In the real world, individuals are exposed to chemicals from sources that vary over space and time. However, traditional risk assessments based on in vivo animal studies typically use a chemical-by-chemical approach and apical disease endpoints. New approach methodologies (NAMs) in toxicology, such as in vitro high-throughput (HTS) assays generated in Tox21 and ToxCast, can more readily provide mechanistic chemical hazard information for chemicals with no existing data than in vivo methods. In this paper, we establish a workflow to assess the joint action of 41 modeled ambient chemical exposures in the air from the USA-wide National Air Toxics Assessment by integrating human exposures with hazard data from curated HTS (cHTS) assays to identify counties where exposure to the local chemical mixture may perturb a common biological target. We exemplify this proof-of-concept using CYP1A1 mRNA up-regulation. We first estimate internal exposure and then convert the inhaled concentration to a steady state plasma concentration using physiologically based toxicokinetic modeling parameterized with county-specific information on ages and body weights. We then use the estimated blood plasma concentration and the concentration-response curve from the in vitro cHTS assay to determine the chemical-specific effects of the mixture components. Three mixture modeling methods were used to estimate the joint effect from exposure to the chemical mixture on the activity levels, which were geospatially mapped. Finally, a Monte Carlo uncertainty analysis was performed to quantify the influence of each parameter on the combined effects. This workflow demonstrates how NAMs can be used to predict early-stage biological perturbations that can lead to adverse health outcomes that result from exposure to chemical mixtures. As a result, this work will advance mixture risk assessment and other early events in the effects of chemicals.

A methodology for quantifying combustion efficiencies and species emission rates of flares subjected to crosswind

Despite strong indications that flares subjected to crosswinds can undergo fuel stripping mechanisms that reduce efficiency and lead to emission of unburned fuel, most published flare experiments have not considered the impact of a crosswind. Knowledge of flare performance in turbulent crosswinds representative of atmospheric wind is especially lacking. Accurate experiments to fill this gap can only be achieved in an environment where the wind conditions can be controlled. This work presents a methodology to determine the carbon conversion efficiency and species emission rates of a flare burning in a closed-loop wind tunnel. The developed methodology is based on solving an unsteady mass balance to relate measured accumulation rates in a closed-loop wind tunnel to emission rates, while extending the applicability and correcting errors of an earlier method. The developed methodology considers complicating factors such as infiltration and exfiltration of gases into and out of the wind tunnel, potential for reburning of products within the wind tunnel, and presence of inert species in the fuel stream. The methodology is assessed with a comprehensive Monte Carlo uncertainty and sensitivity analysis. Results suggest that carbon conversion efficiency can be measured with a systematic bias uncertainty of ≲ ±0.5%, ranging from 0.04% for a 99% efficient flare to 0.55% for a 70% efficient flare at 95% confidence, and methane emission rates can be measured within ±3.25% at 95% confidence. Demonstration experiments in a large closed-loop wind tunnel show that precision (repeatability) uncertainties will be larger but that overall (combined bias and precision) uncertainties of <10% are readily achievable.

Surveillance of long-term environmental elements and PM2.5 health risk assessment in Yangtze River Delta, China, from 2016 to 2020.

PM2.5 metal pollution significantly harms human health. The air quality in Wuxi is poor, especially in winter, and long-term monitoring of PM2.5 elements comprising has not been performed previously. In the present study, 420 PM2.5 samples were collected from January 2016 to December 2020. Eleven elements, including Al, Mn, Ni, Cr, As, Cd, Sb, Hg, Pb, Se, and Tl, were analyzed by inductively coupled plasma mass spectrometry. The mean PM2.5 level was 56.1 ± 31.0μg/m3, with a tendency of yearly decreasing and a significant seasonal distribution variation. The concentration of 11 elements in the PM2.5 samples was 0.38 ± 0.33μg/m3. Al was the highest element with a range of 37.5-2148ng/m3. Meanwhile, the spatial distribution differences were compared by literatures review. Based on the Crystal Ball model, health risks were assessed dynamically using Monte Carlo uncertainty analysis. After 10,000 simulations, the mean value of the hazard index for nine elements was 0.743, and Mn contributed the most to the hazard index among elements, with a correlation of 0.3464. The average carcinogenic risk was 1.01 × 10-5, which indicated that the non-carcinogenic and carcinogenic risks were within the acceptable range. However, considerable attention should be paid to the potential health risks associated with long-term Al, Mn, and As exposure. This study provides detailed data on local atmospheric pollution characteristics, helps identify potential risk elements, and contributes to the development of effective regional air quality management.

Uncertainty appraisal provides useful information for the management of a manual grape harvest

This contribution presents a novel approach to characterise uncertainty in the manual grape harvest of a winery in Tuscany (Italy). After identifying the potential sources of variability arising from randomness, weather, and management options, a model to define useful output variables is built. These output variables include the discrepancy in the harvest date of the vineyards ( harvest date discrepancy) , the discrepancy in the required workforce across harvest dates (labour discrepancy), and, finally, the potential deficit of working hours throughout the grape harvest campaign (labour deficit) . The range spanned by these variables is first assessed through a Monte Carlo uncertainty analysis wherein the model is repeated approximately 16,000 times with variable combinations of the input parameters per their probability distribution. The assessed uncertainty is then apportioned to the input parameters through a global sensitivity analysis. In turn, a regional sensitivity analysis characterises the circumstances producing a deficit of working hours, which corresponds to sufferance in the grape harvest campaign. The discussed approach could be implemented in a user-friendly decision-support tool for risk characterisation and efficient grape harvest management. • We developed a model to define sensitive variables in manual grape harvest. • We characterise their uncertainty through Monte Carlo simulations. • Output uncertainty was apportioned onto inputs with a global sensitivity analysis. • Criticalities were identified and analysed through regional sensitivity analysis. • The approach proposed could support decision making in grape harvest management.

Projection of the Epidemics Trend of COVID-19 in Qom, Iran: A Modeling Study

Background: Coronavirus is one of the major pathogens of the human respiratory system and a major threat to the human health. Objectives: This modeling study aimed to project the epidemics trend of coronavirus disease 2019 (COVID-19) in Qom, Iran Methods: This study projected the COVID-19 outbreak in Qom using a modified susceptible-exposed-infectious-recovered (SEIR) compartmental model by the end of December 2020. The model was calibrated based on COVID-19 epidemic trend in Qom from 1 January to 11 July. The number of infected, hospitalized, and death cases were projected by 31 December. A Monte Carlo uncertainty analysis was applied to obtain 95% uncertainty interval (UI) around the estimates. Results: According to the results, the reduced contact rate and increased isolation rate were effective in reducing the size of the epidemic in all scenarios. By reducing the contact rate from eight to six, the number of new cases on the peak day, as well as the total number of cases admitted to the hospital by the end of the period (31 December), decreased. For example, in Scenario A, compared to Scenario E, with a decrease in contact rate from eight to six, the number of new cases on peak days decreased from 15,700 to 1,100. The largest decrease in the number of new cases on peak days was related to Scenario F with 270 cases. Also, the total number of cases decreased from 948,000 to 222,000 between the scenarios, and the largest decrease in this regard was related to Scenario F, with 188,000 cases. Conclusions: The parameters of contact rate and isolation rate can reduce the number of infected cases and prevent the outbreak, or at least delay the onset of the peak. This can help health policymakers and community leaders to upgrade their health care systems.

Uncertainty and correlation propagation analysis of powertrain mounting systems based on multi-ellipsoid convex model

In engineering practice, uncertainty and correlation may coexist in both input parameters and output responses of the powertrain mounting system (PMS) of a vehicle. A methodology is developed for the uncertainty and correlation propagation analysis of the inherent characteristics of PMSs in this research. In the proposed methodology, the multi-ellipsoid convex model is introduced to quantify multiple groups of uncertain parameters with correlation, where dependent and independent uncertain parameters are considered simultaneously. In the uncertainty propagation analysis, it aims to calculate the interval bounds of system responses. To perform uncertainty propagation analysis, the Monte Carlo uncertainty analysis (MCUA) method is firstly presented based on Monte Carlo simulation, and the first-order perturbation-central difference-Lagrange multiplier (FPCDLM) method and the second-order perturbation-central difference-Lagrange multiplier (SPCDLM) method are then derived to promote the computational efficiency. In the correlation propagation analysis, it aims to compute the correlation between different system responses. To conduct the correlation propagation analysis, the Monte Carlo correlation analysis (MCCA) method is firstly proposed and the second-order perturbation correlation analysis (SPCA) method are then developed to enhance the computational efficiency. Next, the whole procedures for uncertainty and correlation propagation analysis of PMS are established by combining the above uncertainty analysis and correlation analysis methods, and the elliptical domain of any two system responses can be obtained. Finally, numerical examples of the PMS of an electric vehicle are provided to demonstrate the effectiveness of the proposed methodology.

Uncertainty analysis of the optimal health-conscious operation of a hybrid PEMFC coastal ferry

Hydrogen fueled Polymer Electrolyte Membrane Fuel Cells/Lithium-Ion Battery powertrains could be a promising solution for zero-local-emission shipping. The power allocation between PEMFC and LIB and their respective performance degradation play a crucial role in reducing the powertrain operating and maintenance costs. While several research works proposed energy management strategies to face these issues, a long-term operation optimization including the uncertainty in the input parameters of the model has not been extensively addressed. To this purpose, this study couples an operation optimization model of a PEMFC/LIB ferry propulsion system with a Monte-Carlo analysis to investigate the influence of PEMFC, LIB and hydrogen costs on the optimal operation of a hydrogen-powered ferry in the long-term. Hydrogen cost results to be the most influent parameter, in particular toward the end of the plant lifetime, when hydrogen consumption increases by up to 30%. Nevertheless, the variability of optimal ferry operation gradually decreases with the progressive PEMFC/LIB degradation. • Health-conscious energy management strategy of hybrid PEMFC/LIB coastal ferry. • Monte Carlo uncertainty analysis of the long-term operation of a hybrid ship. • Global sensitivity analysis indicates hydrogen fuel cost as the most influent parameter. • PEMFC/LIB degradation causes a 30% increase of hydrogen consumption at end of life. • Variability of powertrain optimal power allocation decreases toward the end of life.