Monte Carlo Uncertainty Analysis Research Articles

SThM-based local thermomechanical analysis: Measurement intercomparison and uncertainty analysis

We assess Scanning Thermal Microscopy (SThM) with a self-heated doped silicon nanoprobe as a method for determining the local phase transition temperature of polymeric materials by means of nano-thermomechanical analysis (nano-TA). Reference semi-crystalline samples and amorphous test samples, characterized first using differential scanning calorimetry (DSC), are studied by nano-TA in the temperature range 50–250 °C. The repeatability, the reproducibility and the reliability of nano-TA are evaluated by three laboratories by applying the same calibration protocol prior to and after the measurements. The calibration of the probe temperature scale and the variability of the sample thermomechanical response are validated by Monte Carlo uncertainty analysis, resulting in a calculated uncertainty between 3 and 5 K. The SThM probe temperature data represented as a function of DSC-measured phase-transition temperatures of the semi-crystalline samples rule out the possibility of a quadratic fit and call for a linear calibration in absence of additional information. The maximum deviation obtained between SThM and DSC temperatures with such linear calibration reaches ± 30 K for melting temperatures and 50 K for glass transition temperatures.

Life cycle assessment of refractory waste management in a Spanish steel works

A life cycle assessment (LCA) is conducted on the current refractory waste management practices in a steel works in Spain producing around 6,000 tonnes of refractory waste in 2018. Management practices included direct reuse of spent magnesia-carbon (MgO-C) bricks, recycling of MgO-C bricks and high-alumina refractories by an external contractor and landfilling of monolithics and isostatic refractories, for which there were yet no established valorization routes. This current situation was compared to a hypothetical scenario where all refractories were disposed in landfill. The LCA included waste management activities (collection, transport, treatment) as well as production of substituted primary materials (dead-burned magnesia, calcined bauxite and new MgO-C bricks) produced in China and Germany. Results are discussed for four indicators: greenhouse-gas emissions, Non-renewable energy demand, land use and water use. Overall, per tonne of produced waste, the current management leads to a reduction or saving of 0.54 tonnes CO2-eq, 3 GJ primary energy from non-renewable sources, the occupation of 10 m2 of land during one year and the abstraction of 5 m3 freshwater. The results also show that reuse, in the particular case of spent MgO-C bricks, leads to higher benefits than recycling by At least a factor four. The robustness of these results is confirmed through the application of a Monte Carlo uncertainty analysis, as well as a sensitivity analysis focusing on a shift from coal to natural gas in the Chinese refractory industry.

Cross-interface transfer of polycyclic aromatic hydrocarbons (PAHs) in a shallow urban lake in Shanghai, China based on the fugacity model

Shallow urban lakes are important urban ecosystems; however, these systems are subject to severe polycyclic aromatic hydrocarbons (PAHs) contamination. An understanding of the distribution and dynamics of PAHs in lakes is required to restore the functions of lake ecosystems and to ensure the ecological security of urban water sources. The Quantitative Water Air Sediment Interaction (QWASI) model and partition coefficient and fugacity fraction methods were applied to estimate the multimedia transfers of PAHs in Dianshan Lake, a typical shallow lake in Shanghai, China. In addition, some new concepts and methods related to PAH transfers were introduced. The results showed that while the gas-solid partition in the area remained in non-equilibrium, the influence of pollution sources tended to weaken. Atmospheric advection was the main source of PAHs to the lake, and a portion of the net loss of advection was transformed into total flux of cross-interface transfers, in which transport fluxes from air to water and from water to sediment were dominant, with a significant correlation between the two types of transfer. The large resuspension of high molecular weight (HMW)-PAHs occurred, possibly related to frequent hydrodynamic disturbances. Furthermore, this study explored the distribution of PAHs among different compartments and the seasonal variation of multimedia transfers. Sensitivity analysis showed that the model is remarkably sensitive to four parameters including temperature and advection. Monte Carlo uncertainty analysis verified that the simulation results were stable and reliable. The results can provide a theoretical basis for the monitoring and control of shallow lake pollution.

Uncertainties of instantaneous influent flow predictions by intelligence models hybridized with multi-objective shark smell optimization algorithm

Reliable prediction of influent time series has demonstrated importance in high-efficiency performance of wastewater treatment and reuse plants. Computational models are powerful tools that has been used for predicting influent time series. But, one of the major drawbacks of developed models is uncertainty analysis in instantaneous and multi-time step prediction of influent time series. For this purpose, a Multi-objective Shark Smell Optimization (MOSSO) algorithm is hybridized with multilayer perceptron (MLP) neural network, radial basis function (RBF) neural networks, and support vector machine (SVM) to optimize the models. These hybrid models of MLP-MOSSO, RBF-MOSSO, and SVM-MOSSO are used for predicting different prediction horizons of immediate, short-term, and long-term of influent time series. Accordingly, there are three objectives in MOSSO: (1) minimize mean absolute error (MAE) in different input vectors for best selection lag time influent time series, (2) achieve the optimal architecture of forecasting models by minimization root mean square error (RMSE) for model parameters, and (3) select accurate activate functions or kernel function of models for finding high accuracy of estimating models. The studied hybrid multi-objective models are modified using Taguchi method for determination of Pareto optimal solution sets. The proposed hybrid models and standalone models of MLP, RBF, and SVM are evaluated using Monte-Carlo uncertainty analysis, common evaluation criteria, and Taylor diagram in evaluation and reliability assessments. The results demonstrated that MLP-MOSSO model produce better results than RBF-MOSSO, SVM-MOSSO, and standalone models in all prediction horizons (S1I4: immediate-Q(t-5min), Q(t-10min); S2I16: short term-Q(t-12hr), Q(t-24hr); S3I15: long term-Q(t-2day), Q(t-4day)). The MLP-MOSSO model for immediate prediction horizon (Q(t-5min), Q(t-10min)) with one hidden layer and five neurons in hidden layer, correlation coefficient (R2) of 0.95, root mean square error (RMSE) of 1.1, Nash-Sutcliffe efficiency (NSE) of 0.91, RMSE-observations standard deviation ratio (RSR) of 0.14, and percent bias (PBIAS) of 12 for testing phase was chosen as the final and best model for predicting influent time series. Furthermore, it was found that all models have some uncertainties, but the uncertainties in the MLP-MOSSO model with immediate prediction horizon was less than other models for predicting influent time series. Finally based on the optimized results, an explicit equation is derived for the best MLP-MOSSO model to predict multi-step influent time series.

Development of a coupled aerosol lidar data quality assurance and control scheme with Monte Carlo analysis and bilateral filtering

Mie-scatter lidar can capture the vertical distribution of aerosols, and a high degree of quantification of lidar data would be capable of coupling with a chemical transport model (CTM). Thus, we develop a data quality assurance and control scheme for aerosol lidar (TRANSFER) that mainly includes a Monte Carlo uncertainty analysis (MCA) and bilateral filtering (BF). The AErosol RObotic NETwork (AERONET) aerosol optical depth (AOD) is utilized as the ground truth to evaluate the validity of TRANSFER, and the result exhibits a sharp 41% (0.36) decrease in root mean square error (RMSE), elucidating an acceptable overall performance of TRANSFER. The maximum removal of uncertainties appears in MCA with an RMSE of 0.08 km−1, followed by denoising (DN) with 50% of MCA in RMSE. BF can smooth interior data without destroying the edge of the structure. The most noteworthy correction occurs in summer with an RMSE of 0.15 km−1 and Pearson correlation coefficient of 0.8, and the least correction occurs in winter with values of 0.07 km−1 and 0.93, respectively. Overestimations of raw data are mostly identified, and representative values occur with weak southerly winds, low visibility, high relative humidity (RH) and high concentrations of both ground fine particulate matter (PM2.5) and ozone. Apart from long-term variations, the intuitional variation in a typical overestimated pollution episode, especially represented by vertical profiles, shows a favorable performance of TRANSFER during stages of transport and local accumulation, as verified by backward trajectories. Few underestimation cases are mainly attributed to BF smoothing data with a sudden decrease. The main limitation of TRANSFER is the zigzag profiles found in a few cases with very small extinction coefficients. As a supplement to the research community of aerosol lidar and an exploration under complicated pollution in China, TRANSFER can aid in the preprocessing of lidar data-powered applications.

Comparison of anaerobic digestion technologies: An Italian case study

The present study focused on a comparison of two technologies applied in the mesophilic anaerobic digestion, namely a conventional wet one and a dry batch one. The considered substrate was the source sorted organic fraction of municipal solid waste (SS-OFMSW) and the input flow rate, for the study case, was 35,000 Mg/year. The analysed systems included the pre-treatment of the SS-OFMSW, anaerobic digestion, upgrading of biogas to biomethane and aerobic post-treatment for the purpose of obtaining compost. The comparison was made by the calculation of three indicators: net present value, total primary energy and CO2 equivalent emissions, with the aim of providing elements for choosing the most appropriate technology for the specific case. The results obtained demonstrated the finding of worse values by the total primary energy indicator for the dry batch technology, providing a saving of approx. 21% lower compared to the wet one. In terms of CO2 equivalent emissions, the dry batch anaerobic digestion technology provided a better indicator than the wet anaerobic digestion system. Sensitivity analysis revealed the finding of an opposite result only when high specific gas production values were assumed for wet anaerobic digestion, and low specific gas production values for the dry batch technology.From an economic perspective, the results indicated a preference for the dry batch technology due to a higher net present value and a shorter period of return of the investment. This finding was also confirmed by a Monte Carlo uncertainty analysis, showing how the dry batch system featured a 90% of possibility of achieving a higher economically sustainability versus the wet technology.

Intelligent Approach to Robust Design Optimization of a Rotor System due to Its Support Stiffness Uncertainty

In this paper, an intelligent robust design approach combined with different techniques such as polynomial chaos expansion (PCE), radial basis function (RBF) neural network, and evolutionary algorithms is presented with a focus on the optimization of the dynamic response of a rotor system considering support stiffness uncertainty. In the proposed method, the PCE method instead of the traditional Monte Carlo uncertainty analysis is applied to analyze the uncertain propagation of system performance. The RBF network is introduced to establish the approximate models of the objective and constraint functions. Taking the low-pressure rotor of a gas turbine with support stiffness uncertainty as an example, the optimization model is established with the mean and variance of unbalanced response of the rotor system at different operating speeds as the objective function, and the maximum unbalance response is less than the upper limit as the constraint function. The polynomial chaos expansion is generated to facilitate a rapid analysis of robustness in the presence of support stiffness uncertainties that is defined in terms of tolerance with good accuracy. The optimal Hypercubus are used as experimental plans for building RBF approximation models of the objective and constraint functions. Finally, the robust solutions are obtained with the multiobject optimization algorithm NSGA-II. Monte Caro simulation analysis demonstrates that the qualified rate of maximum vibration responses of the low-pressure rotor system can be increased from 83.6% to over 99%. This approach to robust design optimization is shown to lead to designs that significantly decrease vibration responses of the rotor system and improved system performance with reduced sensitivity to support stiffness uncertainty.

Characterization of heavy metal pollution in an anthropogenically and geologically influenced semi-arid region of east India and assessment of ecological and human health risks

The present study evaluated the heavy metal contamination in groundwater and associated ecological and human health risks of a geologically and anthropogenically diverse semi-arid region of Birbhum district, India. For a reliable evaluation, concentrations of nine heavy metals in 680 groundwater samples (N = 680) which were collected during premonsoon and postmonsoon seasons of consecutive two years from 170 wells were measured. The human health risk assessment using the USEPA model which is based on single value for each parameter may inherit certain inaccuracy and uncertainties in the evaluation. Unlike earlier studies, a higher degree of accuracy in carcinogenic and noncarcinogenic health risk assessments was achieved through Monte Carlo simulations, sensitivity analysis and uncertainty analysis. The study revealed the occurrence of the target heavy metals in groundwater with mean dominance order of Fe > Zn > Sr > Mn > Cr > Pb > Ni > Cu > Cd where mean concentrations of the carcinogens, Pb and Fe exceeded their maximum permissible limits. The water quality status evaluated using the modified heavy metal pollution index, Nemerow index and Heavy metal evaluation index methods resulted in medium to high heavy metal contamination in groundwater within a large portion of the study area which indicated its unsuitability for drinking purpose. The study suspects a moderate to very high risk for the groundwater dependent ecosystems in major part of the study area. The study further revealed cancer risks, ranging from high to very high within the residents due to accumulative exposure of the carcinogenic heavy metals in groundwater through ingestion and dermal contact. Minor populations of the study area were found to be more vulnerable to the carcinogenic and noncarcinogenic diseases than teenagers and adults, mainly through oral exposure. The study recommends the residents to consume treated groundwater since the primary route of heavy metal exposure was identified to be the ingestion route.

Contamination zoning and health risk assessment of trace elements in groundwater through geostatistical modelling.

Trace elements (TEs) concentration in groundwater is a key factor for health risk assessment (HRA). To achieve high level of accuracy in HRA, the present study performed Monte Carlo simulations, sensitivity analysis and uncertainty analysis to a total of 184 (N=184) groundwater samples, collected during December 2016 from Birbhum district. TEs in samples were detected by anodic stripping voltammetry (ASV). The mean concentration of TEs were found as Fe (855.88μg/L)> Zn (204.0μg/L)> Cu(84.9μg/L)> Ni(47.31μg/L)> Pb(14.43μg/L)> Co(10.58μg/L)> Cd (7.88μg/L). It indicated serious contamination by Fe, Cd. Pb and Ni according BIS, 2012. Pollution indicators such as heavy metal pollution index (HPI) revealed that study area is heavily contaminated by these TEs. Incremental lifetime cancer risk (ILCR) value of TEs showed that Cd is the main offender for cancer risk. Average value of total hazard index (THI), was found to be 2.48. THI through ingestion pathways was found to be more risky than dermal contacts accounting for 88% and 12% health hazard respectively. The sensitivity analysis indicated ingestion rate, exposure time, and TEs concentration were the most influential parameters for all groundwater associated health hazards. The TEs affected areas were mapped through Empirical Bayesian Kriging geostatistical model and health risk prone zones were projected. The study demonstrated that Monte Carlo simulation and EBK can provide better accuracy in health risks prediction and spatial distribution analysis of contaminants in any geographical area. The TEs and their hazard zonation mapping with geostatistical modelling will be helpful for the policy makers and researchers to improve groundwater quality management practices.

Evaluation of the effective parameters on energy losses of rectangular and circular culverts via kernel-based approaches

Abstract Energy dissipation in culverts is a complex phenomenon due to the nonlinearity and uncertainties of the process. In the current study, the capability of Gaussian process regression (GPR) and support vector machine (SVM) as kernel-based approaches and the gene expression programming (GEP) method was assessed in predicting energy losses in culverts. Two types of bend loss in rectangular culverts and entrance loss in circular culverts with different inlet end treatments were considered. Various input combinations were developed and tested using experimental data. The OAT (one-at-a-time), factorial sensitivity analysis and Monte Carlo uncertainty analysis were used to select the effective parameters in modeling. The results of performance criteria proved the capability of the applied methods (i.e. high correlation coefficient (R) and determination coefficient (DC) and low root mean square error (RSME)). For rectangular culverts, the model with parameters Fr (Froude number) and θ (bend angle), and for circular culverts, the model with parameters Fr and Hw/D (depth ratio), were the superior models. It showed that using the bend downstream Froude number caused an increment in model efficiency. Among the four end inlet treatments, mitered flush to 1.5:1 fill slope inlet yielded more accurate prediction. The sensitivity and uncertainty analysis showed that θ and Hw/D had the most significant impact on modeling, and Fr had the highest uncertainty.

Meta analysis of heavy metal pollution and sources in surface sediments of Lake Taihu, China

Heavy metal concentrations in Taihu Lake sediment from studies performed between 2000 and 2018 were analyzed and Monte Carlo uncertainty analysis of heavy metal geo-accumulation, potential ecological risk and toxicity data for Taihu Lake sediment was performed to allow heavy metal pollution of Taihu Lake sediment to be described clearly, objectively, and comprehensively. Five main conclusions were drawn. (1) Most attention should be paid to As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn pollution. (2) The geo-accumulation indices showed that Cd is the most important pollutant and that the probabilities of Taihu Lake sediment being moderately polluted, moderately–heavily polluted, and heavily polluted were found to be 53.6%, 34.9%, and 18.7%, respectively. (3) Cd is the main contributor to potential ecological risks and had cumulative low risk, moderate risk, and considerable risk probabilities of 63.0%, 27.0%, and 10.0%, respectively. (4) Toxicity unit evaluation results indicated that Pb is the main contributor of toxicity in Taihu Lake sediment and had cumulative low toxicity, moderate toxicity, and high toxicity probabilities of 53.0%, 36.8%, and 5.6%, respectively. (5) Positive matrix factorization model results indicated that industrial sources are the main suppliers of heavy metals to Taihu Lake sediment, contributing 64.9% of the heavy metals. The summarized results and conclusions will improve local government awareness of heavy metal pollution in Taihu Lake and will aid in the development of appropriate pollution control measures. The results will also provide reference data for future studies of heavy metal pollution in sediment from Taihu Lake and other lakes.

Mission Design and Performance Analysis of a Smallsat Aerocapture Flight Test

An Earth smallsat mission architecture is developed to demonstrate the feasibility of aerocapture. The mission concept proposes to transfer from a geosynchronous transfer orbit rideshare trajectory to a low Earth orbit. Single-event drag modulation is used as a straightforward means of achieving the control required during the maneuver. Low- and high-fidelity guidance algorithm choices are considered. Numeric trajectory simulations and Monte Carlo uncertainty analyses are performed to show the robustness of the system to day-of-flight environments and uncertainties. Related investigations are described for Mars mission concepts to show the relevance of the proposed flight test to potential future applications.

Exploring a multi-exposure-pathway approach to assess human health risk associated with groundwater fluoride exposure in the semi-arid region of east India

Human health risks associated with groundwater fluoride have been assessed using USEPA method in few parts of India, but those assessments were conducted based on a single value for each parameter, which may lead to certain inaccuracy and uncertainties in results. In this study, a higher degree of accuracy in health risk assessment was achieved through Monte Carlo simulations, sensitivity analysis and uncertainty analysis. As fluoride hazards to human health are chronic, a total of 4560 water samples (N = 4560) were collected during consecutive four seasons (2 pre-monsoon and 2 post-monsoon seasons; 1140 samples/season) from the entire Birbhum district, covering all the blocks and geological settings to obtain the spatiotemporal variation of fluoride level. The Empirical Bayesian Kriging geostatistical model was employed to determine fluoride endemic areas. Amongst all blocks, Nalhati-1 had exhibited the highest fluoride level (18.25 mg/L). The study revealed that most of the blocks are vulnerable to groundwater fluoride due to its occurrences in excess level. The average and 95th percentile values of total hazard index indicate that the infants and children populations of the district are more susceptible than the adults and teens. The sensitivity analysis revealed that water ingestion rate and fluoride concentration are the most influential parameters for higher risk of fluoride-related health hazards. Health risks were evaluated through ingestion and dermal exposure routes for infants, children, teens, and adult residents. The exposure was much higher through ingestion than dermal contacts. Ingestion of defluoridated water will reduce the health risks associated with groundwater fluoride exposure.

Dose-response Analysis in the Presence of Shared and Unshared Uncertainties of the Dose Values.

The dose values used in dose-response analyses are often the result of a computer model. Epistemic uncertainties of the model application make it necessary to perform an uncertainty analysis. Such uncertainties are model parameters, model formulations, and input data subject to either classical or Berkson additive or multiplicative measurement error. Epistemic uncertainties are often shared among the computed dose values of all individuals in a cohort or among groups thereof. The effect of these uncertainties on the estimate of the dose-response parameter in least-squares linear regression is difficult to judge. Additive classical error is known to bias the estimate towards lower values (attenuation). The method suggested in this paper is applicable in situations where any combination of uncertainties mentioned above is involved. All it requires is a random sample of dose vectors taken from their joint subjective probability distribution. Such a sample is the output of a Monte Carlo uncertainty analysis of the model application. The covariance matrix of the vectors is used in the computation of correction factors that are possibly true, given the dose vector used in the estimation of the dose-response parameter. The efficiency of the method is demonstrated with five cases. They differ by the combination of uncertainties involved in the uncertainty analysis of a small illustrative dose reconstruction model.

Monte Carlo uncertainty analysis of an ANN-based spectral analysis method

This work presents the uncertainty analysis of an artificial neural network (ANN)-based method, called multiharmonic ANN fitting method (MANNFM), which is able to obtain, at a metrological level, the spectrum of asynchronously sampled periodical signals. For sinusoidal and harmonic content signals, jitter and quantization noise contributions to uncertainty are considered in order to obtain amplitude and phase uncertainties using Monte Carlo method. The analysis performed identifies also both contributions to uncertainty for different parameters laboratory configurations. The analysis is performed simultaneously with our method and two others: discrete Fourier transform (DFT), for synchronously sampled signals, and multiharmonic sine-fitting method (MSFM), for asynchronously sampled signals, in order to compare them in terms of uncertainty. Regarding asynchronous methods, results show that MANNFM provides the same uncertainties than MSFM, with the advantage of a simpler implementation. Regarding asynchronous and synchronous methods comparison, results for sinusoidal signals show that MANNFM has the same uncertainty as DFT for amplitude and higher uncertainty for phase values; for signals with harmonic content, amplitude conclusions maintain but, regarding phase, both MANNFM and DFT uncertainties become closer as the frequency increases, which implies, in fact, that when synchronous sampling is not possible, spectrum analysis can be performed with asynchronous methods without incurring in uncertainty increases.

Alternative Adjoint Boltzmann Transport Operators in Alternative Hilbert Spaces in Spherical Geometry: Theory and Paradigm Applications

The solution of the customary adjoint Boltzmann equation for linear transport of particles and radiation, referred to as the “adjoint flux,” plays a prominent role in reactor physics, shielding, control, and optimization as a weighting function for cross-section processing, optimization, Monte Carlo acceleration procedures, and sensitivity and uncertainty analyses. All of the textbooks and scientific works published thus far use the same procedure to derive “the” adjoint Boltzmann operator, thereby conveying inadvertently the misleading impression that this traditional procedure is the only way to obtain “the” adjoint Boltzmann operator, and that the form of “the” adjoint operator thus derived is universally unique. None of the works published in the literature thus far touches on the fact that the customary textbook-form of the adjoint Boltzmann operator is actually derived in a particular Hilbert space, which is endowed with a specific inner product that is based on integrating spatially over the domain’s spatial volume such that Gauss’ divergence theorem holds. As this work will show, however, the Hilbert space that has been implicitly used in all of the published works thus far is not the only possible Hilbert space for deriving operators that are adjoint, in the respective Hilbert space, to the forward Boltzmann operator. Alternative Hilbert spaces may be used just as legitimately, and may actually be more suitable than the customary Hilbert space for computing adjoint functions to be used in inner products involving various forward and/or adjoint fluxes and forward and/or adjoint source terms.By presenting paradigm illustrative examples in three-dimensional spherical coordinates, this work shows that although a unique form of the adjoint Boltzmann operator is obtained for each Hilbert space in which the respective adjoint operator is constructed, distinct Hilbert spaces will produce distinct adjoint Boltzmann operators accompanied by distinct forms of the corresponding bilinear concomitants on the respective spatial domain’s boundary. The fundamental practical reason for using alternative Hilbert spaces is to obtain alternative adjoint functions and/or Green’s functions that may be less singular than the customary adjoint function and/or Green’s functions (in the customary Hilbert space) and would consequently be computable numerically. As this work shows, such situations arise when attempting to use the adjoint sensitivity analysis methodology in the conventional Hilbert space for computing sensitivities to cross sections, isotopic number densities, etc., of responses of flux and/or power detectors placed near or at the center of the spherical coordinates. In such sensitivity analysis problems, the singularities of the conventional adjoint Boltzmann equation in the conventional Hilbert space may preclude its use, but the requisite sensitivities can nevertheless be computed efficiently using an alternative adjoint Boltzmann equation in an alternative Hilbert space. The consequences of this powerful breakthrough new concept of using alternative adjoint operators in alternative Hilbert spaces are highlighted by presenting a paradigm benchmark problem that admits a closed-form exact solution. This benchmark problem shows that the customary adjoint equation becomes singular at the sphere’s center, so the conventional adjoint flux is therefore noncomputable there, but the alternative adjoint transport equation in a judiciously chosen alternative Hilbert space is everywhere nonsingular and can therefore be used to compute the requisite sensitivities. By indicating the path for using alternative Hilbert spaces, this work opens new conceptual procedures for solving problems that have hitherto been unsolvable in the framework of the conventional adjoint particle transport equation.

Integrated Markov chains and uncertainty analysis techniques to more accurately forecast floods using satellite signals

Utilizing the readily available, inexpensive, remotely-sensed satellite data products in combination with Markov Chain methods for estimating water levels and discharge anywhere along the vast river networks around the world is one of the most interesting and promising fields in hydrology. This study presents two new extensions of Markov Chain (MC) methods, namely the Online-Markov Chains (O-MC) and Extreme Online-Markov Chains (EO-MC) methods to improve the prediction accuracy. The O-MC method has the advantage of the online implementation of the correct variable states, and the EO-MC method has the advantage of online updating the Markov Matrix (MM) along with the online implementation of the correct variable states. The new O-MC and the EO-MC methods were evaluated using short-term satellites signal predictions for six different case study rivers. The Monte Carlo uncertainty analysis was used to measure the reliability of the new MC-based methods. Each model was developed 1000 times to calculate two indices, namely the 95 Percent Predicted Uncertainties (95PPU) and average distance factor (d-factor). The performances of MC and two extensions of O-MC and EO-MC were also examined for cases where we lack the training data. The Training Percent (TrPr) of the entire dataset gradually decreased from 90% to 1%, and the performance of the models in producing accurate future signals in the non-observed dataset is calculated. The Input Variable Imitation (IVI) problem was considered for the MC-based methods, and the results were compared with the Linear Regression (LR), Multi-Layer Perceptron (MLP), Extreme Learning Machine (ELM), and Radial Basis Function (RBF) regression methods. The results showed that the performance of EO-MC and O-MC are better than the simple MC method. In addition, it is concluded that EO-MC and O-MC have very similar performance in the uncertainty analysis and both methods are robust techniques. The main advantage of EO-MC compared with the O-MC method is highlighted when the number of training samples are very low. Finally, considering the IVI problem, the new O-MC and EO-MC methods significantly outperformed the LR, MLP, ELM, and RBF methods.

Phosphorus loading from onsite wastewater systems to a lake (at long time scales)

Schellenger FL, Hellweger FL. Phosphorus loading from onsite wastewater systems to a lake (at long time scales). Lake Reserv Manage. 35:90–101.Phosphorus from onsite wastewater systems (OWS, septic systems, cesspools) enters the groundwater and may migrate to and contribute to eutrophication of down-gradient lakes. Since phosphorus sorbs relatively strongly to soil solids, this source may be assumed negligible in lake management. However, phosphorus adsorption is reversible, and given enough time, some phosphorus will eventually reach the lake. We investigated the magnitude and timing of phosphorus loading to a case study lake in southeastern Massachusetts, using a model that accounts for cesspools and septic systems installed over a 270-yr time frame, the 1994 laundry detergent phosphorus ban, loss of phosphorus in the vadose zone, and transport and adsorption/desorption (retardation) in the surficial aquifer. We parameterized the model using literature and local information and applied it to all the OWS in a the lake watershed to estimate annual wastewater total phosphorus loading over a long time. Model results were compared to previous phosphorus budgets based on limited field measurements. The model predicts that groundwater transport of phosphorus from OWS by itself can account for the current eutrophic state of the lake and will continue to increase. Our results show that the OWS source can be significant and should not be ignored. We performed parameter sensitivity and Monte Carlo uncertainty analyses and illustrated the effects of removal in the vadose zone and travel time in the aquifer. Our model suggests that past management actions (cesspool to septic system conversion, laundry detergent phosphorus ban) effectively reduced the 2018 load by 31.4%. A centralized wastewater treatment facility would eliminate the OWS loading, but the half-life due to the existing phosphorus reservoir in the aquifer is long (20 yr).

A life cycle assessment-based multi-objective optimization of the purchased, solar, and wind energy for the grocery, perishables, and general merchandise multi-facility distribution center network

Walmart Inc., the U.S. and world’s largest grocery retailer, owns a perishables, grocery, and general merchandise distribution center network, which stores and distributes refrigerated and non-refrigerated food. Finding cost-effective strategies to implement solar and wind-powered electricity in their distribution centers was the central objective of this research. The study analyzed the tradeoffs and effects on costs and climate change impact related to the increase of renewable energy use in the distribution centers. The research combined the life cycle assessment and quantitative methods including the Monte Carlo uncertainty analysis and the multi-objective optimization. A life cycle assessment-based multi-objective optimization model was built to find cost-effective strategies to minimize fossil energy use and mitigate the impact of the Walmart Inc. distribution center network on climate change. The bi-objective and the triple-objective optimization included a number of combinations of minimal costs, non-renewable fossil energy use, and climate change impact criteria. The results of the multi-objective optimizations were Pareto-optimal solutions obtained by weighing the importance of chosen criteria from the baseline to the zero energy scenarios. A selection of the Pareto-optimal solutions included the good, the better, and the zero energy building scenarios. A better building was a Pareto-optimal set of buildings, which demonstrated superiority from the life cycle assessment perspective. The superiority of Pareto-optimal solutions was evaluated using the Monte Carlo pairwise comparison. The good distribution centers were characterized by the Pareto-optimal solutions between the baseline and the better distribution centers. Finally, the zero energy general merchandise distribution centers were mostly the Pareto-optimal solutions with a 100% share of solar energy. For the zero energy grocery and perishables distribution centers the solutions were a combination of solar and supplemental wind energy because refrigerated warehouses are more energy intensive. The study provided the benchmark results that may improve distribution centers and other buildings and a framework to test environmental and renewable energy policies in buildings.

Validation of hybrid angular spectrum acoustic and thermal modelling in phantoms

In focused ultrasound (FUS) thermal ablation of diseased tissue, acoustic beam and thermal simulations enable treatment planning and optimization. In this study, a treatment-planning methodology that uses the hybrid angular spectrum (HAS) method and the Pennes’ bioheat equation (PBHE) is experimentally validated in homogeneous tissue-mimicking phantoms. Simulated three-dimensional temperature profiles are compared to volumetric MR thermometry imaging (MRTI) of FUS sonications in the phantoms, whose acoustic and thermal properties are independently measured. Additionally, Monte Carlo (MC) uncertainty analysis is performed to quantify the effect of tissue property uncertainties on simulation results. The mean error between simulated and experimental spatiotemporal peak temperature rise was +0.33°C (+6.9%). Despite this error, the experimental temperature rise fell within the expected uncertainty of the simulation, as determined by the MC analysis. The average errors of the simulated transverse and longitudinal full width half maximum (FWHM) of the profiles were –1.9% and 7.5%, respectively. A linear regression and local sensitivity analysis revealed that simulated temperature amplitude is more sensitive to uncertainties in simulation inputs than in the profile width and shape. Acoustic power, acoustic attenuation and thermal conductivity had the greatest impact on peak temperature rise uncertainty; thermal conductivity and volumetric heat capacity had the greatest impact on FWHM uncertainty. This study validates that using the HAS and PBHE method can adequately predict temperature profiles from single sonications in homogeneous media. Further, it informs the need to accurately measure or predict patient-specific properties for improved treatment planning of ablative FUS surgeries.