Various Sources Of Uncertainty Research Articles

Distributed economic model predictive control for the joint energy dispatch of wind farms and run-of-the-river hydropower plants

This study addresses the energy dispatch problem of a virtual power plant (VPP) acting as a price-tacker in the day-ahead electricity market. The VPP comprises wind farms and a cascade of run-of-the-river hydropower plants. Even if the storage capacity of the cascade is limited, it can still be exploited to compensate the variability of wind. This implies dispatching the water reservoirs near to real-time, while accounting for complex constraints and various sources of uncertainty. To this aim, we present a control strategy based on economic model predictive control (MPC), which is then decomposed using the auxiliary problem principle. As a distinctive feature, the proposed algorithm is fully-distributed, i.e. no central coordinator is required. Compared to centralized MPC, the distributed algorithm brings a ∼10% reduction in the average execution time of the controller. Moreover, the joint operation of hydropower and wind is shown to enhance the economic value of both assets.

Balanced Lattice Designs under Uncertain Environment

AbstractBalanced lattice designs are vital in numerous fields, especially in experimental design, where controlling variability among experimental units is crucial. In practical experiments, various sources of uncertainty can lead to ambiguous, vague, and imprecise data, complicating the analysis process. To address these indeterminacies, a novel approach using neutrosophic analysis within a balanced lattice design framework is proposed, termed the neutrosophic balanced lattice design (NBLD). This innovative method employs neutrosophic statistics to derive mathematical neutrosophic sums of squares and construct a neutrosophic analysis of variance (NANOVA) table. The effectiveness of the proposed NBLD is demonstrated through a numerical example, showing that it outperforms traditional methods in handling uncertainty.

Safe Robust Adaptive Motion Control for Underactuated Marine Robots.

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources of uncertainties, ensuring the system's safe and robust behavior holds paramount importance in the control literature. To address this concern, this paper introduces a control strategy designed to ensure robustness at both the kinematic and dynamic levels. By emphasizing the compensation for the system uncertainties, the design integrates a straightforward fuzzy system structure. To further ensure the system's safety, a funnel surface is defined, followed by the design of a suitable nonlinear sliding surface as a function of the funnel and tracking error. Using Lyapunov theory, the study formally establishes the Semi-globally Practically Finite-time Stability of the closed-loop system, validated through simulations conducted on underactuated marine robots.

Fuzzy Logic-Based Fragility Curve Development for Steel Moment-Resisting Frames Considering Uncertainties in Seismic Response

In this paper, a comprehensive range of uncertainties is considered to assess the seismic abilities of a moment-resisting system. To incorporate the parameter of construction quality, which has a descriptive nature, a suitable fuzzy logic engine has been developed. This engine, for the first time, addresses the quantitative assessment of construction quality parameters based on linguistic variables, including map accuracy, worker skills, material quality, and site supervision conditions. Instead of using random selection, a self-organizing map (SOM) algorithm is employed to carefully select strong ground motion records, reducing time costs. By applying incremental dynamic analysis (IDA) results, analytical equations are derived for the response surface method. These equations determine the collapse fragility’s mean and standard deviation. The material quality is modeled using the fuzzy inference engine, with the coefficient of logarithm response surface. Collapse fragility curves are created by taking into account many of their material quality values and utilizing the fuzzy model to estimate the modeling parameter based on the logarithm regression coefficients. These curves take into consideration various sources of uncertainty. In countries with inadequate material quality control, it is important to take cognitive uncertainty into account when developing fragility curves. This will help improve the overall risk management strategy.

Reconstruction of environmental site conditions by the integration of SCADA and reanalysis data

For the operational optimization of wind farms, AEP estimation and other tasks, high quality data of environmental conditions at the site are necessary. However, such data is often not available or has insufficient quality. This work tries to fill this gap, by integrating two data sources: the (usually available) operational data from the SCADA (Supervisory Control and Data Acquisition) system, and reanalysis data. SCADA data streams contain measurements from each wind turbine in the farm, but they are affected by various sources of uncertainty (including local flow effects, miscalibration, etc.), and might contain gaps. Meteorological reanalysis datasets can be used to fill gaps and complement SCADA data. However, modelled data can contain a wide range of biases and errors, due to limited model fidelity, coarse spatial and temporal resolution, inaccuracies in the input data feeding the model, etc. This study considers various methods to extract and merge wind speed and direction information from these diverse data sources. The analysis is based on field data measured at two experimental test sites, an offshore site equipped with 111 multi-MW turbines and a lidar buoy, and an onshore site equipped with 14 multi-MW wind turbines and a lidar. The methods are evaluated in the spectral and temporal domains by comparing the reconstructed wind characteristics with measurements from the lidars.

A real‐time decision support system to improve operations in electric bus networks

ABSTRACTElectrifying transit bus networks (TBNs) has recently become a challenging problem that many public transport operators around the world are facing. Due to the limited driving range of electric buses, electric TBNs are more sensitive to operational delays and uncertainties. Moreover, the impact on sustainability is most profound when the buses are powered by renewable energy resources, which are often subject to intermittency and uncertainty. In this work, we tackle the complicated problem of planning charging schedules amid these various sources of uncertainty. We develop a real‐time decision support system that uses real‐time data, predictions, and mathematical optimization to update the charging schedules and mitigate the impact of operational uncertainties. Our results show that the online strategy can maintain higher reliability and renewable energy utilization levels compared to other charging strategies. The study has been carried out in cooperation with the public transport operator in Rotterdam in the Netherlands to assist them in their TBN transition process.

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models.

Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.

Probabilistic three-dimensional finite element analysis of geosynthetic-reinforced pile-supported embankments

The current study focuses on a probabilistic three-dimensional finite element analysis of geosynthetic-reinforced pile-supported embankments. The probabilistic analysis utilizes a finite element technique and was executed using Python scripting in Abaqus. A deterministic model is established for both unreinforced and geosynthetic-reinforced pile-supported embankments, employing a basic linear elastic perfectly plastic Mohr-Coulomb constitutive model. This numerical model adopted a simplified methodology that eliminates the need to simulate actual piles and subsoil. The results are validated across different embankment heights and mesh sizes. Based on results from Monte-Carlo simulations, we aimed to show how results can vary even by considering only one random variable. Probability density functions for various parameters, including pile efficacy, maximum differential settlement, and maximum strain in the reinforcement, are shown. The findings illustrate the ability of probabilistic approaches to predict the most likely occurrences for different governing parameters of geosynthetic-reinforced pile-supported embankments. They also highlight the potential of probabilistic approaches to expand further into a full-scale comprehensive study which can offer deeper insights into the stability of these embankments considering various sources of uncertainties.

Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks

Advanced Air Mobility (AAM) introduces a new, efficient mode of transportation with the use of vehicle autonomy and electrified aircraft to provide increasingly autonomous transportation between previously underserved markets. Safe and efficient navigation of low altitude aircraft through highly dense environments requires the integration of a multitude of complex observations, such as surveillance, knowledge of vehicle dynamics, and weather. The processing and reasoning on these observations pose challenges due to the various sources of uncertainty in the information while ensuring cooperation with a variable number of aircraft in the airspace. These challenges coupled with the requirement to make safety-critical decisions in real-time rule out the use of conventional separation assurance techniques. We present a decentralized reinforcement learning framework to provide autonomous self-separation capabilities within AAM corridors with the use of speed and vertical maneuvers. The problem is formulated as a Markov Decision Process and solved by developing a novel extension to the sample-efficient, off-policy soft actor-critic (SAC) algorithm. We introduce the use of attention networks for variable-length observation processing and a distributed computing architecture to achieve high training sample throughput as compared to existing approaches. A comprehensive numerical study shows that the proposed framework can ensure safe and efficient separation of aircraft in high density, dynamic environments with various sources of uncertainty.

The JCSS – A major contributor to structural safety through half a century

Society expects buildings and structures in the built environment to possess an adequate level of safety. This holds true throughout the whole life cycle of the structure, from the erection stage until the end of life and also during its eventual demolition. In order to achieve this objective, engineers need to have knowledge of structural materials, the use of the structure and the various interactions with the environment. Fundamentally, this knowledge is rarely available in a deterministic form and decisions have to be made taking into account the various sources of uncertainty that affect the structure’s behaviour. The need to answer the question of how to account for such uncertainties and their effects on structural safety was one of the main reasons for the creation of the Joint Committee on Structural Safety over half a century ago. This paper presents an historical account of how it started, the development of the philosophical background and objectives over the course of time, and its main achievements and key publications. The paper closes with a short reflection on what has been achieved and what is required in the future.

Influence of Cross-Correlation on the Modelled Uncertainty in Stress–Strain Behavior of Soft Clays

Reliability-based design is increasingly being applied to geotechnical problems because it allows the robust consideration of various sources of uncertainty, such as the inherent variability of soil properties. Some soil properties, however, are mutually dependent, and ignoring this cross-correlation may lead to biased estimates of the probability of unsatisfactory performance. Hence, in this study, the Gaussian copula was used to evaluate how the applied cross-correlation or independence between the compressibility properties affects the uncertainty in the stress–strain response of two marine soft clays. Two settlement calculation methods were considered: the compression index and Janbu tangential stiffness methods. The correlation coefficients were defined from the site-specific oedometer data at two extensively studied clay sites, and from a database. The simulated oedometer curves were compared to the observed variability in the site-specific data. The settlements in the clay sublayers were then computed, and different cases were compared by means of boxplots. It is concluded that the Janbu method leads to a significant overestimation of uncertainty in settlement if the cross-correlation between the compressibility parameters is ignored. On contrary, the compression index method seems less sensitive to the assumed correlation structure, and as such, the parameters can be treated as independent in most cases.

Health effects associated with chewing tobacco: a Burden of Proof study.

Chewing tobacco use poses serious health risks; yet it has not received as much attention as other tobacco-related products. This study synthesizes existing evidence regarding the health impacts of chewing tobacco while accounting for various sources of uncertainty. We conducteda systematic review and meta-analysis of chewing tobacco and seven health outcomes, drawing on 103 studies published from 1970 to 2023. We use a Burden of Proof meta-analysis to generate conservative risk estimates and find weak-to-moderate evidence that tobacco chewers have an increased risk of stroke, lip and oral cavity cancer, esophageal cancer, nasopharynx cancer, other pharynx cancer, and laryngeal cancer. We additionally find insufficient evidence of an association between chewing tobacco and ischemic heart disease. Our findings highlight a need for policy makers, researchers, and communities at risk to devote greater attention to chewing tobacco by both advancing tobacco control efforts and investing in strengthening the existing evidence base.

COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition.

Automatically recognising apparent emotions from face and voice is hard, in part because of various sources of uncertainty, including in the input data and the labels used in a machine learning framework. This paper introduces an uncertainty-aware multimodal fusion approach that quantifies modality-wise aleatoric or data uncertainty towards emotion prediction. We propose a novel fusion framework, in which latent distributions over unimodal temporal context are learned by constraining their variance. These variance constraints, Calibration and Ordinal Ranking, are designed such that the variance estimated for a modality can represent how informative the temporal context of that modality is w.r.t. emotion recognition. When well-calibrated, modality-wise uncertainty scores indicate how much their corresponding predictions are likely to differ from the ground truth labels. Well-ranked uncertainty scores allow the ordinal ranking of different frames across different modalities. To jointly impose both these constraints, we propose a softmax distributional matching loss. Our evaluation on AVEC 2019 CES, CMU-MOSEI, and IEMOCAP datasets shows that the proposed multimodal fusion method not only improves the generalisation performance of emotion recognition models and their predictive uncertainty estimates, but also makes the models robust to novel noise patterns encountered at test time.

The Influence of Future Changes in Tidal Range, Storm Surge, and Mean Sea Level on the Emergence of Chronic Flooding

AbstractSea‐level rise is leading to increasingly frequent coastal floods globally. Recent research shows that changes in tidal properties and storm surge magnitudes can further exacerbate sea‐level rise‐related increases in flood frequencies. However, such non‐stationarity in tide and storm surge statistics are largely neglected in existing coastal flood projection methodologies. Here we develop a framework to explore the effect that different realizations of various sources of uncertainty have on projections of coastal flood frequencies, including changes in tidal range and storminess. Our projection methodology captures how observed flood rates depend on how storm surges coincide with tidal extremes. We show that higher flood rates and earlier emergence of chronic flooding are associated with larger sea‐level rise rates, lower flood thresholds, and increases in tidal range and skew surge magnitudes. Smaller sea‐level rise rates, higher flood thresholds and decreases in sea level variability lead to commensurately lower flood rates. Percentagewise, changes in tidal amplitudes generally have a much larger impact on flood frequencies than equivalent percentagewise changes in storm surge magnitudes. We explore several implications of these findings. Firstly, understanding future local changes in storm surges and tides is required to fully quantify future flood hazards. Secondly, existing hazard assessments may underestimate future flood rates as changes in tides are not considered. Finally, identifying the flood frequencies and severities relevant to local coastal managers is imperative to develop useable and policy‐relevant projections for decisionmakers.

Active-learning Kriging-assisted robust design optimization of tuned mass dampers: Vibration mitigation of a steel-arch footbridge

Many typical footbridges are designed as slender structures to highlight their aesthetic characteristic. As these bridges can be highly susceptible to pedestrian-induced excitations, tuned mass dampers have been adopted as energy absorbers in many applications to mitigate this issue. The design of tuned mass dampers can be formulated as a robust design optimization problem considering various sources of uncertainties related to the bridge state and environment. To alleviate the computational burden of analyzing the statistical moments, surrogate model-based approaches have been introduced to build cheaper-to-evaluate substitutes, and the analytical expression of the robustness has been discussed to facilitate the optimization procedure. Motivated by this, the present paper derives an analytical expression for the robustness index, considering both deterministic and uncertain variables as inputs of the ordinary Kriging surrogate model. An active-learning framework is proposed to streamline the design optimization of tuned mass dampers for the vibration mitigation of slender footbridges. Two numerical examples are firstly investigated to validate the performance of the proposed method; and then, a practical application of a steel-arch footbridge is carefully studied to demonstrate the engineering feasibility and superiority of the proposed method. The results exhibit that the proposed method can efficiently and accurately facilitate the robust design of vibration mitigation devices for practical engineering projects, and showcases the potential to be used in other engineering problems.

Incorporating uncertainty into the validation of automotive crash simulation models for component analysis

The design and development of crashworthy vehicles depend increasingly on credible crash simulation results. This credibility requires a careful process of verification and validation (V&V). The former relates to assessing the correctness of the calculation while the latter relates to the appropriateness of the physical and numerical test results. In this context, various sources of uncertainty exist in crash tests as well as in simulation models, which can influence the validation outcomes. Hence, it is essential to study statistical validation approaches for crash applications. In this paper, we elaborate on two important aspects of validation for crash simulation models: quantification of the credibility of simulation models, and consideration of uncertainties in the validation. Limited and noisy experimental data, typical in crash applications, impose a challenge on the investigation of validation approaches. To address these challenges, we use synthetic experimental results in our study. A crash-box model is validated using the V&V20-2009 validation standard. Two case studies are considered. The first case study aims to show the application of the V&V20 approach to a crash problem. The second case is a more complex scenario and aims to explore the impact of different experimental factors, such as experimental noise, hidden uncertainties, and the number of experiments on the validation results. In the end, we examine how underlying assumptions in the validation approach affect the conservativity of the validation results.

UNCERTAINTY QUANTIFICATION AND GLOBAL SENSITIVITY ANALYSIS OF SEISMIC FRAGILITY CURVES USING KRIGING

Seismic fragility curves have been introduced as key components of seismic probabilistic risk assessment studies. They express the probability of failure of mechanical structures conditional to a seismic intensity measure and must take into account various sources of uncertainties, the so-called epistemic uncertainties (i.e., coming from the uncertainty on the mechanical parameters of the structure) and the aleatory uncertainties (i.e., coming from the randomness of the seismic ground motions). For simulation-based approaches we propose a methodology to build and calibrate a Gaussian process surrogate model to estimate a family of nonparametric seismic fragility curves for a mechanical structure by propagating both the surrogate model uncertainty and the epistemic ones. Gaussian processes have indeed the main advantage to propose both a predictor and an assessment of the uncertainty of its predictions. In addition, we extend this methodology to sensitivity analysis. Global sensitivity indices such as aggregated Sobol' indices and kernel-based indices are proposed to know how the uncertainty on the seismic fragility curves is apportioned according to each uncertain mechanical parameter. This comprehensive uncertainty quantification framework is finally applied to an industrial test case consisting of a part of a piping system of a pressurized water reactor.

Uncertainties and robustness with regard to the safety of a repository for high-level radioactive waste: introduction of a research initiative

The Federal Company for Radioactive Waste Disposal (BGE mbH) is tasked with the selection of a site for a high-level radioactive waste repository in Germany in accordance with the Repository Site Selection Act. In September 2020, 90 areas with favorable geological conditions were identified as part of step 1 in phase 1 of the Site Selection Act. Representative preliminary safety analyses are to be carried out next to support decisions on the question, which siting regions should undergo surface-based exploration. These safety analyses are supported by numerical simulations building on geoscientific and technical data. The models that are taken into account are associated with various sources of uncertainties. Addressing these uncertainties and the robustness of the decisions pertaining to sites and design choices is a central component of the site selection process. In that context, important research objectives are associated with the question of how uncertainty should be treated through the various data collection, modeling and decision-making processes of the site selection procedure, and how the robustness of the repository system should be improved. BGE, therefore, established an interdisciplinary research cluster to identify open questions and to address the gaps in knowledge in six complementary research projects. In this paper, we introduce the overall purpose and the five thematic groups that constitute this research cluster. We discuss the specific questions addressed as well as the proposed methodologies in the context of the challenges of the site selection process in Germany. Finally, some conclusions are drawn on the potential benefits of a large method-centered research cluster in terms of simulation data management.

Health effects associated with exposure to secondhand smoke: a Burden of Proof study

Despite a gradual decline in smoking rates over time, exposure to secondhand smoke (SHS) continues to cause harm to nonsmokers, who are disproportionately children and women living in low- and middle-income countries. We comprehensively reviewed the literature published by July 2022 concerning the adverse impacts of SHS exposure on nine health outcomes. Following, we quantified each exposure–response association accounting for various sources of uncertainty and evaluated the strength of the evidence supporting our analyses using the Burden of Proof Risk Function methodology. We found all nine health outcomes to be associated with SHS exposure. We conservatively estimated that SHS increases the risk of ischemic heart disease, stroke, type 2 diabetes and lung cancer by at least around 8%, 5%, 1% and 1%, respectively, with the evidence supporting these harmful associations rated as weak (two stars). The evidence supporting the harmful associations between SHS and otitis media, asthma, lower respiratory infections, breast cancer and chronic obstructive pulmonary disease was weaker (one star). Despite the weak underlying evidence for these associations, our results reinforce the harmful effects of SHS on health and the need to prioritize advancing efforts to reduce active and passive smoking through a combination of public health policies and education initiatives.

Assessing mercury contamination in Southern Hemisphere marine ecosystems: The role of penguins as effective bioindicators

Mercury (Hg) is a global pollutant known for its significant bioaccumulation and biomagnification capabilities, posing a particular threat to marine environments. Seabirds have been recognized as effective bioindicators of marine pollution, and, among them, penguins present a unique opportunity to serve as a single taxonomic group (Sphenisciformes) for monitoring Hg across distinct marine ecosystems in the Southern Hemisphere. In this study, we conducted a comprehensive systematic review of Hg concentrations, and performed a meta-analysis that took into account the various sources of uncertainty associated with Hg contamination in penguins. Beyond intrinsic species-specific factors shaping Hg levels, our results showed that the penguin community effectively reflects spatial patterns of Hg bioavailability. We identified geographic Hg hotspots in Australia, the Indian Ocean, and Tierra del Fuego, as well as coldspots in Perú and the South Atlantic. Furthermore, specific penguin species, namely the Southern Rockhopper (Eudyptes chrysocome) and Macaroni penguin (Eudyptes chrysolophus), are highlighted as particularly vulnerable to the toxic effects of Hg. Additionally, we identified knowledge gaps in geographic areas such as the Galápagos Islands, South Africa, and the coast of Chile, as well as in species including Fiordland (Eudyptes pachyrhynchus), Snares (Eudyptes robustus), Erect-crested (Eudyptes sclateri), Royal (Eudyptes schlegeli), Yellow-eyed (Megadyptes antipodes), and Galápagos (Spheniscus mendiculus) penguins. Overall, our study contributes to the growing body of literature emphasizing the role of penguins as bioindicators of Hg pollution, but it also highlights areas where further research and data collection are needed for a more comprehensive understanding of Hg contamination in marine ecosystems in the Southern Hemisphere.