Large Parameter Uncertainties Research Articles

Data-driven hydraulic property analysis and prediction of two-dimensional random fracture networks

The large parameter space and uncertainty of the discrete fracture networks (DFNs) make the hydraulic characteristics have multiple possibilities. Statistical analysis of hydraulic characteristics based on a large amount of data is a standard practice and central to DFN methodology. The present study developed a two-dimensional (2D) DFN numerical seepage model based on Matlab and Comsol from modeling and parameter calculation to simulation to investigate the influence of parameters involved fracture network on seepage characteristics of DFNs. In total, 1728 2D DFN models were generated with increasing fracture density, fracture length, power-law exponent, fracture orientations, and fracture included angles, and 1728 × 3 = 5184 numerical simulations of fluid flow along the x-axis, y-axis, and center through the DFN models are conducted using a self-developed numerical code. The influence of fracture geometric features on connectivity and permeability are estimated, respectively. And to realize the prediction of permeability tensor by known geological data, a theoretical model based on the Snow model and a data mining model based on the back-propagation model are discussed. The analysis helps researchers and engineers can make informed decisions and develop effective strategies for addressing the challenges posed by uncertainty hydraulic property of the fractured rock masses.

On the Formation of Double Neutron Stars in the Milky Way: Influence of Key Parameters

The detection of gravitational wave events has stimulated theoretical modeling of the formation and evolution of double compact objects (DCOs). However, even for the most studied isolated binary evolution channel, there exist large uncertainties in the input parameters and treatments of the binary evolution process. So far, double neutron stars (DNSs) are the only DCOs for which direct observations are available through traditional electromagnetic astronomy. In this work, we adopt a population synthesis method to investigate the formation and evolution of Galactic DNSs. We construct 324 models for the formation of Galactic DNSs, taking into account various possible combinations of critical input parameters and processes such as mass transfer efficiency, supernova type, common envelope efficiency, neutron star kick velocity, and pulsar selection effect. We employ Bayesian analysis to evaluate the adopted models by comparing with observations. We also compare the expected DNS merger rate in the galaxy with that inferred from the known Galactic population of pulsar-neutron star systems. Based on these analyses we derive the favorable range of the aforementioned key parameters.

Improving glacio-hydrological model calibration and model performance in cold regions using satellite snow cover data

Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables.

Adaptive time-varying state safety constraint control for discrete-time nonlinear systems with random actuator failures

This work investigates the adaptive time-varying state safety constraint control problem for a class of discrete-time nonlinear systems subject to large parameter uncertainties and stochastic actuator failures for the first time. The existence of time-varying full-state constraints, large parameter uncertainties and stochastic actuator failures leads to a series of difficulties for realizing all control objectives. Due to the absence of the linearity property of the derivative in discrete-time log type barrier Lyapunov functions, they are not used to deal with the state constraint problem of nonlinear discrete-time systems. The discrete quadratic-fraction time-varying barrier Lyapunov function is employed to guarantee that all states are constrained in the time-varying predefined regions. By virtue of the constructed multiple-model set, an adaptive identification algorithm is developed to deal with large parameter uncertainties and it has a clear advantage that this method does not requires cn (where n is the dimension of the parameter vector and c is an integer) identification models and only relies on n+1 identification models, thereby heavily reducing the computational burden. By employing the discrete-time filter and backstepping techniques, the adaptive failure compensation controller is designed to ensure the boundedness in probability of the considered systems with respect to stochastic actuator failures. Finally, the effectiveness of the proposed scheme is proved by two simulation examples.

Multivariable robust tube-based nonlinear model predictive control of mammalian cell cultures

In this paper, the application of a robust nonlinear model predictive control (NMPC) framework to mammalian cell cultures is proposed, dealing with possible large kinetic parameter uncertainties. Industrial constraints formulated in view of good manufacturing practice and quality-by-design approach are also considered, namely the assurance that all state trajectories are contained within a corridor defined by lower and upper safety bounds. The latter are assimilated to the well-known tube-based paradigm which is used to formulate the corresponding robust NMPC problem. Both classical and tube-based NMPC performances are assessed in numerical simulations where specific key-species are regulated while dealing with an uncertain plant model. The capability of the tube-based method to reduce the impact of the parameter variations on the state trajectories and the violation of the constraints is highlighted, suggesting the transfer of the method on a real pharmaceutical process.

Adaptive attitude control of combined spacecraft with large parametric uncertainties and adversarial disturbance

Adaptive attitude control of combined spacecraft with large parametric uncertainties and adversarial disturbance

Generalized Aerosol/Chemistry Interface (GIANT): A Community Effort to Advance Collaborative Science across Weather and Climate Models

Abstract Atmospheric aerosol and chemistry modules are key elements in Earth system models (ESMs), as they predict air pollutant concentrations and properties that can impact human health, weather, and climate. The current uncertainty in climate projections is partly due to the inaccurate representation of aerosol direct and indirect forcing. Aerosol/chemistry parameterizations used within ESMs and other atmospheric models span large structural and parameter uncertainties that are difficult to assess independently of their host models. Moreover, there is a strong need for a standardized interface between aerosol/chemistry modules and the host model to facilitate portability of aerosol/chemistry parameterizations from one model to another, allowing not only a comparison between different parameterizations within the same modeling framework, but also quantifying the impact of different model frameworks on aerosol/chemistry predictions. To address this need, we have initiated a new community effort to coordinate the construction of a Generalized Aerosol/Chemistry Interface (GIANT) for use across weather and climate models. We aim to organize a series of community workshops and hackathons to design and build GIANT, which will serve as the interface between a range of aerosol/chemistry modules and the physics and dynamics components of atmospheric host models. GIANT will leverage ongoing efforts at the U.S. modeling centers focused on building next-generation ESMs and the international AeroCom initiative to implement this common aerosol/chemistry interface. GIANT will create transformative opportunities for scientists and students to conduct innovative research to better characterize structural and parametric uncertainties in aerosol/chemistry modules, and to develop a common set of aerosol/chemistry parameterizations.

Finite element model correction method based on surrogate model with multiple working conditions and multiple measurement points

The finite element model inversion method has been widely used in recent years for iterative adjustment of finite element model parameters. However, the models constructed in the existing literature are weak and time consuming to adapt to the environment, which makes it difficult to adapt to the current needs of numerical simulations. To address the problem of large uncertainty in the material parameters of real objects and the difficulty of constructing finite element simulation models, a surrogate-based model correction method was proposed for multi-condition and multi-measurement point finite element models. The innovative use of the working condition parameter as one of the training parameters of the surrogate model to construct the optimal mathematical model for parameter correction of the finite element model to variable working conditions. To reduce the number of finite element model calls and speed up the convergence process, an Minimizing Prediction-CV-Voronoi parallel infill sampling method for the surrogate model was proposed to overcome the problems of easily falling into local optima and slow convergence when solving after constructing the surrogate model. The proposed parallel infill sampling method was tested using the test functions. The finite element model correction method with multiple working conditions and multiple measurement points was applied for material parameter correction and identification of aluminum alloys. The superiority of the proposed parallel point addition method in terms of the solution accuracy and speed was demonstrated. The results show that the multi-measurement points have a significant effect on improving the model correction effect, and the constructed multi-condition surrogate model can make fast predictions for arbitrary conditions and has strong environmental adaptability. The finite element model correction method proposed in this paper, with strong environmental adaptability, high accuracy and fast iteration, has been tested to be very effective.

Robust Control of Repeated Drug Administration with Variable Doses Based on Uncertain Mathematical Model.

The aim of this paper was to design a repeated drug administration strategy to reach and maintain the requested drug concentration in the body. Conservative designs require an exact knowledge of pharmacokinetic parameters, which is considered an unrealistic demand. The problem is usually resolved using the trial-and-error open-loop approach; yet, this can be considered insufficient due to the parametric uncertainties as the dosing strategy may induce an undesired behavior of the drug concentrations. Therefore, the presented approach is rather based on the paradigms of system and control theory. An algorithm was designed that computes the required doses to be administered based on the blood samples. Since repeated drug dosing is essentially a discrete time process, the entire design considers the discrete time domain. We have also presented the idea of applying this methodology for the stabilization of an unstable model, for instance, a model of tumor growth. The simulation experiments demonstrated that all variants of the proposed control algorithm can reach and maintain the desired drug concentration robustly, i.e., despite the presence of parametric uncertainties, in a way that is superior to that of the traditional open-loop approach. It was shown that the closed-loop control with the integral controller and stabilizing state feedback is robust against large parametric uncertainties.

CME propagation through the heliosphere: Status and future of observations and model development

The ISWAT (International Space Weather Action Teams) heliosphere clusters H1 and H2 have a focus on interplanetary space and its characteristics, especially on the large-scale co-rotating and transient structures impacting Earth. Solar wind stream interaction regions, generated by the interaction between high-speed solar wind originating in large-scale open coronal magnetic fields and slower solar wind from closed magnetic fields, are regions of compressed plasma and magnetic field followed by high-speed streams that recur at the ∼27 day solar rotation period. Short-term reconfigurations of the lower coronal magnetic field generate flare emissions and provide the energy to accelerate enormous amounts of magnetised plasma and particles in the form of coronal mass ejections into interplanetary space. The dynamic interplay between these phenomena changes the configuration of interplanetary space on various temporal and spatial scales which in turn influences the propagation of individual structures. While considerable efforts have been made to model the solar wind, we outline the limitations arising from the rather large uncertainties in parameters inferred from observations that make reliable predictions of the structures impacting Earth difficult. Moreover, the increased complexity of interplanetary space as solar activity rises in cycle 25 is likely to pose a challenge to these models. Combining observational and modeling expertise will extend our knowledge of the relationship between these different phenomena and the underlying physical processes, leading to improved models and scientific understanding and more-reliable space-weather forecasting. The current paper summarizes the efforts and progress achieved in recent years, identifies open questions, and gives an outlook for the next 5–10 years. It acts as basis for updating the existing COSPAR roadmap by Schrijver et al. (2015), as well as providing a useful and practical guide for peer-users and the next generation of space weather scientists.

Partial-order reduction in reachability-based response-time analyses of limited-preemptive DAG tasks

Response-time analysis (RTA) has been a means to evaluate the temporal correctness of real-time systems since the 1970 s. While early analyses were successful in capturing the exact upper bound on the worst-case response-time (WCRT) of systems with relatively simple computing platforms and task activation models, nowadays we see that most existing RTAs either become pessimistic or do not scale well as systems become more complex (e.g., parallel tasks running on a multicore platform). To make a trade-off between accuracy and scalability, recently, a new reachability-based RTA, called schedule-abstraction graph (SAG), has been proposed. The analysis is at least three orders of magnitude faster than other exact RTAs based on UPPAAL. However, it still has a fundamental limitation in scalability as it suffers from state-space explosion when there are large uncertainties in the timing parameters of the input jobs (e.g., large release jitters or execution-time variations). This could impede its applicability to large industrial use cases, or to be integrated with automated tools that explore alternative design choices. In this paper, we improve the scalability of the SAG analysis by introducing partial-order reduction rules that avoid combinatorial exploration of all possible scheduling decisions. We include systems with dependent and independent task execution models (i.e., with and without precedence constraint). Our empirical evaluations show that the proposed solution is able to reduce the runtime by five orders of magnitude and the number of explored states by 98% in comparison to the original SAG analysis. These achievements come only at a negligible cost of an over-estimation of 0.1% on the actual WCRT. We applied our solution on an automotive case study showing that it is able to scale to realistic systems made of hundreds of tasks for which the original analysis fails to finish.

On Carbon Storage and Substitution Factors of Harvested Wood Products in the Context of Climate Change Impacts of the Norwegian Forest Sector

Harvested wood products (HWP) contribute to climate change mitigation via two main mechanisms: carbon storage and substitution. The authors examined the data on carbon storage and substitution factors of HWPs that are relevant in evaluating the climate change mitigation potential in the context of the Norwegian forest sector. While there seem to be many uncertainties in these parameters, the data suggest that several uses of wood for industrial products come with clear carbon substitution benefits and, in some cases, provide long-term carbon storage. Such wood products could play an important role in climate-friendly bioeconomic transformation. In particular, the authors considered wood- based construction materials, textile fibres, and insulation materials as examples of such products with potential in future bioeconomy. The decay of the carbon stored in HWP pools over time is often modelled using the product half-lives that correspond to the number of years it takes for the carbon in a pool to be reduced to half of its initial value. Using the default half-life values of greenhouse gases reported to the United Nations Framework Convention on Climate Change, the average half-life of carbon in HWPs produced by the forest industry in Norway of today is approximately 21 years. Shifting some of the use of pulpwood and sawn wood chips from producing paper and pellets to produce insulation materials or panels for construction would increase the time carbon is stored in the HWP pool. Accounting for the large uncertainty in the carbon substitution parameters of HWPs found in this study, a cautious estimate of the substitution benefits of HWPs produced in Norway can be considered to amount to at least 5 Mt CO2. Redirecting some pulpwood use from paper production to the production of textile fibres and the above-mentioned construction materials would increase the substitution benefits.

Regionally disparate ecological responses to microplastic slowing of faecal pellets yields coherent carbon cycle response

Microplastic is a ubiquitous marine pollutant whose small dimensions make it biologically available to phytoplankton and zooplankton. These organisms are crucial as the basis of the marine food web and for the export of organic material in the form of faecal pellets from the surface to deeper in the water column, forming a long-term carbon sink. Previous laboratory studies have demonstrated empirically that ingestion of low density microplastics reduces the sinking rates of zooplankton faecal pellets. This study uses a complex earth system model to analyse this effect and assess its wider impacts in a changing climate. Results show that the slowing of faecal pellet sinking stimulates changes to ecosystems regionally and reduces ocean carbon uptake by about 4.4 Pg C between the years 1950-2100, 0.24% of anthropogenic emissions over this time. However, perturbation of organic particle fluxes is significant, especially in gyres, and of the order of climate change impacts over the same time period. We calculate that plastics carbon has a 3 orders of magnitude greater impact on marine ecosystems than atmospheric carbon over our centennial timescale. Large uncertainties in model parameters and simplistic model structure suggest our results should be interpreted as motivation to further investigate parameter estimation, calcification responses to pollution, and the combined effects of multiple impact mechanisms on ecosystems.

Improving the Accuracy of Identification of Non-Stationary Objects Based on the Regulation of Model Variables

Researched and developed mechanisms for optimizing the identification of random time series based on mechanisms for searching for unknown knowledge, hidden properties, patterns, relationships, features of non-stationary objects under the condition of limited a priori information, uncertainty, non-stationary processes. A generalized model for optimizing the identification of RTS based on the use of neural networks, neuro-fuzzy networks of dynamic models, as well as fuzzy logic algorithms is implemented. Instruments for data reliability control are obtained based on statistical, dynamic, intelligent approaches suitable for the conditions of transmission of incomplete, heterogeneous, partially given information with large parametric uncertainty. The principles and methods of data reliability control in a fuzzy environment are developed based on the generalization and use of the properties of neural networks, fuzzy logic and statistical modeling methods. Algorithms for searching for correlations, trends, interrelations and regularities in data, forming training, control and test sets for solving problems of recognition, classifying images of micro-objects and predicting power supply indicators are synthesized. The results of the research are implemented in the form of a software package for identifying non-stationary objects, which ensures the adaptability of model variables, high data processing performance and accuracy of results.

Tracking Small Wildlife With Minimal-Complexity Radio Frequency Transmitters: Near-Optimal Detection

In a range of IoT applications, edge nodes require minimal size, mass, and power/energy use. When node complexity is severely constrained, the burden of detection falls to the receiving system. In this paper we present a method of detection for RF signals motivated by wildlife radio telemetry, where miniature beacons emit VHF pulses that are used to detect and localize animals in the field. We address the challenges of limited transmit power and path losses due to terrain that lead to low received SNR. In addition, the pulse train structure requires joint frequency acquisition, time synchronization, and detection; our approach addresses the key challenge of large uncertainty in signal parameters. Our algorithm explicitly integrates frequency acquisition and time synchronization, using nearly phase-coherent matched filtering followed by non-coherent pulse combining and a multiple hypotheses test to provide near-optimal detection performance in low SNR scenarios. We provide quantitative results showing performance as a function of SNR and signal uncertainty. When compared to the single pulse detection case as used in existing systems, our multipulse combining approach pulses improves detection performance by 4 dB, even under the conditions of temporal and frequency/phase uncertainty resulting from beacon design and manufacturing. We also describe an efficient real-time signal processing implementation of the algorithm, including signal tracking that supports localization after initial signal detection.

Too ill to cure? – An uncertainty-based probabilistic model assessment on one of China’s most eutrophic lakes

Eutrophication is a global challenge, which is exemplified by the tremendous efforts but little results in restoring the sixth largest and also one of the most eutrophic freshwater lakes in China, Lake Dianchi. Considering large parametric uncertainties in water quality modeling, the traditionally used deterministic water quality model is expanded to a probabilistic model to explore the Lake Dianchi's potential responses to different levels of pollutant load reductions. The results show that, given the long pollution history and severe pollution state in Lake Dianchi, a minimum pollution load reduction by half (base year 2003) is required to maintain the water quality state as it is now in 40 years. At least a 60% nutrient load reduction is required to generate any likelihood of water quality improvement, however, the system stabilizes quickly after about 10 years, which may explain why tremendous investments have generated little results. 80% of nutrient load reduction for 40 years has 95% probability of meeting the TN target but only a below 50% (45%) probability in meeting the TP target, and even less to meet water quality target for Chla. The feasibility of ever reaching the Chinese drinking water standards for total phosphorous and total nitrogen is questionable.

Bayesian Calibration of Kinetic Parameters in the CH Chemistry Toward Accurate Prompt-NO Modelling

Abstract Significant efforts made by the gas turbine industry have helped reduce nitrogen oxides (NOx) emissions considerably. To meet and surpass the increasingly stringent regulations, accurate and robust thermochemical mechanisms are needed to help design future sub-10 ppm combustion systems. Uncertainty in kinetic modeling, however, can result in large prediction uncertainty and significant discrepancy between models that hinder the identification of promising combustors with confidence. Direct reaction rate measurements are seldom available for some reactions, especially when involving short-lived radicals such as methylidyne, CH. As the main precursor to the prompt-NO formation pathway, its large parametric uncertainty directly propagates through the nitrogen chemistry preventing accurate and precise emissions predictions. Recent independent CH concentration measurements obtained at various operating conditions are used as indirect rate measurements to perform statistical, or Bayesian, calibration. A subset of important reactions in the CH chemistry affecting peak-CH concentration is identified through uncertainty-weighted sensitivity analysis to first constrain the parametric space of this prompt-NO precursor. Spectral expansion provides the surrogate model used in the Markov-Chain Monte Carlo method to evaluate the posterior kinetic distribution. The resulting constrained CH-chemistry better captures experimental measurements while providing smaller prediction uncertainty of a similar order as the uncertainty of the measurements, which can increase the confidence in simulation results to identify promising future low-emissions configurations. For the quasi-steady-state species CH, fuel decomposition reactions leading to CH production are constrained while little impact is observed for intermediate reactions within the CH-chemistry. The reduction in prediction uncertainty results mainly from the constrained correlations between parameters which greatly limit the set of feasible reaction rate combinations. Additional independent direct and indirect measurements would be necessary to further constrain rate parameters in the CH chemistry, but this calibration demonstrates that predictions of radical species can be improved by assimilating enough data.

Multiaxial fatigue prediction and uncertainty quantification based on back propagation neural network and Gaussian process regression

Engineering structures are often suffering multiaxial stress which leads to multiaxial fatigue failures. Accurate and reliable multiaxial life prediction is a challenging issue in fatigue analysis due to the intricate deterioration mechanisms of the fatigue failure and the large uncertainty in the material parameters at the microscopic scale. Moreover, conventional multiaxial fatigue life prediction models are empirical or semi-empirical. To tackle this problem, a data-driven method is presented, which combines the back propagation neural network (BPNN) and the Gaussian process regression (GPR). The BPNN-GPR method can predict the multiaxial fatigue life and quantify the uncertainty simultaneously. This method is validated using six materials including the uniaxial, multiaxial proportional and multiaxial nonproportional loading cases. All the predicted lives fall within a life factor of ± 3, which indicates that the BPNN-GPR method has the satisfactory capability for multiaxial fatigue life prediction. In addition, results also show the prediction capability of the BPNN-GPR method for unknown multiaxial nonproportional loading paths.

Ice model calibration using semicontinuous spatial data

Rapid changes in Earth’s cryosphere caused by human activity can lead to significant environmental impacts. Computer models provide a useful tool for understanding the behavior and projecting the future of Arctic and Antarctic ice sheets. However, these models are typically subject to large parametric uncertainties, due to poorly constrained model input parameters that govern the behavior of simulated ice sheets. Computer model calibration provides a formal statistical framework to infer parameters, using observational data, and to quantify the uncertainty in projections due to the uncertainty in these parameters. Calibration of ice sheet models is often challenging because the relevant model output and observational data take the form of semicontinuous spatial data with a point mass at zero and a right-skewed continuous distribution for positive values. Current calibration approaches cannot handle such data. Here, we introduce a hierarchical latent variable model that handles binary spatial patterns and positive continuous spatial patterns as separate components. To overcome challenges due to high dimensionality, we use likelihood-based generalized principal component analysis to impose low-dimensional structures on the latent variables for spatial dependence. We apply our methodology to calibrate a physical model for the Antarctic ice sheet and demonstrate that we can overcome the aforementioned modeling and computational challenges. As a result of our calibration, we obtain improved future ice-volume change projections.

Modeling total predation to avoid perverse outcomes from cat control in a data-poor island ecosystem.

Data hungry, complex ecosystem models are often used to predict the consequences of threatened species management, including perverse outcomes. Unfortunately, this approach is impractical in many systems, which have insufficient data to parameterize ecosystem interactions or reliably calibrate or validate such models. Here we demonstrate a different approach, using a minimum realistic model to guide decisions in data- and resource-scarce systems. We illustrate our approach with a case-study in an invaded ecosystem from Christmas Island, Australia, where there are concerns that cat eradication to protect native species, including the red-tailed tropicbird, could release meso-predation by invasive rats. We use biophysical constraints (metabolic demand) and observable parameters (e.g. prey preferences) to assess the combined cat and rat abundances which would threaten the tropicbird population. We find that the population of tropicbirds cannot be sustained if predated by 1607 rats (95% credible interval (CI) [103, 5910]) in the absence of cats, or 21 cats (95% CI [2, 82]) in the absence of rats. For every cat removed from the island, the bird's net population growth rate improves, provided that the rats do not increase by more than 77 individuals (95% CI [30, 174]). Thus, in this context, one cat is equivalent to 30-174 rats. Our methods are especially useful for on-the-ground predator control in the absence of knowledge of predator-predator interactions, to assess whether 1) the current abundance of predators threatens the prey population of interest, 2) managing one predator species alone is sufficient to protect the prey species given potential release of another predator, and 3) control of multiple predator species is needed to meet the conservation goal. Our approach demonstrates how to use limited information for maximum value in data-poor systems, by shifting the focus from predicting future trajectories, to identifying conditions which threaten the conservation goal. This article is protected by copyright. All rights reserved.