Uncertain Input Research Articles

Modelled Cost Reductions for the X-Rotor Offshore Wind Turbine

Abstract The XROTOR project is developing an innovative turbine (X-Rotor Concept - XRC) that is a Vertical Axis Wind Turbine (VAWT)/Horizontal Axis Wind Turbine (HAWT) hybrid. The objective of this paper is to assess the XRC Levelised Cost of Energy (LCoE), applying different sensitivity analysis to determine a realistic LCoE range. This will then be compared with LCoE estimates for three traditional HAWT drivetrain configurations to evaluate the potential cost savings for the XRC. The paper considers a hypothetical farm of 100x5MW XRCs on generic monopiles, 100km from shore and commissioned in 2030 with a 30-year project. Analysis uses the University of Strathclyde Operation and Maintenance (O&M) model and an LCoE tool developed by University College Cork to calculate results. Sensitivity analysis varies key cost driving elements and uncertain inputs including the O&M strategy, distance from shore and the financial assumptions to determine an optimised LCoE estimated range. Results for the XRC are then compared with estimates derived using the same methodology for the HAWT configurations. Analysis indicates that the novel design may facilitate cost reductions, reducing OPEX by removing heavy components that would require costly heavy lift vessels to maintain. It also removes the failure modes around the gearbox, multi-pole generator and yaw system. The XRC could also reduce the capital cost of drivetrains through a power take-off approach that does not require a gearbox or a multi-pole generation but achieves comparable levels of power conversion. Ultimately the XRC could achieve LCoE cost reductions of 10-19%, compared with the traditional HAWTs. Further savings are considered possible but require additional design and analysis that are outside the scope of this paper.

Impact-based forecasting of tropical cyclone-related human displacement to support anticipatory action

Tropical cyclones (TCs) displace millions every year. While TCs pose hardships and threaten lives, their negative impacts can be reduced by anticipatory actions like evacuation and humanitarian aid coordination. In addition to weather forecasts, impact forecast enables more effective response by providing richer information on the numbers and locations of people at risk of displacement. We introduce a fully open-source implementation of a globally consistent and regionally calibrated TC-related displacement forecast at low computational costs, combining meteorological forecast with population exposure and respective vulnerability. We present a case study of TC Yasa which hit Fiji in December 2020. We emphasise the importance of considering the uncertainties associated with hazard, exposure, and vulnerability in a global uncertainty analysis, which reveals a considerable spread of possible outcomes. Additionally, we perform a sensitivity analysis on all recorded TC displacement events from 2017 to 2020 to understand how the forecast outcomes depend on these uncertain inputs. Our findings suggest that for longer forecast lead times, decision-making should focus more on meteorological uncertainty, while greater emphasis should be placed on the vulnerability of the local community shortly before TC landfall. Our open-source codes and implementations are readily transferable to other users, hazards, and impact types.

A novel approach to estimate land surface temperature from landsat top-of-atmosphere reflective and emissive data using transfer-learning neural network

Land Surface Temperature (LST) is a crucial parameter in studies of urban heat islands, climate change, evapotranspiration, hydrological cycles, and vegetation monitoring. However, conventional satellite-based approaches for LST retrieval often require additional data like land surface emissivity (LSE). Meanwhile, traditional machine learning (ML) techniques face challenges in acquiring representative training data and leveraging data from varied sources effectively. To address these issues, we introduce a novel transfer-learning (TL) neural network approach for LST retrieval using top-of-atmosphere (TOA) reflective and emissive data from Landsat. This method not only improves LST retrieval by integrating various data types but also demonstrates the potential of shortwave data in surrogating LSE information, thereby reducing dependence on explicit LSE data. Our TL approach utilized extensive simulations from the radiative transfer model (RTM) and measurements from the real world. The simulations are comprehensive, covering a wide range of atmospheric and surface scenarios, and the inclusion of real-world data mitigates the discrepancy between simulations and actual observations. When applied to a decade of Landsat-8 observations and ground measurements from 241 stations across diverse regions, our TL method significantly outperforms ML, single-channel (SC), and split-window (SW) algorithms in terms of root mean square error (RMSE), with improvements of 0.46 K, 0.84 K, and 0.57 K, respectively. This superiority underscores the advantage of integrating simulated and observed data, as well as the benefit of utilizing both reflective and emissive data without relying on uncertain LSE inputs. Our findings present a promising new TL framework for estimating LST directly from TOA data, offering a robust approach that we have made publicly available through Google Earth Engine (GEE) for broader use. The LST data retrieved by our proposed method can provide valuable insights for environmental research.

Quantifying the Role of Calibration Strategies on Surface‐Subsurface Hydrologic Model Performance

ABSTRACTDistributed, coupled surface‐groundwater hydrologic models are high‐dimensional, given the necessity to reflect the spatially diverse nature of complex hydrologic processes. Furthermore, inverse/inference problems involving these high‐dimensional models are naturally ill‐posed, given the limited information content of state observations that are typically assimilated. Many inversion/inference algorithms do not cope well with high dimensionality, leaving the practitioner to make subjective choices related to uncertain model inputs. The objective of this study is to evaluate the impact of these subjective calibration choices within a formal sensitivity analysis, uncertainty analysis, and parameters estimation (SA‐UA‐PE) framework on model testing for a surface‐subsurface hydrologic model. In doing so, we address the concepts of ‘over‐parameterisation’ and ‘under‐parameterisation’. We completed a series of numerical experiments, testing several otherwise subjective aspects of the calibration process: (1) the number (5, 10, 15, 20) and type (soil, aquifer, land surface, channel) of calibration parameters selected); (2) the type of state observations assimilated (streamflow, groundwater head); and (3) the length of testing period (1 to 14 years), using monthly streamflow and groundwater head as testing data. The experiments were completed for models of the Winnebago River watershed (Minnesota, Iowa), (significant tile drainage) and the Nanticoke River watershed (Delaware, Maryland (significant groundwater‐channel interactions). The selected hydrologic model is SWAT+, using the gwflow module for physically based groundwater storage and flow modelling, and simulations are run for the 2000–2015 period. Through this process, we found that increasing the number of parameters from 5 to 15 improves the representation of streamflow, principally through an improvement of groundwater storage representation and baseflow generation, but minimal improvement when increasing to 20 parameters. Therefore, the SA‐UA‐PE process can be optimised based on an ideal number of parameters that yield adequate results while maintaining a lower computational burden. The method presented here can be used for any watershed, using integrated surface‐subsurface hydrologic models.

Numerical and experimental study of thermal stabilization system for satellite electronics with integrated phase-change capacitor

In this paper experimental and numerical investigations were conducted to analyze the application of phase change material (PCM) for temperature stabilization of small satellite electronics. The reference electronic equipment is a data processing unit generating a nominal power of 9.6 W during its operation. To stabilize its temperature, an integrated PCM module was designed, built, and tested experimentally in a thermal–vacuum chamber. The experimental results were then used to validate a numerical model, developed using commercial software Simcenter FloEFD. The results showed good agreement between measured and predicted temperature evolution in critical locations of the unit. The model predictions were further improved by conducting analysis of uncertain input data, model settings, and by inclusion of the effect of natural convection in the PCM. The improved model showed better agreement between the model predictions and experiments. The comparison showed that the maximum difference between the measured and calculated temperature at the most important location (the heater) was 1.6 °C and difference of the peak values was 0.8 °C. Finally, the model was used to compare the constructed module containing the PCM with a system consisting of an aluminum block of the same volume as PCM. The comparison showed that the use of PCM allows for a reduction of the maximum temperature by 6.2 °C while reducing the mass almost 3.5 times. The results demonstrate that the developed numerical model allows for accurate predictions of temperature distributions in the designed device, and can be effectively used to design PCM modules dedicated to in-space electronics, thereby increasing their peak capabilities and lifetime.

Uncertainty and sensitivity analysis of the fission product behaviour in the Phébus FPT1 test with the system code AC2

The Phèbus FP (Fission Product) research program, conducted by IRSN and CEA, aimed to improve the understanding of the phenomena governing core melt down, fission product release and transport, as well as the fission product behaviour in the containment. The test facility represents a 900 MWe PWR at a 1/5000 scale. The second test, FPT1, was carried out in 1996 and utilised an Ag-In-Cd alloy control rod. There is a growing interest in Best Estimate Plus Uncertainty analysis both in the research, as well as in the licensing. It has a long-standing history in case of thermal–hydraulic system codes focusing on design basis accidents, but its application is limited on the severe accident domain. In recent times however an increasing attention has been paid to evaluating severe accident codes’ uncertainties. This study presents an uncertainty and sensitivity analysis (UaSA) of the FPT1 test, focusing on fission product behavior modeling within the severe accident code system AC2. The detailed application of the coupled codes ATHLET-CD and COCOSYS in this context is novelty. Detailed description of the methodological and practical approach is provided. The uncertain input parameters chosen and quantified are directly related to the phenomena describing the fission product transport and retention phenomena both in the primary circuit, as well as in the containment, while thermal–hydraulic conditions within the primary circuit were kept as much as possible constant. Finally, the study has been complemented by a sensitivity analysis deriving the Spearman's rank correlation coefficient for each uncertain input parameter.

Differential-algebraic equation-based model predictive control for trajectory tracking of spacecraft-mounted continuum manipulators

Spacecraft-mounted continuum manipulators (SMCMs) exhibit great potential for performing dexterous operations in unstructured environments due to their inherent compliance and dexterity. However, the dynamic model of the rigid-flexible coupling SMCM is highly nonlinear and typically formulated as a set of implicit differential-algebraic equations (DAEs), posing significant challenges for precise trajectory tracking control. This paper proposes a novel model predictive control (MPC) framework specifically designed for generic DAEs to achieve precise trajectory tracking of the SMCM under uncertain disturbances and input limitations. The DAE model of the SMCM is discretized into a set of nonlinear algebraic equations. By performing implicit differentiation of these equations with respect to the system state, the state transition matrix (STM) for the DAE model is derived. The optimal control action for the SMCM can be further determined based on the derived STM. Additionally, nonlinear complementary functions are introduced to address the issue of input limitations, allowing the problem of determining the optimal control sequence to be equivalently transformed into a set of nonlinear algebraic equations for solving. Numerical simulations demonstrate that the proposed approach can achieve precise trajectory tracking of the SMCM while strictly adhering to input limitations.

Bayesian Sequential Design of Computer Experiments for Quantile Set Inversion

We consider an unknown multivariate function representing a system—such as a complex numerical simulator—taking both deterministic and uncertain inputs. Our objective is to estimate the set of deterministic inputs leading to outputs whose probability (with respect to the distribution of the uncertain inputs) of belonging to a given set is less than a given threshold. This problem, which we call Quantile Set Inversion (QSI), occurs for instance in the context of robust (reliability-based) optimization problems, when looking for the set of solutions that satisfy the constraints with sufficiently large probability. To solve the QSI problem we propose a Bayesian strategy, based on Gaussian process modeling and the Stepwise Uncertainty Reduction (SUR) principle, to sequentially choose the points at which the function should be evaluated to efficiently approximate the set of interest. We illustrate the performance and interest of the proposed SUR strategy through several numerical experiments.

Input delay compensation of nonlinear systems with uncertain time-varying parameters

A robust adaptive control law is designed for the class of nonlinear systems with uncertain time-varying parametric dynamics and input delays. The Constitution of control law design is based on filtered tracking errors, previous values of control input and estimate of uncertain parameters which is updated by the update law. An arbitrary large input delay compensation is guaranteed by deriving sufficient control gain conditions using Lyapunov-Krassovskii based stability analysis. The efficacy of the design is verified by Simulation results of two-link robot manipulator dynamical system.

Robust [formula omitted] stabilization for systems with uncertain input time-delay

This paper presents a robust H∞ stabilization approach for actively controlled systems that include uncertain input time-delay. The approach comprises two essential steps: (1) converting a controlled system with uncertain time-delay to have only deterministic time-delay through a time-scale transformation; and (2) applying the Chebyshev spectral continuous time approximation (CHsCTA) method and defining appropriate extension matrices to transform the system so there are no time-delays. A robust H∞ control design is implemented on the transformed system, which facilitates the derivation of a robust H∞ controller. An iterative algorithm is proposed for computing the feedback gain matrix, which overcomes the challenges posed by the nonlinear coupling terms when solving the matrix inequalities. Case studies validate the effectiveness of the proposed control algorithm, which, through comparison with existing control algorithms, is shown to be less conservative.

A Multigrid Solver for PDE-Constrained Optimization with Uncertain Inputs

In this manuscript, we present a collective multigrid algorithm to solve efficiently the large saddle-point systems of equations that typically arise in PDE-constrained optimization under uncertainty, and develop a novel convergence analysis of collective smoothers and collective two-level methods. The multigrid algorithm is based on a collective smoother that at each iteration sweeps over the nodes of the computational mesh, and solves a reduced saddle-point system whose size is proportional to the number N of samples used to discretized the probability space. We show that this reduced system can be solved with optimal O(N) complexity. The multigrid method is tested both as a stationary method and as a preconditioner for GMRES on three problems: a linear-quadratic problem, possibly with a local or a boundary control, for which the multigrid method is used to solve directly the linear optimality system; a nonsmooth problem with box constraints and L1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L^1$$\\end{document}-norm penalization on the control, in which the multigrid scheme is used as an inner solver within a semismooth Newton iteration; a risk-averse problem with the smoothed CVaR risk measure where the multigrid method is called within a preconditioned Newton iteration. In all cases, the multigrid algorithm exhibits excellent performances and robustness with respect to the parameters of interest.

Investigating correlation effects of uncertain input variables in reliability-oriented sensitivity analysis for planar linkage mechanism

Investigating correlation effects of uncertain input variables in reliability-oriented sensitivity analysis for planar linkage mechanism

Comparative analysis of data driven rainfall-runoff models in the Kolar river basin

To effectively tackle the challenges posed by climate change, it is crucial to enhance the accuracy of rainfall-runoff models to ensure reliability amidst changing climatic conditions. Neural networks, renowned for their ability to capture complex patterns and relationships within uncertain input and output data, offer valuable tools in this pursuit. This study aims to evaluate the efficacy of two neural network (NN) models: the Radial Basis Function Neural Network (RBFNN) and the Model Tree M5 Neural Network (MTM5NN). These models are assessed both individually and in combination with the Wavelet (WT) data processing technique for rainfall-runoff modeling in the Kolar River watershed located in Madhya Pradesh, India. Fifteen models were developed employing four algorithms: RBFNN models, WRBFNN (RBF model integrating wavelet components of rainfall as inputs), MTM5NN, and WMTM5NN (MT model incorporating wavelet components of rainfall as inputs). Initially, rainfall and runoff data underwent normalization and were applied to RBFNN and MTM5NN networks. Subsequently, time series data for both rainfall and runoff were decomposed using wavelet transforms, resulting in various sub-time series signals such as approximations and decompositions. These derived signals were then utilized as input data for RBFNN and MTM5NN, specifically designated as WRBFNN and WMTM5NN. The most effective model identified was Model 8 of WMTM5NN, which demonstrated R2 values close to 0.97, outperforming the other models. These results underscore the superior performance of the WMTM5NN model, highlighting its effectiveness in achieving heightened accuracy in predicting runoff for this specific watershed.

Probabilistic assessment of short‐term voltage stability under load and wind uncertainty

AbstractContemporary electricity networks are exposed to operational uncertainties, which may jeopardise the stability of the power grid. More specifically, the increasing penetration level of variable renewable energy generation and uncertainty in load demand are key catalysts for these emerging stability issues. A mathematical relationship is established to track the system voltage trajectory with respect to variations in uncertain inputs (associated with wind speed, system load, and wind power penetration levels). Additionally, it demonstrates the consequences of varying uncertain inputs on the short‐term voltage response across different potential operating conditions. The theoretical proposition has further been verified by the simulation studies with two test power networks in DIgSILENT PowerFactory software. The simulation results revealed that uncertain injection sources significantly impacted the system voltage at the receiving end. High uncertainty in wind speed and system loads increased voltage recovery variation, causing delays in voltage response during low wind speeds and high system loads. Additionally, increased wind power penetration levels expanded voltage recovery uncertainties, resulting in decreased system voltage and potentially leading to voltage violations and instability at 30% wind power levels. Moreover, the results showed that the system's response time increased, and in some cases, it collapsed due to increased system capacity (>80%) and dynamic load (>75%), as well as encountering a large disturbance under uncertain circumstances.

Geodesic algorithm: new approach to optimization of temporary fastener arrangement in airframe assembly process

PurposeThe purpose of the current study is development of effective and fast algorithm for optimization the arrangement of temporary fasteners during aircraft assembly.Design/methodology/approachCombinatorial nature, uncertain input data, sensitivity to mechanical properties and geometric tolerances are the specific features of the fastening optimization problem. These characteristics make the problem-solving by standard methods very resource-intensive because the calculation of the objective function requires multiple solution of contact problems. The work provides an extended description of the geodesic algorithm (GA) which is a novel non-iterative optimization approach avoiding multiple objective function calculations.FindingsThe GA makes it possible to optimize the arrangement of temporary fasteners during the different stages of the assembly process. The objective functions for the optimization are number of installed fasteners and quality of contact between joined parts. The mentioned properties of the GA also make it possible to introduce an automatic procedure for optimizing fastener arrangement into everyday practice of aircraft manufacturing.Practical implicationsThe algorithm has been applied to optimization of the assembly process in Airbus company.Originality/valuePerformance of the GA is orders of magnitude greater than standard optimization algorithms while maintaining the quality of results. The use of the assembly process specifics is the main limitation of the GA, because it cannot be automatically applied to optimization problems in other areas. High speed of work and quality of the results make it possible to use it for real optimization problems on assembly line in the production of commercial airliners.

Robust optimization of a novel ultraviolet (UV) photoreactor for water disinfection: A neural network approach

To optimize the ultraviolet (UV) water disinfection process, it is crucial to determine the ideal geometric dimensions of a corresponding model that enhance performance while minimizing the impact of uncertain photoreactor inputs. As water treatment directly affects people's lives, it is crucial to eliminate the risks associated with the non-ideal performance of disinfection photoreactors. Input uncertainties greatly affect photoreactor performance, making it essential to develop a robust optimization algorithm in advance to mitigate these effects and minimize the physical and financial resources required for constructing the photoreactors. In the suggested algorithm, a two-objective genetic algorithm is integrated with a non-intrusive polynomial chaos expansion (PCE) technique. Additionally, the Sobol sampling method is employed to select the necessary samples for understanding the system's behavior. An artificial neural network surrogate model is trained using sufficient data points derived from computational fluid dynamics (CFD) simulations. A novel type of UV photoreactors working based on exterior reflectors is chosen to optimize the process with three uncertain input parameters, including UV lamp power, UV transmittance of water, and diffusive fraction of the reflective surface. In addition, four geometrical design variables are considered to find the optimal configuration of the photoreactor. The standard deviation (SD) and the reciprocal of log reduction value (LRV) are set as the objective functions, calculated using PCE. The optimal design provides a LRV of 3.95 with SD of 0.2. The coefficient of variation (CoV) of the model significantly declines up to 7%, indicating the decreased sensitivity of the photoreactor to the input uncertainties. Additionally, it is discovered that the robust model exhibits minimal sensitivity to changes in reflectivity in various flow rates, and its output variability aligns with the SD obtained through robust optimization.

Evaluation of safe operating envelope for CO2 injection under uncertain rock mechanical parameters and earth stresses

Carbon Capture and Storage (CCS) is a pre-requisite to decarbonize CO2 emissions from industrial sectors and as an industry capable of compensating for hard-to-abate emissions in a net zero scenario. A method was developed to evaluate the geomechanical constraints and safe operating envelope as function of pore pressure and temperature. The probability of failure was estimated from uncertain input stiffness and strength data, and as cooling and re-pressurization shifts the in-situ effective stresses, the safe operating envelope was determined, here given by pressure and temperature.Onshore storages nearby industrial clusters enable energy and cost-effective handling of CO2. In the South-Eastern European region, onshore depleted oil and gas fields located nearby high-emitting industries may developed into CO2 storages. This paper describes a method for determining maximum fluid pressure as function of temperature from geomechanical restrictions. The method was employed on a practical example used to evaluate the safe operation envelope for a pilot CO2 injection site into a depleted onshore naturally fractured carbonate oil and gas field. The tool uses Monte Carlo simulations to perform geomechanical stability analyses by sampling from the inherent uncertainty of the input parameters to probability of failure as function of pressure and temperature. The risk of re-opening natural fractures, induced fracturing and fault reactivation are evaluated so the safe operating envelope can be obtained. The uncertainty of the input parameters is thus directly reflected in the safe operating envelope – thus providing an effective communication of value information to external stake holders when maturing a CO2 storage pilot.

Simulation of pulse-like ground motions with directionality effect for the 2001 Mw 7.6 Bhuj, India earthquake and sensitivity analysis of uncertain model input parameters

Ground motions exhibiting pulse-like (PL) features present a distinct and significant threat to the structural integrity of constructed environments, due to intense pulse, large amplitude, and rapid energy release. The lack of recorded PL ground motion data in the Indian region presents a significant challenge for accurate seismic risk assessment of the built environment. In the absence of recorded PL ground motions, the generation of synthetic PL ground motions can provide earthquake acceleration time histories for seismic response analysis of structures and also to evaluate ground motion prediction equations (GMPEs). Within this framework, the modified stochastic finite-fault approach based on the dynamic corner frequency is used to generate the synthetic ground motions for the 2001 Gujarat earthquake. These artificially generated ground motions are validated using available peak ground acceleration data from thirteen stations. After validation, the ground motions are generated at locations equidistant from the fault in all four directions (i.e., east, west, north, south). The expected acceleration time histories at specific sites, the spatial distribution of ground motion characteristics, and the effect of site non-linearity are investigated. Based on the geographical positioning of sites for the 2001 event, it was observed that 65.15 % of ground motions on the western side, relative to the epicenter, exhibited PL characteristics, whereas 23.64 % of ground motions on the eastern side were observed to have PL properties. The effect of uncertainty in the model input parameters on the simulated ground motions is examined by performing a sensitivity analysis. The reliability and validity of the simulated ground motions are assessed by deriving an empirical equation and compared with the results obtained from available GMPEs for the region.

Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics.

Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample-based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol' sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three-dimensional fluid-structure interaction simulations with affordable one- and zero-dimensional reduced-order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi- (1D/0D) and tri-fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single-fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol' indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response.

Adaptive PI event-triggered control for MIMO nonlinear systems with input delay

An adaptive PI event-triggered control method for a class of multi-input and multi-output (MIMO) nonlinear systems with uncertain input delay is proposed. First, a new variable is designed to eliminate the impact of uncertain input delay based on the Pade approximation and Laplace transform. Then, a nonlinear controller with PI structure is developed, it has a linear structure and the ability to deal with uncertainties. Meanwhile, only one parameter is required to be updated online through RBFNN in the system. Moreover, an event-triggered control strategy is established to dynamically allocate the communication resources based on the PI nonlinear controller. With the proposed method, MIMO nonlinear systems are able to handle input delay of different duration dynamically and still maintain superior tracking performance. Finally, the effectiveness of the proposed method is demonstrated through two sets of simulation experiments.