Search Results (121)

Rotating detonation engines (RDEs) are expected to have higher specific work and efficiency, but the high-temperature transonic flow delivered by the combustor poses relevant design and technological difficulties. This work proposes a 1D model for turbine internal cooling design which can be used to explore multiple design options during the preliminary design of the cooling system. Being based on an energy balance applied to an infinitesimal control volume, the model is general and can be adapted to other applications. The model is applied to design a cooling system for a pre-existing stator blade geometry. Both the inputs and the outputs of the 1D simulation are in good agreement with the values found in the literature. Subsequently, 1D results are compared to a full conjugate heat transfer (CHT) simulation. The agreement on the internal heat transfer coefficient is excellent and is entirely within the uncertainty of the correlation. Despite some criticality in finding agreement with the thermal power distribution, the Mach number, the total pressure drop, and the coolant temperature increase in the cooling channels are accurately predicted by the 1D code, thus confirming its value as a preliminary design tool. To guarantee the integrity of the blade at the extremities, a cooling solution with coolant injection at the leading and trailing edge is studied. A finite element analysis of the cooled blade ensures the structural feasibility of the cooling system. The computational economy of the 1D code is then exploited to perform a global sensitivity analysis using a polynomial chaos expansion (PCE) surrogate model to compute Sobol’ indices. Full article

This study presents a novel framework for uncertainty propagation in power-law, Bingham, and Casson fluids through rectangular ducts under stochastic viscosity (Case I) and pressure gradient conditions (Case II). Using the computationally efficient Stochastic Finite Difference Method with Homogeneous Chaos (SFDHC), validated via comparison with quasi-Monte Carlo simulations, we demonstrate significantly lower computational costs across varying Coefficients of Variation (COVs). For viscosity uncertainty (Case I), results show a 0.54–2.8% increase in mean maximum velocity with standard deviations reaching 75.3–82.5% of the COV, where the power-law model exhibits the greatest sensitivity (velocity variations spanning 71.2–177.3% of the mean at COV = 20%). Pressure gradient uncertainty (Case II) preserves mean velocities but produces narrower and symmetric distributions. We systematically evaluate the effects of aspect ratio, yield stress, and flow behavior index on the stochastic velocity response of each fluid. Moreover, our analysis pioneers a performance hierarchy: Herschel–Bulkley fluids show the highest mean and standard deviation of maximum velocity, followed by power-law, Robertson–Stiff, Bingham, and Casson models. A key finding is the extreme fluctuation of the Robertson–Stiff model, which exhibits the most drastic deviations, reaching up to 177% of the average velocity. The significance of fluid-specific stochastic analysis in duct system design is underscored by these results. This is especially critical for non-Newtonian flows, where system performance and reliability are greatly impacted by uncertainties in viscosity and pressure gradient, which reflect actual operational variations. Full article

To address the challenges of transient voltage stability in modern power systems with high renewables penetration, this paper proposes a multiple stability margin indexes-oriented online risk evaluation and adjustment framework based on a digital twin platform. The System Voltage Deviation Index (S_VDI) is first introduced as a quantitative metric to assess transient voltage stability from time-domain simulation results, capturing the system’s dynamic response under large disturbances. An arbitrary Polynomial Chaos (aPC) expansion combined with Sobol sensitivity analysis is then employed to model the nonlinear relationship between S_VDI and uncertain inputs such as wind power, photovoltaic output, and dynamic load variations, enabling accurate identification of key nodes influencing stability. Furthermore, an emergency control optimization model is developed that jointly considers voltage, frequency, and rotor angle stability margins, as well as the economic costs of load shedding, with a trajectory sensitivity-based local linearization technique applied to enhance computational efficiency. The proposed method is validated on a hybrid AC/DC test system (CSEE-VS), and results show that, compared with a traditional control strategy, the optimized approach reduces total load shedding from 322.59 MW to 191.40 MW, decreases economic cost from 229.18 to 178.11, and improves the transient rotor angle stability index from 0.31 to 0.34 and the transient frequency stability index from 0.3162 to 1.511, while maintaining acceptable voltage stability performance. These findings demonstrate that the proposed framework can accurately assess online operational risks, pinpoint vulnerable nodes, and generate cost-effective, stability-guaranteeing control strategies, showing strong potential for practical deployment in renewable-integrated power grids. Full article

Process designs based on deterministic simulations without considering parameter uncertainty or variability have a high probability of failing to meet specifications. In this work, uncertainty and global sensitivity analyses were applied to a biogas upgrading membrane process implemented in the COCO simulator (CAPE-OPEN to CAPE-OPEN), considering both controlled and non-controlled scenarios. A user-defined model code was developed to simulate gas separation membrane stages, and a preliminary study of membrane parameter uncertainty was performed. In addition, a unit generating combinations of uncertainty factors was developed to interact with the simulator’s parametric tool. Global sensitivity analyses were carried out using the Morris method and Sobol’ indices obtained by Polynomial Chaos Expansion, allowing for the ranking and quantification of the influence of feed variability and membrane parameter uncertainty on product streams and process utilities. Results showed that when feed variability was ±10%, its effect exceeded the uncertainty of the membrane parameters. Uncertainty analysis using the Monte Carlo propagation method provided lower and upper tolerance limits for the main responses. Relative gaps between tolerance limits and mean product flows were 8–9% at a feed variability of 5% and 14–18% at a feed variability of 10%, while relative tolerance gaps resulting from composition were smaller (0.4–1.2%). Full article

The accuracy of the blade pitch angle (BPA) motion in controllable pitch propellers (CPPs) is considered crucial for the efficacy and reliability of marine propulsion systems. The pitch adjustment process of CPPs is highly complex and influenced by various uncertain factors. A parametric kinematic model for the pitch adjustment process for CPPs was established, incorporating the geometric dimensions and material surface friction coefficients caused during workpiece production as uncertainty parameters. The aim was to establish the correspondence between these uncertainty parameters and the BPA of CPPs. A large dataset was generated by batch calling on Adams. Based on the collected dataset, five surrogate models (e.g., deep neural network (DNN), Kriging, support vector regression (SVR), random forest (RF), and polynomial chaos expansion Kriging (PCK)) were constructed to predict the BPA. Among these, the DNN approach demonstrated the highest prediction accuracy. Accordingly, the influence of uncertainties on the BPA was investigated using the DNN model, focusing on variations in the slider width, crank pin diameter, crank disc diameter, piston rod–slider friction coefficient, crank pin–slider friction coefficient, and hub bearing–crank disc friction coefficient. The high-fidelity model established in this study can replace the kinematic model of the CPP pitch adjustment process, significantly improving computational efficiency. The research findings also provide important references for the design optimization of CPPs. Full article

This study presents an innovative approach to quantifying uncertainty in Herschel–Bulkley (H-B) fluid flow through rectangular ducts, analyzing four scenarios: uncertain apparent viscosity (Case I), uncertain pressure gradient (Case II), uncertain boundary conditions (Case III) and uncertain apparent viscosity and pressure gradient (Case IV). Using the stochastic finite difference with homogeneous chaos (SFDHC) method, we produce probability density functions (PDFs) of fluid velocity with exceptional computational efficiency (243 times faster), matching the accuracy of Monte Carlo simulation (MCS). Key statistics and maximum velocity PDFs are tabulated and visualized for each case. Mean velocity shows minimal variation in Cases I, III, and IV, but maximum velocity fluctuates significantly in Case I (63.95–187.45% of mean), Case II (50.15–156.68%), and Case IV (63.70–185.53% of mean), vital for duct design and analysis. Examining the effects of different parameters, the SFDHC method’s rapid convergence reveals the fluid behavior index as the primary driver of maximum stochastic velocity, followed by aspect ratio and yield stress. These findings enhance applications in drilling fluid management, biomedical modeling (e.g., blood flow in vascular networks), and industrial processes involving non-Newtonian fluids, such as paints and slurries, providing a robust tool for advancing understanding and managing uncertainty in complex fluid dynamics. Full article

In renewable energy generation, uncertainties mainly refer to power output fluctuations caused by the intermittency, variability, and forecasting errors of wind and photovoltaic power. These uncertainties have adverse effects on the secure operation of the power systems. Probabilistic load supply capability (LSC) serves as an effective perspective for evaluating power system security under uncertainties. Therefore, this paper studies the influence of renewable energy generation on probabilistic LSC to quantify the impact of these uncertainties on the secure operation of the power systems. Global sensitivity analysis (GSA) is introduced for the first time into probabilistic LSC evaluation. It can quantify the impact of renewable energy generation on the system’s LSC and rank the importance of renewable energy power stations based on GSA indices. GSA necessitates multiple rounds of probabilistic LSC evaluation, which is computationally intensive. To address it, this paper introduces a novel probabilistic repeated power flow (PRPF) algorithm, which employs a basis-adaptive sparse polynomial chaos expansion (BASPCE) model as a surrogate model for the original repeated power flow model, thereby accelerating the probabilistic LSC evaluation. Finally, the effectiveness of the proposed methods is verified through case studies on the IEEE 39-bus system. This study provides a practical approach for analyzing the impact of renewable generation uncertainties on power system security, contributing to more informed planning and operational decisions. Full article

The growing global freshwater scarcity urgently requires innovative wastewater treatment technologies. This study hypothesized that biomimicry-inspired automated machine learning (AML) could effectively manage wastewater variability through adaptive processing techniques. Utilizing decentralized swarm intelligence, specifically the Respected Parametric Insecta Swarm (RPIS), the system demonstrated robust adaptability to fluctuating influent conditions, maintaining stable effluent quality without centralized control. Bio-inspired oscillatory control algorithms maintained stability under dynamic influent scenarios, while adaptive sensor feedback enhanced real-time responsiveness. Machine learning (ML) methods inspired by biological morphological evolution accurately classified influent characteristics (F1 score of 0.91), optimizing resource allocation dynamically. Significant reductions were observed, with chemical consumption decreasing by approximately 11% and additional energy usage declining by 14%. Furthermore, bio-inspired membranes with selective permeability substantially reduced fouling, maintaining minimal fouling for up to 30 days. Polynomial chaos expansions efficiently approximated complex nonlinear interactions, reducing computational overhead by approximately 35% through parallel processing. Decentralized swarm algorithms allowed the rapid recalibration of system parameters, achieving stable pathogen removal and maintaining effluent turbidity near 3.2 NTU (Nephelometric Turbidity Units), with total suspended solids consistently below 8 mg/L. Integrating biomimicry with AML thus significantly advances sustainable wastewater reclamation practices, offering quantifiable improvements critical for resource-efficient water management. Full article

The random fluctuations in inlet flow represent a common uncertainty in aero-engine compressors, necessitating the control of its effects through blade optimization design. To account for the impact of inlet flow fluctuations on performance in blade design optimization, an efficient multi-objective adaptive robust aerodynamic design optimization (ARADO) method is proposed. The optimization method employs a novel sparse polynomial chaos expansion (PCE) and the advanced noisy Gaussian process regression (NGPR) technique is used to establish an initial stochastic surrogate model (SSM) containing statistical moments of aerodynamic performance. By introducing advanced sparse signal processing concepts, the sparce PCE significantly enhances the efficiency of acquiring each training sample for SSM. During the optimization process, the initial SSM autonomously updates based on historical optimization data, without requiring high precision across the entire design space. Compared to traditional model-based aerodynamic robust optimizations, the proposed ARADO method exhibits a faster convergence speed and achieves a superior average level of the optimal solution set. It also better balances various optimization objectives, concentrating the space distribution of optimal solutions closer to the average level. Ultimately, the ARADO is applied to the aerodynamic robust design of a high-load compressor airfoil across all operating incidences. The optimization results enhance aerodynamic performance while reducing performance diversity, thus aligning more closely with practical engineering requirements. Through data analysis of the optimal solutions, robust design guidelines for blade aerodynamic shapes are obtained, along with insights into the flow mechanisms that enhance aerodynamic robustness. Full article

Using non-ergodic ground-motion models (GMMs) in probabilistic seismic hazard analysis (PSHA) for areal sources can lead to large increases in calculation time compared to PSHA based on ergodic GMMs due to the large number of branches on the logic tree required to capture the spatial correlation of the non-ergodic terms. To reduce the computation time, a Polynomial Chaos (PC) expansion with a Taylor series approximation to capture the effects of the spatial correlation effects of the non-ergodic terms is used for the hazard calculations. With these approximate analytical methods, the calculation time for a logic tree with 100 branches for the non-ergodic terms can be reduced by a factor of 50 to 100. Using the proposed analytical approximations, the loss of accuracy of the mean hazard and the epistemic fractiles of the hazard is about 2%. Full article

Time-varying driving loads and uncertain structural parameters affect the transmission accuracy of chain transmission mechanisms. To enhance the transmission accuracy and placement consistency of these mechanisms, a robust optimization design method based on Karhunen–Loeve expansion and Polynomial Chaos Expansion (KL-PCE) is proposed. First, a dynamic model of the chain transmission mechanism, considering multiple contact modes, is established, and the model’s accuracy is verified through experiments. Then, based on the KL-PCE method, a mapping relationship between uncertain input parameters and output responses is established. A robust optimization design model for the chain transmission process is formulated, with transmission accuracy and consistency as objectives. Finally, case studies are used to verify the effectiveness of the proposed method. Thus, the transmission accuracy of the chain transmission mechanism is improved, providing a theoretical foundation for the design of chain transmission mechanisms under time-varying load uncertainties and for improving the accuracy of other complex mechanisms. Full article

Traditional methods for identifying wheel–rail contact loads are based on deterministic models, in which the uncertainties such as material inhomogeneity and geometric tolerance are not considered. For wheel–rail contact load analysis with uncertainties, a novel method named the Interval First-Order Polynomial Chaos Expansion method (IFOPCE) is proposed to propagate the uncertainty in wheel–rail contact systems. In IFOPCE, the polynomial chaos expansion (PCE) is first utilized to approximate the relationship between strain responses, wheel–rail loads, and uncertain variables. The expansion coefficients are calculated using Latin Hypercube Sampling (LHS). To efficiently decouple the wheel–rail loads, the relationship between load and strain is established based on the first-order PCE. By using IFOPCE, the variation range of wheel–rail contact loads can be effectively obtained. It is shown in numerical examples that the IFOPCE achieves high computational accuracy and the uncertainties have a great effect on the identification of wheel–rail loads. Full article

Efficient custom hardware motivates the use of fixed-point arithmetic in the implementation of digital signal-processing (DSP) algorithms. This conversion to finite precision arithmetic introduces quantization noise in the system, which affects the system’s performance. As a result, characterizing quantization noise and its effects within a DSP system is a challenge that must be addressed to avoid over-allocating hardware resources during implementation. Polynomial chaos expansion (PCE) is a method used to model uncertainty in engineering systems. Although it has been employed to analyze quantization effects in DSP systems, previous investigations have been limited in scope and scale. This paper introduces new techniques that allow the application of PCE to be scaled up to larger DSP blocks with many noise sources, as needed for building blocks in software-defined radios (SDRs). Design space exploration algorithms that leverage the accuracy of PCE to estimate bit widths for fixed-point implementations of DSP blocks in an SDR system are explored, and their advantages will be presented. Full article

An aeroengine faces multi-source uncertainty consisting of aeroengine epistemic uncertainty and the control system stochastic uncertainty during operation. This paper investigates actuator fault estimation under multi-source uncertainty to enhance the fault diagnosis capability of aero-engine control systems in complex environments. With the polynomial chaos expansion-based discrete stochastic model quantification, the optimal filter under multi-source uncertainty, the Hyperelliptic Kalman Filter, is proposed. Meanwhile, by treating actuator fault as unknown input, the Two-stage Hyperelliptic Kalman Filter (TSHeKF) is also proposed to achieve optimal fault estimation under multi-source uncertainty. However, considering that the biases of the model are often fixed for the individual, the TSHeKF-based fault estimation is robust and leads to inevitable conservativeness. By adding the additional estimation of the unknown deviation in state function caused by probabilistic system parameters, the hybrid fault observer (HFO) is proposed based on the TSHeKF and realizes conservativeness-reduced estimation for actuator fault under multi-source uncertainty. Numerical simulations show the effectiveness and optimality of the proposed HFO in state estimation, output prediction, and fault estimation for both single and multi-fault modes, when considering multi-source uncertainty. Furthermore, Monte Carlo experiments have demonstrated that the HFO-based optimal fault estimation is less conservative and more accurate than the Two-stage Kalman Filter and TSHeKF, providing better safety and more reliable aeroengine operation assurance. Full article

As magnetic field strength in Magnetic Resonance Imaging (MRI) technology increases, maintaining the specific absorption rate (SAR) within safe limits across human head tissues becomes challenging due to the formation of standing waves at a shortened wavelength. Compounding this challenge is the uncertainty in the dielectric properties of head tissues, which notably affects the SAR induced by the radiofrequency (RF) coils in an ultra-high-field (UHF) MRI system. To this end, this study introduces a computational framework to quantify the impacts of uncertainties in head tissues’ dielectric properties on the induced SAR. The framework employs a surrogate model-assisted Monte Carlo (MC) technique, efficiently generating surrogate models of MRI observables (electric fields and SAR) and utilizing them to compute SAR statistics. Particularly, the framework leverages a high-dimensional model representation technique, which constructs the surrogate models of the MRI observables via univariate and bivariate component functions, approximated through generalized polynomial chaos expansions. The numerical results demonstrate the efficiency of the proposed technique, requiring significantly fewer deterministic simulations compared with traditional MC methods and other surrogate model-assisted MC techniques utilizing machine learning algorithms, all while maintaining high accuracy in SAR statistics. Specifically, the proposed framework constructs surrogate models of a local SAR with an average relative error of 0.28% using 289 simulations, outperforming the machine learning-based surrogate modeling techniques considered in this study. Furthermore, the SAR statistics obtained by the proposed framework reveal fluctuations of up to 30% in SAR values within specific head regions. These findings highlight the critical importance of considering dielectric property uncertainties to ensure MRI safety, particularly in 7 T MRI systems. Full article