Uncertain Operating Conditions Research Articles

A dual‐mode integration framework and application to agile feedback design of launch vehicles

AbstractIn recent decades, the design of complex systems like launch vehicles in the aerospace industry has presented engineers with challenges that go beyond system complexity. Issues such as time‐to‐market pressures and intricate industrial processes have underscored the increasing significance of agile design methodologies. Agile design is derived from the simplification of the design process and enhancing cross‐domain data transmission and feedback. While methods based on model‐based system engineering have improved iteration times in system architecture design, challenges persist in cross‐domain data transmission. Due to the diversity of complex system models and data, a single‐mode integration method is difficult to realize the data link construction of all tools used. To address this challenge, this paper proposes a dual‐mode data integration framework with expansibility, universality, and cost‐efficiency which leverages the benefits of Remote Procedure Call and Intermediate Exchange Module, addressing the challenge of constructing cross‐domain data links under single‐mode integration. In this study, two critical requirements of the first‐ and second‐stage separation systems, namely, weight and minimum separation gap, are selected for data feedback. A Modelica‐based multiphysics simulation model is developed in MWorks; visualization and computation of the minimum gap are carried out in CoppeliaSim. To bridge the gap between domain‐specific tools, Matlab and Functional Mock‐up Unit modules are introduced as middleware, facilitating data feedback linkage. The entire simulation process is orchestrated using activity diagrams in the MagicDraw tool. The study delves into the influence of critical design parameters, such as the initial angular velocity of separation and the thrust of the retro rocket, on the minimum separation gap. It provides an analysis of minimum separation gap variations under uncertain operating conditions and examines design margins. Significantly, the paper highlights the significance of controlling the initial angular velocity during separation and the reliability of the retro rocket, providing essential decision supports and valuable insights to agile the process of system design.

Breakeven analysis of Thermal Energy Storage System under uncertain operating conditions

Since thermal energy storage (TES) systems gained momentum in the global energy market, there is a greater demand to enhance their energy efficiency and, more significantly, lower their costs. The purpose of the study is to assist decision-making under uncertainty and risk associated with stochastic increases in cost connected with Thermal Energy Storage System operation. The Thermal Energy Storage System (TSS) at the Gas District Cooling Plant in Perak, Malaysia is the purpose of this research. When fluctuating cooling demands develop, the Thermal Energy Storage System (TSS) is used to transfer energy use from peak to off-peak hours. For TSS and Gas District Cooling (GDC) systems, electric chillers and thermal storage tanks are required. Thermal energy systems (TES) play a role in the ongoing process of increasing integration across diverse energy systems to achieve a cleaner, more flexible, and long-term use of energy resources. Break-even analysis was used in the study, which is a basic tool for analyzing the feasibility of real-world initiatives with uncertainty. The findings reveal that a single-shift operation is not feasible, and the project will require at least two shifts to be viable.

Uncertainty quantification in autoencoders predictions: Applications in aerodynamics

A data-driven model is compared to classical equation-driven approaches to investigate its ability to predict quantity of interest and their uncertainty when studying airfoil aerodynamics. The focus is on autoencoders and the effect of uncertainties due to the architecture, the hyperparamaters and the choice of the training data (internal or model-form uncertainties). Comparisons with a Gaussian Process regression approach clearly illustrate the autoencoder advantage in extracting useful information on the prediction confidence even in the absence of ground truth data. Simulations accounting for internal uncertainties are also compared to the impact of the variability induced by uncertain operating conditions (external uncertainties) showing the importance of accounting for the total uncertainty when establishing prediction confidence.

Distributed Dynamic Surface Control for a Class of Quadrotor UAVs with Input Saturation and External Disturbance

An adaptive dynamic surface trajectory tracking control method based on the Nussbaum function is proposed for a class of quadrotor UAVs encountering unknown external disturbances and unidentified nonlinearities. By transforming controller expressions into numerical solutions, the challenge of overly complex controller design expressions is addressed, simplifying the overall controller design process and enhancing the efficiency of simulation programs. Additionally, an adaptive controller based on Nussbaum gain is introduced to effectively resolve actuator saturation issues. This approach mitigates complexities associated with traditional control design and ensures smooth operation of the quadrotor UAVs. The proposed methodology offers promising prospects for enhancing the robustness and performance of quadrotor UAVs under uncertain operating conditions. Finally, to validate the effectiveness of the proposed control scheme, a hardware-in-the-loop experimental setup is constructed. The dynamic model of the quadrotor UAVs and the proposed controller scheme are implemented on the Rapid Control Prototype (RCP) and Real-Time Simulator (RTS), respectively. This facilitates a semi-physical simulation experiment, providing a basis for the subsequent application of the control scheme to actual aerial vehicles. The concluding experimental results affirm the effectiveness of the proposed control scheme and highlight its potential for practical applications.

Optimal operational planning of distribution systems: A neighborhood search-based matheuristic approach

This study proposes a strategy for short-term operational planning of active distribution systems to minimize operating costs and greenhouse gas (GHG) emissions. The strategy incorporates network reconfiguration, switchable capacitor bank operation, dispatch of fossil fuel-based and renewable distributed energy resources, energy storage devices, and a demand response program. Uncertain operational conditions, such as energy costs, power demand, and solar irradiation, are addressed using stochastic scenarios derived from historical data through a k-means technique. The mathematical formulation adopts a stochastic scenario-based mixed-integer second-order conic programming (MISOCP) model. To handle the computational complexity of the model, a neighborhood-based matheuristic approach (NMA) is introduced, employing reduced MISOCP models and a memory strategy to guide the optimization process. Results from 69 and 118-node distribution systems demonstrate reduced operational costs and GHG emissions. Moreover, the proposed NMA outperforms two commercial solvers. This work provides insights into optimizing the operation of distribution systems, yielding economic and environmental benefits.

Multi-Stage Dispatch of Seaport Power Systems for Incorporating Logistical Flexibilities in Uncertain Operational Conditions

Multi-Stage Dispatch of Seaport Power Systems for Incorporating Logistical Flexibilities in Uncertain Operational Conditions

Robust shape optimization using artificial neural networks based surrogate modeling for an aircraft wing

Aerodynamic shape optimization is a very active area of research that faces the challenges of highly demanding Computational Fluid Dynamics (CFD) problems, optimization with Partial Differential Equations (PDEs) as constraints, and the appropriate treatment of uncertainties. This includes the development of robust design methodologies that are computationally efficient while maintaining the desired level of accuracy in the optimization process. This paper addresses aerodynamic shape optimization problems involving uncertain operating conditions. After a review of possible approaches to account for uncertainties, an Artificial Neural Network (ANN) model is used to approximate the aerodynamic coefficients when the operating conditions vary. Robust optimization problem-solving approaches based on deterministic measurements are used, inspired by the work of Deb [Deb K., Gupta H. Introducing robustness in multi-objective optimization. KanGAL Report 2004–2016, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, India (2004)]. The first procedure is a direct extension of a technique used for single-objective optimization. The second is a more practical approach allowing a user to define the desired degree of robustness in a problem. These approaches have been tested and validated in the case of the optimization of an aircraft wing profile in the transonic regime considering two uncertain variables: the Mach number and the angle of incidence.

Integrated Sustainable Product Design With Warranty and End-of-Use Considerations

Abstract The concept of integrated sustainable product design has recently emerged, aiming to incorporate downstream life cycle performance into the initial product design to enhance sustainability. Various sustainable product design tools based on life cycle assessment or quality function deployment have been established while the impact of reliability on circular practices has received limited attention. Recognizing the critical role of product reliability in post-design performance, this paper develops a product design optimization model that considers the warranty performance and the effect of end-of-use options. The model takes into account the effect of uncertain operating conditions on product reliability. Two optimization goals including the minimization of expected unit life cycle cost and environmental impact are achieved by the model. To demonstrate the benefits of the integrated approach, the model is applied to an electric motor design problem. The results highlight that integrating end-of-use options in the early design phase leads to adjustments in component selection and reliability design. Moreover, the circular utilization of used products enables cost savings throughout the product’s life cycle and contributes to environmental impact reduction. Lastly, the study analyzes the effects of operating conditions, warranty policies, and take-back prices for used products on design decisions, providing valuable insights for product designers.

Effect of uncertain operating conditions on the aerodynamic performance of high-pressure axial turbomachinery blades

The objective existence of uncertain operating conditions can cause significant variations in the aerodynamic performance of turbomachinery. This study presents a comprehensive investigation into the impact of six uncertain operating conditions on the aerodynamic performance of a turbine. Additionally, a global sensitivity analysis was performed to identify the most important operating condition parameters. To reduce the computational cost of uncertainty quantification (UQ), a new UQ framework, the Nested Sparse-grid Stochastic Collocation Method (NSSCM), is used. It has been observed that uncertain operating conditions can lead to significant variations in energy loss, which in turn directly impacts the operating efficiency of the turbine. This research finds that the suction surface and trailing edge of the blade are particularly sensitive to uncertain operating conditions. The impact of uncertainty on the static pressure coefficient decreases as the flow develops, while the uncertainty of the Mach number and energy loss coefficient increases. Inlet total pressure and outlet static pressure are the primary factors impacting turbomachinery aerodynamics, as determined by sensitivity analysis. This study provides new insights into the impact of uncertain operating conditions on turbine efficiency and can guide the development of more robust turbine designs in the future.

Stochastic Performance of Journal Bearing With Two-Layered Porous Bush—A Machine Learning Approach

Abstract This investigation presents the deterministic and stochastic responses of the journal bearing with a two-layered porous bush. Pressure equations in the porous layers and modified Reynolds equations in the clearance region are governed by the finite difference method (FDM). Stochastic analysis based on Monte Carlo simulation (MCS) is used to investigate the effect of random variation in input parameters caused by uncertain operating conditions, improper installations, and manufacturing imperfections. In order to enhance computational efficiency, this probabilistic study is conducted in conjunction with the machine learning (ML) model based on the support vector machine (SVM) algorithm. The uncertainty in the bearing responses is presented in the form of the probability density function (PDF), considering both the independent and combined effect of the stochastically varied input parameters. Graphical illustration of the data-driven sensitivity represents the relative significance of each input parameter affecting the steady-state responses of the journal bearing with two-layered porous bush. The findings of the present study reveal that the stochastic variations in the input parameters have a profound influence on the operational characteristics of the porous bearing. The outcome of the present study will be helpful in deciding the operational regime of the porous bearing under the practically relevant stochastic environment.

Robust optimization for designing air quality monitoring network in coal ports under uncertainty

Air quality monitoring network (AQMN) is an important management tool to mitigate the impact of air pollution from coal port operations on coastal atmospheric environment and public health. The location of monitoring stations in the AQMN is influenced by wind conditions and emission intensity of air pollutants, which is highly related to the efficiency of coal port operations. Therefore, this paper aims at solving the problem of how to design the AQMN with consideration of uncertain operational efficiency and wind conditions. This study proposes a robust optimization method to address the impact of uncertainty on the design of AQMN in coal ports. Firstly, the deterministic AQMN design problem is summarized as a maximum weighted covering location problem (MWCLP) and formulated as a gradual covering model with the cooperative covering strategy. Then, a linear approximation method is applied to the non-linear model to improve computational efficiency. Secondly, a method for constructing uncertain scenarios is proposed by combining sampling method, port emission inventory, and atmospheric diffusion simulation. After that, a robust optimization model is developed based on the concept of p-robustness to ensure that the relative regret value of each uncertain scenario is less than a specified threshold. Finally, we take a coal port in northern China as a case study. Results show that the average monitoring capacity of robust layout is 5.25% and 1.57% higher than that of conventional layout and deterministic layout. The robust layout ensures a relative regret value of no more than 3.41% for each scenario. The proposed method can provide port and environmental authorities with robust decision support of AQMN design under limited budget and uncertainty of operational efficiency and wind conditions.

Hierarchical optimal control framework to automatic train regulation combined with energy-efficient speed trajectory calculation in metro lines

In high-density metro lines, frequent disturbances could lead to a domino effect of train delays if no adjustment strategy is imposed timely. In this paper, to enhance train timetable adherence and reduce energy consumption, we provide a hierarchical optimal control framework for the automatic train regulation problem with energy-efficient speed trajectory based on the model predictive control method. Specifically, by considering the dynamic passenger flows, the non-linear train regulation model is proposed at the higher level to enhance timetable adherence, while at the lower level the energy-efficient speed trajectories of the trains are generated on-line in a distributed manner. The interaction of real-time information exists between the higher level and the lower level, i.e., the upper model provides the train running time and the numbers of onboard passengers to the lower model, while the lower model feedbacks the updated train information (e.g., the actual arrival time) to the upper model, which provides the potential for the proposed framework to respond to disturbances at the operational stage. Moreover, a customized model predictive control method combined with Radau pseudospectral method (RPM) is designed to generate the energy-efficient train speed trajectory at the lower level in response to uncertain operational conditions. Numerical cases based on the Beijing Metro Yizhuang line demonstrate the effectiveness and robustness of our proposed hierarchical optimal control framework to deal with uncertain disturbances.

Modular Air Quality Calibration and Forecasting Method for Low-Cost Sensor Nodes

The climatic challenges are rising across the globe in general and in worst hit under-developed countries in particular. The need for accurate measurements and forecasting of pollutants with low-cost deployment is more pertinent today than ever before. Low-cost air quality monitoring sensors are prone to erroneous measurements, frequent downtimes, and uncertain operational conditions. Such a situation demands a prudent approach to ensure an effective and flexible calibration scheme. We propose a modular air quality calibration, and forecasting (MAQ-CaF) methodology, that side-steps the challenges of unreliability through its modular machine learning-based design which leverages the potential of IoT framework. It stores the calibrated data both locally and remotely with an added feature of future predictions. Our specially designed validation process and the discussion of the results help to establish the proposed solution’s applicability and flexibility. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {CO}, \text {SO}_{{2}}, \text {NO}_{{2}}, \text {O}_{{3}}, \text {PM}_{{1.0}}, \text {PM}_{{2.5}}~ \text {and}~ \text {PM}_{{10}}$ </tex-math></inline-formula> were calibrated and monitored with reasonable accuracy. Such an attempt is a step toward addressing climate change’s global challenge through appropriate monitoring and air quality tracking across a wider geographical region via affordable monitoring.

Piecewise model-based adaptive compensation control for high-speed trains with unknown actuator failures

Abstract In order to deal with the uncertainties caused by different operation conditions and unknown actuator failures of high-speed trains, an adaptive failures compensation control scheme is designed based on the piecewise model. A piecewise constant model is introduced to describe the variable system parameters caused by the variable operation environments, and a multiple-particle piecewise model of high-speed trains, with unknown actuator failures, is then established. An adaptive failure compensation controller is developed for the multiple-particle piecewise constant model, by using a direct model refering to the adaptive control method. Such an adaptive controller can not only compensate the uncertainties from unknown actuator failures, but also effectively deal with the uncertainties caused by different operating conditions. Finally, a CRH380A high-speed train model is taken as the controlled object for the simulation study. The simulation results show that the proposed controller ensures the desired system performance in the presence of unknown actuator failures and uncertain operation conditions.

Multi-fidelity robust design optimization of an ORC turbine for high temperature waste heat recovery

The ORC technology is subject to manifold sources of uncertainty that can have a severe impact on the thermodynamic and economic efficiency of plant components, particularly when the system is operated at off-design conditions. In this contribution we focus on the development of ORC turbines with stable performance under uncertainty: a novel multi-fidelity robust design optimization (RDO) strategy is used to design the first nozzle of an ORC turbine for high temperature waste-heat recovery. For this kind of application, the turbine inlet and outlet conditions may vary randomly over a large range. The RDO strategy combines parsimonious uncertainty quantification techniques with a multi-objective genetic algorithm optimizer based on surrogate models. The multi-fidelity approach allows to estimate with high accuracy and with a low computational cost the statistical moments of the probability distribution function of the quantity of interest, which here is the entropy generation within the cascade. To improve the accuracy of the surrogate model coupled with the optimizer, the multi-objective expected improvement criterion is adopted. The optimization converges to an efficient optimum solution, ensuring improved and stable performance over the whole considered range of uncertain operating conditions and with a computational cost that is significantly lower than other RDO approaches proposed in literature.

The dynamics of coordination patterns in multiteam systems response to emergency: Case study from a pharmaceutical enterprise.

Effective coordination of multiteam systems (MTSs) can help enterprises respond quickly to complex and uncertain problems under disasters. However, it is unclear how MTS coordination patterns dynamically affect MTS performance in disasters. This study examined how MTSs responded to an emergency production incident at the Zhejiang Huisong Pharmaceutical Company in China during the COVID-19 pandemic through a qualitative and quantitative study. Based on social network theory, we found that a centralized coordination pattern impacts MTS performance by giving play to the leadership team's network centrality position advantage during the crisis outbreak period. In the post-crisis period, the decentralized coordination pattern impacts MTS performance by giving play to the advantages of horizontal coordination. Our results help managers to consider the dynamics of coordination patterns in crisis management in ways that assist them in adapting an effective coordination pattern to changing and uncertain operational conditions.

Flat Unglazed Transpired Solar Collector: Performance Probability Prediction Approach Using Monte Carlo Simulation Technique

Engineering applications including food processing, wastewater treatment, home heating, commercial heating, and institutional heating successfully use unglazed transpired solar collectors (UTCs). Trapping of solar energy is the prime goal of developing an unglazed transpired solar collector. The UTC is usually developed in and around the walls of the building and absorbs the solar energy to heat the air. One of the key challenges faced by the UTC designer is the prediction of performance and its warranty under uncertain operating conditions of flow variables. Some of the flow features are the velocity distribution, plate temperature, exit temperature and perforation location. The objective of the present study was to establish correlations among these flow features and demonstrate a method of predicting the performance of the UTC. Hence, a correlation matrix was generated from the dataset prepared after solving the airflow over a perforated flat UTC. Further, both strong and weak correlations of flow features were captured through Pearson’s correlation coefficient. A comparison between the outcomes from a linear regression model and that of computational simulation was showcased. The performance probability for the UTC was interlinked with correlation matrix data. The Monte Carlo simulation was used to predict the performance from random values of the flow parameters. The study showed that the difference between the free stream value of temperature and the value of temperature inside the UTC’s chamber varied between 15 and 20 °C. The probability of achieving system efficiency greater than 35% was 55.2%. This has raised the hope of recommending the UTC for drying and heating where the required temperature differential is within 20 °C.

Uncertainty analysis of operational conditions in selective artificial ground freezing applications

Artificial ground freezing (AGF) systems are susceptible to uncertain parameters highly affecting their performance. Particularly, selective artificial ground freezing (S-AGF) systems involve several uncertain operational conditions. In this study, uncertainty analysis is conducted to investigate four operational parameters: 1) coolant inlet temperature, 2) coolant flow rate, 3) pipes emissivity, and 4) pipes eccentricity. A reduced-order model developed and validated in our previous work for field-scale applications is exploited to simulate a total of 5,000 cases. The uncertain operational parameters are set according to Monte Carlo analysis based on field observations of a field-scale freeze-pipe in the mining industry extending to 460 m below the ground surface. The results indicate that the freezing time can range between 270 and 350 days with an average of 310 days, whereas the cooling load per one freeze-pipe ranges from 90 to 160 MWh, with an average of 129 MWh. Furthermore, it is observed that the freezing time and energy consumed are mostly dominated by the coolant inlet temperature, while energy dissipated in the passive zone (where ground freezing is not needed) is mostly affected by pipes emissivity. Overall, the conclusions of this study provide useful estimations for engineers and practitioners in the AGF industry.

Data-driven constrained reinforcement learning for optimal control of a multistage evaporation process

It is challenging for reinforcement learning to solve the optimal control problem of industrial processes with constraints under uncertain operating conditions. In this context, this paper proposes a novel data-driven constrained reinforcement learning algorithm for the optimal control of a multistage evaporation process consisting of multiple evaporators in series with coupled liquid levels. We first formulate the optimal control problem as a constrained Markov decision process. Then, with the cumulative tracking error of the outlet liquor density taken as the cumulative constraint, a Lagrangian-based constrained policy optimization is developed. The fast setpoint tracking is achieved by gradient iteration of the policy and the dual variable. An action correction layer based on the online sequential version of random vector functional-link networks is built on the output of the policy network to address the instantaneous constraints of the liquid levels. The infeasible action is corrected in real-time so as to keep the liquid levels in each evaporator within operating range. Finally, we utilize both on-policy and off-policy data generated by the interaction between the constrained policy and the evaporation environment to update our algorithm, which is more data-efficient. Experiments have been carried out on a multistage evaporation system, and the results validate the effectiveness of the proposed algorithm.

DC Shipboard Microgrids With Constant Power Loads: A Review of Advanced Nonlinear Control Strategies and Stabilization Techniques

In modern dc shipboard microgrid (SMG) systems, the propulsion motors and hotel loads are always supplied through tightly regulated point of load converters, which behave as constant power loads (CPLs). The negative incremental impedance due to CPL’s characteristics destabilizes the dc bus voltage of dc SMGs. Due to uncertain operating conditions of maritime ships on the sea, the dc bus voltage robust control is a crucial matter. Therefore, this paper presents a cutting-edge systematic review on advanced nonlinear control strategies to stabilize and control the CPLs in dc SMGs, such as sliding mode control, synergetic control, backstepping control, model predictive control, and passivity-based control. The latest stabilization techniques and the future trends towards an adaptive nonlinear control have been presented throughout this review. Several feedforward control-based observation and estimation techniques have been highlighted. The stability analysis and stability challenges of dc SMGs are also discussed.