Grey-box Model Research Articles

Uncertainty quantification in inerter-based quasiperiodic lattices

Inerter-based periodic structures have attracted significant interest among the research community due to their wide range of applications. However, existing studies on quasiperiodic structures, especially with inerters, are limited. Therefore, this timely work investigates the dynamics of novel one-dimensional inerter-based quasiperiodic lattices with and without local resonators. The quasiperiodicity is introduced through the modulation of both spring and inerter properties resulting in the well-known Hofstadter-like butterfly spectrum having a fractal structure. Moreover, by considering the appropriate boundary conditions and many mass–spring-inerter subsystems, the bulk spectrum of the system is investigated demonstrating the existence of multiple edge states in lower and higher frequency ranges. The deterministic study showed that the effect of inertia amplification is to reduce the frequencies of the Hofstadter-like butterfly and to widen the low frequency band gaps. The second contribution of this work involves investigating the effect of parametric uncertainty for the acoustic-metamaterial-like chain based on Monte Carlo simulations. Moreover, to address the computational aspect of the problem, grey-box modeling using machine learning was performed. A Gaussian process model was trained on a limited dataset and was found to capture the stochastic responses of the lattices adequately. The statistical variation of parameters with different levels of uncertainty demonstrated significant effects on the Hofstadter-like butterfly, band gaps, corresponding edge states and frequency responses. The sensitivity of the dynamic behavior in quasiperiodic lattices to variabilities reveal the need to account for system uncertainties for their targeted performance as future vibration absorbers and energy harvesters. The study also paves the way to utilize the results as useful prior information for robust design optimization of real inerter-based quasiperiodic lattice devices.

CORRIGENDUM to: Grey-box Model Development and MPC Simulation of Campus Office Zone Based on Actual Measurement

CORRIGENDUM to: Grey-box Model Development and MPC Simulation of Campus Office Zone Based on Actual Measurement

Two-Outputs Nonlinear Grey Box Model for Lithium-Ion Batteries

Storing energy efficiently is one of the main factors of a more sustainable world. The battey management system in energy storage plays an extremely important role in ensuring these systems’ efficiency, safety, and performance. This battery management system is capable of estimating the battery states, which are used to give better efficiency, a long life cycle, and safety. However, these states cannot be measured directly and must be estimated indirectly using battery models. Therefore, accurate battery models are essential for battery management systems implementation. One of these models is the nonlinear grey box model, which is easy to implement in embedded systems and has good accuracy when used with a good parameter identification method. Regarding the parameter identification methods, the nonlinear least square optimization is the most used method. However, to have accurate results, it is necessary to define the system’s initial states, which is not an easy task. This paper presents a two-outputs nonlinear grey box battery model. The first output is the battery voltage, and the second output is the battery state of charge. The second output was added to improve the system’s initial states identification and consequently improve the identified parameter accuracy. The model was estimated with the best experiment design, which was defined considering a comparison between seven different experiment designs regarding the fit to validation data, the parameter standard deviation, and the output variance. This paper also presents a method for defining a weight between the outputs, considering a greater weight in the output with greater model confidence. With this approach, it was possible to reach a value 1000 times smaller in the parameter standard deviation with a non-biased and little model prediction error when compared to the commonly used one-output nonlinear grey box model.

A thermodynamics-informed active learning approach to perception and reasoning about fluids

Learning and reasoning about physical phenomena is still a challenge in robotics development, and computational sciences play a capital role in the search for accurate methods able to provide explanations for past events and rigorous forecasts of future situations. We propose a thermodynamics-informed active learning strategy for fluid perception and reasoning from observations. As a model problem, we take the sloshing phenomena of different fluids contained in a glass. Starting from full-field and high-resolution synthetic data for a particular fluid, we develop a method for the tracking (perception) and simulation (reasoning) of any previously unseen liquid whose free surface is observed with a commodity camera. This approach demonstrates the importance of physics and knowledge not only in data-driven (gray-box) modeling but also in real-physics adaptation in low-data regimes and partial observations of the dynamics. The presented method is extensible to other domains such as the development of cognitive digital twins able to learn from observation of phenomena for which they have not been trained explicitly.

Deep Physics Corrector: A physics enhanced deep learning architecture for solving stochastic differential equations

We propose a novel gray-box modeling algorithm for physical systems governed by stochastic differential equations (SDE). The proposed approach, referred to as the Deep Physics Corrector (DPC), blends approximate physics represented in terms of SDE with deep neural network (DNN). The primary idea here is to exploit DNN to model the missing physics. We hypothesize that combining incomplete physics with data will make the model interpretable and allow better generalization. The primary bottleneck associated with training surrogate models for stochastic simulators is often associated with selecting the suitable loss function. Among the different loss functions available in the literature, we use the conditional maximum mean discrepancy (CMMD) loss function in DPC because of its proven performance. Overall, physics-data fusion and CMMD allow DPC to learn from sparse data. We illustrate the performance of the proposed DPC on four benchmark examples from the literature. The results obtained are highly accurate, indicating its possible application as a surrogate model for stochastic simulators.

Investigation of a High-Temperature Heat Pump for Heating Purposes

High-temperature heat pumps consist of a promising choice for substituting conventional boilers and producing hot water with temperature levels in the range of 70–80 °C. The use of high-temperature heat pumps makes it possible to exploit the existing radiators in existing buildings to achieve effective heating of the indoor space and keep thermal comfort at high standards. The goal of this work is the detailed investigation of a high-temperature heat pump, with the R1234ze(E) as the working medium for producing heating at 80 °C for space-heating purposes, in Greek climate conditions. The analysis was conducted at two locations, one in the south (Athens—Zone B) and one in the north (Thessaloniki—Zone C), in order to examine the performance of the study’s technology in the most representative locations of Greece. The thermodynamic analysis of the heat pump was performed with a developed model in Engineering Equation Solver by exploiting real data from the compressor manufacturer and by practically using a gray box model. The heating thermal loads were calculated with a dynamic analysis using TRNSYS software. According to the final results, the high-temperature heat pump was beneficial when compared to a conventional oil boiler from energy, financial, and environmental points of view. The net savings were found at EUR 6448 for Athens and EUR 13,161 for Thessaloniki; the simple payback period resulted in 8.03 years for Athens and 6.11 years for Thessaloniki, while the CO2 emissions avoidance was found at 35% for Athens and 32% for Thessaloniki. Last but not least, the lifetime CO2 emissions reduction was calculated at 20,882 kgCO2 for Athens and 35,278 kgCO2 for Thessaloniki.

Dynamic performance analysis and thermal modelling of a novel two-phase spray cooled rack system for data center cooling

Cooling systems in information technology (IT) equipment account for the greatest share of direct energy consumption in data centers. Conventional air-cooling schemes are thermally inefficient and energy intensive as they require large temperature difference between heat source and cooling medium. To overcome the aforementioned drawbacks of air-cooling, a “chillerless” novel spraying architecture which has the capability of performing high heat flux cooling, is highly scalable and easily adaptable by modern data centers is proposed in this paper. To demonstrate the scalability and performance of this new cooling scheme, a full-scale spray cooled rack system which has the capacity of housing up to 12 server units is developed. The dynamic performance of the spray cooled rack system under variable ambient temperature is studied. It is found that the ambient temperature strongly affects the spray cooling performances and the chamber pressure stability. A grey box model of the system is established. The newly developed model has successfully predicted the temperature variation on both the water side and spray cooling side with reasonable accuracy. Our model sets the foundation for predictive control of the chamber pressure and workload management of the spray cooling scheme for application in large-scale data centers.

Modelling Building Energy Systems using Electric Circuit Analogy

Modelling energy systems is of great interest since it can help to analyse building energy behaviour and to optimize control strategies. Grey-box modelling is one of the three fundamental modelling approaches for developing energy models. Due to its simplicity and offering several benefits, it has been widely used to handle building-technology challenges such as building load estimate, control and optimization, synthetic data generation for prediction, load peak management and grid integration. This review research looked at several areas of grey box modelling for building energy systems. Here we analyse three main directions, first one is modelling thermal dynamics of buildings, second one analysing approach used to model most used building electrical appliances and third one is renewable systems used to produce thermal water for residential use.

PH prediction for a semi-batch cream cheese fermentation using a grey-box model

Abstract Cream cheese, a popular condiment, is widely used in people’s daily diet and in dessert making. To ensure high-quality cream cheese production, the pH value is generally used as the indicator to determine the end point of cream cheese fermentation. The inoculation time and time-dependent concentrations of biomass, lactose, lactic acid are all crucial for pH prediction. However, the inoculation time could vary for industrial applications with multiple fermenters. Moreover, the inoculation time impact on fermentation has not been investigated. This paper aims to build a cream cheese fermentation model predicting pH. The model includes a semi-batch kinetic model and an artificial neural network (ANN) model. The outcome of the model will help the cream cheese industries understand the inoculation time impact on fermentation time and organise better fermenter scheduling.

Perspective: Disentangling the effects of tES on neurovascular unit.

Transcranial electrical stimulation (tES) can modulate the neurovascular unit, including the perivascular space morphology, but the mechanisms are unclear. In this perspective article, we used an open-source "rsHRF toolbox" and an open-source functional magnetic resonance imaging (fMRI) transcranial direct current stimulation (tDCS) data set to show the effects of tDCS on the temporal profile of the haemodynamic response function (HRF). We investigated the effects of tDCS in the gray matter and at three regions of interest in the gray matter, namely, the anodal electrode (FC5), cathodal electrode (FP2), and an independent site remote from the electrodes (PZ). A "canonical HRF" with time and dispersion derivatives and a finite impulse response (FIR) model with three parameters captured the effects of anodal tDCS on the temporal profile of the HRF. The FIR model showed tDCS onset effects on the temporal profile of HRF for verum and sham tDCS conditions that were different from the no tDCS condition, which questions the validity of the sham tDCS (placebo). Here, we postulated that the effects of tDCS onset on the temporal profile of HRF are subserved by the effects on neurovascular coupling. We provide our perspective based on previous work on tES effects on the neurovascular unit, including mechanistic grey-box modeling of the effects of tES on the vasculature that can facilitate model predictive control (MPC). Future studies need to investigate grey-box modeling of online effects of tES on the neurovascular unit, including perivascular space, neurometabolic coupling, and neurovascular coupling, that can facilitate MPC of the tES dose-response to address the momentary ("state") and phenotypic ("trait") factors.

A Route Map of Machine Learning Approaches in Heterogeneous CO2 Reduction Reaction

Machine learning (ML) with its indigenous predicting ability has been influential in the current scientific world and has enabled a paradigm shift in the field of CO2 reduction reaction (CO2RR). In this perspective, current research progress of ML approaches in heterogeneous electrocatalytic CO2RR has been demonstrated. The important findings related to the ML systems comprising features, output descriptors, and ML models have been summarized. Further, the opportunities and challenges in using the state-of-the-art ML methodologies along with the ways of circumventing those challenges are discussed. Finally, the interpretation of black box ML models and extensive usages of interpretable glass box and gray box models for CO2RR are encouraged for obtaining proper physical interpretations. The future directions on utilizing several such evolving ML methods to predict catalytic activity descriptors can help in a broader way to explore novel and efficient heterogeneous CO2RR and other similar catalytic reactions.

Development of control-oriented models for a building under regular heating, ventilation and air-conditioning operation - a comparative simulation study and an experimental validation

The development of models is a major barrier to the fast and widespread adoption of model predictive control for building HVAC systems. This paper proposes the subspace identification technique, refined through the prediction error method, to quickly obtain a model for the accurate indoor temperature prediction, even with little identification data, even in the presence of large unmeasured disturbances and noisy identification data, and even using data which was collected during the regular HVAC operation of a building. The identification issues associated with grey-box models were thoroughly investigated. In particular, the development of a grey-box model was found to be a complex, lengthy and computationally intensive process, even for a single-zone building, and the models were not physically meaningful. The proposed method was found to be much easier and faster, with a potential for direct practical application. Analysis on experimental data from an existing building provided promising results.

Electricity Consumption Optimization Using Thermal and Battery Energy Storage Systems in Buildings

Energy storage system (ESS) plays a key role in peak load shaving to minimize power consumption of buildings in peak hours. This paper proposes a novel energy management unit (EMU) to define an optimal operation schedule of ESSs by employing metaheuristic and mathematical optimization approaches. The proposed EMU uses a thermal energy storage system (TESS) and a battery energy storage system (BESS) to store the energy in off-peak periods and discharge it in high load demands. We formulate the charging/discharging schedule of TESS and BESS as an optimization problem. Then, particle swarm optimization (PSO) is employed to obtain the optimal schedule due to its computational time efficiency. The mathematical approach is also applied to prove the convexity of the problem and the uniqueness of the solution. Due to the different characteristics of the building loads, this paper divides the total load into shiftable and fixed loads. Moreover, to model the building components and loads, grey-box modeling is adopted. Results show that employing a combination of TESS and BESS achieves peak load shaving while reducing 42.2% of the required BESS capacity compared with the case where the BESS is only used. In addition, the results indicate the effectiveness and robustness of the proposed algorithm.

Genetic Algorithm Assisted Parametric Design of Splitting Inductance in High Frequency GaN-Based Dual Active Bridge Converter

Splitting and placing interfacing inductance on both sides of the transformer has been proven to be an effective method, which extends the zero-voltage switching region for all the switching devices in the dual active bridge (DAB) converter. With the trend toward operating in higher frequency, achieving higher power density and higher efficiency, the converter model becomes more complex due to the non-negligible parasitic components that brings new challenges to DAB converter design. Traditional analytical methods have made it hard to imitate the proposed converter easily and precisely. Thus, artificial intelligence techniques are able to be utilized to assist the design process. When considering the converter system as a gray-box model, the metaheuristic algorithm can be implemented for the targeted design inside such a gray-box. In this article, a genetic algorithm (GA) is employed in the DAB converter parametric design with an explicit fitness desire to help in discovering the high frequency oscillation (HFO) problem. Consequently, the splitting inductance tuning method is proposed for eliminating the HFO problem and minimizing inductors’ loss. The methodology of implementing GA into converter parametric design, and the proposed splitting inductance tuning method are introduced and verified with a 1 MHz gallium nitride high-electron-mobility transistor based DAB converter prototype. The comparitive experimental results prove the effectiveness of the splitting inductance tuning method and achieve 4% efficiency enhancement with 200 W power delivering.

Comparison of Data-Driven Modeling and Identification Approaches for a Self-Balancing Vehicle

This paper gives a systematic comparison of different state–of–the–art modeling approaches and the corresponding parameter identification processes for a self–balancing vehicle. In detail, a nonlinear grey box model, its extension to consider friction effects, a parametric black box model based on regression neural networks, and a hybrid approach are presented. The parameters of the models are identified by solving a nonlinear least squares problem. The training, validation, and test datasets are collected in full–scale experiments using a self–balancing vehicle. The performance of the different models used for ego–motion prediction are compared in full–scale scenarios, as well. The investigated model architectures can be used to improve both, simulation environments and model–based controller design. This paper shows the upsides and downsides arising from using the different modeling approaches. Videos showing the self–balancing vehicle in action are available at: https://tinyurl.com/mvn8j7vf

城市可持续发展新型定量研究方法进展

PDF HTML阅读 XML下载 导出引用 引用提醒 城市可持续发展新型定量研究方法进展 DOI: 10.5846/stxb202111253331 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目(42171287, 41590840); 科技部第二次青藏高原综合科学考察专题项目(2019QZKK1005) Advances in the innovatively quantitative research approach for urban sustainable development Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:城市体现了集约化的发展方式,以仅占陆地3%的面积承载了世界一多半的人口,因此城市是人类社会可持续发展的主阵地,城市可持续发展是区域可持续发展的重要实践。研究试图从可持续发展的基本内涵着手,聚焦于城市尺度的研究,梳理了相关理论和概念演变历程,着重对新型定量方法--评价指标体系法、复杂系统建模、资源环境可持续性探索等进行综述。研究发现指标体系法常用于传统的城市可持续发展评估,大多是针对表象的分析,隔离了系统内部的组织关联,对系统机理探索不够。资源环境对人类社会的发展起到基础支撑作用,从资源环境可持续性出发研究可持续发展诞生了一些较为创新的方法,但研究集中在描述人类对自然的压力和影响,而对于自然对人类的反馈以及人对自然的积极改造探索不足,也很难直接表达系统整体的可持续性。弹性思维和复杂系统科学近年来被多次用于解释城市运行的灰箱模型,但研究稍浅。研究提出可将弹性思维和复杂系统科学结合起来,借助数据科学和计算科学的新兴方法,分析城市的可持续发展,深入探索可持续性的科学表达和深层机理,为解决复杂的城市问题提供科学指导,为城市可持续发展提供理论依据。 Abstract:Cities embody the intensive development mode, with only 3% of the land area to carry more than half of the world's population, so cities are the main front of sustainable development of human society. Urban sustainable development is an important practice of regional sustainable development. Starting from the basic connotation of sustainable development, this study focuses on urban sustainable development research, sorts out the evolution process of related theories and concepts, and reviews the research methods, especially new quantitative methods including evaluation index system method, complex system modeling, resource and environment sustainability exploration, etc. It is found that the index system method is often used in previous urban sustainable development assessments, but most assessments stay only on appearance and isolate the systemically internally organizational association, and the exploration of the system mechanism is not enough. The resources and environment play a fundamental role in supporting the development of human society, so some relatively innovative methods have been developed to study sustainable development from the perspective of resource and environmental sustainability. Human pressure and influence on nature were studied more, and the feedback of nature to human beings and the positive transformation of human beings to nature were not explored enough, also it is difficult to directly express the sustainability of the whole system. In recent years, resilience thinking and complex system science have been used many times to explain the grey box model of urban operation, but the research is slightly superficial. This study proposes research prospects which can combine resilience thinking and complex system science to analyze the sustainable urban development with new approaches in data science and computing science, and explore the scientific expression and deep mechanism of sustainability. Our study could provide scientific guidance for solving the complicated urban problems, and provide a theoretical basis for urban sustainable development. 参考文献 相似文献 引证文献

A Viscoelastic Compensator for Force Sensors With Soft Materials

Model-based compensators have been widely applied in force sensors with soft materials to eliminate viscoelasticity that causes creep output, slow dynamic response, and limits the performance of the sensors. However, the lack of accurate characterization of high-order viscoelasticity and the high computational complexity restricts the performance and application of existing methods. For the above problem, we propose a first-order Volterra grey box model-based viscoelastic compensator for force sensors with soft materials. First, the measurement mechanism of the soft-material-based force sensor is analyzed, and a grey box model for accurately characterizing the high-order linear viscoelasticity by the first-order Volterra series is proposed, without needing high-order spring-damping parameters and their exponential operations. Then, the high-order viscoelasticity compensator is designed and directly linearly fitted with the variation of the historical output, which avoids the computational complexity of the high-order creep compliance and its exponential parameters fitting. Finally, the proposed method is verified with a self-developed soft-material-based capacitive force sensor. The real-time experimental results show that, compared with existing methods, the proposed compensator not only has better compensation performance, but also has a shorter calculation time for force sensors with soft materials.

Hydraulic Parameter Estimation in District Heating Networks

Using hydraulic models in control of district heating networks can increase pumping efficiency and reduce sensitivity to hydraulic bottlenecks. These models are usually white-box, as they are obtained based on full knowledge of the district heating network and its components. This type of model is time-consuming to obtain, and might differ from the actual behavior of the system. In this paper, a method is proposed to obtain a grey-box hydraulic model for tree-shaped district heating systems: hydraulic parameters are estimated based on pressure measurements in only two locations. While previous works only estimate parameters related to pressure losses in pipes, this work also includes customers valves in the grey-box model structure, an important inclusion for control-oriented applications. Finally, a numerical example illustrates the proposed method on a small district heating network, showing its ability to obtain an accurate model on the basis of noisy measurements.

Modeling and real-time control of a hydrogen refueling station with uncertain demand

Hydrogen-fueled vehicles are a promising technology for reducing CO2 emissions in the mobility sector. This paper presents a stochastic receding horizon controller for operating a real-world hydrogen refueling station in a cost-efficient manner, while considering uncertainty in the hydrogen demand. We derive grey-box models for the system and we use them to formulate a predictive control taking the form of a mixed-integer linear program. We solve it by relying on the scenario approach and we ensure a reduced computational footprint by resorting on a non-uniform prediction horizon discretization and on a Lagrangian-based decomposition method.

Analytical Construction of Koopman EDMD Candidate Functions for Optimal Control of Ackermann-Steered Vehicles

The path-tracking control performance of an autonomous vehicle (AV) is crucially dependent upon modeling choices and subsequent system-identification updates. Traditionally, automotive engineering has built upon increasing fidelity of white- and gray-box models coupled with system identification. While these models offer explainability, they suffer from modeling inaccuracies, non-linearities, and parameter variation. On the other end, end-to-end black-box methods like behavior cloning and reinforcement learning provide increased adaptability but at the expense of explainability, generalizability, and the sim2real gap. In this regard, hybrid data-driven techniques like Koopman Extended Dynamic Mode Decomposition (KEDMD) can achieve linear embedding of non-linear dynamics through a selection of “lifting functions”. However, the success of this method is primarily predicated on the choice of lifting function(s) and optimization parameters. In this study, we present an analytical approach to construct these lifting functions using the iterative Lie bracket vector fields considering holonomic and non-holonomic constraints on the configuration manifold of our Ackermann-steered autonomous mobile robot. The prediction and control capabilities of the obtained linear KEDMD model are showcased using trajectory tracking of standard vehicle dynamics maneuvers and along a closed-loop racetrack.