Simulation-based Control Research Articles

Coupled building simulation and CFD for real-time window and HVAC control in sports space

Accurately predicting players’ thermal comfort in large sports spaces for real-time window and HVAC operations presents significant challenges due to non-uniform thermal distributions. Standalone building energy simulation (BES) typically assumes the entire targeted space as a uniform thermal zone, which fails to capture these variances. Conversely, Computational Fluid Dynamics (CFD) simulation can predict indoor thermal environments with precision but often struggles with determining accurate boundary conditions. This study introduces a coupled BES and CFD simulation method tailored for real-time window and HVAC control in sports spaces. A case study was conducted in a prototypical national fitness hall to evaluate the effectiveness of the proposed method. The thermal comfort and cooling energy results from the co-simulation-based control were compared with those from standalone EnergyPlus simulation-based control and fixed-schedule window and HVAC operations, which served as baselines. The results indicate that the proposed method enhanced thermal comfort by 68.5 % compared to constantly-scheduled window operations and achieved daily energy savings of up to 43.5 % versus constantly-scheduled HVAC operations. Furthermore, significant discrepancies in Average Predicted Mean Vote (PMV‾) or Average Adaptive Predicted Mean Vote (aPMV‾) as well as daily cooling energy consumption between the BES-CFD co-simulation and standalone EnergyPlus simulation were identified, ranging from -0.53 to 1.83 for PMV‾ (aPMV‾) and -2444.6 to 1266.5 kWh for cooling energy. This study contributes novel methods for real-time window and HVAC control in sports buildings towards thermally comfortable and energy-efficient sports environments.

Explainable proactive control of industrial processes

One of the goals of Industry 4.0 is the adoption of data-driven models to enhance various aspects of the manufacturing process, such as monitoring equipment conditions, ensuring product quality, detecting failures, and preparing optimal maintenance plans. However, many machine-learning algorithms require a large amount of training data to reach desired performance. In numerous industrial applications, such data is either not available or its acquisition is a costly process. In such cases, simulation frameworks are employed to replicate the behavior of real-world facilities and generate data for further analysis. Simulation frameworks typically provide high-quality data but are often slow which can be problematic when real-time decision-making is required. Control approaches based on simulation-based data commonly face challenges related to inflexibility, particularly in dynamic production environments undergoing frequent reconfiguration and upgrades. This paper introduces a method that seeks to strike a balance between the reliance on simulated data and the limited robustness of simulation-based control methods. This balance is achieved by supplementing available data with additional expert knowledge, enabling the matching of similar data sources and their combination for reuse. Furthermore, we augment the methods with an explainability layer, facilitating collaboration between the human expert and the AI system, leading to informed and actionable decisions. The performance of the proposed solution is demonstrated through a case study on gas production from an underground reservoir resulting in reduced downtime, heightened process reliability, and enhanced overall performance. This paper builds upon our conference paper (Kuk et al., 2023), addressing the same problem with an extended, more generic methodology, and presenting entirely new results.

Quantifying land impacts of wind energy: a regional-scale assessment in Finland

AbstractLarge-scale deployment of wind energy raises ecological concerns about land use changes. Here, we conducted a quantitative assessment of land impacts of 42 wind farms (513 turbines) from Ostrobothnia, Finland. Located on the Baltic Sea coast, Ostrobothnia is a strategic region for national wind energy targets. Our main objectives were (1) to calculate landscape metrics (e.g., habitat composition, loss and fragmentation) within wind farm project areas, (2) compare the amount of building-related deforestation between project areas and simulation-based control areas and (3) compute the overlap between project areas and regional land use plans. According to CORINE Land Cover datasets, the predominant habitats within project areas were forests (371.7 km2; 81%) and transitional forests (52.7 km2; 11%). The remaining 8% were agricultural areas (17.6 km2; 4%), waters (12.5 km2; 3%) and artificial surfaces (2.9 km2; 1%). Wind farm infrastructure corresponded to 1.4-6.0% of project areas and caused similar degrees of habitat fragmentation. Deforestation levels in project areas were higher (~ 6 times on average) than in control areas, and the micro-siting of turbines did not avoid mature forests by favouring e.g., transitional forests (which are ecologically less valuable). We found some spatial incongruence between project areas and regional land use plans, with 78 turbines falling outside the established boundaries. Our results highlight the need for careful ecological considerations in wind energy spatial planning in Finland (and countries with similar landscapes and land uses) to limit forest loss and its detrimental impacts on forest-dependent species, but also on carbon storage and sequestration.

Integrating Digital Twins with BIM for Enhanced Building Control Strategies: A Systematic Literature Review Focusing on Daylight and Artificial Lighting Systems

In the architecture, engineering, and construction industries, the integration of Building Information Modeling (BIM) has become instrumental in shaping the design and commissioning of smart buildings. At the center of this development is the pursuit of more intelligent, efficient, and sustainable built environments. The emergence of smart buildings equipped with advanced sensor networks and automation systems increasingly requires the implementation of Digital Twins (DT) for the direct coupling of BIM methods for integral building planning, commissioning, and operational monitoring. While simulation tools and methods exist in the design phase of developing advanced controls, their mapping to construction or post-construction models is less well developed. Through systematic, keyword-based literature research on publisher-independent databases, this review paper gives a comprehensive overview of the state of the research on BIM integration of building control systems with a primary focus on combined controls for daylight and artificial lighting systems. The review, supported by a bibliometric literature analysis, highlights major development fields in HVAC controls, failure detection, and fire-detection systems, while the integration of daylight and artificial lighting controls in Digital Twins is still at an early stage of development. In addition to already existing reviews in the context of BIM and Digital planning methods, this review particularly intends to build the necessary knowledge base to further motivate research activities to integrate simulation-based control methods in the BIM planning process and to further close the gap between planning, implementation, and commissioning.

Model-based data center cooling controls comparative co-design

This article presents a comparative simulation-based control logic design process. It uses the Control Description Language (CDL) and the ASHRAE Guideline 36 high-performing building control sequences with the Modelica Buildings Library (MBL) to demonstrate a comparative analysis of two control designs for a data center chilled water plant. Details include a description of the closed-loop plant and control design methodology, including sizing and parameterization, base and alternative (Guideline 36) control logic with software implementation structure, and outline of the simulation experimentation process. The selected control designs are paired with comparable chilled water plant configurations. The models include a chiller, a water-side economizer, and an evaporative cooling tower. The plant provides cooling at 27ºC zone supply air temperature to a data center in Sacramento, CA. The comparative simulation results examined the impacts of a selected control logic detail, and present an example model-based design application. Overall, the simulation results showed a 25% annual and a 18% summer energy use reduction for alternative controls. This shows that simulation-based control logic design performance evaluation can improve energy efficiency and resilience aspects of system controls at large.

Quality-based scheduling for a flexible job shop

Recent developments in food consumption open up new challenges in production control in manufacturing systems with low levels of automation. The demand for fresh and healthy products, in combination with the demand for processed products, requires short throughput times and attribution of exposure to process-specific, time-sensitive negative quality influences on certain products. Therefore, this article presents a framework incorporating quality aspects into production planning and control by developing mixed-integer linear programming for scheduling and integrating it into simulation-based control for sequencing. The article applies this framework to a flexible job shop problem derived from a real-world use case. The resulting schedules are used in a simulation model using different order release schemes and priority rules to test how the scheduling approach could deal with dynamic manufacturing environments. The evaluation shows the satisfying integration of quality issues in a flexible job shop problem by minimizing the deterioration while not compromising on makespan and utilization.

Adaptive traffic signal control using deep Q-learning: case study on optimal implementations

Deep reinforcement learning has found great successes in addressing many challenging control problems; however, real-world implementations are still scarce if not non-existent. This is primarily due to three main challenges pertaining to the implementation, the stability, the optimal settings, and a lack of knowledge on methods that can be applied to field settings. This research attempts to address these issues with an adaptive simulation-based control framework proposed specifically for the training and evaluation. The control framework has implemented simulation models to conduct an extensive sensitivity analysis on the effects of key design variables, including rewarding schemes, state spaces, and model training parameters. The feasibility of transfer learning as a training strategy is also studied on scenarios with different layouts and different driver behavior models. Complex scenarios are also evaluated and used as test cases, including multiphase ring-and-barrier control and multi-intersection control. The research has contributed a significant amount of evidence on several critical design and implementation-related questions such as input representation, data (technology) requirements, training methods, and model transferability.

A digital twin in transportation: Real-time synergy of traffic data streams and simulation for virtualizing motorway dynamics

The introduction of digital twins is expected to fundamentally change the technology in transportation systems, as they appear to be a compelling concept for monitoring the entire life cycle of the transport system. The advent of widespread information technology, particularly the availability of real-time traffic data, provides the foundation for supplementing predominated (offline) microscopic simulation approaches with actual data to create a detailed real-time digital representation of the physical traffic. However, the use of actual traffic data in real-time motorway analysis has not yet been explored. The reason is that there are no supporting models and the applicability of real-time data in the context of microscopic simulations has yet to be recognized. Thus, this article focuses on microscopic motorway simulation with real-time data integration during system run-time. As a result, we propose a novel paradigm in motorway traffic modeling and demonstrate it using the continuously synchronized digital twin model of the Geneva motorway (DT-GM). We analyze the application of the microscopic simulator SUMO in modeling and simulating on-the-fly synchronized digital replicas of real traffic by leveraging fine-grained actual traffic data streams from motorway traffic counters as input to DT-GM. Thus, the detailed methodological process of developing DT-GM is presented, highlighting the calibration features of SUMO that enable (dynamic) continuous calibration of running simulation scenarios. By doing so, the actual traffic data are directly fused into the running DT-GM every minute so that DT-GM is continuously calibrated as the physical equivalent changes. Accordingly, DT-GM raises a technology dimension in motorway traffic simulation to the next level by enabling simulation-based control optimization during system run-time that was previously unattainable. It, thus, forms the foundation for further evolution of real-time predictive analytics as support for safety–critical decisions in traffic management. Simulation results provide a solid basis for the future real-time analysis of an extended Swiss motorway network.

Framework for simulation-based control design evaluation for a snake robot as an example of a multibody robotic system

Snake robots are the multibody mechanisms allowing us to solve specific problems efficiently, i.e., navigate into diverse environments and maneuver through tight spaces or uneven grounds in a way that resembles living organisms. However, the path following and controlling such systems is challenging due to nonlinear dynamics, coupling between links, and nonstandard definitions of the set-point that differ from industrial applications. This paper describes a framework for simulation and evaluation of the controller design for snake robot as the set of tools for the 3D design and robot dynamic simulation. Combined with a theoretical background (equations of robot dynamics), it allows testing new solutions and strategies of robot control design. Firstly, based on the proposed methodology, we provide a mechanical design of a ten-link snake robot. We present control algorithms enabling point-to-point tracking of the robot position in two cases: (i) tracking the center of gravity of the robot and (ii) tracking the position of the head of the robot. Then we provide a simulation-based robustness analysis of a simple fault-tolerant control algorithm, where some snake robot joints are broken. The proposed framework can be used efficiently to study control strategies for multibody mechanisms.

수중 사출에 의한 소형 무인잠수정의 전동기 역기전력 보호회로 설계 및 모델링 방안

In this paper, we identify the electric power problem of the high-speed underwater discharge in a small autonomous underwater vehicle(AUV) and propose a circuit design method to protect it. High-speed underwater discharge of an AUV arouse propeller rotates due to the water flow. Excessive initial propeller rotation generates a back EMF of the motor and causes a voltage increase at the DC terminal of the inverter. To figure out this problem, a simulation was performed to limit the back EMF in a simplified environment. Then, a simulation-based controller protection circuit was proposed. Finally, the validity of the proposed simulation and the superiority of the protection circuit were confirmed through the actual underwater discharge test.

Modular hydronic subsystem models for testing and improving control algorithms of air-handling units

To reduce the energy consumption of building energy systems, efficient and well-tuned control strategies are needed. However, developing and benchmarking control strategies is a challenging task. Here, simulation-based controller tuning and testing is beneficial and more cost-efficient compared to experiments. However, generating a model for this purpose is time-consuming and profound expert knowledge is needed. An approach to reduce the modeling effort is to divide the overall system into frequently used subsystems and develop one model for each type of subsystem. In this paper, we present modular models for frequently used hydronic subsystems in air-handling units (AHU). The models are implemented in the modeling language Modelica with a high level of detail and provide a standardized connection interface to different controller models for testing and tuning. The developed subsystem models are validated using measured data of a real-world air-handling unit. The results show that the models have nearly the same static and dynamic behavior as the real system. The mean absolute percentage error (MAPE) between the simulation and measured data is in the range of 0.14% to 11.3% for all compared sensor values. In two case studies, the usage of the models for controller testing and tuning is demonstrated: the first case study deals with the tuning of a PI controller, and in the second case study we compare two control strategies regarding the control quality and energy consumption.

A Reliability-Based Formulation for Simulation-Based Control Co-Design Using Generalized Polynomial Chaos Expansion

Abstract Combined plant and control design (control co-design (CCD)) methods are often used during product development to address the synergistic coupling between the plant and control parts of a dynamic system. Recently, a few studies have started applying CCD to stochastic dynamic systems. In their most rigorous approach, reliability-based design optimization (RBDO) principles have been used to ensure solution feasibility under uncertainty. However, since existing reliability-based CCD (RBCCD) algorithms use all-at-once formulations, only most-probable-point (MPP) methods can be used as reliability analysis techniques. Though effective for linear/quadratic RBCCD problems, the use of such methods for highly nonlinear RBCCD problems introduces solution error that could lead to system failure. A multidisciplinary feasible (MDF) formulation for RBCCD problems would eliminate this issue by removing the dynamic equality constraints and instead enforcing them through forward simulation. Since the RBCCD problem structure would be similar to traditional RBDO problems, any of the well-established reliability analysis methods could be used. Therefore, in this work, a novel reliability-based MDF formulation of multidisciplinary dynamic system design optimization has been proposed for RBCCD. To quantify the uncertainty propagated by the random decision variables, Monte Carlo simulation has been applied to the generalized polynomial chaos expansion of the probabilistic constraints. The proposed formulation is applied to two engineering test problems, with the results indicating the effectiveness of both the overall formulation as well as the reliability analysis technique for RBCCD.

Controlling Switchable Electrochromic Glazing for Energy Savings, Visual Comfort and Thermal Comfort: A Model Predictive Control

Dynamic façades play an important role in enhancing the overall performance of buildings: they respond to the environmental conditions and adjust the amount of transmitted solar radiation. This paper proposes a simulation-based framework to evaluate the energy and comfort performance of different control strategies for switchable electrochromic glazing (EC). The presented method shows the impact of a model predictive control (MPC) on energy savings and on visual and thermal comfort for different orientations compared to other strategies. Besides manual operation and conventional rule-based controls, the benchmark in this study was a simulation-based control (multi-objective penalty-based control) with optimal performance. The hourly results of various control cases were analyzed based on the established performance indicators and criteria. The cumulative annual results show the capabilities and limitations of each control strategy for an EC glazing. For a temperate climate (Mannheim, Germany), results showed that an MPC for EC glazing provides visual and thermal comfort while saving energy of up to 14%, 37%, 37%, and 34% respectively for facing north, east, south, and west relative to the base-case.

Iterative simulation-based techniques for control of laser powder bed fusion additive manufacturing

One of the challenges for process control of laser powder bed fusion additive manufacturing lies in thermal control. Excessively low laser power may lead to incomplete melting, while too high laser power can lead to keyholing, increasing the porosity of parts. Considering a thermal finite-element model from our prior work, a secant-based iterative method is proposed and implemented in this work to control the simulated laser power to attain a constant melt-pool size. Several experimental samples of Inconel 625 are designed and built with the EOSINT M280 system, and cross-sectioned to evaluate the effectiveness of the iterative simulation-based controller of laser power. Cross-sectional area statistics are collected near laser turnarounds, where the melt pool is most dynamic. The iterative simulation-based controller reduces the variation of melt pool size by between 13.4% and 48.8% compared to applying constant laser power for all configurations. With the extra iterations from the secant method, the controlled simulations take roughly 2.3 times longer than the simulations under constant laser power.

New models and algorithms to solve integrated problems of production planning and control taking into account worker skills in flexible manufacturing systems

The paradigm of the cyber-physical manufacturing system is playing an increasingly important role in the development of production systems and management of manufacturing processes. This paper presents an optimization model for solving an integrated problem of production planning and manufacturing control. The goal is to create detailed production plans for a complex manufacturing system and to control the skilled manual workers. The detailed optimization model of the problem and the developed approach and algorithms are described in detail. To consider the impact of human workers performing the manufacturing primary operations, we elaborated an extended simulation-based procedure and new multi-criteria control algorithms that can manage varying availability constraints of parallel workstations, worker-dependent processing times, different product types and process plans. The effectiveness of the proposed algorithms is demonstrated by numerical results based on a case study.

Machine learning-based novel DSP controller for PV systems

As fossil fuels are getting depleted it is very much essential to harvest solar energy. The harvesting and conversion methods available in literature are simulation-based. A very few work has been reported using hardware circuitry. However, machine learning-based DSP controller for solar energy harvesting is not available in literature. In the proposed study, machine learning-based DSP controller is implemented. The genetic algorithm (GA)-based DSP controller has been designed for enhancing the efficiency of solar PV. In the proposed work, perturb and observe (P&O) technique and genetic algorithm (GA) have been considered to achieve maximum power point and precise control parameters of PID controller respectively. Single DSPTMS320F28377s has been used to implement both P&O and GA and it is revealed that the proposed DSP-based hardware model provides better speed, efficiency and reliability than the existing simulation-based controller. The proposed work will bring a paradigm shift in solar energy harvesting and control.

A Study on the Simulation-Based Electric Control Panel Distance Learning Model

A Study on the Simulation-Based Electric Control Panel Distance Learning Model

Error handling method for digital twin-based plasma radiation detection

Abstract In Industry 4.0 several new methods have been introduced to approximate the real and digital world. Digital twins represent real object together with their data and functions in the digital world. The latest developments in digital twin researches extend the application of following areas like simulation-based system engineering, simulation-based optimization and simulation-based control. Application of digital twin in plasma radiation detection gives a new opportunity to model the procedure of detection and discover the possible errors. This paper proposes a fuzzy based mathematical model of metal absorber–metal resistor bolometer as a digital twin. The use of a fuzzy logic based approach is herewith proposed and implemented, where fuzzy membership functions are assigned to different physical and geometrical properties of the metal resistor → metal absorber → camera housing → numerical assumptions back-calculation chain. Our fuzzy based digital twin is tested on results of numerical plasma simulations the uncertainties (as a possible sources of detection error) of which are also handled via fuzzy logic. The resulted mathematical model (camera + plasma) is a suitable tool to be able to estimate the overall error and uncertainty of radiation detection.

Solution for Voltage and Frequency Regulation in Standalone Microgrid using Hybrid Multiobjective Symbiotic Organism Search Algorithm

Voltage and frequency regulation is one of the greatest challenges for proper operation subsequent to the isolated microgrid. To validate the satisfactory electric power quality supply to customers, the proposed manuscript tries to enhance the quality of energy provided by DG (Distributed generation) units connected to the subsequent isolated grid. Microgrid and simulation-based control structure including voltage and current control feedback loops is proposed for microgrid inverters to recover voltage and frequency of the system subsequently for any fluctuations in load change. The proportional-integral (PI) controller connected to the voltage controller is an end goal to obtain smooth response in most of the consistent frameworks. The present controller creates the space vector pulse width modulation signals which are given to the three-leg inverter. The objective elements of the multiobjective optimization issue are voltage overshoot and undershoot, rise time, settling time, and integral time absolute error (ITAE). The hybrid Multiobjective Symbiotic Organism Search (MOSOS) calculation is associated for self-tuning of control parameters keeping in mind the end goal to deal with the voltage and frequency. The proposed PI controller, along with the hybrid Multiobjective Symbiotic Organism Search algorithm, provides the solution for the greatest challenge of voltage and frequency regulation in an isolated-microgrid operation.

Optimization-based operations control for public transportation service with transfers

This research develops a real-time simulation-based control framework that attempts to coordinate the operations of public transportation services to allow smoother transfers and to maintain service regularity. The control actions, which include holding and change speed, are set as the solution of an optimization problem with the objective to minimize total passengers’ time in the system within a prediction horizon. The prediction horizon is defined by a number of downstream stops and subsequent buses. The predictions made include the arrival and departure times of vehicles at downstream stops and the passenger demands they are expected to serve. The model is demonstrated with a case study of three lines of the BRT system in Haifa, Israel. The results show that the model outperforms headway control and no-control alternatives in terms of total passengers’ time and on-time performance. It is only slightly worst then the headway control in terms of waiting times and denied boarding at stops. The combination of holding and change speed actions provides substantially better results than each of them individually.