In this paper, we propose an integrated simulation framework for a mobile robot to analyze the dependencies among system components, particularly electrical power and battery models, rather than treating them independently. The framework captures the joint effects of mechanical power, computational power, and computational load offloading on total energy consumption and long-term battery health. It models mechanical, computational, and communication energy while accounting for their dependencies. As a case study, we apply the holistic framework to an energy-aware mission planning problem in which the robot travels to a target location and returns to its starting point. Along the planned path, computational tasks may be offloaded to base stations (BSs) when communication coverage is available. Because the robot operates on battery power, energy efficiency directly influences routing decisions and battery aging. The spatial distribution of BSs affects offloading opportunities, motion planning, resource allocation, and overall energy consumption, ultimately impacting battery degradation. Results from the case study demonstrate that the proposed integration of computational, mechanical, and communication energy models into routing optimization reduces total energy consumption by approximately 13% and battery degradation by 24% compared to approaches that optimize only mechanical and computational energy without considering load offloading.

We introduce a computationally simple model for predicting the dynamical response of sensible heat packed bed thermal energy storages (TES). The proposed system of differential-algebraic equations arises from thermodynamic power balance considerations. We derive the governing equations and present model validation by Computational Fluid Dynamics (CFD). The numerical results demonstrate that the proposed model can accurately represent different flow configurations within the TES.

Urbanization has drastically increased sealed surfaces, leading to higher stormwater runoff and subsequent associated risks such as flooding and water quality degradation. Nature-based solutions such as bioretention cells are a sustainable approach to manage stormwaters. The present work studies the effects of soil layer porosity on water detention efficiency of a bioretention cell, using computational fluid dynamics (CFD). Three porosities (0.34, 0.40, and 0.43) and multiple soil layering configurations were analyzed under two inflow rates (1 l/s and 1.5 l/s). Results reveal that higher porosity layers reduce bypass runoff compared to lower porosity layers. Configurations with porosity increasing from bottom to top in order from 0.34, 0.40 and 0.43 achieved the lowest runoff, reducing it by up to 22.14\% for 1 l/s. Configurations with low-porosity layers at the top produced the highest runoff, emphasizing the importance of layer arrangement.

Digital Twins (DTs) are increasingly applied for optimization of operations in logistics, healthcare, smart cities, and beyond. However, implementing high-fidelity DTs remains challenging in computationally intensive domains such as Computational Fluid Dynamics (CFD). While simplified models can facilitate real-time operation, they often lack physical fidelity. This article presents an open-source scalable software framework along with a case study of CFD-based digital twining on stormwater management. The presented framework enables online execution of CFD-based models by containerizing and integrating them into OpenShift platform, providing a two-way communication channel for simulation parameters and results. The framework is capable of dynamically scaling computing resources to run computationally-intensive CFD-models. In the case study, we present a novel CFD simulation model of a bioretention cell intended to reduce runoff volumes of urban stormwater. The simulation model, implemented in OpenFOAM, is then integrated into the presented software framework to create the DT. The framework source code, simulation model and the DT are made publicly available to promote future research.

Energy management of mechanical and cyber parts in mobile robots consists of two processes operating concurrently at runtime. Both the two processes can significantly improve the robots' battery lifetime and further extend mission time. In each process, information on energy consumption of one of the two parts is captured and analyzed to manipulate various mechanical/computational actuators in a robot, such as motor speed and CPU voltage/frequency. In this article, we show that considering management of mechanical and computational segments separately does not necessarily result in an energy-optimal solution due to their co-dependence; as a consequence, a runtime co-management scheme is required. We propose a proactive energy optimization methodology in which dynamically trained internal models are utilized to predict the future energy consumption for the mechanical and computational parts of a mobile robot, and based on that, the optimal mechanical speed and CPU voltage/frequency are determined at runtime. The experimental results on a ground wheeled robot show up to 36.34% reduction in the overall energy consumption compared to the state-of-the-art methods.

Reinforcement structures involve the use of advanced materials, optimization techniques, and computational methods to enhance the strength and performance of components. Modern multi-material three-dimensional printers can manufacture reinforced structures with complex topologies. We present a computational design method to reinforce an object through the Solid Isotropic Material with a Penalization method decreasing the structure's compliance by pinpointing particular areas in the structure that need strengthening while preserving the initial dimensions of the model. We demonstrate mathematically the typical topology optimization approach remains effective when substituting voids with an alternative material. The rationale of our approach is to utilize the Solid Isotropic Material with a Penalization for modelling a two-material structure, in which the second material replaces the voids indicated by the Solid Isotropic Material with a Penalization process. We apply our approach to a two-dimensional cantilever beam and perform a mesh convergence study to confirm our method's validity. Moreover, we compare the displacement and von Mises results of a baseline beam that has only a weak material and a reinforced beam that has two materials (weak and strong materials). The results indicate that von Mises stress is higher when the maximum displacement is lower in the reinforced beam.

Hydrogels with shear-thinning properties are being investigated for use in biomedical applications such as drug delivery, tissue engineering, and 3D bioprinting. For 3D bioprinting, it is very important not to change materials such as the survival of cells during the printing process. This study aims to determine the best model for predicting viscosity using Computational Fluid Dynamics (CFD) simulations and further investigate the relationship between material properties and nozzle geometrical parameters to optimize 3D printing processes. Three types of Alginate gels with different shear-thinning behaviour are considered and their viscosity is modeled by: Power Law, Herschel–Bulkley, and Bird-Carreau models. The most accurate model is used in the further CFD simulations of these materials through two different types of 3D printing nozzles: 18G and 22G. The results showed how shear stress is influenced by the viscosity properties. Moreover, the effect of shear-thinning properties on the printability of materials is discussed.

Optimal control of electric vehicle (EV) batteries for maximal energy efficiency, safety and lifespan requires that the Battery Management System (BMS) has accurate real-time information on both the battery State-of-Charge (SoC) and its dynamics, i.e. long-term and short-term energy supply capacity, at all times. However, these quantities cannot be measured directly from the battery, and, in practice, only SoC estimation is typically carried out. In this article, we propose a novel parametric algebraic voltage model coupled to the well-known Manwell-McGowan dynamic Kinetic Battery Model (KiBaM), which is able to predict both battery SoC dynamics and its electrical response. Numerical simulations, based on laboratory measurements, are presented for prismatic Lithium-Titanate Oxide (LTO) battery cells. Such cells are prime candidates for modern heavy offroad EV applications.

Ship fairways in archipelagos pass close to small islands with complex terrain and forests. In such areas, local wind conditions deviate from regional forecasts, creating a complex and challenging environment when navigating large vessels. In this article, we carried out Computational Fluid Dynamics (CFD) simulations for wind flows over a site in the Turku Archipelago, in Finland. We assessed local wind conditions along the fairway with the intent of improving safe passage for large vessels. All simulations were carried out with and without the surrounding forests to elucidate the role of forests in shaping the local wind and turbulence conditions over the site. Our results show that in some regions of the fairway, forests can lower the wind speed to one-third of the magnitude obtained by using terrain-only models. Moreover, turbulence can locally be increased six-fold. We expect that these results will facilitate the development of new smart fairways for ensuring safe navigation through the archipelago in the future.

Batteries are becoming one important part to power varieties of devices including electro-mechanical robots and vehicles. Understanding the behaviour of the battery and its state of charge can help the control systems to significantly improve the decision-making and risk management at run-time, after the device starts its operation. Currently, there is an increased interest in tracking battery dynamics as a function of health in both academia and industry. In this paper, we propose a light-weight approach for modeling the state of charge of lithium-ion (Li-ion) batteries during the life-time of the system. We also consider the battery capacity of charge degradation over its usage. To do that, we use electrical equivalent circuit model (EECM) modeling as the basis for modeling the battery and add the aging model to it to consider the effect of battery usage in the long term. Experimental results show that our proposed technique successfully estimates the battery state of charge at different states of health for the National Aeronautics and Space Administration (NASA) randomized usage battery dataset in comparison with the state-of-the-art. The obtained estimation error in the worst case is 2.2%.

Finding the optimal distribution of exerted effort by an athlete in competitive sports has been widely investigated in the fields of sport science, applied mathematics and optimal control. In this article, we propose a reinforcement learning-based solution to the optimal control problem in the running race application. Well-known mathematical model of Keller is used for numerically simulating the dynamics in runner's energy storage and motion. A feed-forward neural network is employed as the probabilistic controller model in continuous action space which transforms the current state (position, velocity and available energy) of the runner to the predicted optimal propulsive force that the runner should apply in the next time step. A logarithmic barrier reward function is designed to evaluate performance of simulated races as a continuous smooth function of runner's position and time. The neural network parameters, then, are identified by maximizing the expected reward using on-policy actor-critic policy-gradient RL algorithm. We trained the controller model for three race lengths: 400, 1500 and 10000 meters and found the force and velocity profiles that produce a near-optimal solution for the runner's problem. Results conform with Keller's theoretical findings with relative percent error of 0.59% and are comparable to real world records with relative percent error of 2.38%, while the same error for Keller's findings is 2.82%.

Understanding battery capacity degradation is instrumental for designing modern electric vehicles. In this paper, a Semi-Empirical Model for predicting the Capacity Loss of Lithium-ion batteries during Cycling and Calendar Aging is developed. In order to redict the Capacity Loss with a high accuracy, battery operation data from different test conditions and different Lithium-ion batteries chemistries were obtained from literature for parameter optimization (fitting). The obtained models were then compared to experimental data for validation. Our results show that the average error between the estimated Capacity Loss and measured Capacity Loss is less than 1.5% during Cycling Aging, and less than 2% during Calendar Aging. An electric mining dumper, with simulated duty cycle data, is considered as an application example.

This article describes ModelConductor-eXtended (MCX), which is an open-source software architecture for digital twins. The MCX framework facilitates co-execution of, and asynchronous data communication between, physical systems and their digital simulation models. MCX supports running FMUs (simulation models packaged according to the FMI specification) as well as machine learning models and customized models. We propose extensions to the previously published ModelConductor framework for higher performance and better scalability. The extensions include decoupling of the queue and the model computation module, utilization of a standard data transmission protocol and implementation of the facility to run time-consuming simulation models in a time synchronous manner. Additionally, three new validation case studies are presented. A performance evaluation shows that the extensions improve the average response time almost 4 times in three specific experiments.

In this article we introduce a software architecture, along with an experimental implementation called ModelConductor, for input-output data management in on-line digital twin (DT) simulation model applications. The goal here is to facilitate asynchronous and standardized data interexchange between a (potentially computationally expensive) computer simulation model and a physical device (with potential latency in measurement data). The presented object-oriented architecture defines a DT design pattern by introducing a set of abstract methods for data retrieval, manipulation and consumption for communication of physical and digital assets. As a concrete example, we demonstrate the use of ModelConductor in machine learning based on-line prediction of diesel engine nitrogen oxide (NOx) emissions, using simulated live data from a real engine.