Flexible Simulation Framework Research Articles

Building an Accessible and Flexible Multi-User Robotic Simulation Framework with Unity-MATLAB Bridge

Multi-user collaborative robotic simulation has great potential for applications in industry and education. Unity is a powerful software for simulation and online multi-user experience, which can be enhanced with third-party mathematical analysis and multiplayer servers. Unity can become a much more capable and user-friendly robotic simulation package through integration with other software. These include MATLAB for computations and the Photon Unity Engine (PUN) for online multi-user capabilities. This study developed a flexible robotic simulation framework that can be adapted to different scenarios for industrial and educational applications. Several simulation scenarios were developed to identify the most efficient data communication methods between MATLAB and Unity. TCP/IP, Shared Memory, Firebase, and MQTT, were selected to assess their performance and interaction with data in Unity and MATLAB. Next, an independent PUN application was created. Both components were integrated into the simulator for evaluation and performance optimization. The performance of this simulation framework was assessed through two case studies. The results demonstrated that the integrated framework is viable, efficient, and flexible for robotic simulation and digital twins. Future research will expand the framework by adding diverse functionalities to provide users with a better interface, enhancing its performance, and integrating additional software packages.

Exploring the role of hydrogen in decarbonizing energy-intensive industries: A techno-economic analysis of a solid oxide fuel cell cogeneration system

Industry is nowadays one of the most energy-demanding sectors representing a major contributor of global greenhouse gas emissions. The simultaneous need for electricity and high-temperature heat is what makes some industrial processes difficult to decarbonize via current commercially available technologies. As the demand for materials and goods is expected to grow in the upcoming years, it is crucial to define which strategies and technologies will serve as the cornerstone of sustainable development. This study addresses the imperative need for emission reduction of energy-intensive sectors by proposing a novel hydrogen-based cogeneration system in the framework of the paper and pulp industry, with the aim of providing general insights relevant to a broader spectrum of similar applications. The comparative analysis presented in this work focuses on three cogeneration options aimed at satisfying the paper mill energy needs: a conventional natural gas-fuelled gas turbine, a Solid Oxide Fuel Cell (SOFC) fed with grey hydrogen produced via steam methane reforming, and a SOFC operating using green hydrogen produced on-site. The latter involves an integrated multi-energy system with photovoltaic panels, electrolyzers, compressors, and storage tanks. Indeed, the SOFC potential of supplying electricity and high-temperature heat in the form of pressurized steam for industrial applications has not been well investigated yet and represents one of the main objectives of this work. Building on the real consumption profiles of a paper mill facility, techno-economic analyses are carried out for many system configurations, varying components size and layout to assess their performance with respect to CO2 emissions and two key economic parameters, the Levelized Cost of Hydrogen (LCOH) and the net present cost. An in-house-developed flexible simulation framework is presented and expanded for the purposes of this study, including a detailed model that accounts for design and off-design performance of a SOFC cogeneration unit. Results demonstrate that integrating a SOFC with a heat recovery steam generator result in a 75% reduction in the mass flow of generable pressurized steam in comparison to a gas turbine. Additionally, in the cost-optimal scenario, CO2 emissions are 25% lower than the conventional gas turbine-based configuration, achieving complete independence from the electricity grid and an LCOH of 5.81€/kg without considering revenues from electricity sold.

Towards biologically plausible phosphene simulation for the differentiable optimization of visual cortical prostheses.

Blindness affects millions of people around the world. A promising solution to restoring a form of vision for some individuals are cortical visual prostheses, which bypass part of the impaired visual pathway by converting camera input to electrical stimulation of the visual system. The artificially induced visual percept (a pattern of localized light flashes, or 'phosphenes') has limited resolution, and a great portion of the field's research is devoted to optimizing the efficacy, efficiency, and practical usefulness of the encoding of visual information. A commonly exploited method is non-invasive functional evaluation in sighted subjects or with computational models by using simulated prosthetic vision (SPV) pipelines. An important challenge in this approach is to balance enhanced perceptual realism, biologically plausibility, and real-time performance in the simulation of cortical prosthetic vision. We present a biologically plausible, PyTorch-based phosphene simulator that can run in real-time and uses differentiable operations to allow for gradient-based computational optimization of phosphene encoding models. The simulator integrates a wide range of clinical results with neurophysiological evidence in humans and non-human primates. The pipeline includes a model of the retinotopic organization and cortical magnification of the visual cortex. Moreover, the quantitative effects of stimulation parameters and temporal dynamics on phosphene characteristics are incorporated. Our results demonstrate the simulator's suitability for both computational applications such as end-to-end deep learning-based prosthetic vision optimization as well as behavioral experiments. The modular and open-source software provides a flexible simulation framework for computational, clinical, and behavioral neuroscientists working on visual neuroprosthetics.

An evaluation framework for operational interventions on urban mass public transport during a pandemic

Decision making in a rapidly changing context, such as the development and progression of a pandemic, requires a dynamic assessment of multiple variable and competing factors. Seemingly beneficial courses of action can rapidly fail to deliver a positive outcome as the context changes. In this paper, we present a flexible data-driven agent-based simulation framework that considers multiple outcome criteria to increase opportunities for safe mobility and economic interactions on urban transit networks while reducing the potential for Covid-19 contagion in a dynamic setting. Using a case study of the Victoria line on the London Underground, we model a number of operational interventions with varied demand levels and social distancing constraints including: alterations to train headways, dwell times, signalling schemes, and train paths. Our model demonstrates that substantial performance gains ranging from 12.3–195.7% can be achieved in metro service provision when comparing the best performing operational scheme and headway with those realised on the Victoria line during the pandemic.

Large‐Domain Multisite Precipitation Generation: Operational Blueprint and Demonstration for 1,000 Sites

AbstractStochastic simulations of spatiotemporal patterns of hydroclimatic processes, such as precipitation, are needed to build alternative but equally plausible inputs for water‐related design and management, and to estimate uncertainty and assess risks. However, while existing stochastic simulation methods are mature enough to deal with relatively small domains and coarse spatiotemporal scales, additional work is required to develop simulation tools for large‐domain analyses, which are more and more common in an increasingly interconnected world. This study proposes a methodological advancement in the CoSMoS framework, which is a flexible simulation framework preserving arbitrary marginal distributions and correlations, to dramatically decrease the computational burden and make the algorithm fast enough to perform large‐domain simulations in short time. The proposed approach focuses on correlated processes with mixed (zero‐inflated) Uniform marginal distributions. These correlated processes act as intermediates between the target process to simulate (precipitation) and parent Gaussian processes that are the core of the simulation algorithm. Working in the mixed‐Uniform space enables a substantial simplification of the so‐called correlation transformation functions, which represent a computational bottle neck in the original CoSMoS formulation. As a proof of concept, we simulate 40 years of daily precipitation records from 1,000 gauging stations in the Mississippi River basin. Moreover, we extend CoSMoS incorporating parent non‐Gaussian processes with different degrees of tail dependence and suggest potential improvements including the separate simulation of occurrence and intensity processes, and the use of advection, anisotropy, and nonstationary spatiotemporal correlation functions.

Thermal, Lighting and IAQ Control System for Energy Saving and Comfort Management

The present work proposes a simulation and control framework for home and building automation, focusing on heating, ventilating, and air conditioning processes. Control systems based on different advanced control architectures and different control policies are simulated and compared, highlighting control performances, and energy-saving results in terms of CO2 emissions reduction. Heat, lighting, and natural ventilation phenomena were modelized through first-principles and empirical equations, obtaining a reliable and flexible simulation framework. Energy-consuming and green energy-supplying renewable sources were integrated into the framework, e.g., heat pumps, artificial lights, fresh air flow, and natural illuminance. Different control schemes are proposed, based on proportional–integral–derivative advanced control architectures and discrete event dynamic systems-based supervisors; different control specifications are included, resulting in a multi-mode control system. The specifications refer to energy savings and comfort management, while minimizing overall costs. Comfort specifications include thermal comfort, lighting comfort, and a good level of indoor air quality. Simulations on different scenarios considering various control schemes and specifications show the reliability and soundness of the simulation and control framework. The simulated control and energy performances show the potential of the proposed approach, which can provide energy-saving results greater or equal to 6 [%] (in each season) and 19 [%] (in one year) with respect to more standard approaches.

Balancing consumer and business value of recommender systems: A simulation-based analysis

Automated recommendations can nowadays be found on many e-commerce platforms, and such recommendations can create substantial value for consumers and providers. Often, however, not all recommendable items have the same profit margin, and providers might thus be tempted to promote items that maximize their profit. In the short run, consumers might accept non-optimal recommendations, but they may lose their trust in the long run. Ultimately, this leads to the problem of designing balanced recommendation strategies, which consider both consumer and provider value and lead to sustained business success.This work proposes a simulation framework based on agent-based modeling designed to help providers explore longitudinal dynamics of different recommendation strategies. In our model, consumer agents receive recommendations from providers, and the perceived quality of the recommendations influences the consumers’ trust over time. We design several recommendation strategies which either give more weight on provider profit or on consumer utility. Our simulations show that a hybrid strategy that puts more weight on consumer utility but without ignoring profitability considerations leads to the highest cumulative profit in the long run. This hybrid strategy results in a profit increase of about 20% compared to pure consumer or profit oriented strategies. We also find that social media can reinforce the observed phenomena. In case when consumers heavily rely on social media, the cumulative profit of the best strategy further increases. To ensure reproducibility and foster future research, we publicly share our flexible simulation framework.

Genomic epidemiological models describe pathogen evolution across fitness valleys.

Genomics is fundamentally changing epidemiological research. However, systematically exploring hypotheses in pathogen evolution requires new modeling tools. Models intertwining pathogen epidemiology and genomic evolution can help understand processes such as the emergence of novel pathogen genotypes with higher transmissibility or resistance to treatment. In this work, we present Opqua, a flexible simulation framework that explicitly links epidemiology to sequence evolution and selection. We use Opqua to study determinants of evolution across fitness valleys. We confirm that competition can limit evolution in high-transmission environments and find that low transmission, host mobility, and complex pathogen life cycles facilitate reaching new adaptive peaks through population bottlenecks and decoupling of selective pressures. The results show the potential of genomic epidemiological modeling as a tool in infectious disease research.

DL-RSIM: A Reliability and Deployment Strategy Simulation Framework for ReRAM-based CNN Accelerators

Memristor-based deep learning accelerators provide a promising solution to improve the energy efficiency of neuromorphic computing systems. However, the electrical properties and crossbar structure of memristors make these accelerators error-prone. In addition, due to the hardware constraints, the way to deploy neural network models on memristor crossbar arrays affects the computation parallelism and communication overheads. To enable reliable and energy-efficient memristor-based accelerators, a simulation platform is needed to precisely analyze the impact of non-ideal circuit/device properties on the inference accuracy and the influence of different deployment strategies on performance and energy consumption. In this paper, we propose a flexible simulation framework, DL-RSIM, to tackle this challenge. A rich set of reliability impact factors and deployment strategies are explored by DL-RSIM, and it can be incorporated with any deep learning neural networks implemented by TensorFlow. Using several representative convolutional neural networks as case studies, we show that DL-RSIM can guide chip designers to choose a reliability-friendly design option and energy-efficient deployment strategies and develop optimization techniques accordingly.

Modelling and Simulation/Optimization of Austria’s National Multi-Energy System with a High Degree of Spatial and Temporal Resolution

The European Union and the Austrian government have set ambitious plans to expand renewable energy sources and lower carbon dioxide emissions. However, the expansion of volatile renewable energy sources may affect today’s energy system. To investigate future challenges in Austria’s energy system, a suitable simulation methodology, temporal and spatially resolved generation and consumption data and energy grid depiction, is necessary. In this paper, we introduce a flexible multi-energy simulation framework with optimization capabilities that can be applied to a broad range of use cases. Furthermore, it is shown how a spatially and temporally resolved multi-energy system model can be set up on a national scale. To consider actual infrastructure properties, a detailed energy grid depiction is considered. Three scenarios assess the potential future energy system of Austria, focusing on the power grid, based on the government’s renewable energy sources expansion targets in the year 2030. Results show that the overwhelming majority of line overloads accrue in Austria’s power distribution grid. Furthermore, the mode of operation of flexible consumer and generation also affects the number of line overloads as well.

Recommender systems effect on the evolution of users’ choices distribution

Recommender systems’ (RSs) research has mostly focused on algorithms aimed at improving platform owners’ revenues and user’s satisfaction. However, RSs have additional effects, which are related to their impact on users’ choices. In order to avoid an undesired system behaviour and anticipate the effects of an RS, the literature suggests employing simulations.In this article we present a novel, well grounded and flexible simulation framework. We adopt a stochastic user’s choice model and simulate users’ repeated choices for items in the presence of alternative RSs. Properties of the simulated choices, such as their diversity and their quality, are analysed. We state four research questions, also motivated by identified research gaps, which are addressed by conducting an experimental study where three different data sets and five alternative RSs are used. We identify some important effects of RSs. We find that non-personalised RSs result in choices for items that have a larger predicted rating compared to personalised RSs. Moreover, when a user’s awareness set, which is the set containing the items that she can choose from, increases, then choices are more diverse, but the average quality (rating) of the choices decreases. Additionally, in order to achieve a higher choice diversity, increasing the awareness of the users is shown to be a more effective remedy than increasing the number of recommendations offered to the users.

Simulating Large-Scale Events as a Network of Heterogeneous Queues: Framework and Application

Large-scale planned special events (PSEs) can pose unique transportation and logistics challenges. Data collection and simulation are important tools to address these challenges, although they are often difficult because of event size and complexity. This paper discusses methods to address the challenge of multimodal simulation at large PSEs through the context of AirVenture, a large week-long airshow organized by the Experimental Aircraft Association in Oshkosh, Wisconsin. Sampling and data collection techniques are discussed for a variety of modal processes like private vehicles, pedestrians, and shuttles, and for different situations like vehicle arrivals and departures, pedestrian queues, and shuttle systems. A flexible simulation framework for integrating these three modes and numerous activities is developed as a network of heterogeneous queues and queue-dependent choices. The simulation tested a variety of proposed policy changes around the site, including rerouting shuttle lines, and adjusting the system of vehicle arrivals to the site. Results of this study demonstrate the effectiveness and flexibility of the data collection and simulation methodologies. The techniques developed in this work can be used to improve planning and transportation systems at many other forms of PSE.

Mobility of Circular and Elliptical Si Nanowire Transistors Using a Multi-Subband 1D Formalism

We have studied the impact of the cross-sectional shape on the electron mobility of n-type silicon nanowire transistors (NWTs). We have considered circular and elliptical cross-section NWTs including the most relevant multisubband scattering processes involving phonon, surface roughness, and impurity scattering. For this purpose, we use a flexible simulation framework, coupling 3D Poisson and 2D Schrodinger solvers with the semi-classical Kubo-Greenwood formalism. Moreover, we consider cross-section dependent effective masses calculated from tight binding simulations. Our results show significant mobility improvement in the elliptic NWTs in comparison to the circular one for both [100] and [110] transport directions.

Overcoming Challenges in Large-Core SI-POF-Based System-Level Modeling and Simulation

The application areas for plastic optical fibers such as in-building or aircraft networks usually have tight power budgets and require multiple passive components. In addition, advanced modulation formats are being considered for transmission over plastic optical fibers (POFs) to increase spectral efficiency. In this scenario, there is a clear need for a flexible and dynamic system-level simulation framework for POFs that includes models of light propagation in POFs and the components that are needed to evaluate the entire system performance. Until recently, commercial simulation software either was designed specifically for single-mode glass fibers or modeled individual guided modes in multimode fibers with considerable detail, which is not adequate for large-core POFs where there are millions of propagation modes, strong mode coupling and high variability. These are some of the many challenges involved in the modeling and simulation of POF-based systems. Here, we describe how we are addressing these challenges with models based on an intensity-vs-angle representation of the multimode signal rather than one that attempts to model all the modes in the fiber. Furthermore, we present model approaches for the individual components that comprise the POF-based system and how the models have been incorporated into system-level simulations, including the commercial software packages SimulinkTM and ModeSYSTM.

An IoT-enabled simulation approach for process planning and analysis: a case from engine re-manufacturing industry

ABSTRACTEngine re-manufacturing factory runs comparatively smaller but more changeable business than normal engine manufacturing factory. Therefore, it needs a simple, quick, effective, but less costly approach for process planning. This paper proposes a novel rapid simulation approach, named as Efficiency Validate Analysis, which can serve as process planning and process analysis tools. This approach is developed based on the flexible simulation framework and discrete event system specification formalism (DEVS). Internet-of-Things (IoT) data, in terms of the historical records of sensors and radio frequency identifications (RFIDs), is adopted to build the foundation of simulation. This new simulation approach is evaluated with a case in an engine re-manufacturing plant in China. By comparing with traditional methods, the results show that the new approach supports process planning tasks more effectively. This approach offers a simple but practical way to combine the virtual information world with the entity physical world and drive their bidirectional mapping in re-manufacturing process planning, analysis and optimisation.

Accelerated 3D T2 mapping with dictionary-based matching for prostate imaging.

To develop a fast and accurate method for 3D T2 mapping of prostate cancer using undersampled acquisition and dictionary-based fitting. 3D high-resolution T2 -weighted images (0.9 × 0.9 × 3 mm3 ) were obtained with a multishot T2 -prepared balanced steady-state free precession (T2 -prep-bSSFP) acquisition sequence using a 3D variable density undersampled Cartesian trajectory. Each T2 -weighted image was reconstructed using total variation regularized sensitivity encoding. A flexible simulation framework based on extended phase graphs generated a dictionary of magnetization signals, which was customized to the proposed sequence. The dictionary was matched to the acquired T2 -weighted images to retrieve quantitative T2 values, which were then compared to gold-standard spin echo acquisition values using monoexponential fitting. The proposed approach was validated in simulations and a T1 /T2 phantom, and feasibility was tested in 8 healthy subjects. The simulation analysis showed that the proposed T2 mapping approach is robust to noise and typically observed T1 variations. T2 values obtained in the phantom with T2 prep-bSSFP and the acquisition-specific, dictionary-based matching were highly correlated with the gold-standard spin echo method (r = 0.99). Furthermore, no differences were observed with the accelerated acquisition compared to the fully sampled acquisition (r = 0.99). T2 values obtained in prostate peripheral zone, central gland, and muscle in healthy subjects (age, 26 ± 6 years) were 97 ± 14, 76 ± 7, and 36 ± 3 ms, respectively. 3D quantitative T2 mapping of the whole prostate can be achieved in 3 minutes.

Automated Mobility-on-Demand vs. Mass Transit: A Multi-Modal Activity-Driven Agent-Based Simulation Approach

Among the new transportation services made possible by the introduction of automated vehicles, automated mobility-on-demand (AMoD) has attracted a lot of attention from both industry and researchers. AMoD provides a service similar to taxi or ride-sharing services, while being driverless. It is expected to attract a huge fraction of travelers currently using mass transit or private vehicles and will have a disruptive effect on urban transportation. While most studies have focused on the operational efficiency of the technology itself, our work aims to investigate its impact on urban mobility. Our contribution is two-fold. First, we present a flexible AMoD modeling and simulation framework developed within a multi-modal agent-based urban simulation platform (SimMobility). The framework allows the detailed simulation and assessment of different AMoD operations together with an activity-based framework that accounts for changes in demand, such as activity participation, trip making, mode, destination, or route choice decisions. Second, we focus our attention on the role of mass transit in a futuristic urban system where AMoD is widely available. Mass transit is already challenged by current ride-sharing services, for example, Uber and Lyft, which provide comparatively better and cheaper services. This trend will plausibly be exacerbated with the introduction of AMoD, which may indirectly act as a replacement to mass transit. Our simulation results show that mass transit is irreplaceable, despite the high efficiency of AMoD, in order to avoid congestion and maintain a sustainable urban transportation system with acceptable levels of service.

Understanding the Effect of Statins and Patient Adherence in Atherosclerosis via a Quantitative Systems Pharmacology Model Using a Novel, Hybrid, and Multi-Scale Approach.

Background and Objective: Statins are one of the most prescribed drugs to treat atherosclerosis. They inhibit the hepatic HMG-CoA reductase, causing a reduction of circulating cholesterol and LDL levels. Statins have had undeniable success; however, the benefits of statin therapy crystallize only if patients adhere to the prescribed treatment, which is far away from reality since adherence decreases with time with around half of patients discontinue statin therapy within the first year. The objective of this work is to; firstly, demonstrate a formal in-silico methodology based on a hybrid, multiscale mathematical model used to study the effect of statin treatment on atherosclerosis under different patient scenarios, including cases where the influence of medication adherence is examined and secondly, to propose a flexible simulation framework that allows extensions or simplifications, allowing the possibility to design other complex simulation strategies, both interesting features for software development.Methods: Different mathematical modeling paradigms are used to present the relevant dynamic behavior observed in biological/physiological data and clinical trials. A combination of continuous and discrete event models are coupled to simulate the pharmacokinetics (PK) of statins, their pharmacodynamic (PD) effect on lipoproteins levels (e.g., LDL) and relevant inflammatory pathways whilst simultaneously studying the dynamic effect of flow-related variables on atherosclerosis progression.Results: Different scenarios were tested showing the impact of: (1) patient variability: a virtual population shows differences in plaque growth for different individuals could be as high as 100%; (2) statin effect on atherosclerosis: it is shown how a patient with a 1-year statin treatment will reduce his plaque growth by 2–3% in a 2-year period; (3) medical adherence: we show that a patient missing 10% of the total number of doses could increase the plaque growth by ~1% (after 2 years) compared to the same “regular” patient under a 1-year treatment with statins.Conclusions: The results in this paper describe the effect of pharmacological intervention combined with biological/physiological or behavioral factors in atherosclerosis progression and treatment in specific patients. It also provides an exemplar of basic research that can be practically developed into an application software.

Techno-economic modeling and optimization of solar-driven high-temperature electrolysis systems

We present a techno-economic analysis of solar-driven hightemperature electrolysis systems used for the production of hydrogen and synthesis gas. We consider different strategies for the incorporation of solar energy, distinguished by the use of differing technologies to provide solar power and heat: i) thermal approaches (system 1) using concentrated solar technologies to provide heat and to generate electricity through thermodynamic cycles, ii) electrical approaches (system 2) using photovoltaic technologies to provide electricity and to generate heat through electrical heaters, and iii) hybrid approaches (system 3) utilizing concentrated solar technologies and photovoltaics to provide heat and electricity. We find that system 3 generates hydrogen at a high efficiency (ηSTF = 9.9%, slightly lower than the best performing system 1 with 10.6%) and at a low cost (Cfuel = $4.9/kg, lowest cost of all three systems) at reference conditions, providing evidence for the competitiveness of this hybrid approach for scaled solar hydrogen generation. Sensitivity analysis indicates an optimal working temperature for system 3 of 1350 K, which balances the increased thermal receiver losses with the reduced electrolysis cell potential when increasing the temperature. Lower working pressure always favors high system efficiency and low cost. The working current densities for thermoneutral voltage were determined for various temperature and pressure combinations, and trends for efficient and cost-effective thermoneutral operation were identified. The water conversion extent was optimized to avoid mass transport limitations in the electrodes while ensuring large fuel generation rates. For synthesis gas production, a H2/CO molar ratio of 2 can be achieved by tuning the inlet feeding molar ratio of CO2/H2O, temperature, and pressure. This study introduces a flexible simulation framework of solar-driven high-temperature electrolysis systems allowing for the assessment of competing solar integration approaches and for the guidance of the operational conditions maximizing efficiency and minimizing cost, providing pathways for scalable solar fuel processing.

Macroscopic urban dynamics: Analytical and numerical comparisons of existing models

Large-scale network modeling using the Macroscopic Fundamental Diagram (MFD) is widely based on the single-reservoir model, where the variation of the accumulation of circulating vehicles in the reservoir equals inflow minus outflow. However, inconsistent lags for information propagation between boundaries may be observed with this single accumulation-based model. For example, outflow is reacting too fast when inflow varies rapidly, whereas this information should be carried by vehicles that are never driving faster than the free-flow speed. To overcome this limitation, a trip-based model has been recently proposed, but whose solution cannot be obtained analytically.In this paper we compare both models under piecewise linear MFD and a piecewise constant demand. These assumptions allow to establish the exact solution of the accumulation-based model, and continuous approximations of the trip-based model at any order using Taylor series. Moreover, a flexible event-based simulation framework is implemented to solve the latter model, making it a promising tool to account for heterogeneity in distance traveled. Thanks to these resolution schemes we are able to measure the inaccuracy of the accumulation-based approach when the demand varies rapidly, and propose a validity domain for this model. Other applications with different trip lengths and supply limitations are also discussed.