Simulation Architecture Research Articles

A Strategic Pathway from Cell to Pack-Level Battery Lifetime Model Development

The automotive energy storage market is currently dominated by the existing Li-ion technologies that are likely to continue in the future. Thus, the on-road electric (and hybrid) vehicles running on the Li-ion battery systems require critical diagnosis considering crucial battery aging. This work aims to provide a guideline for pack-level lifetime model development that could facilitate battery maintenance, ensuring a safe and reliable operational lifespan. The first of the twofold approach is a cell-level empirical lifetime model that is developed from a lab-level aging dataset of commercial LTO cells. The model is validated with an exhaustive sub-urban realistic driving cycle yielding a root-mean-square error of 0.45. The model is then extended to a 144S1P modular architecture for pack-level simulation. The second step provides the pack electro-thermal simulation results that are upscaled from a cell-level and validated 1D electrical model coupled with a 3D thermal model. The combined simulation framework is online applicable and considers the relevant aspects into account in predicting the battery system’s lifetime that results in over 350,000 km of suburban driving. This robust tool is a collaborative research outcome from two Horizon2020 EU projects—GHOST and Vision xEV, showcasing outstanding cell-level battery modeling accuracies.

A review of fog computing and its simulators

Fog computing is defined as the distribution of computing resources between the data devices and the cloud or any other data centre in a distributed computing infrastructure or process. This paper briefly reviews the various definitions, applications, architecture and fog simulators proposed by researchers over the years. In this paper, a comparison table is presented which highlights the key features of simulators available like FogtorchII, iFogSim, Fogbus, MyiFogSim etc.

Computer Simulation as a Tool of Psychological Readiness for Employment Process for Migrant Students

Background: Nowadays the number of students studying abroad increases rapidly. Migrant students represent a highly motivated and achieving category and contribute a lot to the economies of the host countries. Coming to the host country they face different challenges, one of them being employment. Previous studies reveal that migrant students during the period of their acculturation undergo various difficulties, and even after graduation, there are such hurdles as pressure, immigration policies, and an uncertain future. If they want to pursue a career in the host country, new barriers such as documentation requirements, problems with language and discrimination could appear.Purpose: The necessity to create a safe environment that can help them acquire the necessary skills needed at a job interview made the research authors consider the use of a VR tool.Materials and Methods: 171 migrant students took part in the research. Based on a needs analysis to depict the barriers migrant students face while seeking a job in a host country the architecture of a computer simulator “Employment navigator” was designed.Results: The identified needs of migrant students, as well as the main barriers to their adaptation, can influence the optimization of the system of adaptation of foreign students in the conditions of host universities. The ‘Employment Navigator’ virtual reality simulator can be used as a tool to assess the readiness of a migrant student to undergo a job interview successfully.Implication: Virtual reality simulators make it possible to repeat actions as many times as necessary to achieve the desired result and proved their efficiency applied to all vulnerable categories of citizens.

A unified cloud-enabled discrete event parallel and distributed simulation architecture

Cloud infrastructure provides rapid resource provision for on-demand computational requirements. Cloud simulation environments today are largely employed to model and simulate complex systems for remote accessibility and variable capacity requirements. In this regard, scalability issues in Modeling and Simulation (M&S) computational requirements can be tackled through the elasticity of on-demand Cloud deployment. However, implementing a high performance cloud M&S framework following these elastic principles is not a trivial task as parallelizing and distributing existing architectures is challenging. Indeed, both the parallel and distributed M&S developments have evolved following separate ways. Parallel solutions has always been focused on ad-hoc solutions, while distributed approaches, on the other hand, have led to the definition of standard distributed frameworks like the High Level Architecture (HLA) or influenced the use of distributed technologies like the Message Passing Interface (MPI). Only a few developments have been able to evolve with the current resilience of computing hardware resources deployment, largely focused on the implementation of Simulation as a Service (SaaS), albeit independently of the parallel ad-hoc methods branch. In this paper, we present a unified parallel and distributed M&S architecture with enough flexibility to deploy parallel and distributed simulations in the Cloud with a low effort, without modifying the underlying model source code, and reaching important speedups against the sequential simulation, especially in the parallel implementation. Our framework is based on the Discrete Event System Specification (DEVS) formalism. The performance of the parallel and distributed framework is tested using the xDEVS M&S tool, Application Programming Interface (API) and the DEVStone benchmark with up to eight computing nodes, obtaining maximum speedups of 15.95× and 1.84×, respectively.

Evaluation of the Communication Delay in a Hybrid Real-Time Simulator for Weak Grids

Real-time Simulation (RTS) is one of the effective means via which to study device level or system level dynamics, such as power converter online testing, evaluation, and control, and power system stability analysis. The RTS -enabled design-chain offers a time -effective, low-cost, and fail-safe development process. As the penetration of renewable energy is becoming higher, the demand in hybrid system real-time simulation becomes imperative, where fast-dynamic device level power converters and slow -dynamic large -scale power systems are simulated at the same time. This paper introduces a novel hybrid real-time simulation architecture based on the central processing unit (CPU) and the field-programmable gate array (FPGA). Compared with the off-the-shelf power system real-time simulation system, it offers both wide time scale simulation and high accuracy. The multi-time scale model can perform electromechanical electromagnetic transient hybrid simulation, which can be applied to the research of power systems penetrated with power converters. In the proposed simulation platform, the communication delay is introduced when different RTS platforms exchange real-time data. The communication delay should be considered in the stability analysis of the grid-connected inverters in a weak grid environment. Based on the virtual impedance characteristic formed by the control loop with and without communication delay, the impedance characteristics are analyzed and inter-simulator delay impacts are revealed in this paper. Theoretical analysis indicates that the communication delay, contrary to expectation, can improve the virtual impedance characteristics of the system. With the same hardware simulation parameters, the grid-converter system is verified on both the Typhoon system alone and the Typhoon-dSPACE-SpaceR hybrid simulation platform. The THD value of grid current in a weak grid environment that works in the Typhoon system is 4.98%, and 2.38% in the Typhoon-dSPACE-SpaceR hybrid simulation platform. This study eventually reveals the fact that the inter-simulation delay creates the illusion that the control system built in the novel hybrid real-time simulation is more stable under weak grid conditions.

Architectural Simulations on Spatio-Temporal Changes of Settlement Outdoor Thermal Environment in Guanzhong Area, China

This paper aims to provide data support for rural sustainable development through analyzing the spatio-temporal characteristics of the interactions of the outdoor thermal environment. The ordinary and representative rural settlements in the Guanzhong area were selected to analyze the dynamic process of the rural thermal environment through field measurements and numerical simulations. RMSE (root mean square error) and MAPE (mean absolute percentage) were used to verify the numerical simulation model, and physiological equivalent temperature (PET) was used to evaluate the outdoor thermal environment. Results show that the ENVI-met model reliably predicts the thermal environment of a rural settlement, as the air temperature and relative humidity values range of the RMSE and MAPE were 0.85–1.79 and 2.04–5.11%, respectively. Moreover, the air temperature rose by 3.08% and relative humidity dropped by 4.42% from 2003 to 2018 as the amount of artificial surfaces increased by 35.4% and the PET index gradually increased by 27.43% at daytime and 34.03% at nighttime. Furthermore, trees could improve the outdoor thermal environment significantly, mainly because the average air temperature decreased by 3.6% and relative humidity increased by 8%, and the PET index decreased by 12.4% and 13.1%, respectively, for daytime and nighttime. This case study is representative of rural settlements in the Guanzhong plain, and thus is an appeal to rural planners to pay attention to the thermal environment issues caused by increased artificial underlay surfaces and to focus on trees in rural areas.

Edge Intelligence for Rendering Green Camera-Network-as-a-Service

This work proposes the Green Camera-Network-as-a-Service (G-CNaaS) architecture, which provides on-demand camera networks to multiple end-users simultaneously while utilizing minimal energy. G-CNaaS simultaneously reduces the carbon footprint and eliminates the single application-centric approach of traditional camera networks (TCNs) by enabling each camera to participate in multiple Virtual-Camera-Networks (VCNs) and selecting an optimal set of cameras for each VCN. We couple each camera node in every VCNs with a learning model suitable for the requested application. We assign an intelligent edge device to each VCN to analyze time-sensitive events. We introduce the camera selection factor by leveraging the properties of cameras: 1) field-of-view (FoV); 2) angular-distance; 3) observation range; and 4) residual energy to select the optimal camera set. The results of the extensive simulation of the G-CNaaS architecture show that it excels in performance concerning attributes such as the average lifetime, fair distribution of the work among the camera owners, and cost-effectiveness compared to the TCNs. We observe that the expenditure of a user using the TCN varies by 88.7%, while in the case of G-CNaaS, the expenditure varies by 10.28% with the increase in time from 1–60 months. On the other hand, the average energy consumed increases by 59.88% and 99.5% in the presence of 10 and 20 camera sensor owners.

A Low-Latency Communication Design for Brain Simulations

Brain simulation, as one of the latest advances in artificial intelligence, facilitates better understanding about how information is represented and processed in the brain. The extreme complexity of the human brain makes brain simulations only feasible on high-performance computing platforms. Supercomputers with a large number of interconnected graphical processing units (GPUs) are currently employed for supporting brain simulations. Therefore, high-throughput low-latency inter-GPU communications in super-computers play a crucial role in meeting the performance requirements of brain simulation as a highly time-sensitive application. In this article, we first provide an overview of the current parallelizing technologies for brain simulations using multi-GPU architectures. Then we analyze the challenges to communications for brain simulation and summarize guidelines for communication design to address such challenges. Furthermore, we propose a partitioning algorithm and a two-level routing method to achieve efficient low-latency communications in multi-GPU architecture for brain simulation. We report experiment results obtained on a supercomputer with 2000 GPUs for simulating a brain model with 10 billion neurons (digital twin brain, DTB) to show that our approach can significantly improve communication performance. We also discuss open issues and identify some research directions for low-latency communication design for brain simulations.

Distributed agent-based deep reinforcement learning for large scale traffic signal control

Traffic signal control (TSC) is an established yet challenging engineering solution that alleviates traffic congestion by coordinating vehicles’ movements at road intersections. Theoretically, reinforcement learning (RL) is a promising method for adaptive TSC in complex urban traffic networks. However, current TSC systems still rely heavily on simplified rule-based methods in practice. In this paper, we propose: (1) two game theory-aided RL algorithms leveraging Nash Equilibrium and RL, namely Nash Advantage Actor–Critic (Nash-A2C) and Nash Asynchronous Advantage Actor–Critic (Nash-A3C); (2) a distributed computing Internet of Things (IoT) architecture for traffic simulation, which is more suitable for distributed TSC methods like the Nash-A3C deployment in its fog layer. We apply both methods in our computing architecture and obtain better performance than benchmark TSC methods by 22.1% and 9.7% reduction of congestion time and network delay, respectively.

Optimized management of ultra-wideband photonics switching systems assisted by machine learning.

Recent years have seen an unprecedented growth of data traffic driven by a continuous increase of connected devices and new applications. This trend will tend to saturate transparent optical networks that are the backbone of the whole telecommunication infrastructure. To improve the capacity of already deployed network infrastructures and maximize operators CAPEX returns, band-division multiplexing (BDM) has emerged as a promising solution to expand the fiber bandwidth beyond the existing C-band. Along with this, the demand for flexible and dynamically reconfigurable functionalities in each network layer is increasing. In this regard, optical networking is fast evolving towards the applications of the software-defined networking (SDN) paradigm down to the physical layer. The implementation of optical SDN requires the full abstraction and virtualization of each network element in order to enable complete control by a centralized network controller. To pursue this objective, photonics transmission components and their transmission functionalities must be abstracted to allow the definition of the control states and a real-time quality-of-transmission (QoT) evaluation of transparent lightpaths (LP). In this work, we propose an SDN based model of a photonic switching fabric that allows determining the control state and evaluating QoT degradation. Our investigations present a wideband optical switch design based on photonic integrated circuits (PICs), where QoT degradation is abstracted using a structure-agnostic approach based on machine learning (ML). The ML engine training and testing datasets are generated synthetically by software simulation of the photonic switch architecture. Results show the potential of the proposed technique to predict QoT impairments with high accuracy, and we envision its application in a real-time control plane.

Simulating the Cortical Microcircuit Significantly Faster Than Real Time on the IBM INC-3000 Neural Supercomputer.

This article employs the new IBM INC-3000 prototype FPGA-based neural supercomputer to implement a widely used model of the cortical microcircuit. With approximately 80,000 neurons and 300 Million synapses this model has become a benchmark network for comparing simulation architectures with regard to performance. To the best of our knowledge, the achieved speed-up factor is 2.4 times larger than the highest speed-up factor reported in the literature and four times larger than biological real time demonstrating the potential of FPGA systems for neural modeling. The work was performed at Jülich Research Centre in Germany and the INC-3000 was built at the IBM Almaden Research Center in San Jose, CA, United States. For the simulation of the microcircuit only the programmable logic part of the FPGA nodes are used. All arithmetic is implemented with single-floating point precision. The original microcircuit network with linear LIF neurons and current-based exponential-decay-, alpha-function- as well as beta-function-shaped synapses was simulated using exact exponential integration as ODE solver method. In order to demonstrate the flexibility of the approach, additionally networks with non-linear neuron models (AdEx, Izhikevich) and conductance-based synapses were simulated, applying Runge–Kutta and Parker–Sochacki solver methods. In all cases, the simulation-time speed-up factor did not decrease by more than a very few percent. It finally turns out that the speed-up factor is essentially limited by the latency of the INC-3000 communication system.

MIMO Radar Parallel Simulation System Based on CPU/GPU Architecture.

The data volume and computation task of MIMO radar is huge; a very high-speed computation is necessary for its real-time processing. In this paper, we mainly study the time division MIMO radar signal processing flow, propose an improved MIMO radar signal processing algorithm, raising the MIMO radar algorithm processing speed combined with the previous algorithms, and, on this basis, a parallel simulation system for the MIMO radar based on the CPU/GPU architecture is proposed. The outer layer of the framework is coarse-grained with OpenMP for acceleration on the CPU, and the inner layer of fine-grained data processing is accelerated on the GPU. Its performance is significantly faster than the serial computing equipment, and satisfactory acceleration effects have been achieved in the CPU/GPU architecture simulation. The experimental results show that the MIMO radar parallel simulation system with CPU/GPU architecture greatly improves the computing power of the CPU-based method. Compared with the serial sequential CPU method, GPU simulation achieves a speedup of 130 times. In addition, the MIMO radar signal processing parallel simulation system based on the CPU/GPU architecture has a performance improvement of 13%, compared to the GPU-only method.

Architecture and Application Co-Design for Beyond-FPGA Reconfigurable Acceleration Devices

In recent years, field-programmable gate arrays (FPGAs) have been increasingly deployed in datacenters as programmable accelerators that can offer software-like flexibility and custom-hardware-like efficiency for key datacenter workloads. To improve the efficiency of FPGAs for these new datacenter use cases and data-intensive applications, a new class of reconfigurable acceleration devices (RADs) is emerging. In these devices, the FPGA fine-grained reconfigurable fabric is a component of a bigger monolithic or multi-die system-in-package that can incorporate general-purpose software-programmable cores, domain-specialized accelerator blocks, and high-performance networks-on-chip (NoCs) for efficient communication between these system components. The integration of all these components in a RAD results in a huge design space and requires re-thinking the implementation of applications that need to be migrated from conventional FPGAs to these novel devices. In this work, we introduce RAD-Sim, an architecture simulator that allows rapid design space exploration for RADs and facilitates the study of complex interactions between their various components. We also present a case study that highlights the utility of RAD-Sim in re-designing applications for these novel RADs by mapping a state-of-the-art deep learning (DL) inference FPGA overlay to different RAD instances. Our case study illustrates how RAD-Sim can capture a wide variety of reconfigurable architectures, from conventional FPGAs to devices augmented with hard NoCs, specialized matrix-vector blocks, and 3D-stacked multi-die devices. In addition, we show that our tool can help architects evaluate the effect of specific RAD architecture parameters on end-to-end workload performance. Through RAD-Sim, we also show that novel RADs can potentially achieve 2.6× better performance on average compared to conventional FPGAs in the key DL application domain.

Evaluating Tradeoffs for Swarm Reconnaissance With Autonomous Ground Vehicles

AbstractAutonomous ground vehicles (AGVs) operating collaboratively have several advantages over vehicles operating alone. An AGV team may be more resilient and efficient than a single AGV. Other advantages of AGV teams include increased coverage and multiple viewing angles of terrain features as well as resistance to failure from any single AGV. Additionally, AGV teams can explore large terrains more quickly and thoroughly than a single system. In this work, the feasibility of using a team of high-mobility AGV to explore a navigation corridor, map the terrain, and autonomously flag obstacles for future navigation is evaluated. Focusing on negative obstacles, the value of using multiple vehicles to map a navigation corridor is quantified. This study is the first to evaluate large teams of AGV collaborating in realistic off-road, 3D environments. The feasibility of the large-scale AGV team is demonstrated while avoiding the high cost of purchasing and testing large numbers of vehicles using the Mississippi State University autonomous vehicle simulator (MAVS), a high-fidelity, physics-based simulation tool. The cost and benefits of increasing the AGV team size are evaluated. The simulation results show how factors like fuel use, map coverage, and obstacle detection are influenced by increasing numbers of AGV in the team. The simulation architecture is presented and experiments quantifying the performance of the simulator are shown. Finally, a model for evaluating the tradeoff between mission effectiveness and fuel use is developed and presented to demonstrate the utility of this approach.

Reactive online scheduling of mobile resources for adaptive layout evolution in line-less assembly system

Assembly systems are required to be more flexible due to unpredictable resource and demand fluctuations. Line-less assembly systems (LAS) implement flexible job routes through multi-purpose resources and flexible transportation systems. Moreover, a completely reactive layout through (re-)arrangement of stations with mobile resources enables reconfigurations without interrupting production. A scheduling that can handle the complexity of dynamic events is necessary to plan job routes and control transportation in such an assembly system. Since the system layout and the job scheduling influence each other, only an integrated view of layout planning and scheduling implements a reactive system behaviour. Existing pre-planning methods with fixed cycle times are not designed for reactive layout optimization during line-less assembly operations with mobile resources. Therefore, the contribution of this paper is an integrated online optimization architecture for layout evolution using mobile resources and simultaneous job scheduling to increase the efficiency of LAS. The optimization is implemented using modular matching and scheduling algorithms considering temporary station setup states and parallel processing based on zone restrictions. The proposed algorithms are connected to a modular discrete-event simulation and scenario analysis tool of LAS. The simulation and control architecture is validated by applying an industry-based use case. The reactive layout evolution, including station exchanges and mobile resource repositioning are benchmarked with nonflexible control methods. Results show that the developed control system allows for shorter average lead-time and increased throughput and utilization of (mobile) resources.

Choosing the optimal approach to building a recommender system based on movie data

Recommender systems have become increasingly popular recently because they can address the problem of information overload by suggesting items of interest to the users. The volume of information with each year increases and more opportunities for Internet business, so it becomes easier to get anything through the internet, such as goods, books, movies, news. Work in the field of creating recommendation systems can be both commercial and research in nature, and need to address a number of issues. The paper studies the problem of choosing the optimal approach to building a recommendation system by repulsive from the available data. It is not possible to create a recommendation system without data. The data is usually available in an explicit or implicit way. Data that is called explicit can be collected by finding reviews and sharing of user views about different products. However, the implicit data is related to the search magazine and the data history available on the system. Choosing an optimal approach to building a recommendation system will allow the machine learning algorithm to be used. Simulated recommendation system architecture that helps to understand how to build a user-defined recommendation process. Recommender systems are classified in the manner of sampling the necessary material for the user system. Two basic approaches are primarily applied: collaborative filtering and content-oriented filtering. There is also a hybrid filtering that combines both collaborative and content-oriented filtering. The work carried out analysis and comparison of recommendation systems by types. Comparing types of recommendation system based on basic characteristics: implementation complexity, implementation accuracy, job speed, dependency on system users. Analysis of the core content-oriented filtering machine learning algorithms have been conducted.

MarsSim: A high-fidelity physical and visual simulation for Mars rovers

Simulation has deeply infiltrated into the development and validation of planetary rover technologies concerning mobility and autonomy, by providing large amounts of data in a variety of conditions and environments, as well as high-fidelity prototypes. Prevalent approaches for rover simulation focus either on physical behaviors or visual scenarios. In this paper, we present a simulator named MarsSim developed upon ROS/Gazebo platform that supports both physical and visual realistic simulations. The interaction between the wheel and the rough deformable terrain is modeled with improved contact solutions, and the model is integrated into a refined physical simulation architecture as an independent terramechanics plugin. Using multi-scale planetary data and representation models, Martian scenarios are constructed, considering characteristics in terms of the surface, rock and atmosphere. Compared with experiments, the terramechanics plugin shows great modelling accuracy in fitting interactive force/torque and slip-sinkage phenomenon. The simulation of traverses with a six- wheeled planetary rover on Martian landscape are demonstrated with near-photoreal images, coupled with physical-realistic locomotive data under different terrains.

Architectural Simulation of Hybrid Energy Harvesting: A Design Experiment in Lanzarote Island

This study conducts research on an architectural design based on energy harvesting technology. The research subject is a pergola-style structure to be built in a square in Arrecife, the Spanish territory of Lanzarote Island. The architectural design based on the energy harvesting technology developed in this research utilizes solar energy. To install a solar panel on the roof of the pergola, the optimal tilt angle from January to December was derived by using a function that considered the latitude and solar declination value of the study site, and the amount of renewable energy generation was calculated. The architectural design based on energy harvesting also utilizes wind power. To transform wind power into renewable energy, piezoelectric materials that trigger renewable energy with the micro-vibrations generated by wind power are applied to the architectural design. The amount of energy generation was calculated considering the wind power and wind direction in the location where the pergola should be built; in addition, this calculation used information from prior studies on piezoelectric materials. This article is significant, as it has developed an architectural design where hybrid energy harvesting technology that utilizes two types of natural energy (solar and wind) is applied to a building façade.

An Architecture of Procedures in Modeling for Manufacturing Logistics

The concepts of Modeling and Simulation (M&S) are gradually becoming a fundamental sector which attracts the attention of researchers in the modern age. Operational systems in Small and Medium-sized Enterprises (SMEs) have to be investigated using these concepts to achieve quality results. Until now, many scholastic contributions have been published regarding the various perspectives and aspects of M&S. However, there are gaps in the discussion of the complete cycle of modeling and simulation which incorporates the verification and development of an architecture that can be used in manufacturing logistics for Product Development Schemes (PDS). In that case, this paper presents the Simulation Procedures (SP) and architecture can be used to comprehend the systems for product development. This contribution focusses on the validation and verification of the architecture as well. As such, this contribution fills the prevailing gap between the architectural design/development and the validation/verification of modeling and simulation procedures. These are the fundamentals in the application of the M&S approach to comprehend the systems of product development in SMEs.

Boosting Inter-process Communication with Architectural Support

IPC (inter-process communication) is a critical mechanism for modern OSes, including not only microkernels such as seL4, QNX, and Fuchsia where system functionalities are deployed in user-level processes, but also monolithic kernels like Android where apps frequently communicate with plenty of user-level services. However, existing IPC mechanisms still suffer from long latency. Previous software optimizations of IPC usually cannot bypass the kernel that is responsible for domain switching and message copying/remapping across different address spaces; hardware solutions such as tagged memory or capability replace page tables for isolation, but usually require non-trivial modification to existing software stack to adapt to the new hardware primitives. In this article, we propose a hardware-assisted OS primitive, XPC (Cross Process Call), for efficient and secure synchronous IPC. XPC enables direct switch between IPC caller and callee without trapping into the kernel and supports secure message passing across multiple processes without copying. We have implemented a prototype of XPC based on the ARM AArch64 with Gem5 simulator and RISC-V architecture with FPGA boards. The evaluation shows that XPC can reduce IPC call latency from 664 to 21 cycles, 14×–123× improvement on Android Binder (ARM), and improve the performance of real-world applications on microkernels by 1.6× on Sqlite3.