Complex approaches for the calculation of EMC problems of large systems

Multi-scale methods such as wavelet and multigrid schemes are efficient algorithms for the numerical solution of large classes of partial differential equations (PDEs). In this chapter we illustrate the basic ideas behind iterative methods for the solution of large linear systems which arise from the discretisation of PDEs. Besides describing the concepts of wavelet preconditioning, we summarise the main ingredients of multigrid methods. Examples from computational geosciences will show the importance of multi-scale methods for the simulation of large and complex systems.

Current simulation tools for multibody dynamics are not problem dependent, they use the same modeling process to all cases regardless of their characteristics. In addition, real-time simulation of small multibody systems is achievable by existing simulation tools, however, real-time simulation of large and complex systems is not possible with existing methods. This is a challenge that needs to be addressed before further advances in mechanical simulation with hardware-in-the-loop and man-in-the-loop, as well as virtual prototyping are made possible. This paper addresses the issue of how the modeling process-dependent versus independent coordinates, and descriptor form versus state-space form of the equations of motion-affects the dynamic simulation of multibody systems and how it may be taken into account and added to the concept of intelligent simulation. With this new concept all the factors involved in the simulation process — modeling, equations, solution, etc. — are chosen and combined depending upon the characteristics of the system to be simulated. It is envisioned that this concept will lead to faster and more robust real-time simulators.

Systems engineering is the branch of engineering concerned with the development of large and complex systems, where a system is understood to be an assembly or combination of interrelated elements or parts working together toward a common objective. Past experience has shown that formal systems engineering methodologies have not always been successfully applied to large and complex cybersecurity systems. These complex systems have become commonplace when applying cyberstrategies in cybersecurity operations. The ability to build, operate and maintain such systems is crucial to the effectiveness of cybersecurity operations. Most importantly, a cyberstrategy program must surround these systems on a global scale across multiple inter-related platforms. In this chapter, the authors demonstrate why a systems engineering approach is best suited for large and complex information systems used in cybersecurity, as well as the overall cyberstrategies that must also reside over these systems.

How to predict very large and complex crystal structures

This study examines the performance of integration methods for hybrid simulation of large and complex structural systems in the context of structural collapse due to seismic excitations. The target application is not necessarily for real-time testing, but rather for models that involve large-scale physical sub-structures and highly nonlinear numerical models. Four case studies are presented and discussed. In the first case study, the accuracy of integration schemes including two widely used methods, namely, modified version of the implicit Newmark with fixed-number of iteration (iterative) and the operator-splitting (non-iterative) is examined through pure numerical simulations. The second case study presents the results of 10 hybrid simulations repeated with the two aforementioned integration methods considering various time steps and fixed-number of iterations for the iterative integration method. The physical sub-structure in these tests consists of a single-degree-of-freedom (SDOF) cantilever column with replaceable steel coupons that provides repeatable highlynonlinear behavior including fracture-type strength and stiffness degradations. In case study three, the implicit Newmark with fixed-number of iterations is applied for hybrid simulations of a 1:2 scale steel moment frame that includes a relatively complex nonlinear numerical substructure. Lastly, a more complex numerical substructure is considered by constructing a nonlinear computational model of a moment frame coupled to a hybrid model of a 1:2 scale steel gravity frame. The last two case studies are conducted on the same porotype structure and the selection of time steps and fixed number of iterations are closely examined in pre-test simulations. The generated unbalance forces is used as an index to track the equilibrium error and predict the accuracy and stability of the simulations.

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Construction of a Knowledge Base for Simulation and Control of Large Scale and Complex Systems. Applications was published in Systems Analysis and Simulation 1988, I: Theory and Foundations. Proceedings of the International Symposium held in Berlin (GDR), September 12–16, 1988 on page 387.

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet-TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet-LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed-memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.

Molecular dynamics simulation is currently a theoretical technique eligible in simulating of a wide range of systems, from soft condensed matter to biological systems. Excellent outcomes have resulted from using this technique; however, the implementation of this approach remains computationally expensive to some extent. Novel computing technologies may help reducing the computational simulation time, particularly, by using Graphical Processing Units (GPUs). Calculations on GPUs make possible to carry out simulations of large and complex molecular systems at a significantly reduced time. In this manuscript, the implementations of measure-preserving geometric integrator in the canonical ensemble coded in Compute Unified Device Architecture (CUDA) language are presented. The performance and validation of our High-throughput Molecular Dynamics (HIMD) code was done by calculating the thermodynamic properties of a Lennard-Jones fluid. From our tested systems, an excellent agreement was achieved with the reported of literature, compared with calculations carried out on Central Processing Units (CPUs). The implementation of the HIMD code performs time integration on Nosé-Hoover chains (NHC) faster in comparison to the NHC method implemented in LAMMPS code tested with one CPU vs one GPU. Along this work, the scope and limitations in performing simulations by using our HIMD code under rigorous statistical thermodynamics in the canonical ensemble are discussed and analyzed.KeywordsMolecular Dynamics (MD)Graphical Processing Units (GPUs)Nosé-Hoover Chains (NHC)Canonical Ensemble (NVT)

Testing consumes significant human and machine resources, especially for large, complex systems such as database servers. While a variety of testing approaches have been proposed to improve the efficiency of the testing process, it is difficult to evaluate these approaches. Mutation testing has been proposed as a way to assess the adequacy of a test suite, assigning a score that can be used to compare testing approaches. While promising, serious obstacles appear to prevent mutation testing with large software systems. Recent advances in mutation testing have scaled to medium-sized programs of around 100,000 lines of code but to our knowledge there are no reported studies working with large systems with millions of lines of code and other features of database systems that complicate testing. In this paper we explore using mutation testing on a database server to evaluate its suitability for comparing test suites or testing approaches.

Modelling and simulation is of crucial importance for the understanding of system dynamics. In aircraft, simulation has been strong in the area of flight control. Modelling and simulation of the hydraulic systems has also a long tradition. The rapid increase in computational power has now come to a point where complete modelling and simulation of all the sub systems in an aircraft is not far away. This means new challenges in dealing with very complex multi domain systems. Of all the systems in an aircraft fluid power systems is one of the most difficult to handle from a numerical point of view. They are characterised by difficulties such as discontinuities, very strong non-linearities, stiff differential equations and a high degree of complexity. A considerable effort has therefore been made to develop methods suitable for simulation of such systems. This means that it is possible to simulate fluid power systems much more efficiently than before, and that a lot of systems can even be simulated in real time. The distributed modelling technique used, also makes it possible to partition the system for parallel processing. This means that it is possible to simulate much larger systems within reasonable time. It also is a valid paradigm for multi domain system. The rapid development in simulation methods and the general increase in hardware performance imply that simulations can be run thousands of times faster than just a few years ago. As a consequence, there is no longer such need to use very economic, more or less analytical methods. This means that design methods based on different kinds of numerical optimisation for system design, are becoming much more important.

Mass and energy rate (ER) data have been collected for a wide variety of (complex) systems from the biological, cultural, and cosmological realms. They range from the cytochrome oxidase protein (10-22 kg and 6x10-19 W) to the observable universe (1.5x1053 kg and 1048 W) and, thus, span 75 mass and 66 ER orders of magnitude. Many of these systems are relevant for the big history (BH) narrative, i.e., the development of complexity over “big time” from the Big Bang up to the human society on Earth of today. The purpose of this paper is not per se to describe their history though, but to explore a master plot of ER vs. mass. Notably, the development of systems over big time has followed a rather tortuous path criss-crossing over this ER vs. mass master plot. The true mass of the system as a whole is used (for example trees including the non-living wood, living organisms including their intrinsic water, and social systems including the built constructs), because these inactive parts are essential for the performance of the system and facilitate its ER. A double logarithmic master plot of all ER vs. mass data shows clusters of data points. To some extent, this provides quantitative support for the distinction between the (sub-)realms, which is based on a qualitative description of their material structure and energy processing. In the master plot, small systems with low mass and ER converge into larger systems with larger mass and ER, which is typically accompanied by a decrease of the energy rate density (ERD = ER/mass). Correlation of ER with mass for various groups of systems demonstrates both sub- and supra-linear scaling with the power law β constant varying between 0.5 and 4.0, showing that the mechanisms of self-organisation are quite different for the corresponding system groups. The combination of convergence and scaling with β always larger than zero explains why the ER & mass data points fall in a diagonal band with a width of 17 orders of magnitude.
 ER and mass have changed over wide ranges during the evolution of groups of systems, suggesting that evolution can be viewed as a process of systems exploring a larger ER vs. mass area until they run into ER and/or mass limitations. Indeed, there is a diagonal ER vs. mass limit for stable systems in all realms, corresponding to an ERD value of around 105 W/kg. Systems with ER & mass combinations above this limit, such as bombs, super-novae and cosmological transients, are unstable and “explosive”. This raises the interesting question of whether such an ERD maximum puts a limit on the development of complexity over big time. It seems that the low, right side of the master plot is empty. However, it is argued here that it is full of systems with low ER, such as dormant, living organisms, technological systems with their power adjusted or even switched off, as well as cooling, cosmological objects. Such systems are typically considered of less interest in a BH context, but they are viewed here as simple, complex systems which are out of equilibrium with matter, energy and information stored in their structure. While ERD appears to increase with the ‘advancement’ of systems over big time [5,51,52], there are quite some confounding factors regarding the efficacy of ERD as a metric for complexity in BH. For example, ERD decreases during the lifetime of a human and the human society (the mass of human-made constructs has grown faster than the global energy consumption), as well as during the evolution of living organisms and stars, whereas complexity is considered to increase. High ERD values of system parts may be illustrative for the complexity of the larger system, but are not representative for ERD of the system itself. Machines with an increased efficiency of energy conversion have a lower ERD, but could be considered more complex. The smallest and largest ERD values observed for the various realms appear to correlate with activity level and reciprocally with size, which do not per se reflect complexity. It is hoped that the raw data collected and the major trends observed in this paper will offer new insights into various aspects of the evolution of the universe over big time, and serve as an important resource for other related studies.

Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods

The specification and distributed control of discrete event robotic manufacturing systems using Petri nets are considered, and a methodology of decomposition and coordination is presented for hierarchical and distributed control. First, a task specification is defined as a Petri net model at the conceptual level, and then transformed to the detailed Petri net representation of the subtasks. Finally, the overall Petri net is decomposed and the constituent subnets are assigned to local Petri net based controllers. The controllers are coordinated by the coordinator so that the decomposed transitions fire at the same time. System coordination algorithm through communication between the coordinator and the controllers, is presented. By the proposed method, modeling, simulation and control of large and complex manufacturing systems can be performed consistently using Petri nets.

Epidemic episodes such as the COVID-19 has shown the need for simulation tools to support decision making, predict the results of control actions, and mitigating the effects of the virus. Simulation methods have been widely used by healthcare researchers and practitioners to improve the planning and management of hospitals and predict the spread of disease. Simulating all involved aspects of an epidemic episode requires the modelling and simulation of large and complex Discrete Event Systems (DESs), supported by modular and hierarchical models easy to use for experts, and that can be translated to efficient code for distributed simulation. This paper presents a model driven engineering (MDE) approach to support the modelling of healthcare systems (HS) in epidemic episodes combining different perspectives, and the translation to efficient code for scalable distributed simulations.