An Efficient Modelling and Simulation Framework of a Decanter Centrifuge for Solid-Liquid Separations

PurposeThis paper aims to propose an alternate, efficient and scalable modeling framework to simulate large-scale bike-sharing systems using discrete-event simulation. This study uses this model to evaluate several initial bike inventory policies inspired by the operation of the bike-sharing system in Mexico City, which is one of the largest around the world. The model captures the heterogeneous demand (in time and space) and this paper analyzes the trade-offs between the performance to take and return bikes. This study also includes a simulation-optimization algorithm to determine the initial inventory and present a method to deal with the bias caused by dynamic rebalancing on observed demand.Design/methodology/approachThis paper is based on the analysis of an alternate and efficient discrete-event simulation modeling framework. This framework captures the heterogeneity of demand and allows one to experiment with large-scale models. This study uses this model to test several initial bike inventory policies and also combined them with an optimization engine. The results, provide valuable insights not only for the particular system that motivated the study but also for the administrators of any bike-sharing system.FindingsThe findings of this paper include: most of the best policies use a ratio of bikes: docks near to 1:2; however, it is important the way they are initially allocated; a policy that contradicts the demand profile of the stations can lead to poor performance, regardless the quick and dynamic changes of bike locations during the morning period; the proposed simulation-optimization algorithm achieves the best results.Research limitations/implicationsThe findings are limited to the initial inventory of the system under study. The model assumes a homogeneous probability distribution function for the travel time. This assumption seems reasonable for the system under study. This paper limits the tested inventory policies to simple practical rules. There might be other sophisticated methods to obtain better solutions, but they might be system-specific.Practical implicationsThe insights of this paper are valuable for operators of bike-sharing systems because this study focuses on the analysis of the impact of the initial inventory assuming that dynamic rebalancing may not be existing during the morning peak-time. This paper finds that initial inventory has a great impact on the performance, regardless of how quickly the bikes are dispersed across the system. This study also provides insights into the effect of dynamic rebalancing on observed demand.Social implicationsIncreasing knowledge about the operation of the bike-sharing system has a positive effect on society because more cities around the world could consider implementing these systems as a public transportation mode. Furthermore, delivering suggestions on how to increase the user service level could incentivize people to adopt bikes as a mobility option, which would contribute to improve their health and also reduce air pollution caused by motorized vehicles.Originality/valueThis paper considers that the contributions of this work to existing literature are the following: this study proposes a novel efficient and scalable simulation framework to evaluate initial bike inventory policies; the analysis presented in the paper includes an approach to deal with the bias in the observed demand caused by dynamic rebalancing and the analysis includes the value of demand information to determine an effective initial bike inventory policy.

Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA – for example, on the same transcript – was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology.

Efficient simulation of generalized SABR and stochastic local volatility models based on Markov chain approximations

Chapter 20 - DEVS Representation of Iteratively Specified Systems

Ultrasound (US) imaging is essential for monitoring thermotherapies, but accurate simulation of heating-induced artifacts remains challenging. Conventional single-sound-path (SSP) models approximate thermal effects as a channel-independent axial shift, failing to reproduce thermo-acoustic lens (TAL) artifacts observed in thermotherapy experiments. This study proposes a physically realistic multi-sound-path (MSP) simulation framework that captures TAL artifacts arising from temperature-induced speed-of-sound inhomogeneity in array-based US imaging. Transient temperature fields are computed using an alternating direction implicit solver, and US images are reconstructed via delay-and-sum beamforming. Compared to SSP, the MSP model successfully reproduces characteristic TAL artifacts, including lateral resolution degradation (up to 52.0% increase in full width at half maximum) and speckle brightness reduction (up to 49.1%). When applied to speckle tracking and thermal strain imaging, MSP also reveals substantial decorrelation and displacement errors consistent with experimental observations. By providing a computationally efficient imaging simulation framework that avoids the prohibitive cost of full-wave solvers, the proposed framework is well-suited for large-scale parametric studies and evaluation of US-based thermometry and other speckle-based functional imaging methods.

Atomistic simulation toward real-scale microprocessor circuits

Reliability assessment of generic geared wind turbines by GTST-MLD model and Monte Carlo simulation

As SystemC gains popularity as a modelling language of choice for system-on-chip (SOC) designs, heterogeneous modelling in SystemC and efficient simulation become increasingly important. However, in the current reference implementation, all SystemC models are simulated through a non-deterministic discrete-event simulation kernel, which schedules events at run-time. This sometimes results in too many delta cycles hindering the simulation performance of the model. The SystemC language also seems to target this simulation kernel as the target simulation engine making it difficult to express different models of computation (MOC) naturally in SystemC. In an SOC model, different components may need to be naturally expressible in different MOCs. Some of these components may be amenable to static scheduling based simulation or other pre-simulation optimization techniques. Our goal is to create a simulation framework for heterogeneous SystemC models, to gain efficiency and ease of use within the framework of SystemC reference implementation. In this work, we focus on synchronous data flow (SDF) models, where the rates of data produced and consumed by a data flow node/block are known a priori. Compile time knowledge of these rates allows the use of static scheduling resulting to significant improvement in simulation efficiency. We propose source level hints to be provided by the model designer to help express SDF more naturally and to make the new simulation kernel execute special functionalities. Our experiments show significant improvement in simulation time over the original models.

As SystemC gains popularity as a modeling language of choice for system-on-chip (SOC) designs, heterogeneous modeling in SystemC and efficient simulation become increasingly important. However, in the current reference implementation, all SystemC models are simulated through a non-deterministic Discrete-Event simulation kernel, which schedules events at run-time. This sometimes results in too many delta cycles hindering the simulation performance of the model. The SystemC language also seems to target this simulation kernel as the target simulation engine. This makes it difficult to express different Models Of Computation naturally in SystemC. In an SOC model, different components may need to be naturally expressible in different Models Of Computations. Some of these components may be amenable to static scheduling based simulation or other pre-simulation optimization techniques. Our goal is to create a simulation framework for heterogeneous SystemC models, to gain efficiency and ease of use within the framework of SystemC reference implementation. In this paper, we focus on Synchronous Data Flow (SDF) models, where the rates of data produced and consumed by a data flow node/block are known a priori. In digital signal processing (DSP) applications where relative sample rates are specified for each DSP component, such models are quite common. Compile time knowledge of these rates allow the use of static scheduling resulting in significant improvement in simulation efficiency. We describe an alternate SystemC kernel that exploits such static scheduling of SDF models. Our experiments show improvement in simulation time over the original models and over the latest efficiency results from [20].

Simulation framework for automatic load frequency control studies of VSC-based AC/DC power grids

Despite analog SPICE-like simulators have reached their maturity, most of them were not originally conceived for simulating faulty circuits. With the advent of smart systems, fault testing has to deal with models encompassing both analog and digital blocks. Due to their complexity, the industry is still lacking of effective testing approaches for these analog and mixed-signal (AMS) models. The current problem is the computational time required for implementing an analog fault simulation campaign. To this end, the work presented in this paper is an automatic procedure which: 1) injects faults in an analog circuit, 2) abstracts both faulty and fault-free models from the circuit to the functional level, 3) builds an efficient fault simulation framework. The processes of fault injection, faulty model abstraction and framework generation are reported in details, as well as how simulation is carried out. This abstraction process, which preserves the faulty behaviors, allows to reach a speed-up of some orders of magnitude and thus, making feasible an extensive analog faults campaign.

Multi-physics simulations are at the heart of today's engineering applications. The trend is towards more realistic and detailed simulations, which demand highly resolved spatial and temporal scales of various physical mechanisms to solve engineering problems in a reasonable amount of time. As a consequence, numerical codes need to run efficiently on high-performance computers. Therefore, the framework Zonal Flow Solver (ZFS) featuring lattice-Boltzmann, finite-volume, discontinuous Galerkin, level set and Lagrange solvers has been developed. The solvers can be combined to simulate, e.g. quasi-incompressible and compressible flow, aeroacoustics, moving boundaries and particle dynamics. In this manuscript, the multi-physics implementation of the coupling mechanisms are presented. The parallelisation approach, the involved solvers and their scalability on state-of-the-art heterogeneous high-performance computers are discussed. Various multi-physics applications complement the discussion. The results show ZFS to be a highly efficient and flexible multi-purpose tool that can be used to solve varying classes of coupled problems.

Reservoir simulation with the cubic plus (cross-) association equation of state for water, CO2, hydrocarbons, and tracers

A computationally efficient and cost effective simulation framework has been implemented to perform design space exploration and multi-objective optimization for a conceptual regenerative rotorcraft powerplant configuration at mission level. The proposed framework is developed by coupling a comprehensive rotorcraft mission analysis code with a design space exploration and optimization package. The overall approach is deployed to design and optimize the powerplant of a reference twin-engine light rotorcraft, modeled after the Bo105 helicopter, manufactured by Airbus Helicopters. Initially, a sensitivity analysis of the regenerative engine is carried out to quantify the relationship between the engine thermodynamic cycle design parameters, engine weight, and overall mission fuel economy. Second, through the execution of a multi-objective optimization strategy, a Pareto front surface is constructed, quantifying the optimum trade-off between the fuel economy offered by a regenerative engine and its associated weight penalty. The optimum sets of cycle design parameters obtained from the structured Pareto front suggest that the employed heat effectiveness is the key design parameter affecting the engine weight and fuel efficiency. Furthermore, through quantification of the benefits suggested by the acquired Pareto front, it is shown that the fuel economy offered by the simple cycle rotorcraft engine can be substantially improved with the implementation of regeneration technology, without degrading the payload-range capability and airworthiness (one-engine-inoperative) of the rotorcraft.

Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in many scenarios, including safety-critical applications such as autonomous driving. In this context, besides energy efficiency and performance, reliability plays a crucial role since a system failure can jeopardize human life. As with any other device, the reliability of hardware architectures running DNNs has to be evaluated, usually through costly fault injection campaigns. This paper explores approximation and fault resiliency of DNN accelerators. We propose to use approximate (AxC) arithmetic circuits to agilely emulate errors in hardware without performing fault injection on the DNN. To allow fast evaluation of AxC DNN, we developed an efficient GPU-based simulation framework. Further, we propose a fine-grain analysis of fault resiliency by examining fault propagation and masking in networks.