Discrete event performance prediction of speculatively parallel temperature-accelerated dynamics

Due to its unrivaled ability to predict the dynamical evolution of interacting atoms, molecular dynamics (MD) is a widely used computational method in theoretical chemistry, physics, biology, and engineering. Despite its success, MD is only capable of modeling timescales within several orders of magnitude of thermal vibrations, leaving out many important phenomena that occur at slower rates. The temperature-accelerated dynamics (TAD) method overcomes this limitation by thermally accelerating the state-to-state evolution captured by MD. Due to the algorithmically complex nature of the serial TAD procedure, implementations have yet to improve performance by parallelizing the concurrent exploration of multiple states. Here we utilize a discrete-event-based application simulator to introduce and explore a new speculatively parallel TAD (SpecTAD) method. We investigate the SpecTAD algorithm, without a full-scale implementation, by constructing an application simulator proxy (SpecTADSim). Following this method, we discover that a non-trivial relationship exists between the optimal SpecTAD parameter set and the number of CPU cores available at run-time. Furthermore, we find that a majority of the available SpecTAD boost can be achieved within an existing TAD application using relatively simple algorithm modifications.

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Using PRRSV-Resilient Sows Improve Performance in Endemic Infected Farms with Recurrent Outbreaks

Animals ◽

10.3390/ani11030740 ◽

2021 ◽

Vol 11 (3) ◽

pp. 740

Author(s):

Gloria Abella ◽

Adela Pagès-Bernaus ◽

Joan Estany ◽

Ramona Natacha Pena ◽

Lorenzo Fraile ◽

...

Keyword(s):

Simulation Model ◽

Production Cost ◽

Discrete Event ◽

Reproductive Parameters ◽

Productive Performance ◽

Improve Performance ◽

Gross Margin ◽

Event Based ◽

Improve Disease Control ◽

Selection Of

The selection of porcine reproductive and respiratory syndrome (PRRS) resilient sows has been proposed as a strategy to control this disease. A discrete event-based simulation model was developed to mimic the outcome of farms with resilient or susceptible sows suffering recurrent PRRSV outbreaks. Records of both phenotypes were registered in a PRRSV-positive farm of 1500 sows during three years. The information was split in the whole period of observation to include a PRRSV outbreak that lasted 24 weeks (endemic/epidemic or En/Ep) or only the endemic phase (En). Twenty simulations were modeled for each farm: Resilient/En, Resilient/En_Ep, Susceptible/En, and Susceptible/En_Ep during twelve years and analyzed for the productive performance and economic outcome, using reference values. The reproductive parameters were generally better for resilient than for susceptible sows in the PRRSV En/Ep scenario, and the contrary was observed in the endemic case. The piglet production cost was always lower for resilient than for susceptible sows but showed only significant differences in the PRRSV En/Ep scenario. Finally, the annual gross margin by sow is significantly better for resilient than for susceptible sows for the PRRSV endemic (12%) and endemic/epidemic scenarios (17%). Thus, the selection of PRRSV resilient sows is a profitable approach for producers to improve disease control.

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Event-based Fault Diagnosis of Networked Discrete Event Systems

IEEE Transactions on Circuits & Systems II Express Briefs ◽

10.1109/tcsii.2021.3120486 ◽

2021 ◽

pp. 1-1

Author(s):

Kexin Ren ◽

Zhipeng Zhang ◽

Chengyi Xia

Keyword(s):

Fault Diagnosis ◽

Discrete Event Systems ◽

Discrete Event ◽

Event Systems ◽

Event Based

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Discrete-Event Based Monitoring and Diagnosis of Manufacturing Processes

1993 American Control Conference ◽

10.23919/acc.1993.4793123 ◽

1993 ◽

Cited By ~ 7

Author(s):

Sujeet Chand

Keyword(s):

Discrete Event ◽

Manufacturing Processes ◽

Monitoring And Diagnosis ◽

Event Based

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Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499

Applied Sciences ◽

10.3390/app10051611 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1611

Author(s):

Michael H. Spiegel ◽

Edmund Widl ◽

Bernhard Heinzl ◽

Wolfgang Kastner ◽

Nabil Akroud

Keyword(s):

Real Time ◽

Discrete Event ◽

Simulation Models ◽

Hybrid Plant ◽

Seamless Integration ◽

Quality Of Control ◽

Automation Systems ◽

Coupling Algorithm ◽

Event Based ◽

Iec 61499

Various development and validation methods for cyber-physical systems such as Controller-Hardware-in-the-Loop (C-HIL) testing strongly benefit from a seamless integration of (hardware) prototypes and simulation models. It has been often demonstrated that linking discrete event-based control systems and hybrid plant models can advance the quality of control implementations. Nevertheless, high manual coupling efforts and sometimes spurious simulation artifacts such as glitches and deviations are observed frequently. This work specifically addresses these two issues by presenting a generic, standard-based infrastructure referred to as virtual component, which enables the efficient coupling of simulation models and automation systems. A novel soft real-time coupling algorithm featuring event-accurate synchronization by extrapolating future model states is outlined. Based on considered standards for model exchange (FMI) and controls (IEC 61499), important properties such as real-time capabilities are derived and experimentally validated. Evaluation demonstrates that virtual components support engineers in efficiently creating C-HIL setups and that the novel algorithm can feature accurate synchronization when conventional approaches fail.

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Optimal Tolerance Allocation of Automotive Pneumatic Control Valves Based on Product and Process Simulations

Volume 1: 32nd Design Automation Conference, Parts A and B ◽

10.1115/detc2006-99592 ◽

2006 ◽

Author(s):

Naesung Lyu ◽

Amane Shimura ◽

Kazuhiro Saitou

Keyword(s):

Product Quality ◽

Production Cost ◽

Radial Basis Function Network ◽

Discrete Event ◽

Computational Method ◽

Tolerance Allocation ◽

Production Volume ◽

Pneumatic Control ◽

Multi Objective ◽

Trade Offs

This paper discusses a computational method for optimally allocating dimensional tolerances for an automotive pneumatic control valve. Due to the large production volume, costly tight tolerances should be allocated only to the dimensions that have high influence to the quality. Given a parametric geometry of a valve, the problem is posed as a multi-objective optimization with respect to product quality and production cost. The product quality is defined as 1) the deviation from the nominal valve design in the linearity of valve stroke and fluidic force, and 2) the difference in fluidic force with and without cavitation. These quality measures are estimated by using Monte Carlo simulation on a Radial-Basis Function Network (RBFN) trained with computational fluid dynamics (CFD) simulation of the valve operation. The production cost is estimated by the tolerance-cost relationship obtained from the discrete event simulations of valve production process. A multi-objective genetic algorithm is utilized to generate Pareto optimal tolerance allocations with respect to these objectives, and alternative tolerance allocations are proposed considering the trade-offs among multiple objectives.

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Simulation of Wind Farm Operations and Maintenance

Volume 8: Supercritical CO2 Power Cycles; Wind Energy; Honors and Awards ◽

10.1115/gt2013-94300 ◽

2013 ◽

Cited By ~ 2

Author(s):

Eduardo Pérez ◽

Lewis Ntaimo ◽

Yu Ding

Keyword(s):

Wind Farm ◽

Discrete Event ◽

Maintenance Scheduling ◽

Simulation Framework ◽

Structural Element ◽

Maintenance Strategies ◽

Random Events ◽

Operations And Maintenance ◽

Event Based ◽

Commercial Size

We develop a discrete event-based simulation framework that mimics the operations of a commercial size wind farm. Each turbine is treated as separate module, so that the simulation can be easily scaled up to more than one hundred turbines for a farm. Each turbine module includes a structural element sub-module, degradation sub-module, power generation sub-module, sensing and maintenance scheduling sub-module. The simulator is specially designed to handle a large number of unorganized random events (turbine failures, waiting for parts, weather disruptions) and reflect in the simulator’s outputs the variation from parameters and operations. We report on implementation results and provide insights into wind farm operations under different maintenance strategies.

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