Simulation tools for process operability and control

2000 ◽  
Author(s):  
D. Scott
Keyword(s):  
Simulation Tools ◽  
And Control

OPEN ONTOLOGY-BASED INTEGRATION PLATFORM FOR MODELING AND SIMULATION IN ENGINEERING

2012 ◽  
Vol 03 (02) ◽  
pp. 1250004 ◽  
Author(s):  
TOMMI KARHELA ◽  
ANTTI VILLBERG ◽  
HANNU NIEMISTÖ
Keyword(s):  
Modeling And Simulation ◽  
System Integration ◽  
Data Modeling ◽  
Simulation Tools ◽  
Semantic Data ◽  
Engineering Information ◽  
Mapping Techniques ◽  
Aided Design ◽  
And Control ◽  
Semantic Data Modeling

The benefits of the use of modeling and simulation in engineering are acknowledged widely. It has proven its advantages e.g., in virtual prototyping i.e., simulation aided design and testing as well as in training and R&D. It is recognized to be a tool for modern decision making. However, there are still reasons that slow down the wider utilization of modeling and simulation in companies. Modeling and simulation tools are separate and are not an integrated part of the other engineering information management in the company networks. They do not integrate well enough into the used CAD, PLM/PDM and control systems. The co-use of the simulation tools themselves is poor and the whole modeling process is considered often to be too laborious. In this article we introduce an integration solution for modeling and simulation based on the semantic data modeling approach. Semantic data modeling and ontology mapping techniques have been used in database system integration, but the novelty of this work is in utilizing these techniques in the domain of modeling and simulation. The benefits and drawbacks of the chosen approach are discussed. Furthermore, we describe real industrial project cases where this new approach has been applied.

scholarly journals Shifts in Estimated Preferred Directions During Simulated BMI Experiments With No Adaptation

2021 ◽  
Vol 15 ◽  
Author(s):  
Miri Benyamini ◽  
Miriam Zacksenhouse
Keyword(s):  
Theoretical Analysis ◽  
Motor Neurons ◽  
Motor Units ◽  
Neural Adaptation ◽  
Experimental Conditions ◽  
Simulation Tools ◽  
Preferred Direction ◽  
Cortical Motor ◽  
Optimal State Estimation ◽  
And Control

Experiments with brain-machine interfaces (BMIs) reveal that the estimated preferred direction (EPD) of cortical motor units may shift following the transition to brain control. However, the cause of those shifts, and in particular, whether they imply neural adaptation, is an open issue. Here we address this question in simulations and theoretical analysis. Simulations are based on the assumption that the brain implements optimal state estimation and feedback control and that cortical motor neurons encode the estimated state and control vector. Our simulations successfully reproduce apparent shifts in EPDs observed in BMI experiments with different BMI filters, including linear, Kalman and re-calibrated Kalman filters, even with no neural adaptation. Theoretical analysis identifies the conditions for reducing those shifts. We demonstrate that simulations that better satisfy those conditions result in smaller shifts in EPDs. We conclude that the observed shifts in EPDs may result from experimental conditions, and in particular correlated velocities or tuning weights, even with no adaptation. Under the above assumptions, we show that if neurons are tuned differently to the estimated velocity, estimated position and control signal, the EPD with respect to actual velocity may not capture the real PD in which the neuron encodes the estimated velocity. Our investigation provides theoretical and simulation tools for better understanding shifts in EPD and BMI experiments.

Simulation and Ergonomics Approach for Service & Manufacturing Process Improvement

2012 ◽  
Vol 502 ◽  
pp. 121-125 ◽  
Author(s):  
A. Sánchez-Lite ◽  
M. García-García
Keyword(s):  
Process Improvement ◽  
Process Design ◽  
Human Factor ◽  
Process Development ◽  
3D Design ◽  
Simulation Tools ◽  
Work Related ◽  
Work Related Musculoskeletal Disorders ◽  
And Control ◽  
Detection Evaluation

The human factor is a key variable in the efficiency of the product-process development system. The ability to be able to predict the influence of this factor in the process is a significant challenge in manufacturing engineering, as well as the consequences that the process designed has on this factor. All ergonomic considerations have traditionally been evaluated without using 3D product design. Nowadays, 3D process design technologies and simulation tools allow us the opportunity from the earliest stages of the design process. They can also be used to improve current processes in order to increase human comfort, productivity and safety. This paper describes a methodology using 3D design and simulation tools to improve industrial and service processes. This methodology has as an objective the detection, evaluation and control of work-related musculoskeletal disorders (WMSDs).

scholarly journals Simulation Tools for Powertrain Design and Control

2009 ◽  
Vol 64 (3) ◽  
pp. 215-222
Author(s):  
António Pires da Cruz ◽  
Christian Angelberger ◽  
Adlène Benkenida
Keyword(s):  
Simulation Tools ◽  
And Control ◽  
Powertrain Design

Impact of Brine Flow and Mixing in the Reservoir on Scale Control Risk Assessment and Subsurface Treatment Options: Case Histories

2005 ◽  
Vol 127 (3) ◽  
pp. 201-213 ◽  
Author(s):  
Eric J. Mackay ◽  
Myles M. Jordan
Keyword(s):  
Project Success ◽  
Treatment Options ◽  
Formation Damage ◽  
Case Histories ◽  
Simulation Tools ◽  
Offshore Production ◽  
Production Environments ◽  
Subsurface Treatment ◽  
And Control ◽  
Scale Control

As offshore production environments become ever more complex, particularly in deepwater regions, the risks associated with formation damage due to precipitation of inorganic scales may increase to the point that production by conventional waterflooding may cease to be viable. The ability to predict and control such formation damage can thus become critical to project success under such circumstances. The work described in this paper presents how the risk may be managed from early in the CAPEX phase of projects through to the OPEX phase by use of reservoir simulation tools to better understand the scaling potential in a reservoir and the possibilities for effective scale control. This process is illustrated by reference to a number of field examples where specific scaling problems have been identified, and the ability to implement effective scale management has been impacted by detailed fluid flow and brine-mixing calculations.

SwarmSim: a framework for modeling swarming unmanned aerial vehicles using hardware-in-the-loop

2017 ◽  
pp. 154851291771515
Author(s):  
Derek B Worth ◽  
Brian G Woolley ◽  
Douglas D Hodson
Keyword(s):  
Control Strategies ◽  
Hardware In The Loop ◽  
Simulation Framework ◽  
Functional Capability ◽  
Simulation Tools ◽  
Steady Growth ◽  
Aerial Vehicle ◽  
The Cost ◽  
Software Extension ◽  
And Control

Unmanned Aerial Vehicle (UAV) swarm applications, algorithms, and control strategies have experienced steady growth and development over the past 15 years. Yet, to date, most swarm development efforts have gone untested and unimplemented. The major inhibitors to successful swarm implementation seem to include the cost of aircraft systems, government imposed airspace restrictions, and the lack of adequate modeling and simulation tools. This paper examines how the open-source OpenEaagles simulation framework was leveraged to bridge this gap to create Hardware-in-the-Loop (HIL) simulations. Leveraging OpenEaagles through software extension to create HIL simulations provides developers with a functional capability with which to develop and test the behaviors of scalable and modular swarms of autonomous UAVs. Using HIL-based simulations in this capacity provides assurance that defined behaviors will propagate to live flight tests in the real world. The demonstrations in the work show how the framework enhances and simplifies swarm development through encapsulation, possesses high modularity, provides realistic aircraft modeling, and is capable of simultaneously accommodating multiple hardware-piloted and purely simulated swarming UAVs during simulation.

scholarly journals BIST Analysis of an Embedded Memory Associated Logic

VLSI Design ◽  
2001 ◽  
Vol 12 (4) ◽  
pp. 563-578
Author(s):  
Jacob Savir
Keyword(s):  
Design Rule ◽  
Random Pattern ◽  
Data Path ◽  
Embedded Memory ◽  
Control Logic ◽  
Simulation Tools ◽  
Cutting Algorithm ◽  
Self Test ◽  
And Control ◽  
Embedded Memories

Of late some interesting and useful work has been done on the problem of testing logic surrounding embedded memories. This work assumes that the logic surrounding the memory is functionally partitioned and that the different partitions are logically isolated one from the other.This paper expands upon past work using a more flexible design rule which allows feed-forward connections between the data-path Prelogic and Postlogic. The connections are such that there is no feedback from the memory outputs to its inputs, and both the Prelogic and the Postlogic are disconnected from the Address and Control logic. Under this design rule we show the auxiliary circuits used to determine the random pattern testability of faults in the circuitry driving the address inputs and the controls of the two-port memory.The techniques described herein are intended to be used in conjunction with the cutting algorithm for testability measurement in built-in self-test (BIST) designs [2, 11, 17], but may also be suitable for use with other detection probability tools [9, 19], and simulation tools [20].

Coupling of Multibody and Control Simulation Tools for the Design of Mechatronic Systems

2001 ◽  
Author(s):  
Ondřej Vaculín ◽  
Wolf-Reiner Krüger ◽  
Martin Spieck
Keyword(s):  
Model Complexity ◽  
Concurrent Design ◽  
Multibody Simulation ◽  
Mechatronic Systems ◽  
Control Engineering ◽  
Active Suspensions ◽  
Simulation Tools ◽  
Design Environments ◽  
Computer Aided ◽  
And Control

Abstract With the increasing importance of integrating different simulation tools into concurrent design environments, the demands on model complexity and interdisciplinarity have led to a growing number of interfaces between the various tools. This paper presents examples of such interfaces for the connection between multibody simulation and computer-aided control engineering tools. The interfaces introduced in this paper are classified. The application of the different interfaces is presented in two examples. Both are dealing with control of semi-active suspensions, but different methodologies are applied.

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