Hider: A Methodology for Early-Stage Exploration of Design Space

1996 ◽  
Author(s):  
Sudhakar Y. Reddy
Keyword(s):  
Design Space ◽  
Optimization Problems ◽  
Early Stage ◽  
Simulation Models ◽  
Design Tool ◽  
System Level ◽  
Complex Simulation ◽  
Machine Learning Approach ◽  
Detailed Simulation ◽  
And Performance

Abstract This paper describes HIDER, a methodology that enables detailed simulation models to be used during the early stages of system design. HIDER uses a machine learning approach to form abstract models from the detailed models. The abstract models are used for multiple-objective optimization to obtain sets of non-dominated designs. The tradeoffs between design and performance attributes in the non-dominated sets are used to interactively refine the design space. A prototype design tool has been developed to assist the designer in easily forming abstract models, flexibly defining optimization problems, and interactively exploring and refining the design space. To demonstrate the practical applicability of this approach, the paper presents results from the application of HIDER to the system-level design of a wheel loader. In this demonstration, complex simulation models for cycle time evaluation and stability analysis are used together for early-stage exploration of design space.

Towards Automated Evaluation of Vehicle Dynamics in System-Level Designs

2012 ◽  
Author(s):  
Zsolt Lattmann ◽  
Adam Nagel ◽  
Jason Scott ◽  
Kevin Smyth ◽  
Chris vanBuskirk ◽  
...  
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Test Bench ◽  
Simulation Models ◽  
Mobility Model ◽  
System Level ◽  
Bond Graphs ◽  
Automated Evaluation ◽  
Design Alternatives ◽  
Automatic Composition

We describe the use of the Cyber-Physical Modeling Language (CyPhyML) to support trade studies and integration activities in system-level vehicle designs. CyPhyML captures parameterized component behavior using acausal models (i.e. hybrid bond graphs and Modelica) to enable automatic composition and synthesis of simulation models for significant vehicle subsystems. Generated simulations allow us to compare performance between different design alternatives. System behavior and evaluation are specified independently from specifications for design-space alternatives. Test bench models in CyPhyML are given in terms of generic assemblies over the entire design space, so performance can be evaluated for any selected design instance once automated design space exploration is complete. Generated Simulink models are also integrated into a mobility model for interactive 3-D simulation.

scholarly journals Distributed processing in virtual simulation using Cloud Computing Environment

2018 ◽  
Vol 210 ◽  
pp. 04018
Author(s):  
Jarosław Koszela ◽  
Maciej Szymczyk
Keyword(s):  
Cloud Computing ◽  
Distributed Processing ◽  
Simulation Models ◽  
Virtual Simulation ◽  
Performance Tests ◽  
Computing Environment ◽  
Computing Power ◽  
Cloud Computing Environment ◽  
Detailed Simulation ◽  
And Performance

Today’s hardware has computing power allowing to conduct virtual simulation. However, even the most powerful machine may not be sufficient in case of using models characterized by high precision and resolution. Switching into constructive simulation causes the loss of details in the simulation. Nonetheless, it is possible to use the distributed virtual simulation in the cloud-computing environment. The aim of this paper is to propose a model that enables the scaling of the virtual simulation. The aspects on which the ability to disperse calculations depends were presented. A commercial SpatialOS solution was presented and performance tests were carried out. The use of distributed virtual simulation allows the use of more extensive and detailed simulation models using thin clients. In addition, the presented model of the simulation cloud can be the basis of the “Simulation-as-a-Service” cloud computing product.

Probabilistic Analyses for the Performance Characteristics of the Main Bearings in an Operating Engine Due to Variability in Bearing Properties

2003 ◽  
Author(s):  
Jin Wang ◽  
Nickolas Vlahopoulos ◽  
Zissimos P. Mourelatos ◽  
Omidreza Ebrat ◽  
Kumar Vaidyanathan
Keyword(s):  
Design Space ◽  
Combustion Engine ◽  
Hydrodynamic Lubrication ◽  
Simulation Models ◽  
Threshold Value ◽  
Integrated System ◽  
Probabilistic Methods ◽  
System Level ◽  
Oil Viscosity ◽  
Main Bearing

This paper presents the development of surrogate models (metamodels) for evaluating the bearing performance in an internal combustion engine. The metamodels are employed for performing probabilistic analyses for the engine bearings. The metamodels are developed based on results from a simulation solver computed at a limited number of sample points, which sample the design space. An integrated system-level engine simulation model, consisting of a flexible crankshaft dynamics model and a flexible engine block model connected by a detailed hydrodynamic lubrication model, is employed in this paper for generating information necessary to construct the metamodels. An optimal symmetric Latin hypercube algorithm is utilized for identifying the sampling points based on the number and the range of the variables that are considered to vary in the design space. The development of the metamodels is validated by comparing results from the metamodels with results from the actual simulation models over a large number of evaluation points. Once the metamodels are established they are employed for performing probabilistic analyses. The initial clearance between the crankshaft and the bearing at each main bearing and the oil viscosity comprise the random variables in the probabilistic analyses. The maximum oil pressure and the percentage of time (the time ratio) within each cycle that a bearing operates with oil film thickness less than a user defined threshold value at each main bearing constitute the performance variables of the system. The availability of the metamodels allows comparing the performance of several probabilistic methods in terms of accuracy and computational efficiency. A useful insight is gained by the probabilistic analysis on how variability in the bearing characteristics affects its performance.

scholarly journals Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests

2021 ◽  
Author(s):  
Max-Arno Meyer ◽  
Lina Sauter ◽  
Christian Granrath ◽  
Hassen Hadj-Amor ◽  
Jakob Andert
Keyword(s):  
Data Exchange ◽  
Time Synchronization ◽  
Simulation Models ◽  
Simulation Platform ◽  
Automated Driving ◽  
Complex Simulation ◽  
Simulation Protocol ◽  
Environment Simulation ◽  
Cross Platform ◽  
And Performance

AbstractTo meet the challenges in software testing for automated vehicles, such as increasing system complexity and an infinite number of operating scenarios, new simulation methods must be developed. Closed-loop simulations for automated driving (AD) require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context. For the realization of such models, different simulation domains must be coupled with co-simulation. However, widely supported model integration standards such as functional mock-up interface (FMI) lack native support for distributed platforms, which is a key feature for AD due to the computational intensity and platform exclusivity of certain models. The newer FMI companion standard distributed co-simulation protocol (DCP) introduces platform coupling but must still be used in conjunction with AD co-simulations. As part of an assessment framework for AD, this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform. A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave. A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models. A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP. Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA. The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors. This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability, reuse potential, and performance.

Multilevel Hierarchical Decomposition: Identification Procedures for Edge of Design Space Feasibility

2006 ◽  
Author(s):  
Somanath Nagendra ◽  
Jeff Midgley ◽  
Joseph B. Staubach
Keyword(s):  
High Performance ◽  
Design Space ◽  
Optimization Problems ◽  
Functional Dependence ◽  
Minimum Weight ◽  
Design Feature ◽  
Turbine Disk ◽  
System Level ◽  
Optimum Shape ◽  
Design Variables

In high performance machines, multiple active MDO constraints dictate the edge of feasibility, i.e. boundary of the design space. It is essential to have an accurate description of the boundary in terms of design variables. Given a sample of data, the recognition of a design feature (e.g. design shape) is not usually familiar to the design domain experts but must be extracted based on data-driven procedures. The “edge of feasibility” could be evaluated as a continuous or piece wise continuous function of active constraints. In this work, the focus is on a class of quasiseparable optimization problems. The subsystems for these problems involve local design shape variables and global system variables, but no variables from other subsystems. The system in this particular case is the engine component (i.e. HPT) and the subsystem is the turbine disk. The system is hierarchically decomposed to the system and subsystem components respectively. The HPT flowpath and its defined thermodynamic and geometric parameters define the system. The subsystem is the HPT turbine disk and its associated geometric shape variables. A system level DOE determines the design space of the HPT system. The optimized subsystem turbine disk is the solution to the DOE of the system and feasible disk designs are the shapes that can withstand the design loads and stresses. The focus of the paper is to develop a methodology that would systematically utilize minimum weight optimum shape designs across the design space and predict new designs close to being optimal in performance for a specified range of design conditions. The shape of minimum weight disks are identified as a solution of a system of inverse response surface equations that can determine disk shapes with good confidence. The methodology is developed using synthetic turbine disk problems with known regions of feasibility and infeasibility. The edge of feasibility is determined and the functional dependence on the design variables estimated.

Quick Prototyping Design for More than Moore era

2019 ◽  
Vol 2019 (DPC) ◽  
pp. 001209-001231
Author(s):  
Yoko Fujita ◽  
Kazukani Koga ◽  
Shintaro Ohtani ◽  
Daisuke TsuTsui
Keyword(s):  
Flip Chip ◽  
Early Stage ◽  
System Level ◽  
Shielding Effect ◽  
Electrical Characteristics ◽  
Wafer Level ◽  
Design Environment ◽  
Design Data ◽  
And Performance ◽  
Prototype Design

[Background and Motivation] System in Package (SiP) is pulling the evolution of technology instead of deep submicron of LSI. SiP assists coming AI/IoT/5G world. SiP has many implementation variations. When we examine the chip and package implementation technique for the target product, it's important task to find the optimal solution for the product considering trade-off between cost and performance. Compared with mass production products, it's important to optimize costs especially for small volume production products. Moreover, in terms of performance, compared to conventional packages, for high frequency products and SiP, since the margin becomes small and confirming system level electrical characteristics are necessary, in addition to conventional conceptual design and detail design, prototyping design is required to test its electrical characteristics while keeping lower cost. [Technical callenge] To do prototyping design at the early stage of product and find the optimum solution of the product, we created short turn-around time(TAT) iteration environment and distributed prototyping design. Quick Prototyping, which is a short TAT iteration environment, is based on the LSI/Package/PCB integrated co-design environment, estimates the chip size from the signal number of product, performs LSI design prototyping, using package automatic net assignment and the substrate auto routing, proceeds package prototype design and then confirms electrical characteristics. In the above, itfs possible to consider multiple implementation variations. In the distribution of prototype design, we utilize the LPB format (IEC 63055/IEEE 2401), have eliminated the interface(I/F) barriers between companies and tools, and have developed an environment for design prototypes and checking their characteristics throughout the industry. [Results and Conclusions] In collaboration with Semiconductor vendor Socionext Inc., as the above benchmark, 3D Flip Chip-Chip Scale Package(FCCSP) (with memory package with Package on Package(PoP) method) and 3D Fan-Out Wafer Level Package(FOWLP) (similarly in PoP method) as samples, the prototype design was created out. By comparison of the electrical characteristics, the Self Inductance of the FOWLP becomes half or less than FCCSP, the power supply noise could be suppressed. The shielding effect of FOWLP reduced the influence of crosstalk. We confirmed that FOWLP has advantage of electrical characteristics. On the cost side, in the LSI implementation of the FOWLP side, only the function blocks requiring performance were replaced with the fine node by chip partitioning, we examined to suppress the increase in cost while maintaining the performance advantage of FOWLP. With Quick Prototyping, cost and performance balance can be examined in the product review early stages, also by utilizing the LPB format for Quick Prototyping, from the negotiation stage with business partners, we can share the design data, focus on tuning the product itself and improve the competitive SiP products.

Learning abstract models for system design

1996 ◽  
Vol 10 (2) ◽  
pp. 167-169
Author(s):  
Sudhakar Y. Reddy
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Simulation Models ◽  
Preliminary Design ◽  
Engineering Systems ◽  
Design Decisions ◽  
Detailed Design ◽  
Stage Design ◽  
Detailed Simulation ◽  
Early Stage Design

AbstractThough simulation models are extensively used for detailed design analysis, they find limited role in preliminary design decisions. We have developed a machine learning based approach to enable detailed simulation models to be harvested for supporting early-stage design of engineering systems.

Dynamic Systems With SES Process for Vehicle Integration

2009 ◽  
Author(s):  
Kevin Chang ◽  
Sung Kwon ◽  
Kasra Naghshineh
Keyword(s):  
Simulation Models ◽  
System Level ◽  
High Fidelity ◽  
Electronic Components ◽  
Electrical Control ◽  
Control Behavior ◽  
Integrated Environment ◽  
Engineering Environment ◽  
And Performance ◽  
Test Process

This paper describes an integrated engineering environment developed by BAE Systems which combines an integration and test process called Simulation-Emulation-Stimulation (SES) using physics-based high-fidelity dynamic simulation models. This environment creates real-time vehicle simulations of system and electrical control behavior that enable the visibility of electronic component messages and signals at subsystem and system level. It is used to integrate tactical software and electronic components as well as to test and verify vehicle subsystem and system level requirements and performance. To further enhance the SES environment capability, high fidelity electronics simulation models utilized during integration to extend internal signal visibility and aid troubleshooting. With this integrated environment, vehicle electronics and software integration issues can be identified and resolved in a lab before on-vehicle integration occurs. This significantly reduces overall project risk to both schedule and cost.

The Environmental Design Space: Modeling and Performance Updates

2021 ◽  
Author(s):  
Luis Salas Nunez ◽  
Jimmy C. Tai ◽  
Dimitri N. Mavris
Keyword(s):  
Design Space ◽  
Environmental Design ◽  
Space Modeling ◽  
And Performance
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