• Video
  • 21-Mar-2012 09:57 EDT

Estimating Return on Investment for SAVI (a Model-Based Virtual Integration Process)

00:19:32
Length:

Purchase Required to View Video

Short Preview Below

The System Architecture Virtual Integration (SAVI) program is a collaboration of industry, government, and academic organizations within the Aerospace Vehicle System Institute (AVSI) with the goal of structuring a new integration process that relies on a single-truth architectural framework. The SAVI approach of Integrate, then Build provides a modern distributed development environment which arrests the propagation of requirements errors through the development life cycle. It does so by capturing design assumptions and shared properties of the system design in an authoritative, annotated architectural model. This reference model provides a common, analyzable framework for confirming that system requirements remain complete, consistent, and correct at all levels of system decomposition. Core concepts of SAVI include extensive use of model-based system engineering tools and use of a single-truth reference architectural model. From the outset SAVI developers anticipated that a quantified prediction of the productivity of the SAVI Virtual Integration Process (VIP) would be necessary to close the business case for using it. Therefore, the SAVI statement of work at each stage of its feasibility demonstration carried a task to estimate the Return on Investment (RoI). AVSI participants needed a prediction of what the resources poured into SAVI development and deployment would produce. This paper lays out the work done so far to produce these RoI estimates and the assumptions that have gone into them. The paper goes on to illustrate example results for two of the major types of participants in SAVI, and details the current state of the evolving estimation capability. The most important result of this RoI work is a substantial positive RoI predicted for using SAVI's VIP. Initial estimates of RoI for a first application to a commercial aircraft development indicated an expected value of annual RoI for an OEM on the order of 40%. Later estimates gave similar, but more positive, results with modified assumptions. But the range of variation of the estimates has been reduced to less than 1/3 of the original prototype estimator's variation. Savings for suppliers heavily engaged and at risk in the development are also predicted to have double digit annual RoIs, with the exact value of annual RoI rate dependent on the level of involvement of the supplier. The minimum value of annual RoI for the same commercial airliner development was calculated to be 2%, using the initial prototype estimator, but that minimum value of annual arithmetic RoI grew to over 70% per year in the refined estimator.

Presenter
Steven Helton

Buy
Select
Price
List
Purchase to View
$19.00
Learn More
11VATC40701
Estimating Return on Investment for SAVI (a Model-Based Virtual Integration Process)
2011-10-21
ESTIMATING RETURN ON INVESTMEN
Share
HTML for Linking to Page
Page URL
Grade
Rate It
No ratings yet

View More Video

Video
2012-03-21
In recent years, all major microprocessor manufacturers are transitioning towards the deploymenet of multiple processing cores on every chip. These multi-core architectures represent the industry consensus regarding the most effective utilization of available silicon resources to satisfy growing demands for processing and memory capacities. Porting off-the-shelf software capabilities to multi-core architectures often requires significant changes to data structures and algorithms. When developing new software capabilities specifically for deployment on SMP architectures, software engineers are required to address specific multi-core programming issues, and in the ideal, must do so in ways that are generic to many different multi-core target platforms. This talk provides an overview of the special considerations that must be addressed by software engineers targeting multi-core platforms and describes how the Java language facilitates solutions to these special challenges.
Video
2011-12-05
These advanced checks have resulted in development of many new diagnostic monitors, of varying types, and a whole new internal software infrastructure to handle tracking, reporting, and self-verification of OBD related items. Due to this amplified complexity and the consequences surrounding a shortfall in meeting regulatory requirements, efficient and thorough validation of the OBD system in the powertrain control software is critical. Hardware-in-the-Loop (HIL) simulation provides the environment in which the needed efficiency and thoroughness for validating the OBD system can be achieved. A HIL simulation environment consisting of engine, aftertreatment, and basic vehicle models can be employed, providing the ability for software developers, calibration engineers, OBD experts, and test engineers to examine and validate both facets of OBD software: diagnostic monitors and diagnostic infrastructure (i.e., fault memory management).
Video
2012-01-24
A combination of laboratory reactor measurements and vehicle FTP testing has been combined to demonstrate a method for diagnosing the formation of NO2 from a diesel oxidation catalyst (DOC). Using small cores from a production DOC and simulated diesel exhaust, the laboratory reactor experiments are used to support a model for DOC chemical reaction kinetics. The model we propose shows that the ability to produce NO2 is chemically linked to the ability of the catalyst to oxidize hydrocarbon (HC). For thermally damaged DOCs, loss of the HC oxidation function is simultaneous with loss of the NO2 production function. Since HC oxidation is the source of heat generated in the DOC under regeneration conditions, we conclude that a diagnostic of the DOC exotherm is able to detect the failure of the DOC to produce NO2. Vehicle emissions data from a 6.6 L Duramax HD pick-up with DOC of various levels of thermal degradation is provided to support the diagnostic concept.
Video
2012-02-15
Virtual testing is a method that simulates lab testing using multi-body dynamic analysis software. The main advantages of this approach include that the design can be evaluated before a prototype is available and virtual testing results can be easily validated by subsequent physical testing. The disadvantage is that accurate specimen models are sometimes hard to obtain since nonlinear components such as tires, bushings, dampers, and engine mounts are hard to model. Therefore, virtual testing accuracy varies significantly. The typical virtual rigs include tire and spindle coupled test rigs for full vehicle tests and multi axis shaker tables for component tests. Hybrid simulation combines physical and virtual components, inputs and constraints to create a composite simulation system. Hybrid simulation enables the hard to model components to be tested in the lab.

Related Items

Training / Education
2017-10-19
Training / Education
2017-06-15
Technical Paper / Journal Article
1988-03-01
Training / Education
2010-03-15
Training / Education
1997-05-29