The use of agents and objects to integrate virtual enterprises

The manufacturing complex for the Department of Energy (DOE) is distributed: design laboratories, manufacturing facilities, and industrial partners. Designers must have a concurrent engineering environment to support all aspects of the cradle-to-grave product realization process across the distributed sites. Engineers must be able to analyze and simulate processes, retrieve and process heterogeneous information, both archived and current, and access multiple databases. Manufacturers must be able to coordinate activities of various manufacturing centers, which may involve a negotiation process. Furthermore, Sandia must be able to export manufacturing capabilities, such as on-machine acceptance, to outside suppliers. A key element to making this a reality is a flexible information architecture. The DOE information architecture must support a wide-area virtual enterprise, with distributed intelligent software components. The architecture must provide for asynchronous communication; multiple programming languages and operating systems; incorporation of geographically distributed manufacturing services; various hardware platforms; and heterogeneous workstations, PC`s, machine tool controllers, and special-purpose compute engines. Further, it is critical that manufacturing facilities are not isolated from design, planning, and other business activities and that information flows easily and bidirectionally between these activities. To accomplish this seamlessly, heterogeneous knowledge must be exchanged across both domain and organizational boundaries. Distributed object and software agent technologies are two methods for connecting such engineering and manufacturing systems. The two technologies have overlapping goals - interoperability and architectural support for integrating software components - though to date little or no integration of the two technologies has been made

Development of the RIOT Web Service and Information Technologies to enable mechanism reduction for HCCI simulations.

Abstract. New approaches are being explored to facilitate multidisciplinary collaborative research of Homogenous Charge Compression Ignition (HCCI) combustion processes. In this paper, collaborative sharing of the Range Identification and Optimization Toolkit (RIOT) and related data and models is discussed. RIOT is a developmental approach to reduce the computational of detailed chemical kinetic mechanisms, enabling their use in modeling kinetically controlled combustion applications such as HCCI. These approaches are being developed and piloted as a part of the Collaboratory for Multiscale Chemical Sciences (CMCS) project. The capabilities of the RIOT code are shared through a portlet in the CMCS portal that allows easy specification and processing of RIOT inputs, remote execution of RIOT, tracking of data pedigree, and translation of RIOT outputs to a table view and to a commonly-used mechanism format. Introduction The urgent need for high-efficiency, low-emission energy utilization technologies for transportation, power generation, and manufacturing processes presents difficult challenges to the combustion research community. The needed predictive understanding requires systematic knowledge across the full range of physical scales involved in combustion processes -from the properties and interactions of individual molecules to the dynamics and products of turbulent multi-phase reacting flows. Innovative experimental techniques and computational approaches are revolutionizing the rate at which chemical science research can produce the new information necessary to advance our combustion knowledge. But the increased volume and complexity of this information often makes it even more difficult to derive the systems-level knowledge we need. Combustion researchers have responded by forming interdisciplinary communities intent on sharing information and coordinating research priorities. Such efforts face many barriers, however, including lack of data accessibility and interoperability, missing metadata and pedigree information, efficient approaches for sharing data and analysis tools, and the challenges of working together across geography, disciplines, and a very diverse spectrum of applications and funding. This challenge is especially difficult for those developing, sharing and/or using detailed chemical models of combustion to treat the oxidation of practical fuels. This is a very complex problem, and the development of new chemistry models requires a series of steps that involve acquiring and keeping track of a large amount of data and its pedigree. Also, this data is developed using a diverse range of codes and experiments spanning ab initio chemistry codes, laboratory kinetics and flame experiments, all the way to reacting flow simulations on massively parallel computers. Each of these processes typically requires different data formats, and often the data and/or analysis codes are only accessible by personally contacting the creator. Chemical models are usually shared in a legacy file format, such as Chemki

Enabling HCCI modeling: The RIOT/CMCS Web Service for Automatic Reaction Mechanism Reduction

New approaches are being developed to facilitate multidisciplinary collaborative research of Homogeneous Charge Compression Ignition (HCCI) combustion processes. In this paper, collaborative sharing of the Range Identification and Optimization Toolkit (RIOT) and related data and models is discussed. RIOT is a developmental approach to reduce the computational complexity of detailed chemical kinetic mechanisms, enabling their use in modeling kinetically-controlled combustion applications such as HCCI. These approaches are being developed and piloted as a part of the Collaboratory for Multiscale Chemical Sciences (CMCS) project. The capabilities of the RIOT code are shared through a portlet in the CMCS portal that allows easy specification and processing of RIOT inputs, remote execution of RIOT, tracking of data pedigree and translation of RIOT outputs (such as the reduced model) to a table view and to the commonly-used CHEMKIN mechanism format. The reduced model is thus immediately ready to be used for more efficient simulation of the chemically reacting system of interest. This effort is motivated by the need to improve computational efficiency in modeling HCCI systems. Preliminary use of the web service to obtain reduced models for this application has yielded computational speedup factors of up to 20 as presented in this paper

Smart sensor technology for joint test assembly flights.

The world relies on sensors to perform a variety of tasks from the mundane to sophisticated. Currently, processors associated with these sensors are sufficient only to handle rudimentary logic tasks. Though multiple sensors are often present in such devices, there is insufficient processing power for situational understanding. Until recently, no processors that met the electrical power constraints for embedded systems were powerful enough to perform sophisticated computations. Sandia performs many expensive tests using sensor arrays. Improving the efficacy, reliability and information content resulting from these sensor arrays is of critical importance. With the advent of powerful commodity processors for embedded use, a new opportunity to do just that has presented itself. This report describes work completed under Laboratory-Directed Research and Development (LDRD) Project 26514, Task 1. The goal of the project was to demonstrate the feasibility of using embedded processors to increase the amount of useable information derived from sensor arrays while improving the believability of the data. The focus was on a system of importance to Sandia: Joint Test Assemblies for ICBM warheads. Topics discussed include: (1) two electromechanical systems to provide data, (2) sensors used to monitor those systems, (3) the processors that provide decision-making capability and data manipulation, (4) the use of artificial intelligence and other decision-making software, and (5) a computer model for the training of artificial intelligence software

Agent-based enterprise integration

The authors are developing and deploying software agents in an enterprise information architecture such that the agents manage enterprise resources and facilitate user interaction with these resources. The enterprise agents are built on top of a robust software architecture for data exchange and tool integration across heterogeneous hardware and software. The resulting distributed multi-agent system serves as a method of enhancing enterprises in the following ways: providing users with knowledge about enterprise resources and applications; accessing the dynamically changing enterprise; locating enterprise applications and services; and improving search capabilities for applications and data. Furthermore, agents can access non-agents (i.e., databases and tools) through the enterprise framework. The ultimate target of the effort is the user; they are attempting to increase user productivity in the enterprise. This paper describes their design and early implementation and discusses the planned future work

Development of the RIOT Web Service and Information Technologies to Enable Mechanism Reduction for HCCI Simulations

New approaches are being explored to facilitate multidisciplinary collaborative research of Homogeneous Charge Compression Ignition (HCCI) combustion processes. In this paper, collaborative sharing of the Range Identification and Optimization Toolkit (RIOT) and related data and models is discussed. RIOT is a developmental approach to reduce the computational of detailed chemical kinetic mechanisms, enabling their use in modeling kinetically controlled combustion applications such as HCCI. These approaches are being developed and piloted as a part of the Collaboratory for Multiscale Chemical Sciences (CMCS) project. The capabilities of the RIOT code are shared through a portlet in the CMCS portal that allows easy specification and processing of RIOT inputs, remote execution of RIOT, tracking of data pedigree, and translation of RIOT outputs to a table view and to a commonly-used mechanism format