System-Level and Architectural Trade-offs

2011 ◽  
pp. 19-43
Author(s):  
Emanuele Lopelli ◽  
Johan van der Tang ◽  
Arthur van Roermund
Keyword(s):  
System Level ◽  
Trade Offs

scholarly journals Rapid design of aircraft fuel quantity indication systems via multi-objective evolutionary algorithms

2020 ◽  
pp. 1-18
Author(s):  
David Judt ◽  
Craig Lawson ◽  
Albert S.J. van Heerden
Keyword(s):  
System Level ◽  
Fluid Systems ◽  
Trade Offs ◽  
Aircraft Fuel ◽  
Definition Of ◽  
Tank Geometry ◽  
Probe Set ◽  
Accuracy Constraints ◽  
Good Agreement

The design of electrical, mechanical and fluid systems on aircraft is becoming increasingly integrated with the aircraft structure definition process. An example is the aircraft fuel quantity indication (FQI) system, of which the design is strongly dependent on the tank geometry definition. Flexible FQI design methods are therefore desirable to swiftly assess system-level impact due to aircraft level changes. For this purpose, a genetic algorithm with a two-stage fitness assignment and FQI specific crossover procedure is proposed (FQI-GA). It can handle multiple measurement accuracy constraints, is coupled to a parametric definition of the wing tank geometry and is tested with two performance objectives. A range of crossover procedures of comparable node placement problems were tested for FQI-GA. Results show that the combinatorial nature of the probe architecture and accuracy constraints require a probe set selection mechanism before any crossover process. A case study, using approximated Airbus A320 requirements and tank geometry, is conducted and shows good agreement with the probe position results obtained with the FQI-GA. For the objectives of accessibility and probe mass, the Pareto front is linear, with little variation in mass. The case study confirms that the FQI-GA method can incorporate complex requirements and that designers can employ it to swiftly investigate FQI probe layouts and trade-offs.

ANALYSIS AND TOOLS FOR THE DESIGN OF VLIW EMBEDDED SYSTEMS IN A MULTI-OBJECTIVE SCENARIO

2007 ◽  
Vol 16 (05) ◽  
pp. 819-846
Author(s):  
VINCENZO CATANIA ◽  
MAURIZIO PALESI ◽  
DAVIDE PATTI
Keyword(s):  
Embedded Systems ◽  
Instruction Scheduling ◽  
Design Flow ◽  
System Level ◽  
Instruction Level Parallelism ◽  
Accurate Estimation ◽  
Subsequent Stage ◽  
Multi Objective ◽  
Trade Offs ◽  
Starting Point

The use of Application-Specific Instruction-set Processors (ASIP) in embedded systems is a solution to the problem of increasing complexity in the functions these systems have to implement. Architectures based on Very Long Instruction Word (VLIW) have found fertile ground in multimedia electronic appliances thanks to their ability to exploit high degrees of Instruction Level Parallelism (ILP) with a reasonable trade-off in complexity and silicon costs. In this case the ASIP specialization involves a complex interaction between hardware- and software-related issues. In this paper we propose tools and methodologies to cope efficiently with this complexity from a multi-objective perspective. We present EPIC-Explorer, an open platform for estimation and system-level exploration of an EPIC/VLIW architecture. We first analyze the possible design objectives, showing that it is necessary, given the fundamental role played by the VLIW compiler in instruction scheduling, to evaluate the appropriateness of ILP-oriented compilation on a case-by-case basis. Then, in the architecture exploration phase, we will use a multi-objective genetic approach to obtain a set of Pareto-optimal configurations. Finally, by clustering the configurations thus obtained, we extract those representing possible trade-offs between the objectives, which are used as a starting point for evaluation via more accurate estimation models at a subsequent stage in the design flow.

scholarly journals Design of Complex Engineered Systems Using Multi-Agent Coordination

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Nicolás F. Soria Zurita ◽  
Mitchell K. Colby ◽  
Irem Y. Tumer ◽  
Christopher Hoyle ◽  
Kagan Tumer
Keyword(s):  
Autonomous Agents ◽  
System Level ◽  
Coordination Problem ◽  
Design Decisions ◽  
Formula Sae ◽  
Complex Engineered Systems ◽  
Trade Offs ◽  
Potential System ◽  
Multi Agent ◽  
Agent Coordination

In complex engineering systems, complexity may arise by design, or as a by-product of the system's operation. In either case, the cause of complexity is the same: the unpredictable manner in which interactions among components modify system behavior. Traditionally, two different approaches are used to handle such complexity: (i) a centralized design approach where the impacts of all potential system states and behaviors resulting from design decisions must be accurately modeled and (ii) an approach based on externally legislating design decisions, which avoid such difficulties, but at the cost of expensive external mechanisms to determine trade-offs among competing design decisions. Our approach is a hybrid of the two approaches, providing a method in which decisions can be reconciled without the need for either detailed interaction models or external mechanisms. A key insight of this approach is that complex system design, undertaken with respect to a variety of design objectives, is fundamentally similar to the multi-agent coordination problem, where component decisions and their interactions lead to global behavior. The results of this paper demonstrate that a team of autonomous agents using a cooperative coevolutionary algorithm (CCEA) can effectively design a complex engineered system. This paper uses a system model of a Formula SAE racing vehicle to illustrate and simulate the methods and potential results. By designing complex systems with a multi-agent coordination approach, a design methodology can be developed to reduce design uncertainty and provide mechanisms through which the system level impact of decisions can be estimated without explicitly modeling such interactions.

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