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The lifecycle of a system is extended from its early conception to its retirement of service. The lifespan of unmanned ground vehicles (UGVs) can be expected to last over 50 years in the defense market. In this context, the rising complexity of UGV systems imposes engineering steps that would ensure both capabilities of the system and resilience to its future inclusion in a system-of system context. During its operational usage, the UGV is supposed to be maneuvered for specifically designed purposes following user manual datasheet of the components off-the-shelf (COTS) that were integrated. This paper exposes the public user datasheet relevance compared to the system engineering requirements that are the artifacts of system design architecture. The use of connecting COTS user manual to system requirements is discussed, all the more if the systems are to be re-used in a system production line. This article is intended to explore system of system conception methods for future robotized battlefield.

During the initial concept development phase, systems engineers focus on defining the problem space and system functions to explore candidate concepts that may address the systems engineers' problems. Model-based conceptual design (MBCD) techniques may be used to assist the customer and other stakeholders develop a greater understanding of the system concept, as well as identifying areas in the system that are affected by changes in requirements. This approach has generally been documented for describing the system concept in the early stages in the lifecycle, without significant focus on the test and evaluation (T&E) space that would be needed to evaluate these concepts, or identifying where the T&E space would be affected with a change in requirements. Our hypothesis is that decision makers would equally gain insight into the T&E considerations as well as system space considerations using MBCD techniques. An approach is offered to extend the previously published MBCD methodology to better consider the T&E space.

A complex system is characterized by emergence of global properties which are very difficult, if not impossible, to anticipate just from complete knowledge of component behaviors. Emergence, hierarchical organization, and numerosity are some of the characteristics of complex systems. Recently, there has been an exponential increase on the adoption of various neural network-based machine learning models to govern the functionality and behavior of systems. With this increasing system complexity, achieving confidence in systems becomes even more difficult. Further, ease of interconnectivity among systems is permeating numerous system-of-systems, wherein multiple independent systems are expected to interact and collaborate to achieve unparalleled levels of functionality. Traditional verification and validation approaches are often inadequate to bring in the nuances of potential emergent behavior in a system-of-systems, which may be positive or negative. This paper describes a novel approach towards application of machine learning based classifiers and formal methods for analyzing and evaluating emergent behavior of complex system-of-systems that comprise a hybrid of constituent systems governed by conventional models and machine learning models. The proposed approach involves developing a machine learning classifier model that learns on potential negative and positive emergent behaviors, and predicts the behavior exhibited. A formal verification model is then developed to assert negative emergent behavior. The approach is illustrated through the case of a swarm of autonomous UAVs flying in a formation, and dynamically changing the shape of the formation, to support varying mission scenarios. The effectiveness and performance of the approach are quantified.

In current practice, a verification strategy is defined at the beginning of an acquisition program and is agreed upon by customer and contractor at contract signature. Hence, the resources necessary to execute verification activities at various stages of the system development are allocated and committed at the beginning, when a small amount of knowledge about the system is available. However, contractually committing to a fixed verification strategy at the beginning of an acquisition program fundamentally leads to suboptimal acquisition performance. Essentially, the uncertain nature of system development will make verification activities that were not previously planned necessary and will make some of the planned ones unnecessary. To cope with these challenges, this paper presents an approach to apply set-based design to the design of verification activities to enable the execution of dynamic contracts for verification strategies, ultimately resulting in more valuable verification strategies than current practice.

The production of single use medical devices, particularly for home use by patients, continues to grow, and the reliability of these devices is a primary concern for manufacturers and end-users. The systems engineer tasked with the device development needs methods and tools to establish reliability requirements and provide cost estimates for the testing necessary to show compliance with those requirements. This paper examines methods for determining reliability requirements, the cost of reliability testing for single use medical devices in the design input phase of product development, and how the costs of testing and potential errors can be used to perform trade-off analysis between reliability tolerance and confidence level.

Model-based systems engineering depends on correct models. However, thus far, relatively little attention has been paid to ensuring their correctness. This paper describes a methodology for performing verification and validation on models written in SysML. The methodology relies on a catalog of candidate requirements that can be tailored for a specific project. Both manual and automated methods are used to verify and validate these requirements. Manual methods are necessary where knowledge of the domain and other extrinsic characteristics are necessary. Automated methods can be used where the requirements cover the use of SysML. Examples from a public domain SysML model of a satellite are presented to demonstrate application of automated requirements verification.

Systems architecture design is a key activity that affects the overall systems engineering cost. Hence it is fundamental to ensure that the system architecture reaches a proper quality. In this paper, we leverage model-based systems engineering (MBSE) approaches and complement them with simulation techniques, as a promising way to improve the quality of the system architecture definition, and to come up with innovative solutions while securing the systems engineering process.

With the increasing complexity that is being introduced to engineered systems, the literature suggests that verification may benefit from theoretical foundations. In practice and in teaching of system engineering (SE), we typically define a verification model (simulation, test article, etc.) under the assumption that the model is a valid representation of the system design. Is this assumption always true? In this article, we explore the use of system theoretic morphisms to mathematically characterize the validity of representativeness between verification models and corresponding system design.

Like so many aspects of life, we are looking for value-for-money. But we need to consider the value in terms of both short and long-term gains. Although certification standards require verification that requirements have been met, we need to recognize that verification is also there to bring value to a project and to the business as a whole. However, prioritizing the value to the project over the value to the business can result in sub-optimization and an overall higher cost to the business. This paper examines a specific case, the prediction of the fatigue lives of critical parts in gas turbine engines, to illustrate the more general case of performing tests to calibrate models that then have general applicability across multiple projects, rather than focusing testing on the needs of a specific project. In some circumstances, testing may not even be the best approach to take; if some level of error escape into service is acceptable (unlike the life prediction example given in this paper) then more focus on requirements validation and design review may provide a more cost-effective approach. This is where the linkage in a systems engineering model between requirements, functions, failure modes and effects analysis, verification test cases, and available calibrated models can help with identifying opportunities and risks.