Insight最新文献

Resilience is a much-needed characteristic in systems that are expected to operate in uncertain environments for extended periods with a high likelihood of disruptive events. Resilience approaches today employ ad hoc methods and piece-meal solutions that are difficult to verify and test, and do not scale. Furthermore, it is difficult to assess the long-term impact of such ad hoc “resilience solutions.” This paper presents a flexible contract-based approach that employs a combination of formal methods for verification and testing and flexible assertions and probabilistic modelling to handle uncertainty during mission execution. A flexible contract (FC) is a hybrid modelling construct that facilitates system verification and testing while offering the requisite flexibility to cope with non-determinism. This paper illustrates the use of FCs for multi-UAV swarm control in, partially observable, dynamic environments. However, the approach is sufficiently general for use in other domains such as self-driving vehicle and adaptive power/energy grids.

Part of the Presidential Policy Directive 21 (PPD-21) (PPD 2013) mandate includes evaluating safety, security, and safeguards (or nonproliferation) mechanisms traditionally implemented within the nuclear reactors, materials, and waste sector of critical infrastructure—including a complex, dynamic set of risks and threats within an all-hazards approach. In response, research out of Sandia National Laboratories (Sandia) explores the ability of systems theory principles (hierarchy and emergence) and complex systems engineering concepts (multidomain interdependence) to better understand and address these risks and threats. This Sandia research explores the safety, safeguards, and security risks of three different nuclear sector-related activities—spent nuclear fuel transportation, small modular reactors, and portable nuclear power reactors—to investigate the complex and dynamic risk related to the PPD-21-mandated all-hazards approach. This research showed that a systems-theoretic approach can better identify inter-dependencies, conflicts, gaps, and leverage points across traditional safety, security, and safeguards hazard mitigation strategies in the nuclear reactors, materials, and waste sector. As a result, mitigation strategies from applying systems theoretic principles and complex systems engineering concepts can be (1) designed to better capture interdependencies, (2) implemented to better align with real-world operational uncertainties, and (3) evaluated as a systems-level whole to better identify, characterize, and manage PPD-21's all hazards strategies.

Systems Modeling Language (SysML) is a tool for guiding engineers in designing power grid circuits sufficiently robust to withstand known electromagnetic pulses (EMPs). Careful examination of existing data shows that EMPs, and sometimes geomagnetically induced currents (GICs) that accompany EMPs are truly a powerful threat to power grid survival. Systems engineers, employing SysML can isolate power grid failure susceptibilities and areas for necessary power grid design improvements with selected SysML packages defined as enclaves associated with risk. These enclaves can be decomposable into stereotyped components available for risk categorization, building simulation libraries, or follow–on tests. As an example, a stereotype Source, instantiated as a Photovoltaic (PV) Inverter, increasingly important in microgrid renewable energy, is linked to a high frequency alternating current (HFAC) microgrid risk enclave package. Simulation allows evaluation of SysML use cases with EMP Actors. Real world test, construction, and strategic grid readjustment can then segue quickly.

System characteristics include what it is (structure, state), what it does (function, behavior), where it resides (environment, containing whole), what it uses (resources, energy source, raw material), what it contains (content), and why it exists (value delivery). An adversity produces a disturbance that can induce stress in a system so it may suffer some loss within one or more of these characteristics. Loss-driven systems engineering (LDSE) is an approach to address systemic loss in all forms helping ensure value delivery. LDSE domains include reliability, sustainability, survivability, risk management, resistance, resilience, agility, safety, and security which all work in harmony to avoid, withstand, and recover from loss. Traditional systems engineering treats these as separate domains with varying degrees of detail, rigor, and results. LDSE proposes consolidating these domains for a comprehensive, cohesive, and consistent approach to address system loss. This paper establishes interrelationships among the LDSE domains to harmonize role, fit, function, and impact among the domains focusing on sustaining value-delivery.

Prevailing modeling and simulation (M&S) techniques have struggled to provide meaningful quantitative results in M&S of complex system of systems (SoSs) in the face of an environment filled with complex interacting uncertainties. This paper reports on systems thinking applied to “how” M&S techniques should shift to allow a next generation of quantitative tools and techniques. The imperative is to provide quantitative performance results across the constituent interfaces in a modeled architecture. A five-step statistical and parametric algorithm tool that addresses uncertainty quantification (UQ) is presented. [Improving the utility of UQ data evaluation] A quantitative approach to managing complex uncertainties across modeled interfaces using graph theory is proposed. A future vision for SoS engineering (SoSE) that uses graph theory-based modeling is suggested to improve the utility of tools such as UQ is suggested.

Uncertainty is a large part of the systems engineering development process. Particularly absent is the quantification of uncertainty of the threat, operating environment, and friendly force factors at each step of this lifecycle. This paper will explore a methodology to quantify the amount of uncertainty and the interdependencies of the uncertainty factors during the development. Included for consideration are internal and external factors and their contribution to the overall system uncertainty. An illustrative example is provided to exercise this methodology.

In early design stages, business developers and systems engineers deal with uncertainties on the business problem, in line with the company's strategy. Before designing the system, the business developers need to set the boundaries of the business problem: What are the values to deliver to which stakeholders? What are their preferences? What are the future trends or the evolution of the markets and the external context? These questions regarding the uncertainties on the definition of the problem may not have answers and need to be investigated to assess the value robustness of the possible design alternatives. The aim of this work is to support decision-making in business and system design thanks to a broad and rapid analysis of a large amount of business design alternatives under uncertainty. We introduce a decision-making support method, called ValXplore, based on visual analysis and data analytics to explore the uncertainties on and in the business problem. The method was tested and validated on an industrial case study to assess the benefits and limits of the semi-reusability of a launch vehicle. Both business developers and systems engineers can rapidly explore a broad space of alternatives to increase the value to the stakeholders, by performing sensitivity and uncertainty analyses.

Although theoretically independent, requirements within a decomposition level of a system architecture are not isolated elements. For an existing design, a change of a requirement may endanger or facilitate fulfillment of other requirements within the same level of the decomposition. The present research suggests a requirement connectivity metric to evaluate the potential consequences that changing a requirement may have on a system with respect to fulfillment of other requirements. A particular aspect of the present research is the assumption that connectivity accounts only for requirements within the same decomposition level of an architecture, not for those flowing up or down the decomposition. The metric is used to evaluate different cases in which requirements are changed due to triggering of uncertain events during a project life cycle.

Although a number of recent studies on using Bayesian Networks (BN) for system reliability estimation have been proposed, these studies are based on the assumption that a pre-built BN was designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. However, the process of building a system-specific BN is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a holistic method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model and estimation of the system reliability. Moreover, very limited human intervention is sufficient for the process of BN construction and reliability estimation.