Industrial plants are complex structures, highly vulnerable with respect to seismic loading. Past seismic events have demonstrated the devastating impact and huge economic losses that an industrial plant can experience not only due to physical damage of equipment, but also due to interruption of the production processes. In order to quantify these economic losses, plant seismic resilience evaluation is required. The current paper presents a probabilistic process flow-based framework for assessment of industrial plant resilience and economic losses in case of seismic events. Uncertainties are considered in the ability of plant equipment to withstand the perturbation, and also in the recovery process including equipment recovery durations and recovery costs. Monte Carlo Simulation is used to account for the uncertainties of the model. A black carbon plant is used as a case study to show the applicability of the model. Results and capability of the proposed model shows that it can be a useful tool for decision makers, plant owners, insurance companies, emergency managers and plant designers in their decision making process.

Wind-energy systems are strongly affected by uncertainty and variability. Therefore, uncertainty sources should be considered during the economic evaluation of this type of system. In the literature, a framework for the economic performance assessment of wind-power systems has been proposed. Furthermore, in another contribution, the random discontinuities of political and regulatory scenarios have been included by using scenario analysis. However, the implemented models neglected the uncertainty related to disruptive events and the effect of climate change on the wind resource. To fill this gap, in this paper, climate change and disruptive events are included in a new model for evaluating the economic performance of wind turbine systems using scenario analysis. Analysis of a numerical example has been carried out to show the framework’s capabilities and to evaluate the effects of the added issues. The main results confirm previous findings on the necessity of including regulatory and political risks to achieve a proper economic evaluation. Additionally, they show that disruptive events increase the variability of the expected value of the Net Present Value (NPV). Therefore, even though climate change is expected to increase wind producibility in the numerical example location, the inclusion of disruptive events constrains the NPV growth.

The recent evolution of the space industry, commonly referred to as New Space, has changed the way space missions are conceived, developed, and executed. In contrast to traditional approaches, the current paradigm emphasizes accessibility, commercial competitiveness, and rapid and sustainable innovation. This study proposes a research methodology for selecting relevant literature to identify the key design drivers and associated enablers that characterize the New Space context from an engineering design perspective. These elements are then organized into three categories: the evolution of traditional drivers, emerging manufacturing and integration practices, and sustainability and technology independence. This categorization highlights their role and relevance, providing a baseline for the development of systems for New Space missions. The results are further contextualized within three major application domains, namely Low Earth Orbit (LEO) small satellite constellations, operations and servicing in space, and space exploration, to illustrate their practical role in engineering space systems. By linking high-level industry trends to concrete design choices, this work aims to support the early design phases of New Space innovative systems and promote a more integrated approach between strategic objectives and technical development.

The supply of parts to the workstations of assembly lines is a critical design and operational issue. Even though automation and collaborative robots are increasingly used in industry, human operators are frequently employed in the picking and assembly of manual parts. These are time-consuming activities required to be efficiently performed at a high rate and for prolonged periods. Therefore, ergonomic analysis is necessary to reduce the risk of work-related musculoskeletal injuries due to the biomechanical loads. The proper layout of shelves storing part containers along the production line, and the location of the containers on the shelves, may improve picking efficiency and reduce biomechanical risk. Several manufacturing companies use computer-aided ergonomic tools to improve the design of manual production lines, racking, shelving, and workstations. This paper describes the development of a support tool to configure industrial light shelves for feeding the assembly lines. The approach includes the development of a knowledge base to support the geometrical configuration of the shelving and an ergonomic analysis based on the RULA method, considering the shelf’s position and the operator’s postures. As a test case, the model has been used to evaluate the ergonomic score of some configured shelving based on a prescribed picking sequence. The results show that the proposed approach can help in comparing the ergonomic score of candidate shelving layouts to improve the design of parts storage systems to reduce operators’ workload and ergonomic risk.