![]() ![]() A more reliable estimate could come from a bottom-up cost analysis, referred to the specific plant layout and technical features. Nevertheless, the design-saving factor that is expected to decrease construction costs of SMRs is strictly dependent on the specific reactor concept. Some general considerations on cost reduction by design may be drawn from several innovative SMR features, such as the integration of primary loop into the reactor vessel, with the elimination of large loss-of-coolant accident (LOCA), the wide use of passive safety systems with natural circulation of coolant in case of accident, and the elimination of some active components and safety systems. The smaller the reactor unit size, the higher must be the design cost savings in order to have the same generation costs as LRs. SMRs rely on the ‘economy of multiples’ but also on the ‘economy of small’ in the sense that design-related cost savings are necessary to recover economic competitiveness. Revised, simplified and more cost-effective plant layout becomes possible, with favourable impact on costs ( Carelli et al., 2008a, 2008b). As a result, the elimination of some engineered safety systems might be possible and/or the safety downgrading of some other components. relying on physical laws and not on human intervention for the activation), but small-scale plants can take maximum advantage of such features, due to their physically smaller size or lower power densities, and consequential lower power output. Most Gen III + reactor designs include some features that may be regarded as passive (i.e. The 300–400 MWe safe integral reactor (SIR) in the 1990s and the international reactor innovative and secure (IRIS) in early 2000s paved the way to the understanding of an innovative technological and economic paradigm. SMR economic rationale also lies on the enhanced passive safety features and design simplifications, often enabled by a small plant scale. Usually SMRs are not a mere re-sizing of larger units they do not represent a way back, but, on the contrary, a further progress in the technology evolution path.Īt a smaller size, different design concepts might be possible, which could lead to a more significant capital cost reduction than simple application of the scaling laws from large design would predict ( Hayns and Shepherd, 1991). In designing a plant with smaller output, it does not necessarily make sense to just scale down a large system. Large plants have been optimized for their particular power output. While modularization deals with a design and fabrication methodology, design factor is related to the specific and peculiar features and enhancements of a given design concept, in order to meet operating requirements with optimized safety, simplicity and economics. Locatelli, in Handbook of Small Modular Nuclear Reactors (Second Edition), 2021 10.5.2 Design factor Read moreĮconomics and financing of small modular reactors (SMRs) Covid-19 accelerated these transformations both in terms of scope, scale, and speed. These factors create markets that are better described by power-laws distributions. internal groups driven by continuous experimentation, the creation of software models, and their operationalization.Īt macro level we can observe organizations that increasingly compete on innovation, endowed of close-to-zero marginal costs and total agility and scalability. digitally transformed firms are endowed with A.I. This deeply changes the internal balance within organizations and shapes competition. moves exploitation to code changing the focus to exploration, to the discovery and adoption of new innovations. on organization goes beyond concrete tasks and organizational functions such as coordination. Tasks, even if very complicated, with a narrow scope that demand no “common-sense” and where knowledge is highly codified are prime targets for automation. ![]() has limits in terms of the tasks that can be accomplished. The choice between augmentation versus automation depends on many factors, among them cultural or organizational ones. but also the transformation of these modules into software in the cloud, enjoying the benefits of close-to-zero marginal costs and total scalability. ![]() Modularization and decomposition of jobs allow not only the massive integration of A.I. ![]()
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