The values of Q=(fusion power)/(auxiliary heating power) predicted for ITER by three different methods, i.e., transport model based on empirical confinement scaling, dimensionless scaling technique, and theory-based transport models are compared. The energy confinement time given by the ITERH-98(y,2) scaling for an inductive scenario with plasma current of 15 MA and plasma density 15% below the Greenwald value is 3.6 s with one technical standard deviation of ±14%. These data are translated into a Q interval of [7-13] at the auxiliary heating power P aux = 40 MW and [7 Ð 28] at the minimum heating power satisfying a good confinement ELMy H-mode. Predictions of dimensionless scalings and theory-based transport models such as Weiland, MMM and IFS/PPPL overlap with the empirical scaling predictions within the margins of uncertainty.

In the last few years, significant progress has been made in the understanding of H-mode plasmas (e.g. ion temperature profile stiffness, pedestal physics, etc). Based on this improved understanding, a set of rules (models) comprising a physics picture of the H-mode has been implemented in the ASTRA code in order to improve the understanding of experimental observations and ultimately to provide a predictive capability for ITER complementary to the scaling relations. The model has been verified for consistency with experimental observations in ASDEX-UG and JET plasmas. Numerical coefficients for the transport, required because of simplifications or missing quantitative information, are determined for one plasma (e.g. from JET) and then held constant for all others (JET, ASDEX-UP, ITER).After benchmarking the model to experimental results, it was also applied to ITER. It predicts that Q = 10 can be achieved in ITER but only with at least a 50% deep fuelling contribution (inside the H-mode pedestal). However, in existing machines as well as in our model runs for existing machines, gas puffing is sufficient to achieve the observed density pedestal and line average densities. A second important result from the predictive runs for ITER is that electron energy transport in the plasma core, the neoclassical transport in the pedestal and the CX losses at the plasma edge are important constraints for a better performance. Thus future theoretical and experimental work should concentrate on these areas in order to improve our predictions.

Recent studies provide convincing evidence that the anomalous transport in a tokamak is governed by the combination of ion temperature gradient and trapped electron mode turbulence. In this paper, we employ the theory-based transport model GLF23 to study the density profile formation in ITER. It is shown that the plasma density can be noticeably more peaked than is usually assumed. The peaking is mainly due to the curvature drift as described by the GLF model and, independently, by the turbulent equipartition theory. This results in an approximately 30% enhancement of the fusion power in comparison with the standard conjecture of the flat density profile. The physical background of this density peaking and its possible further impact on the reactor performance are also discussed.

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The subject of the research is methods of relational database mining. The purpose of the research is to develop scientifically grounded models for supporting intelligent technologies for integrating and managing information resources of distributed computing systems. Explore the features of the operational specification of the relational data model. To develop a method for evaluating a relational data model and a procedure for constructing functional associative rules when solving problems of mining relational databases. In accordance with the set research goal, the presented article considers the following tasks: analysis of existing methods and technologies for data mining. Research of methods for representing intelligent models by means of relational systems. Development of technology for evaluating the relational data model for building functional association rules in the tasks of mining relational databases. Development of design tools and maintenance of applied data mining tasks; development of applied problems of data mining. Results: The analysis of existing methods and technologies for data mining is carried out. The features of the structural specification of a relational database, the formation of association rules for building a decision support system are investigated. Information technology has been developed, a methodology for the design of information and analytical systems, based on the relational data model, for solving practical problems of mining, practical recommendations have been developed for the use of a relational data model for building functional association rules in problems of mining relational databases, conclusion: the main source of knowledge for database operation can be a relational database. In this regard, the study of data properties is an urgent task in the construction of systems of association rules. On the one hand, associative rules are close to logical models, which makes it possible to organize efficient inference procedures on them, and on the other hand, they more clearly reflect knowledge than classical models. They do not have the strict limitations typical of logical calculus, which makes it possible to change the interpretation of product elements. The search for association rules is far from a trivial task, as it might seem at first glance. One of the problems is the algorithmic complexity of finding frequently occurring itemsets, since as the number of items grows, the number of potential itemsets grows exponentially.