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Nuclear reactor simulators implementing the widespread two-steps deterministic calculation scheme tend to produce a large volume of intermediate data at the interface of their two subcodes – up to dozens or even hundred of gigabytes – which can be so cumbersome that it hinders the global performance of the code. The vast majority of this data consists of ‘‘few-groups homogenized cross-sections’’, nuclear quantities stored in the form of tabulated multivariate functions which can be precomputed to a large extent.It has been noticed in Tomatis (2021) that few-groups homogenized cross-sections are highly redundant— that is, they exhibit strong correlations, which paves the way for the use of compression techniques. Wehere pursue this line of work by introducing a new coupled compression/surrogate modeling tool based on the Empirical Interpolation Method, an algorithm originally developed in the framework of partial differential equations (Barrault et al., 2004). This EIM-compression method is based on the infinite norm ∥ ⋅ ∥∞, and proceeds in a greedy manner by iteratively trying to approximate the data and incorporating the chunks of information which cause the largest error. In the process, it generates a vector basis and a set of interpolation points, which provide an elementary surrogate model that can be used to approximate future data from little information. The algorithm is also very suitable for parallelization and out-of-core computation (processing of data too large for the computer RAM) and very easy to apprehend and implement. This method enables us to both efficiently compress cross-sections and spare a large fraction of the required lattice calculations. We investigate its performance on large realistic nuclear data replicating the notorious VERA benchmark (Godfrey, 2014) (20 energy groups, pin-by-pin homogenization, 10 particularized isotopes). Compression loss, memory savings and speed are analyzed both from a data-centric point of view in the perspective of applications in neutronics, and by comparison with an existing and widely-used method – stochastic truncated SVD – to assess mathematical efficiency. We discuss the usage of our surrogate model and its sensitivity to the choice of the training set. The method is shown to be competitive in terms of accuracy and speed, provide important memory savings and spare a large amount of physics code computation; all this could facilitate the adoption of fine-grain modelization schemes (pin-by-pin and many-groups homogenization, particularization of many isotopes) in industrial setups. A Github repository is available,1 which contains all the methods used for the article. International audience

This article describes an implementation of Depletion Perturbation Theory (DPT) using deterministic neutronics code APOLLO3® alongside depletion code MENDEL. We use DPT to calculate the sensitivity coefficients of decay heat (DH) to various nuclear data such as reaction cross-sections, fissions yields, and reaction energy.DPT allows for the coupling of the Bateman, Boltzmann, and normalization equations, and has the advantage of needing only one calculation per cooling time to compute the sensitivity coefficient to all input parameters, as opposed to usual finite differences schemes which need as many calculation as there are input parameters.The calculation of sensitivity coefficients is done using both the DPT methodology and a finite differencesscheme as a mean of numerical verification. Both methods showed good agreement, although we found that DPT should be used with care when studying short cooling times. Sensitivity coefficients were then used to perform deterministic uncertainty propagation, the resulting uncertainty calculation were used to compare the effect of various coupling configurations. International audience

In the design and synthesis of energy systems, mathematical tools such as optimization algorithms are often used. While using them, environmental indicators are increasingly used as optimization criteria to exploit the possible environmental benefits of these systems. The problem is that the high number of environmental indicators poses a problem for optimization algorithms in terms of convergence, computational time and visualization. In this paper, this problem is addressed through a many-objective search of solutions using a state-of-the-art evolutionary algorithm, NSGA-III. Furthermore, the performance of this algorithm is tested using different settings in the PCA-based objective reduction framework. The original 14 indicators are reduced to seven, four, three and two, which reveal important insights about the use of NSGA-III, objective reduction and a combination of the two. It was found that by using objective reduction, the performance of NSGA-III can be further improved in terms of the quality of solutions and computational time. However, beyond a certain point, further objective reduction leads to a trade-off between solution quality and computational time. For this case study, the best quality solutions were obtained in the PCA reduction procedure when the CUT value was maintained at 99.99% without additional reduction in the last step, using a correlation matrix. The algorithms were applied to a real-life sizing case study involving hydrogen production from polymer-electrolyte-membrane (PEM) water electrolysis, for which the demand is furnished by the electricity spot market, solar photovoltaics (PV) or wind turbine in Marseille, France. These results will be useful for future applications of many-objective optimization and objective reduction. They will also be practical for including environmental indicators in the many-objective search for solutions. International audience

International audience

International audience; Nuclear power and solar photovoltaic energy conversion often compete for policy support, which governs economic viability. This paper compares current subsidization of the nuclear industry with providing equivalent support to manufacturing photovoltaic modules. Current U.S. indirect nuclear insurance subsidies are reviewed and the power, energy and financial outcomes of this indirect subsidy are compared to equivalent amounts for indirect subsidies (loan guarantees) for photovoltaic manufacturing using a model that holds economic values constant for clarity. The preliminary analysis indicates that if only this one relatively ignored indirect subsidy for nuclear power was diverted to photovoltaic manufacturing, it would result in more installed power and more energy produced by mid-century. By 2110 cumulative electricity output of solar would provide an additional 48,600TW-hrs over nuclear worth $5.3 trillion. The results clearly show that not only does the indirect insurance liability subsidy play a significant factor for nuclear industry, but also how the transfer of such an indirect subsidy from the nuclear to photovoltaic industry would result in more energy over the life cycle of the technologies.

Much of the existing literature on the relationship between nuclear energy consumption and gross domestic product (GDP) deals only with the causal links between these two variables. However, very little attention has been paid to the structure and form of this relationship. This paper first uses panel cointegration techniques to illustrate the form of an inverted U-shaped curve that arises from pooled data, then, applies the iterative empirical Bayesian procedure in order to account for the heterogeneity in the coefficients of the long-term relationship. The empirical results from a multivariate framework involving carbon dioxide (CO2) emissions reveal that for only 3 of the 21 nuclear countries studied, a linear form of the relationship can be justified and that nuclear energy goes from being a normal good to being an inferior good for the majority of the sample countries. International audience

Extracting, processing, and delivering energy requires energy itself, which reduces the net energy available to society and yields considerable socioeconomic implications. Yet, most mitigation pathways and transition models overlook net energy feedbacks, specifically related to the decline in the quality of fossil fuel deposits, as well as energy requirements of the energy transition. Here, we summarize our position across 8 key points that converge to form a prevailing understanding regarding EROI (Energy Return on Investment), identify areas of investigation for the Net Energy Analysis community, discuss the consequences of net energy in the context of the energy transition, and underline the issues of disregarding it. Particularly, we argue that reductions in net energy can hinder the transition if demand-side measures are not implemented and adopted to limit energy consumption. We also point out the risks posed for the energy transition in the Global South, which, while being the least responsible for climate change, may be amongst the most impacted by both the climate crisis and net energy contraction. Last, we present practical avenues to consider net energy in mitigation pathways and Integrated Assessment Models (IAMs), emphasizing the necessity of fostering collaborative efforts among our different research communities. International audience

Abstract The principal peaceful application of nuclear energy is that of electricity generation. The nuclear industry is a young one, which is today confronted with difficult choices, essentially because this activity generates fear. This fear is partly related to the generation of electricity in power plants but is particularly present in relation to the transport, reprocessing, management and underground disposal of nuclear waste. This paper examines, respectively, the nuclear technologies available today (1), the future perspectives for nuclear energy on a worldwide basis (2) and the controversial question of the management of nuclear waste and the insurability of risks (3). Nuclear energy can be considered as an alternative to fossil fuels in the context of policies to reduce emissions of greenhouse gases. The potential technological progress is a key element of the future of nuclear energy; but the crux of the problem remains the long-term management of waste.