Next Issue
Volume 2, March

J. Nucl. Eng., Volume 1, Issue 1 (December 2020) – 6 articles

Cover Story (view full-size image): The possibility is investigated of using artificial neural networks (ANNs) as surrogate models for uncertainty quantification and data assimilation in fuel performance studies. The focus is on steady-state cases with given densification, creep, fission gas, etc. Results show that one can make use of simple rectangular ANNs with one or two hidden layers, with improved results when a non-linear activation function is employed in at least one of the layers. By using 1000 labeled data and a two-layer ANN, one can reduce the computational cost of DA studies by several tens of times while negligibly affecting the result of the DA process. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
Order results
Result details
Select all
Export citation of selected articles as:
Open AccessArticle
Nuclear Data Uncertainty Propagation in Complex Fusion Geometries
J. Nucl. Eng. 2020, 1(1), 63-69; https://0-doi-org.brum.beds.ac.uk/10.3390/jne1010006 - 02 Dec 2020
Cited by 1 | Viewed by 492
Abstract
The ASUSD program package was designed to automate and simplify the process of deterministic nuclear data sensitivity and uncertainty quantification. The program package couples Denovo, a discrete ordinate 3D transport solver, as part of ADVANTG and SUSD3D, a deterministic first order perturbation theory [...] Read more.
The ASUSD program package was designed to automate and simplify the process of deterministic nuclear data sensitivity and uncertainty quantification. The program package couples Denovo, a discrete ordinate 3D transport solver, as part of ADVANTG and SUSD3D, a deterministic first order perturbation theory based Sensitivity/Uncertainty code, using several auxiliary programs used for input data preparation and post processing. Because of the automation employed in ASUSD, it is useful for Sensitivity/Uncertainty analysis of complex fusion geometries. In this paper, ASUSD was used to quantify uncertainties in the JET KN2 irradiation position. The results were compared to previously obtained probabilistic-based uncertainties determined using TALYS-based random nuclear data samples and MCNP in a Total Monte Carlo computation scheme. Results of the two approaches, deterministic and probabilistic, to nuclear data uncertainty propagation are compared and discussed. ASUSD was also used to perform preliminary Sensitivity/Uncertainty (S/U) analyses of three JET3-NEXP streaming benchmark experimental positions (A1, A4 and A7). Full article
(This article belongs to the Special Issue Selected Papers from PHYSOR 2020)
Show Figures

Figure 1

Open AccessArticle
Artificial Neural Networks as Surrogate Models for Uncertainty Quantification and Data Assimilation in 2-D/3-D Fuel Performance Studies
J. Nucl. Eng. 2020, 1(1), 54-62; https://0-doi-org.brum.beds.ac.uk/10.3390/jne1010005 - 10 Nov 2020
Viewed by 543
Abstract
This paper preliminarily investigates the use of data-driven surrogates for fuel performance codes. The objective is to develop fast-running models that can be used in the frame of uncertainty quantification and data assimilation studies. In particular, data assimilation techniques based on Monte Carlo [...] Read more.
This paper preliminarily investigates the use of data-driven surrogates for fuel performance codes. The objective is to develop fast-running models that can be used in the frame of uncertainty quantification and data assimilation studies. In particular, data assimilation techniques based on Monte Carlo sampling often require running several thousand, or tens of thousands of calculations. In these cases, the computational requirements can quickly become prohibitive, notably for 2-D and 3-D codes. The paper analyses the capability of artificial neural networks to model the steady-state thermal-mechanics of the nuclear fuel, assuming given released fission gases, swelling, densification and creep. An optimized and trained neural network is then employed on a data assimilation case based on the end of the first ramp of the IFPE Instrumented Fuel Assemblies 432. Full article
(This article belongs to the Special Issue Selected Papers from PHYSOR 2020)
Show Figures

Figure 1

Open AccessArticle
Preliminary Core Design Study of Small Supercritical Fast Reactor with Single-Pass Cooling
J. Nucl. Eng. 2020, 1(1), 46-53; https://0-doi-org.brum.beds.ac.uk/10.3390/jne1010004 - 07 Nov 2020
Viewed by 561
Abstract
A supercritical water-cooled reactor (SCWR) adopts a once-through direct cycle, which is compatible with a small modular reactor class (SMR) plant system. The core is cooled by supercritical light water, which does not exhibit phase change, but undergoes large temperature and density changes. [...] Read more.
A supercritical water-cooled reactor (SCWR) adopts a once-through direct cycle, which is compatible with a small modular reactor class (SMR) plant system. The core is cooled by supercritical light water, which does not exhibit phase change, but undergoes large temperature and density changes. A super fast reactor (Super FR) is a fast reactor type concept of SCWR. Unlike other SCWR core concepts, it adopts the single coolant pass flow scheme, in which the coolant passes the core only once from the bottom to the top without any reverse flows or preheating stages. In the meantime, reducing the core size tends to increase the core power peaking and reduce criticality. Therefore, the key issues with the small Super FR core design is reducing the core power peaking and achieving high average core outlet temperature with the single coolant pass scheme. This study aims to highlight the design issues through conceptual core designs of SMR class Super FR. To evaluate the core characteristics, three-dimensional coupled core calculations are carried out. The proposed design with small fuel assemblies, which are equivalent to those of boiling water reactors, attains a high core average outlet temperature of about 500 °C, which is compatible to that of typical large SCWR core design. Full article
(This article belongs to the Special Issue Selected Papers from PHYSOR 2020)
Show Figures

Figure 1

Open AccessArticle
First-Order Comprehensive Adjoint Method for Computing Operator-Valued Response Sensitivities to Imprecisely Known Parameters, Internal Interfaces and Boundaries of Coupled Nonlinear Systems: II. Application to a Nuclear Reactor Heat Removal Benchmark
J. Nucl. Eng. 2020, 1(1), 18-45; https://0-doi-org.brum.beds.ac.uk/10.3390/jne1010003 - 09 Sep 2020
Cited by 1 | Viewed by 404
Abstract
This work illustrates the application of a comprehensive first-order adjoint sensitivity analysis methodology (1st-CASAM) to a heat conduction and convection analytical benchmark problem which simulates heat removal from a nuclear reactor fuel rod. This analytical benchmark problem can be used to verify the [...] Read more.
This work illustrates the application of a comprehensive first-order adjoint sensitivity analysis methodology (1st-CASAM) to a heat conduction and convection analytical benchmark problem which simulates heat removal from a nuclear reactor fuel rod. This analytical benchmark problem can be used to verify the accuracy of numerical solutions provided by software modeling heat transport and fluid flow systems. This illustrative heat transport benchmark shows that collocation methods require one adjoint computation for every collocation point while spectral expansion methods require one adjoint computation for each cardinal function appearing in the respective expansion when recursion relations cannot be developed between the corresponding adjoint functions. However, it is also shown that spectral methods are much more efficient when recursion relations provided by orthogonal polynomials make it possible to develop recursion relations for computing the corresponding adjoint functions. When recursion relations cannot be developed for the adjoint functions, the collocation method is probably more efficient than the spectral expansion method, since the sources for the corresponding adjoint systems are just Dirac delta functions (which makes the respective computation equivalent to the computation of a Green’s function), rather than the more elaborated sources involving high-order Fourier basis functions or orthogonal polynomials. For systems involving many independent variables, it is likely that a hybrid combination of spectral expansions in some independent variables and collocation in the remaining independent variables would provide the most efficient computational outcome. Full article
Open AccessArticle
First-Order Comprehensive Adjoint Method for Computing Operator-Valued Response Sensitivities to Imprecisely Known Parameters, Internal Interfaces and Boundaries of Coupled Nonlinear Systems: I. Mathematical Framework
J. Nucl. Eng. 2020, 1(1), 3-17; https://0-doi-org.brum.beds.ac.uk/10.3390/jne1010002 - 08 Sep 2020
Cited by 2 | Viewed by 548
Abstract
This work presents the first-order comprehensive adjoint sensitivity analysis methodology (1st-CASAM) for computing efficiently the first-order sensitivities (i.e., functional derivatives) of operator-valued responses (i.e., model results) of general models of coupled nonlinear physical systems characterized by imprecisely known or and/or uncertain parameters, external [...] Read more.
This work presents the first-order comprehensive adjoint sensitivity analysis methodology (1st-CASAM) for computing efficiently the first-order sensitivities (i.e., functional derivatives) of operator-valued responses (i.e., model results) of general models of coupled nonlinear physical systems characterized by imprecisely known or and/or uncertain parameters, external boundaries, and internal interfaces between the coupled systems. The explicit mathematical formalism developed within the 1st-CASAM for computing the first-order sensitivities of operator-valued response to uncertain internal interfaces and external boundaries in the models’ phase–space enables this methodology to generalize all of the previously published methodologies for computing first-order response sensitivities. The computational resources needed for using forward versus adjoint operators in conjunction with spectral versus collocation methods for computing the response sensitivities are analyzed in detail. By enabling the exact computations of operator-valued response sensitivities to internal interfaces and external boundary parameters and conditions, the 1st-CASAM presented in this work makes it possible, inter alia, to quantify the effects of manufacturing tolerances on operator-valued responses of physical and engineering systems. Full article
Show Figures

Figure 1

Open AccessEditorial
Introducing the Journal of Nuclear Engineering: An Interdisciplinary Open Access Journal Dedicated to Publishing Research in Nuclear and Radiation Sciences and Applications
J. Nucl. Eng. 2020, 1(1), 1-2; https://0-doi-org.brum.beds.ac.uk/10.3390/jne1010001 - 28 May 2020
Viewed by 539
Abstract
In 1938, Strassmann, Hahn and Meitner discovered neutron-induced nuclear fission in uranium, forever changing our world and opening multiple paths to developing nuclear energy, nuclear medicine, instrumentation, space propulsion, environmental monitoring, remediation and nuclear security [...] Full article
Next Issue
Back to TopTop