Physics-related epistemic uncertainties in proton depth dose simulation

A set of physics models and parameters pertaining to the simulation of proton energy deposition in matter are evaluated in the energy range up to approximately 65 MeV, based on their implementations in the Geant4 toolkit. The analysis assesses several features of the models and the impact of their associated epistemic uncertainties, i.e. uncertainties due to lack of knowledge, on the simulation results. Possible systematic effects deriving from uncertainties of this kind are highlighted; their relevance in relation to the application environment and different experimental requirements are discussed, with emphasis on the simulation of radiotherapy set-ups. By documenting quantitatively the features of a wide set of simulation models and the related intrinsic uncertainties affecting the simulation results, this analysis provides guidance regarding the use of the concerned simulation tools in experimental applications; it also provides indications for further experimental measurements addressing the sources of such uncertainties.Comment: To be published in IEEE Trans. Nucl. Sc

ORCSim: a generalized Organic Rankine cycle simulation tool

An increasing interest in organic Rankine cycle (ORC) technology has led to numerous simulation and optimization studies. In the open-literature different modeling approaches can be found, but general software tools available to the academic/industrial community are limited. A generalized ORC simulation tool, named ORCSim, is proposed in this paper. The framework is developed using object-oriented programming that easily allows improvements and future extensions. Currently two cycle configurations are implemented, i.e. a basic ORC and an ORC with liquid-flooded expansion. The software architecture, the thermo-physical property wrappers, the component library and the solution algorithm are discussed with particular emphasis on the ORC with liquid-flooded expansion. A thorough validation both at component and cycle levels is proposed by considering the aforementioned cycle architectures

A methodology for full-system power modeling in heterogeneous data centers

The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous data centers that overcomes those limitations. It derives a single model per platform, which works with high accuracy for heterogeneous applications with different patterns of resource usage and energy consumption, by systematically selecting a minimum set of resource usage indicators and extracting complex relations among them that capture the impact on energy consumption of all the resources in the system. We demonstrate our methodology by generating power models for heterogeneous platforms with very different power consumption profiles. Our validation experiments with real Cloud applications show that such models provide high accuracy (around 5% of average estimation error).This work is supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2015-65316-P, by the Gener- alitat de Catalunya under contract 2014-SGR-1051, and by the European Commission under FP7-SMARTCITIES-2013 contract 608679 (RenewIT) and FP7-ICT-2013-10 contracts 610874 (AS- CETiC) and 610456 (EuroServer).Peer ReviewedPostprint (author's final draft

Prediction of residual stresses in girth welded pipes using an artificial neural network approach

Management of operating nuclear power plants greatly relies on structural integrity assessments for safety critical pressure vessels and piping components. In the present work, residual stress profiles of girth welded austenitic stainless steel pipes are characterised using an artificial neural network approach. The network has been trained using residual stress data acquired from experimental measurements found in literature. The neural network predictions are validated using experimental measurements undertaken using neutron diffraction and the contour method. The approach can be used to predict through-wall distribution of residual stresses over a wide range of pipe geometries and welding parameters thereby finding potential applications in structural integrity assessment of austenitic stainless steel girth welds

A multiplex oligonucleotide ligation-PCR as a complementary tool for subtyping of Salmonella Typhimurium

Subtyping below the serovar level is essential for surveillance and outbreak detection and investigation of Salmonella enterica subsp. enterica serovar Typhimurium (S. Typhimurium) and its monophasic variant 1,4,[5],12:i:- (S. 1,4,[5],12:i:-), frequent causes of foodborne infections. In an attempt to overcome the intrinsic shortcomings of currently used subtyping techniques, a multiplex oligonucleotide ligation-PCR (MOL-PCR) assay was developed which combines different types of molecular markers in a high throughput microsphere suspension array. The 52 molecular markers include prophage genes, amplified fragment length polymorphism (AFLP) elements, Salmonella genomic island 1 (SGI1), allantoinase gene allB, MLVA locus STTR10, antibiotic resistance genes, single nucleotide polymorphisms (SNPs) and phase 2 flagellar gene fljB. The in vitro stability of these markers was confirmed in a serial passage experiment. The validation of the MOL-PCR assay for subtyping of S. Typhimurium and S. 1,4,[5],12:i:- on 519 isolates shows that the method is rapid, reproducible, flexible, accessible, easy to use and relatively inexpensive. Additionally, a 100 % typeability and a discriminatory power equivalent to that of phage typing were observed, and epidemiological concordance was assessed on isolates of 2 different outbreaks. Furthermore, a data analysis method is provided so that the MOL-PCR assay allows for objective, computerised data analysis and data interpretation of which the results can be easily exchanged between different laboratories in an international surveillance network

Validation and Verification of Aircraft Control Software for Control Improvement

Validation and Verification are important processes used to ensure software safety and reliability. The Cooper-Harper Aircraft Handling Qualities Rating is one of the techniques developed and used by NASA researchers to verify and validate control systems for aircrafts. Using the Validation and Verification result of controller software to improve controller\u27s performance will be one of the main objectives of this process. Real user feedback will be used to tune PI controller in order for it to perform better. The Cooper-Harper Aircraft Handling Qualities Rating can be used to justify the performance of the improved system