BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Current possibilities for performing licensing analyses for accidents in NPP

Within the licensing process of the Atucha-2 PHWR (Pressurized Heavy Water Reactor) the BEPU (Best Estimate Plus Uncertainty) approach has been selected for issuing of the Chapter 15 on FSAR (Final Safety Analysis Report). The key steps of the entire process are basically two: a) the selection of PIE (Postulated Initiating Events) and, b) the analysis by best estimate models supported by uncertainty evaluation. Otherwise, key elements of the approach are: 1) availability of qualified computational tools including suitable uncertainty method; 2) demonstration of quality; 3) acceptability and endorsement by the licensing authority. The effort of issuing Chapter 15 is completed and the safety margins available for the operation of the concerned NPP (Nuclear Power Plant) have been quantified

The BEPU Challenge in Current Licensing of Nuclear Power Reactors

Several ways can be adopted to define BEPU, one of this being “BEPU connection between SYS TH code development and V&V, Scaling, Uncertainty on the one side and licensing process on the other side.” Then, “BEPU is the application of SYS TH codes.” The BEPU process shall be associated with the licensing process. The licensing process noticeably includes the PSA together with a number of methods not discussed in the present book; those methods including PSA need a cross-link with BEPU. AA is part of the licensing process. Among the general attributes of AA, the first one shall be the compliance with the established regulatory requirements. The second attribute deals with the adequacy and the completeness of the selected spectrum of events. The achievement of the spectrum of events (or envelope) shall be the result of the combined applications of deterministic and probabilistic methods. The third attribute is connected with the knowledge base including that captured by the qualified computational tools and analytical procedures suitable for the analysis of transient conditions envisaged for individual (concerned) NPP. The complexity of a NPP and/or the accident scenarios may prove challenging for a conservative analysis, thus justifying the choice for a BEPU approach. This implies two main needs for nuclear thermal-hydraulics: (a) to adopt the current computational tools, proving (to the regulatory authority) an adequate quality via suitable V&V and (b) to adopt a qualified uncertainty method

ICONE12-49036 INVESTIGATION OF A POSSIBLE EMERGENCY PROCEDURE FOR THE VVER 1000 NPP IN CASE OF A TOTAL LOSS OF FEEDWATER AND A MAIN STEAM LINE BREAK NOMENCLATURE AFW Auxilliary Feed Water AM Accident Management BDBA Beyond Design Basis Accident BRU Bruzod

ABSTRACT Accident management procedures associated with nuclear power plant beyond design basis accidents should be developed with the aid of the more recent versions of advanced computational tools (best estimate codes) in order to verify the effectiveness of normal operation systems of the plant to avoid or minimize core damage. A. Madeira showed in her paper "A PWR Recovery Option for a Total Loss of Feedwater Beyond Design Basis Scenario" that it is possible to safely control a total loss of feedwater scenario in the Angra2 nuclear power plant, using two emergency procedures, namely the opening of the steam generator (SG) relief valves, and on the primary side the complete manual opening of all pressurizer relief and safety valves. This paper investigates the effectiveness of the procedure opening of the SG relief valves, followed by primary side feed and bleed for a generic VVER-1000 NPP in case of a total loss of feed water. The results indicate that the procedure is successful in reducing the primary side pressure and temperature to safe conditions, i.e. long term core cooling is achievable

Analysis of measured and calculated counterpart test data in PWR and VVER 1000 simulators

This paper presents an over view of the "scaling strategy", in particular the role played by the counter part test methodology. The recent studies dealing with a scaling analysis in light water reactor with special regard to the VVER 1000 Russian reactor type are presented to demonstrate the phenomena important for scaling. The adopted scaling approach is based on the selection of a few characteristic parameters chosen by taking into account their relevance in the behavior of the transient. The adopted computer code used is RELAP5/Mod3.3 and its accuracy has been demonstrated by qualitative and quantitative evaluation. Comparing experimental data, it was found that the investigated facilities showed similar behavior concerning the time trends, and that the same thermal hydraulic phenomena on a qualitative level could be predicted. The main results are: PSB and LOBI main parameters have similar trends. This fact is the confirmation of the validity of the adopted scaling approach and it shows that PWR and VVER reactor type behavior is very similar. No new phenomena occurred during the counter part test, despite the fact that the two facilities had a different lay out, and the already known phenomena were predicted correctly by the code. The code capability and accuracy are scale-independent. Both character is tics are necessary to permit the full scale calculation with the aim of nuclear power plant behavior prediction.

F. D'Auria, et al.: Analysis of Mea sured and Cal cu lated Coun ter part Test Data... 3 ANALYSIS OF MEA SURED AND CAL CU LATED COUN TER PART TEST DATA IN PWR AND VVER 1000 SIM U LA TORS by

This pa per pres ents an over view of the “scal ing strat egy”, in par tic u lar the role played by the coun ter part test meth od ol ogy. The re cent stud ies deal ing with a scal ing anal y sis in light wa ter reactor with spe cial re gard to the VVER 1000 Rus sian re ac tor type are pre sented to dem on strate the phe nom ena im por tant for scal ing. The adopted scal ing ap proach is based on the se lec tion of a few char ac ter is tic pa ram e ters cho sen by tak ing into ac count their rel e vance in the be hav ior of the tran sient. The adopted com puter code used is RELAP5/Mod3.3 and its ac cu racy has been dem on strated by qual i ta tive and quan ti ta tive eval u a tion. Com par ing ex per i men tal data, it was found that the in ves ti gated fa cil i ties showed sim i lar be hav ior con cern ing the time trends, and that the same ther mal hy drau lic phe-nom ena on a qual i ta tive level could be pre dicted. The main re sults are: PSB and LOBI main pa ram e ters have sim i lar trends. This fact is the con fir ma tion of the va lid ity of the adopted scal ing ap proach and it shows that PWR and VVER re ac tor type be hav ior is very sim i lar. No new phe nom ena oc curred dur ing the coun ter part test, de spite the fact that the two fa cil i ties had a dif fer ent lay out, and the al ready known phe nom ena were pre dicted cor rectly by the code. The code ca pa bil ity and ac cu racy are scale-in de pend-ent. Both char ac ter is tics are nec es sary to per mit the full scale cal cu la tion with the aim of nu clear power plant be hav ior pre dic tion. Key words: nu clear re ac tor safety, scal ing anal y sis, VVER re ac tor, RELAP

Optimizing the Initial Pressure of Accumulators for the Atucha-2 Nuclear Power Plant using an Optimization Method

Accumulators (ACC) constitute passive systems essential part of the Emergency Core Cooling Systems (ECCS) of any water cooled and moderated reactor. Key design parameters for the ACC are the pressure and the volume; in this last case liquid and gas volumes are distinguished. In relation to pressure: a high initial value brings to early ACC intervention in case of Loss of Coolant Accident (LOCA), increasing the ACC fluid mass lost to the break; a low initial value brings to late actuation and danger of high rod surface temperature at the time of actuation. The optimized design value of initial pressure is more difficult to be attained in case of Small Break LOCA because of the several possible accident scenarios. A procedure based on ‘analytical optimization methods’ has been set up and tested to identify the best initial pressure for the Atucha-II Nuclear Power Plant under final stage of construction in Argentina

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License