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Enregistrement W6925986438 · doi:10.20381/ruor-28116

Swelling, Thermal, and Hydraulic Properties of a Bentonite-Sand Barrier in a Deep Geological Repository for Radioactive Wastes: Effect of Groundwater Chemistry, Temperature and Physical Factors

2022· article· en· W6925986438 sur OpenAlexaboutno aff

Notice bibliographique

RevueuO Research (University of Ottawa) · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueMicrobial Natural Products and Biosynthesis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRadioactive wasteBentoniteNuclear powerGroundwaterHydraulic conductivitySedimentary rockWaste disposalHigh-level wasteSpent nuclear fuel

Résumé

récupéré en direct d'OpenAlex

Electricity generation at nuclear power plants produces a large amount of high-level radioactive waste (HLW) every year, which has long-term detrimental effects on humans and the environment. Other applications of nuclear technology (e.g., medicine, research, nuclear weapons, industry) also produce radioactive waste (e.g., low-level radioactive waste, LLW, Intermediate-level waste, ILW). The potential of deep geological repositories (DGRs) as an option for disposal of radioactive waste (HLW, ILW, LLW) has been examined in several countries, including Bulgaria, Canada, China, Finland, France, Germany, India, Japan, Russia, Spain, Sweden, Switzerland, Ukraine and the United Kingdom and are still under discussion. In Ontario, Canada, DGRs with a multi-barrier system comprised of a sedimentary rock formation (i.e., a natural barrier) and an engineered barrier system (EBS) are currently under consideration. An EBS consists of various components, such as waste containers, buffer, backfill, and tunnel sealing materials, intended to prevent the release of radionuclides. Several engineered barrier materials, including a mixture of bentonite and sand, are currently being considered for use in DGRs for nuclear waste in Ontario. Bentonite has some advantageous physical and chemical properties, such as low permeability, high plasticity, and high swelling potential, which provide it with a good sealing ability and thus make it an effective barrier. However, interaction between the compacted bentonite–sand mixture and underground water chemistry fluids (chemical factor) in the DGR could significantly alter the favourable properties of bentonite (e.g., swelling potential), thus influencing its performance when used in an EBS and eventually jeopardizing the overall safety of DGRs. In addition, other parameters, such as the clay content, initial dry density and moisture content of the compacted barrier (physical factors), as well as the presence of salts in groundwater may affect the physical and physiochemical properties of barrier materials. Moreover, during the lifetime of a DGR for used spent fuel, the bentonite–based barrier material will not only be exposed to a broad range of groundwaters with different chemical compositions, but also to high temperatures (heat generated by the nuclear wastes) (thermal factor). Thus, the interaction between the compacted bentonite–sand mixture, the surrounding groundwater and the heat from the nuclear waste material could jeopardize the favourable properties of the bentonite-based (bentonite-sand) barrier material. Properties of a bentonite-sand barrier is an important characteristic to study while designing and constructing an EBS for a DGR. Thus, to understand and assess the operations of DGRs in Ontario, comprehensive studies must be performed on engineering properties like swelling behaviour, permeability, and thermal conductivity. The goal of this research study is to experimentally investigate the physical, chemical and thermal factors that influencing the engineering properties of a barrier material made up of bentonite-sand composite used in DGRs for nuclear waste in Ontario. Compacted samples are subjected to one-dimensional free swell test to understand the swelling behaviour of the material. Hydraulic conductivity was investigated using a flexible wall permeability test. Thermal conductivity and diffusivity were tested using Decangon KD2 Pro with TR-1 and and KS-1 sensors. The specimens contain different bentonite–sand mixture ratios (20:80, 30:70, 50:50, and 70:30 dry mass), and they are tested under conditions with differing bentonite content, dry density, groundwater chemistry, and temperature. Additional tests were conducted to investigate the microstructure of the specimens. These tests include X-ray diffraction (XRD) analysis, mercury intrusion porosimetry (MIP), and thermogravimetric analyses (TG/DTG). The results reveal that the time and strain required to achieve maximum swelling of compacted bentonite–sand specimens increase with the increase of initial dry density. The simulated saline solutions of Guelph and Trenton groundwater are found to suppress the swelling of the bentonite–sand specimens. This in turn leads to the increase of hydraulic conductivity and decrease of thermal properties of the barrier material. However, the impact of the salinity is significantly reduced by increasing the dry densities and sand content of the compacted material. Moreover, the coupled effect of salinity and temperature decreases the swelling potential of the bentonite-sand mixture. Also, some transformation of Na-montmorillonite into Ca-Montmorillonite was observed. The results also indicate that some montmorillonites might have been transformed into illites, thereby further decreasing the swelling potential of the bentonite-based barrier.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,017

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,020
Tête enseignante GPT0,245
Écart entre enseignants0,226 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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