SearcharxivSearch

arXiv · 2606.27524

Quantitative interpretation of Brookfield DV3TLV measurements: shear rate conversion, correction factors, and applicability limits

Abstract

The flow behavior and hydrodynamic characteristics of fluids in rotational viscometry systems are investigated using the Brookfield DV3TLV viscometer, with emphasis on measurement reliability and applicability limits of different measuring geometries. The results are compared and validated using the high-precision MCR 302 rheometer manufactured by the Austrian company Anton Paar. Both Newtonian (water and glycerol) and non-Newtonian fluids (guar-based gels), exhibiting fundamentally different viscosity-shear rate behavior, were included in the study. Based on the comparison of measurements obtained with the Brookfield DV3TLV viscometer and the MCR 302 rheometer, empirical coefficients were determined that relate the spindle rotational speed to the shear rate, taking into account the geometry of the measuring systems. Analysis of the Reynolds number range showed that laminar flow conditions were maintained for all measurement systems, which justifies the application of quasi-static models that neglect possible flow turbulence within them. Comparison with high-precision measurements performed on the MCR 302 rheometer showed that, with appropriate interpretation, the data obtained using the Brookfield instrument can be used to estimate the real viscosity of process fluids with an accuracy specific to each geometry and its operating conditions. The proposed methodology enables reliable characterization of flow properties in rotational systems and can be applied in engineering practice and laboratory analysis of complex fluids, especially at oil and food production facilities where high-end rheometers are unavailable or impractical to use. The study is formulated within the framework of experimental fluid mechanics and non-Newtonian flow characterization.

Explore related subjects

Keep this discovery

BibTeXRIS

A. E. Vasiliev, A. S. Besov, D. O. Andreev. 2026-06-25. Quantitative interpretation of Brookfield DV3TLV measurements: shear rate conversion, correction factors, and applicability limits. https://arxiv.org/abs/2606.27524

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Holistic law of aftershocks

The paper is devoted to the phenomenological theory of aftershocks occurring in the source of a tectonic earthquake following the main shock. The theory was developed by the author jointly with A.D. Zavyalov and O.D. Zotov during the course of a long-term study of aftershocks. The theory is based on the concepts of source deactivation and the source's proper time. The holistic law governing the decay of aftershock activity over proper time follows from the theory. The damping decrement is equal to the source deactivation coefficient. The main focus of this paper is the analysis of the logical structure of the theory. The paper also contains a brief description of the experimental results obtained using the theory. Keywords: earthquake source, aftershocks, Omori's law, Utsu's law, deactivation coefficient, proper time, underground clock, foreshock convergence, aftershock divergence.

physics.geo-ph

Bayesian deep learning integration of geophysical and drilling data for 3D prediction of copper mineralization and drill targeting: a case study from the Kogodai prospect, Rudny Altai

Exploration drill targeting in structurally complex terranes is hindered by sparse sampling, heterogeneous datasets, and the ambiguity of geophysical inversions. Here, we present an uncertainty-aware 3D workflow for the acceleration of time-to-discovery in brownfield explorations and apply it to the Kogodai prospect in the Rudny Altai metallogenic province. We jointly analyse existing drilling and geophysical data in a comprehensive approach, revealing hidden patterns in already available data. Drillholes and trenches were desurveyed to a common 3D reference frame, and assays were composited to a consistent spatial support to facilitate joint modelling with geophysical inputs. We develop Bayesian deep-learning models to predict 3D fields of Cu grade together with chargeability and apparent resistivity while quantifying epistemic uncertainty via Monte Carlo sampling. The original contribution of this work is to treat the problem not as pointwise regression between co-located observations, but as joint learning of spatially continuous 3D fields from sparse, heterogeneous exploration evidence. The resulting 3D predictions delineate a principal mineralized trend and several localized candidate zones that coincide with elevated induced polarization (IP) responses, while uncertainty mapping highlights where predictions are robust versus where additional drilling would be most informative. The continuous Cu-grade field can also be thresholded to produce binary prospectivity maps, allowing the sensitivity of target delineation to the chosen cutoff to be evaluated. The outputs are intended for qualitative interpretation and risk-aware drill targeting rather than resource estimation, and we discuss key limitations arising from incomplete provenance metadata for geophysical products and heterogeneity of historical sampling.

physics.geo-ph

PyelogP: Automated Energy-Based Determination of Preconsolidation Pressure in Clay Deposits

Estimating the preconsolidation pressure ($\sigma'_p$) from one-dimensional consolidation (oedometer) tests is critical in geotechnical engineering for settlement analysis. Traditional graphical methods, such as the Casagrande procedure, may introduce uncertainties, particularly when interpreting rounded $e$-log($P$) curves typical of disturbed specimens of soft clays and silt deposits. This paper introduces PyelogP, an open-source Python library designed to calculate $\sigma'_p$ using the strain-energy method proposed by Becker et al. (1987) as an automated and reproducible alternative. The algorithm combines natural cubic spline interpolation, knee-point detection via the Kneedle algorithm, and split-point linear regression within the work-pressure space. Physically informed thresholds, including overconsolidation ratio limits and second-derivative maxima (${d^2 e}/{d(\log \sigma')^2}$), are incorporated to establish pre-yield and post-yield fitting boundaries. The performance of PyelogP is evaluated against a suite of 22 experimental consolidation datasets covering various clay deposits, including Saint-Alban clay and San Francisco Old Bay Clay. The results demonstrate strong agreement with the published values ($R^2$ = 0.912, RMSE = 0.374, MBE = -0.080), while the $O(N^2)$ algorithm requires only a few milliseconds per curve for typical oedometer datasets and less than 150 milliseconds for the largest datasets.

physics.geo-ph