Practical Modeling Approach Research Articles

Evaluation of a practical approach for field scale moisture flow modeling in heterogeneous media at a semiarid site

AbstractA practical approach for modeling field‐scale moisture flow in a highly heterogeneous unsaturated medium is described in this study. The validity of this approach is demonstrated through comparison of the numerical simulations with field observations at a semiarid site located in southcentral Washington State. The methodology is based on upscaling the core scale hydraulic properties and combining power‐law and tensorial connectivity‐tortuosity (PA‐TCT) approaches to derive macroscopic anisotropy parameters for each hydrostratigraphic unit (HSU) identified in the field. Each heterogeneous HSU is approximated by an equivalent homogeneous medium (EHM) model for which PA‐TCT parameters are used in the flow simulations. The available field data on moisture content and matric potential are compared with steady‐state flow simulations based on the mean form of Richards' equation. While the homogenization or averaging of heterogeneities, embedded in the EHM modeling approximation, cannot capture all of the field‐scale variability, the simulated steady‐state moisture and matric potential profiles capture well the central tendency of the field data. This approach is deemed practical for assessing the fate and transport of contaminants in highly heterogeneous unsaturated media at the transport scale of hundreds of meters.

Flexure Performance of Textile-Reinforced Cementitious Composites with Novel Inclined Reinforcements.

Textile-reinforced cementitious composites have great potential to offer novel design opportunities for thin-section structures thanks to their superior material capabilities. In this work, new cementitious composites with novel reinforcement configurations are developed, which have superior mechanical properties. The cementitious composites contain inclined through-the-thickness reinforcements, and their enhanced performance on thin-section material hardening under flexural loading is demonstrated. Furthermore, a new practical FE modeling approach is proposed that involves the combined use of multiple cohesive regions and 1D reinforcement elements that pass through these regions with a bilinear material law. This approach provides a new computationally efficient modelling framework whereby reinforcement pull-out during hardening is readily captured without resorting to computationally demanding interface laws between the reinforcement and the cementititous matrix. The model can model enhanced hardening of new configurations and provides comparable results with the experimental findings. The model can be used in the modelling and design of novel cementitious composites with engineered reinforcement configurations. Overall, this study aims to open up new avenues for the smart material design of cementitious composites with novel structural reinforcements.

Subsidence associated with dewatering and gas extraction from coal seams: Contribution of desorption-induced coal shrinkage

Several coal seams are contained within the formations of Great Artesian Basin (GAB), the largest natural underground water reservoir in Australia and the world. The extraction of coal seam gas (CSG) and its associated water from thousands of wells within the coal measures of the GAB has led to tens of millimetres of subsidence in CSG development areas. Since highly developed farming systems located in these areas rely on very low slope landforms, even this scale of subsidence has caused significant community concern about the potential for CSG extraction to impair farming operations and productivity through changes in land slope and drainage. Coal seam compaction associated with dewatering and gas extraction has two key components: poromechanical compaction and desorption-induced bulk shrinkage. The former results from pore pressure depletion (due to dewatering and gas extraction) and an increase of vertical effective stress, while the latter is induced by gas desorption from the coal matrix, leading to further deformation. While poromechanical compaction of fluid-bearing formations has been extensively addressed in the literature, relatively little research has been conducted on the role of coal shrinkage in CSG-induced subsidence. This paper introduces an innovative practical modelling approach for assessing CSG-induced subsidence at a subregional/regional scale. The approach utilises an analytical model for CSG-induced subsidence derived from constitutive stress–strain relations for poroelastic-sorptive media. This is a novel approach in the context of CSG-induced subsidence which considers both poromechanical compaction and desorption-induced shrinkage. A further distinction to previous work is the integration of the geomechanical-sorptive subsidence model with a numerical groundwater model. Based on this approach, this paper examines the effect of coal shrinkage on subsidence, and its proportions with respect to total compaction for one of the major coal measures in the Surat Basin. Input data are derived from three-dimensional geological, geomechanical, and groundwater models, as well as methane adsorption and desorption reports. Results show that (a) the impact of coal shrinkage on CSG-induced subsidence is likely to be significant in the study area and (b) the contribution of coal shrinkage to CSG-induced subsidence depends on gas content, Langmuir isotherm, shrinkage strain parameters, and the saturation state of coal. This study provides important insights into CSG-induced subsidence and lays the foundation for the development of robust, efficient, and localised predictive models to support environmental impact assessment and management.

Evaluation of Ground Pressure, Bearing Capacity, and Sinkage in Rigid-Flexible Tracked Vehicles on Characterized Terrain in Laboratory Conditions.

Trafficability gives tracked vehicles adaptability, stability, and propulsion for various purposes, including deep-sea research in rough terrain. Terrain characteristics affect tracked vehicle mobility. This paper investigates the soil mechanical interaction dynamics between rubber-tracked vehicles and sedimental soils through controlled laboratory-simulated experiments. Focusing on Bentonite and Diatom sedimental soils, which possess distinct shear properties from typical land soils, the study employs innovative user-written subroutines to characterize mechanical models linked to the RecurDyn simulation environment. The experiment is centered around a dual-tracked crawler, which in itself represents a fully independent vehicle. A new three-dimensional multi-body dynamic simulation model of the tracked vehicle is developed, integrating the moist terrain's mechanical model. Simulations assess the vehicle's trafficability and performance, revealing optimal slip ratios for maximum traction force. Additionally, a mathematical model evaluates the vehicle's tractive trafficability based on slip ratio and primary design parameters. The study offers valuable insights and a practical simulation modeling approach for assessing trafficability, predicting locomotion, optimizing design, and controlling the motion of tracked vehicles across diverse moist terrain conditions. The focus is on the critical factors influencing the mobility of tracked vehicles, precisely the sinkage speed and its relationship with pressure. The study introduces a rubber-tracked vehicle, pressure, and moisture sensors to monitor pressure sinkage and moisture, evaluating cohesive soils (Bentonite/Diatom) in combination with sand and gravel mixtures. Findings reveal that higher moisture content in Bentonite correlates with increased track slippage and sinkage, contrasting with Diatom's notable compaction and sinkage characteristics. This research enhances precision in terrain assessment, improves tracked vehicle design, and advances terrain mechanics comprehension for off-road exploration, offering valuable insights for vehicle design practices and exploration endeavors.

Forecasting acute rainfall driven E. coli impacts in inland rivers based on sewer monitoring and field runoff

Surface water quality is frequently impacted by acute rainfall driven pollutant sources such as sewer overflows. Understanding the risk of exposure from faecal pollution from short term impacts is challenging due to a paucity of high-resolution data from river systems. This paper proposes practical modelling approach for forecasting arrival time and durations of elevated E. coli levels based on hydrological routing of catchment source loadings, characterized by distributed and remote sensing techniques (including sewer overflow monitoring). The model is calibrated and validated using new high resolution E. coli datasets from a UK catchment featuring both diffuse field runoff and storm overflow impacts. Hourly/Bihourly sampling of E. coli was undertaken in the river following different rainfall events across a range of seasonal conditions. The model provides a good estimate of arrival times and durations of elevated E. coli periods following rainfall events. Model simulations suggest that key sources in the catchment are event specific, with sewer overflow spills being more significant following short, intense rainfall events.

A practical approach for numerical modeling of a complex and data-limited hydrological system

Abstract By their nature, wetlands represent an ecosystem base for many concurrent heterogeneous interactions, where the mission of numerical modeling requires a wide range of consistent and reliable datasets from various sources, spatially and temporally. Such a mission usually collides with the existence of tremendous missing in time-series dataset(s). In this context, MIKE SHE was used to construct an integrated surface–subsurface flow model for the Paya Indah wetland in Malaysia where huge gaps exist in the historical datasets of water level and flow rate. To calibrate and validate the model to a satisfactory level, a tri-criteria simulation approach was applied to overcome the occasional missing values in these datasets. This goal was accomplished by calibrating the surface water level and channel flow while simultaneously matching the steady-state subsurface portion of the system wherever water table depth data allowed. Quantitatively, the integrated model scored the highest values of R (0.765 − 0.927) and CE (0.748 − 0.828) during the validation. However, large RMSE values were calculated for the flow rate during calibration at SWL2 (outlet; 0.766) and during validation at Langat River (0.780). This bias was attributed to the low or occasional absence of variation in the historical time-series datasets necessary for the simulation process.

A Practical Approach for Biochemical Modeling in the CFD Evaluation of Novel Anaerobic Digester Concepts for Biogas Production

The detailed physics-based description of anaerobic digesters is characterized by their multiscale and multiphysics nature, with Computational Fluid Dynamics (CFD) simulations being the most comprehensive approach. In practice, difficulties in obtaining a detailed characterization of the involved biochemical reactions hinder its application in the design of novel reactor concepts, where all physics interplays in the reactor must be considered. To solve this limitation, a practical approach is introduced where a calibration step using actual process data was applied for the simplified biochemical reactions involved, allowing us to efficiently manage uncertainties arising when characterizing biochemical reactions with lab scale facilities. A complete CFD modeling approach is proposed for the anaerobic digestion of wastewater, including heat transfer and multiphasic flow. The proposed multiphase model was verified using reference data and, jointly with the biochemical modeling approach, applied to a lab-scale non-conventional anaerobic digester for winery wastewater treatment. The results showed qualitative improvement in predicting methane production when the diameter of the particles was reduced, since larger particles tend to move downwards. The biochemistry of the process could be simplified introducing a preexponential factor of 380 (kmol/m3)(1 – n)/s for each considered chemical reaction. In general, the proposed approach can be used to overcome limitations when using CFD to scale-up optimization of non-conventional reactors involving biochemical reactions.

Optimal intensity measure selection and probabilistic seismic demand model of pile group supported bridges in sandy soil considering variable scour effects

Scour of pile group foundations is a common hazard for cross-river bridges and can produce considerable damage to piles in earthquake-prone regions. The fragility-based performance assessment for pile group supported bridges subjected to earthquake and scour hazards has drawn increasing attention from engineering practice and academic communities. Such assessment requires numerical models that are computationally efficient and accurate in describing the mechanical behavior associated with the complex soil-pile interaction of these soil-bridge systems. Additionally, selecting an optimal ground motion intensity measure (IM) is generally required for such assessments. However, modeling the pile group effect for seismically excited pile groups and the optimal IM for the scoured bridges have not been well addressed in previous related studies. To this end, a practical pile group effect modeling approach is adopted in this study and validated by a set of shake table tests on scoured pile group bridge available in the literature. Based on the validated numerical modeling approach for the soil pile interaction, a series of coupled soil-bridge models are generated considering the structural and soil parameter uncertainty and analyzed in the OpenSees platform. The optimal IM for various engineering demand parameters (EDPs) is identified from thirty-nine scalar IM candidates through the criteria of efficiency, practicality, sufficiency, and hazard computability. Finally, a straightforward form of probabilistic seismic demand model (PSDM), incorporating the effect of scour depth changes, is developed for pile group supported bridges. Results show that the increase of scour depth significantly increases the curvature demand of a pile group and statistically reduces the embedded depth of the belowground plastic hinge of the pile shaft. The rank of optimal IM candidates directly depends on the selection criteria of an optimal IM and the selection of EDP. In addition, the scour depth slightly affects the rank of optimal IMs. Generally, VSI, PGV, and Sv,1.0 are recommended for the probabilistic seismic demand analysis of pile group supported bridges in sandy soils with scour potentials.

SMA-braced steel frames influenced by temperature: Practical modelling strategy and probabilistic performance assessment

This study presents a practical and efficient modelling approach for shape memory alloy (SMA)-braced steel frames under various temperatures, and examines their probabilistic performance affected by temperature. A one-dimensional constitutive model is developed in OpenSees to capture the superelasticity (SE), partial SE, shape memory effect (SME), and permanent yielding behaviors of SMA elements subjected to varying temperatures. The proposed modelling approach is partly validated against an experimental study newly conducted in the present work. The material-level investigation is followed by a comprehensive probabilistic performance assessment of a typical steel frame employing SMA-based self-centering braces (SMA-SCBs), where a practical and realistic modelling strategy for the SMA-SCB is developed to capture the degradation behavior, especially at low temperatures. Seismic risk assessment is conducted to evaluate the collapse resistance and resilience performance of the considered structures, with the influence of temperature highlighted. Among other important findings, it is shown that the decrease in environment temperature significantly increases the peak inter-story drift (PID) and residual inter-story drift (RID) responses. Under the maximum considered earthquake (MCE), the probability of collapse increases from 2.7% to 19.7% when the temperature decreases from 30 °C to −15 °C. From a self-centering capability point of view, the probabilistic of exceedance of 0.5% RID threshold soars from negligible to nearly 100%, considering the same temperature variation. The study highlights the fact that temperature plays a significant role in the annual rate of exceedance of all damage states, and warns that the idealized flag-shaped modelling approach for SMA components is extremely unsafe at low temperatures.

A practical multivariate approach to testing volatility spillover

We propose an asymptotic N(0,1) inferential strategy to test for volatility spillover between markets consisting of multiple sectors. First, we use nonparametric kernel method to derive test statistics that assign flexible weight to each lag order and are able to check a growing number of lags as the sample size increases. Second, we propose a practical multivariate volatility modeling approach — which enjoys estimation consistency and simplicity — to facilitate higher dimensional spillover testing. Simulations show the reasonable finite sample performance of the proposed econometric strategy in a relatively large system. An empirical application highlights the merits of the proposed approach.

A practical environment potential field modelling method for complex geometric objects

AbstractSeveral studies have been conducted on collision avoidance (CA) and path planning for maritime autonomous surface ships (MASS) based on artificial potential field (APF) and electronic navigation chart (ENC) data. However, to date, accurate, highly efficient, and automatic modelling of complicated geometry environment potential fields (EPFs) has not been realised. In this study, an accurate EPF model is established using ENC data to describe different types of obstacles, navigable areas, and non-navigable areas. The implicit equations of complex polygons are constructed based on the R-function theory, and the discrete-convex hull method is introduced to realise the automatic modelling of EPF. Moreover, collaborative CA and obstacle avoidance (OA) experiments are designed and conducted in a simulated environment and based on the ENC data. The results show that the proposed EPF modelling method is accurate, reliable, and time-efficient even with numerous ENC data and complex shapes owing to the R-function representation for geometric objects and discrete-convex hull method. The combination of improved APF and EPF models is proven to be effective for CA and OA. This paper presents a practical EPF modelling approach for APF-based ship path planning.

Cyclic behavior of non-seismically detailed full-scale exterior beam-column subassemblies with setback in columns

Discontinuity in cross-section depth may exist in RC columns. If present in a non-seismically detailed frame, such a discontinuity (termed herein setback in columns, SBC) may trigger unexpected seismic damage. However, this typology of irregularity within connections has long been overlooked. This paper offers initial experimental evidence showing how vulnerable these irregular, non-conforming RC connections would be under earthquake-like loading. Four full-scale exterior beam-column subassemblies with SBC were constructed and cyclically tested, including three “as-built” units and a control one improved with seismic detailing. Results indicated that all the units succumbed to joint shear failure, and exhibited distinctly asymmetric responses in different loading directions. Particularly, when subjected to a positive (sagging) beam moment, the “as-built” units displayed a very steep joint cracking pattern and, hence, remarkably lower peak loads as compared to the case where a negative (hogging) beam moment was applied. Possible causes inducing such a salient failure mode are discussed, and a detailed account of the cyclic behaviors of the four units presented. Moreover, based on the concept of wide and narrow joint panels, existing major code provisions and analytical procedures for regular connections were adapted to evaluate the joint shear capacity of the current irregular subassemblies. A practical modeling approach was also developed through ABAQUS user-defined subroutine for capturing the observed global hysteretic responses. Overall, the outcomes of this study highlight the vulnerability of non-seismically detailed exterior connections with SBC, and provide a reference for future related research.

Survivability and Disaster Recovery Modeling of Cellular Networks Using Matrix Exponential Distributions

Cellular network design must incorporate disaster response and repair scenarios. Requirements for high reliability and low latency often fail to incorporate network survivability for mission critical services. This paper defines a practical modeling approach using a Markov chain Matrix Exponential (ME) model. Transient and the steady state representations of system repair models, namely, fast and slow (i.e., crew-based) repairs for networks consisting of a multiple repair crews have been analyzed. Failures are exponentially modeled as per common practice, but ME distributions describe the more complex recovery processes. The model used in this paper is evaluated for varying number of repair crews, base stations, repair models and squared coefficient of variation values. This model is scalable for larger networks, calculates the restoration and network availability times, consists of asymptotic approximations to estimate network availability and determines the optimal number repair crews required. This ME model shows how survivable networks can be designed based on controlling numbers of crews, times taken for individual repair stages in the repair process, and the balance between fast and slow repairs.

Personalized 0D-1D multiscale hemodynamic modeling and wave dynamics analysis of cerebral circulation for an elderly patient with dementia.

Senile dementia is associated with pronounced alterations in cerebral circulation. A fundamental understanding of intracranial hemodynamics and wave dynamics is essential for assessing dementia risk. Recent findings suggest that higher carotid artery wave intensity (WI) can predict future cognitive impairments in the elderly. However, wave power (WP) is more advantageous for assessing the risk of cognitive impairment and dementia because of its conservative form, which allows quantification of detailed WP distribution among the entire cerebrovascular network. Unfortunately, intracranial hemodynamics and wave dynamics in elderly patients with dementia remain poorly understood due to ethical issues and technical challenges. In this paper, we proposed a novel and easily achievable personalized methodology for the 0D-1D model of cerebral circulation using widely available clinical data on transcranial Doppler ultrasonography velocity, cerebral artery anatomy from magnetic resonance imaging, and brachial artery pressure. Using the proposed model, we simulated the cerebral blood flows and compared the wave dynamics between a healthy elderly subject and one living with dementia. Moreover, we performed a variance-based global sensitivity analysis to quantify the model-predicted WI and WP sensitivity to the uncertainties of model inputs. This provided more precise information for model personalization and further insights into the wave dynamics of cerebral circulation. In conclusion, the proposed personalized model framework provides a practical approach for patient-specific modeling and WI/WP analysis of cerebral circulation through noninvasive clinical data. The wave dynamics features of higher WI and lower WP in cerebral arteries may be an invaluable biomarker for assessing dementia risk.

A Practical Modeling Approach using Room and Pillar Mining Method for the Exploitation of Ankpa Coal Deposit

A Practical Modeling Approach using Room and Pillar Mining Method for the Exploitation of Ankpa Coal Deposit

Estimating wildfire fuel consumption with multitemporal airborne laser scanning data and demonstrating linkage with MODIS-derived fire radiative energy

Characterizing pre- and post-fire fuels remains a key challenge for estimating biomass consumption and carbon emissions from wildfires. Airborne laser scanning (ALS) data have demonstrated effectiveness for estimating canopy, and to a lesser degree, surface fuel components at fine-scale (i.e., 30 m) across landscapes. Using pre- and post-fire ALS data and corresponding field data, this study estimated consumption of canopy fuel (ΔCF), understory fuel (ΔUF), total fuel (ΔTF), and canopy bulk density (ΔCBD) for the 2012 Pole Creek fire in Oregon, USA (10,760 ha), and portions of the 2011 Las Conchas fire in New Mexico, USA (4,934 ha). Additionally, the feasibility of predicting fuel consumption was tested using separate pre- and post-fire models (PrePost), models combining all pre- and post-fire data (Pooled), and models using all data from both fires (Global). Estimates of ΔTF were then compared to fire radiative energy (FRE, units: MJ) derived from Fire Radiative Power (FRP, units: MW) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA Terra and Aqua satellites to mechanistically derive a biomass combustion coefficient (BCC, units: kg MJ−1). The PrePost and Pooled approaches yielded similar results at Las Conchas, but at Pole Creek insufficient pre-fire field data resulted in erroneous fuel consumption estimates outside the fire perimeter using the PrePost models. These results demonstrated that pre-fire field data were less important for these models than having field data which represent the full range of fuel conditions likely to exist across the landscape. Estimated total biomass consumed for the PrePost, Pooled, and Global models were 226 Gg, 224 Gg, and 224 Gg at Las Conchas, and 581 Gg, 713 Gg, and 552 Gg at Pole Creek. Comparisons between estimated ΔTF and FRE yielded an average BCC for both fires of 0.367 (s.d. ± 0.049) kg MJ−1 based on pixels with at least five MODIS observations. Both higher MODIS observations per pixel and accounting for canopy occlusion of FRE improved the relationship between ΔTF and MODIS-FRE. This study suggested a practical modelling approach for future efforts using only post-fire field observations and quantified a landscape-scale relationship between MODIS-derived FRE and fine-scale fuel consumption consistent with prior experiments.

Modeling Indoor Particulate Matter and Small Ion Concentration Relationship—A Comparison of a Balance Equation Approach and Data Driven Approach

In this work we explore the relationship between particulate matter (PM) and small ion (SI) concentration in a typical indoor elementary school environment. A range of important air quality parameters (radon, PM, SI, temperature, humidity) were measured in two elementary schools located in urban background and suburban area in Belgrade city, Serbia. We focus on an interplay between concentrations of radon, small ions (SI) and particulate matter (PM) and for this purpose, we utilize two approaches. The first approach is based on a balance equation which is used to derive approximate relation between concentration of small ions and particulate matter. The form of the obtained relation suggests physics based linear regression modelling. The second approach is more data driven and utilizes machine learning techniques, and in this approach, we develop a more complex statistical model. This paper attempts to put together these two methods into a practical statistical modelling approach that would be more useful than either approach alone. The artificial neural network model enabled prediction of small ion concentration based on radon and particulate matter measurements. Models achieved median absolute error of about 40 ions/cm3 and explained variance of about 0.7. This could potentially enable more simple measurement campaigns, where a smaller number of parameters would be measured, but still allowing for similar insights.

Joint design of control chart, production and maintenance policy for unreliable manufacturing systems

PurposeThe purpose of this study is to develop a joint production, maintenance and quality control strategy involving a periodic preventive maintenance policy.Design/methodology/approachThe proposed integrated policy is defined and modeled mathematically.FindingsThe paper focuses on finding simultaneously the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval and the control chart limits, such that the expected total cost per time unit is minimized.Practical implicationsThe paper attempts to integrate in a single model the three main aspects of any manufacturing system: production, maintenance and quality. The considered system consists of one machine subject to a degradation process that directly affects the quality of products. The process and product quality control is carried out using an “x-bar” control chart. In the proposed model, a preventive maintenance action is performed every inspections of product quality in order to reduce the shift rate to the “out-of-control” state. A corrective maintenance action is undertaken once the control limits are exceeded. In order to palliate perturbations caused by the stopping of the machine to undergo maintenance actions, a buffer stock is built up to ensure the continuous supply of the subsequent machine. The main goal of this work is to develop a model that captures the underlying link between the preventive maintenance policy, the buffer stock size and the parameters of an “x-bar” control chart used to control the quality of the product. Numerical experiments and a study of the effects of the input parameters variation on the obtained results are performed.Originality/valueThe existing models that simultaneously consider maintenance, inventory and control charts consist of a condition-based maintenance (CBM) policy. Periodic preventive maintenance (PM) has not been considered in such models. The proposed integrated model is original, in that it links production through buffer stocks, quality through a control chart and maintenance through periodic preventive maintenance (different practical settings and modeling approach than when CBM is used). Hence, this paper addresses practical situations where, for economic or technical reasons, only systematic periodic preventive maintenance is possible.

Simple Contact Stiffness Model Validation for Tie Bolt Rotor Design With Butt Joints and Pilot Fits

Abstract Tie bolt rotors for centrifugal compressors comprise multiple shaft components that are held together by a single tie bolt. The axial connections of these rotors—including butt joints, Hirth couplings, and Curvic couplings—exhibit a contact stiffness effect, which tends to lower the shaft bending frequencies compared to geometrically identical monolithic shafts. If not accounted for in the design stage, shaft bending critical speed margins can be compromised after a rotor is built. A previous paper had investigated the effect of tie bolt force on the bending stiffness of stacked rotor assemblies with butt joint interfaces, both with and without pilot fits. This previous work derived an empirical contact stiffness model and developed a practical finite element modeling approach for simulating the axial contact surfaces, which was validated by predicting natural frequencies for several test rotor configurations. The present work built on these previous results by implementing the same contact stiffness modeling approach on a real tie bolt rotor system designed for a high pressure centrifugal compressor application. Each joint location included two axial contact faces, with contact pressures up to five times higher than previously modeled, and a locating pilot fit. The free-free natural frequencies for different amounts of tie bolt preload force were measured, and the frequencies exhibited the expected stiffening behavior with increasing preload. However, a discontinuity in the data trend indicated a step-change increase in the contact stiffness. It was shown that this was likely due to one or more of the contact faces becoming fully engaged only after sufficient tie bolt force was applied. Finally, a design calculation was presented that can be used to estimate whether contact stiffness effects may be ignored, which could simplify rotor analyses if adequate contact pressure is used.

Smart literature review: a practical topic modelling approach to exploratory literature review

Manual exploratory literature reviews should be a thing of the past, as technology and development of machine learning methods have matured. The learning curve for using machine learning methods is rapidly declining, enabling new possibilities for all researchers. A framework is presented on how to use topic modelling on a large collection of papers for an exploratory literature review and how that can be used for a full literature review. The aim of the paper is to enable the use of topic modelling for researchers by presenting a step-by-step framework on a case and sharing a code template. The framework consists of three steps; pre-processing, topic modelling, and post-processing, where the topic model Latent Dirichlet Allocation is used. The framework enables huge amounts of papers to be reviewed in a transparent, reliable, faster, and reproducible way.