Correlations of Rock-Physics Model Parameters From Bayesian Analysis: Pressure- and Porosity-Dependent Models Applied to Unconsolidated Sands

Correlations of rock-physics model inputs are important to know to help design informative prior models within integrated reservoir-characterization workflows. A Bayesian framework is optimal to determine such correlations. Within that framework, we use velocity and porosity measurements on unconsolidated, dry, and clean sands. Three pressure- and three porosity-dependent rock-physics models are applied to the data to examine relationships among the inputs. As with any Bayesian formulation, we define a prior model and calculate the likelihood in order to evaluate the posterior. With relatively few inputs to consider for each rock-physics model, we found that sampling the posterior exhaustively to be convenient. The results of the Bayesian analyses are multivariate histograms that indicate most likely values of the input parameters given the data to which the rock-physics model was fit. When the Bayesian procedure is repeated many times for the same data, but with different prior models, correlations emerged among the input parameters in a rock-physics model. These correlations were not known previously. Implications, for the pressure- and porosity-dependent models examined here, are that these correlations should be utilized when applying these models to other relevant data sets. Furthermore, additional rock-physics models should be examined similarly to determine any potential correlations in their inputs. These correlations can then be taken advantage of in forward and inverse problems posed in reservoir characterization.

Revealing the compressive and flow properties of novel bone scaffold structure manufactured by selective laser sintering technique

Additive manufacturing is revolutionizing the field of medical sciences through its key application in the development of bone scaffolds. During scaffold fabrication, achieving a good level of porosity for enhanced mechanical strength is very challenging. The bone scaffolds should hold both the porosity and load withstanding capacity. In this research, a novel structure was designed with the aim of the evaluation of flexible porosity. A CAD model was generated for the novel structure using specific input parameters, whereas the porosity was controlled by varying the input parameters. Poly Amide (PA 2200) material was used for the fabrication of bone scaffolds, which is a biocompatible material. To fabricate a novel structure for bone scaffolds, a Selective Laser Sintering machine (SLS) was used. The displacement under compression loads was observed using a Universal Testing Machine (UTM). In addition to this, numerical analysis of the components was also carried out. The compressive stiffness found through the analysis enables the verification of the load withstanding capacity of the specific bone scaffold model. The experimental porosity was compared with the theoretical porosity and showed almost 29% to 30% reductions when compared to the theoretical porosity. Structural analysis was carried out using ANSYS by changing the geometry. Computational Fluid Dynamics (CFD) analysis was carried out using ANSYS FLUENT to estimate the blood pressure and Wall Shear Stress (WSS). From the CFD analysis, maximum pressure of 1.799 Pa was observed. Though the porosity was less than 50%, there was not much variation of WSS. The achievement from this study endorses the great potential of the proposed models which can successfully be adapted for the required bone implant applications.

A cost-minimisation analysis of performing point-of-care ultrasonography on patients with vaginal bleeding in early pregnancy in general practice: a decision analytical model

Background Spotting and light vaginal bleeding are common and usually harmless symptoms in early pregnancy. Still, vaginal bleeding may be the first sign of an abortion and often causes distress to pregnant women and leads to an expectation of an ultrasonography examination of the uterus. As point-of-care ultrasonography (POCUS) is increasingly being integrated into general practice, these patients may be clinically evaluated and managed by general practitioners (GPs). This can potentially reduce referrals of patients from the primary to the secondary healthcare sector resulting in societal cost-savings. The primary purpose of this study was to investigate whether the accessibility of POCUS in general practice for patients with vaginal bleeding in early pregnancy is cost-saving compared to usual practice where GPs do not have access to POCUS. A secondary purpose of this study was to estimate a remuneration for GPs performing POCUS on these patients in general practice. Methods A cost-minimisation analysis was based on a decision tree model reflecting the two alternatives: general practice with and without GPs having access to POCUS. The robustness of the model results was investigated using probabilistic sensitivity analysis and the following deterministic sensitivity analyses: one-way analyses for the model input parameters and a scenario analysis with a change from a societal to a healthcare sector perspective. An expected remuneration reflecting the add-on cost of Danish GPs performing POCUS was estimated based on the related costs: cost of an ultrasonography scanner, GP’s time consumption, ultrasonography training, and utensils per scanning. Results The difference in average cost between the two alternatives from a societal perspective was estimated to be €110, in favour of general practice with GPs using POCUS. The deterministic sensitivity analyses demonstrated robustness of the results to plausible changes in the input parameters. The expected remuneration for performing POCUS in this specific setting was estimated to be €32 per examination. Conclusion Having GPs perform POCUS on patients with vaginal bleeding in early pregnancy is cost-saving compared to usual practice. The results should be taken with caution as this study was based on early modelling with uncertainties associated with the input parameters in the model.

Uncertainty analysis of galloping based piezoelectric energy harvester system using polynomial neural network

This paper deals with the impact of uncertain input parameters on the electrical power generation of galloping-based piezoelectric energy harvester (GPEH). A distributed parameter model for the system is derived and solved by using Newmark beta numerical integration technique. Nonlinear systems tend to behave in a completely different manner in response to a slight change in input parameters. Due to the complex manufacturing process and various technical defects, randomness in system properties is inevitable. Owing to the presence of randomness within the system parameters, the actual power output differs from the expected one. Therefore, stochastic analysis is performed considering uncertainty in aerodynamic, mechanical, and electrical parameters. A polynomial neural network (PNN) based surrogate model is used to analyze the stochastic power output. A sensitivity analysis is conducted and highly influenced parameters to the electric power output are identified. The accuracy and adaptability of the PNN model are established by comparing the results with Monte Carlo simulation (MCS). Further, the stochastic analyses of power output are performed for various degrees of randomness and wind velocities. The obtained results showed that the influence of the electromechanical coefficient on power output is more compared to other parameters.

Split-Plot I-Optimal Design Optimisation of Combined Oil-Based and Friction Stir Rotation-Assisted Heating in SPIF of Ti-6Al-4V Titanium Alloy Sheet under Variable Oil Pressure

The aim of this paper is to determine the optimal input parameters for the process in order to ensure the maximum formable wall angle is obtained in a conical frustum with a varying wall angle fabricated using Single Point Incremental Forming (SPIF). The test material was 0.8-mm-thick Ti-6Al-4V titanium alloy sheets, and the test used a tungsten carbide tool with a rounded tip with a radius of 4 mm. Complete workpieces were heated using hot oil with a temperature of about 200 °C, and in addition, the high rotation speed of the forming tool generated an amount of friction heat. The input parameters were tool rotational speed, feed rate, step size, and tool rotation direction. Various oil pressures were used to improve both the accuracy of the components formed and the friction heating process. On the basis of calculations performed by means of the response surface methodology, split-plot I-optimal design responses were obtained by means of polynomial regression models. Models were fitted using REstricted Maximum Likelihood (REML), and p-values are derived using the Kenward–Roger approximation. Observation of the fracture surface of Ti-6Al-4V drawpieces showed that the destruction is as a result of ductile fracture mode. Tool rotational speed and step size are the most significant factors that affect the axial force, followed by feed rate. It was also found that step size is the most significant factor that affects the in-plane SPIF force.

Analysis of vibrations in a diesel engine produced by Jatropha biodiesel using heterogeneous catalyst

Extensive consumption of fossil fuel has contributed to the worldwide decline of its reserves and detrimental effect on the environment. Therefore, it is essential to explore alternative option of fuel for diesel engine. The main objective of this research article is to optimize vibrations in a single-cylinder variable compression ratio diesel engine driven by Jatropha biodiesel blend. The heterogeneous catalyst (calcium oxide) is used to manufacture of biodiesel from Jatropha curcas oil by a process of transesterification. The optimization technique (Response Surface Methodology) has been employed to optimize root mean square acceleration of vibration by taking load, compression ratio (CR), and fuel injection pressure (FIP) as engine input parameters. Experiments were designed according to central composite design. The amplitude of the frequency domain signals is determined using Fast Fourier Transform and the influence of input parameters has been investigated in the frequency domain analysis of the vibration signatures. The adequacy and significance of the models have been checked by p-value and F value tests. Regression coefficients Adj. R2, R2, Pred. R2 were also found in acceptable range. The experimental outcome reveals that biodiesel yield of 81.6% was obtained at methanol-to-oil molar ratio of 12:1, reaction temperature of 65°C, reaction time of 3 h, and catalyst concentration of 5 wt%. Simultaneously, the model obtained a series of solutions based on the desirability criteria and proposed optimum setting of engine input parameters at a load of 2.59 kg, 17.94 CR, and 268.76 bar FIP for B30 blend. B30 blend generated root mean square acceleration of 4.46 m/s2 at above optimized conditions. A validation trial was conducted and the percentage of error for root mean square acceleration was found to be 2.3356% and 1.3039%, respectively, for B0 and B30 blend.