Life Cycle Assessment of Thermoelectric Generators (TEGs) in an Automobile Application

In this paper, a possibility to reduce the environmental burdens by employing thermoelectric generators (TEGs) was analyzed with a cradle-to-grave LCA approach. An upscaling technique was newly introduced to assess the environmental impacts of TEGs over its life cycle. In addition to CO2 emissions, other environmental impacts as well as social impacts were assessed using the Life Cycle Impact Assessment Method based on Endpoint Modeling (LIME2). The analysis was conducted under two scenarios, a baseline scenario with a 7.2% conversion efficiency and a technology innovation scenario with that of 17.7% at different production scales. The results showed that while GHG emissions were positive over the life cycle under the baseline scenario, it became negative (−1.56 × 102 kg-CO2 eq/kg) under the technology innovation scenario due to GHG credits in the use phase. An increase in the conversion efficiency of the TEG and a decrease in the amount of stainless steel used in TEG construction are both necessary in order to reduce the environmental impacts associated with TEG manufacture and use. In addition, to accurately assess the benefit of TEG deployment, the lifetime driving distance needs to be analyzed together with the conversion efficiency.

Strut Diameter Uncertainty Prediction by Deep Neural Network for Additively Manufactured Lattice Structures

Additive manufacturing (AM) introduces geometric uncertainties on the fabricated strut members of lattice structures. These uncertainties result in deviations between the modeled and fabricated geometries of struts. The use of deep neural networks (DNNs) to accurately predict the statistical parameters of the effective strut diameters to account for the AM-introduced geometric uncertainties with a small training dataset for constant process parameters is studied in this research. For the training data, struts with certain angle and diameter values are fabricated by the material extrusion process. The geometric uncertainties are quantified using the random field theory based on the spatial strut radius measurements obtained from the microscope images of the fabricated struts. The uncertainties are propagated to the effective diameters of the struts using a stochastic upscaling technique. The relationship between the modeled strut diameter and the characterized statistical parameters of the effective diameters are used as the training data to establish a DNN model. The validation results show that the DNN model can predict the statistical parameters of the effective diameters of the struts modeled with angle and diameters different from the ones used in the training data with good accuracy even if the training data set is small. Developing such a DNN model with a small data will allow designers to use the fabricated results in the design optimization processes without requiring additional experimentations.

Upscaling Technique for Highly Heterogeneous Reservoirs Based on Flow and Storage Capacity and the Lorenz Coefficient

Summary Field-scale representation of highly heterogeneous reservoirs remains a challenge in numerical reservoir simulation. In such reservoirs, detailed geological models are important to properly represent key heterogeneities. However, high computational costs and long simulation run times make these detailed models unfeasible to use in dynamic evaluations. Therefore, the scaling up of geological models is a key step in reservoir-engineering studies to reduce computational time. Scaling up must be carefully performed to maintain integrity; both truncation errors and the smoothing of subgrid heterogeneities can cause significant errors. This work evaluates the latter—the effect of averaging small-scale heterogeneities in the upscaling process—and proposes a new upscaling technique to overcome the associated limitations. The technique is dependent on splitting the porous media into two levels guided by flow- and storage-capacity analysis and the Lorenz coefficient (LC), both calculated with static properties (permeability and porosity) from a fine-scale reference model. This technique allows the adaptation of a fine highly heterogeneous geological model to a coarse-scale simulation model in a dual-porosity/dual-permeability (DP/DP) approach and represents the main reservoir heterogeneities and possible preferential paths. The new upscaling technique is applied to different reservoir-simulation models with water injection and immiscible gas injection as recovery methods. In deterministic and probabilistic studies, we show that the resulting coarse-scale dual-permeability models are more accurate and can better reproduce the fine-scale results in different upscaling ratios (URs), without using any simulation results of the reference fine-scale simulation models, as some of the current alternative upscaling methods do.

Super Resolution - An image Enhancement and Upscaling Technique for Satellite Imagery

Satellite imagery is the phenomenon of extracting information from the images takes by satellite which is owned by the government or business throughout the world. In other words, we can say satellite imagery is the spaceborne photography which zooms enough to take photos of the surface of the earth and other planets of the solar system. Most of the time images captured are not in sync which makes it really complex to study. Nowadays satellites are used by the government for patrolling border security and terrorist activities. To clarify such extra zoomed image for human visibility ‘’super-resolution’’ was introduced. Super-resolution is an image processing based resolution enhancement technique which improves the details available in an image. An image with power order of resolution is taken an upscaled to its higher-order using multi-surface fitting and image pixel transformations. Our proposed paper is to process the image in the proper order to enhance the image quality so that we can increase the zooming capability of satellite imagery.

Numerical local upscaling of elastic geomechanical properties for heterogeneous continua

An efficient, numerical local upscaling technique for estimating elastic geomechanical properties in heterogeneous continua is proposed. The upscaled anisotropic elastic properties are solved locally with various boundary conditions and reproduce the anisotropic geomechanical response of fine-scale simulations of sand–shale sequence models with horizontal and inclined shale bedding planes. The algorithm is automated in a parallel program and can be used to determine optimum upscaling ratios in different regions of the reservoir. The successful application of the proposed upscaling method in a field-scale coupled reservoir–geomechanics simulation demonstrates an improvement in overall computational efficiency while maintaining accuracy in the geomechanical response and reservoir performance.