Applying system engineering to reduce capital construction costs in oil and gas fields

The article proposes solving way the problem of reduction in the expenditure on the permanent construction of oil and gas fields using systems engineering methods. There is shown system engineering ability to reduce permanent construction costs. The text considered an example of systems engineering using the conceptual design stage for the location of a well cluster.

Integrating 3D Modeling, Sustainability and Cost Estimating at the Conceptual Design Stage of Bridges

Integrated 3D Modeling adaptation on infrastructure has been slow due to technical challenges, such as the lack of interoperability between software. In an effort to partially fill this gap, a versatile model with 3D Modeling capabilities was developed to assist in designing sustainable bridges at the conceptual design stage. The model incorporates a rule-based expert system and four modules, namely: 3D CAD modeling, Bridge Sustainability Rating System (BrSRS), Bridge Environmental Performance Strategy Mapping (BrEPSM), and conceptual cost estimating. The 3D CAD module was developed using the Graphics.DrawLine method. The BrSRS and BrEPSM modules were developed by the amalgamation of identified pertinent sustainable and footprint indicators. The model aims to provide bridge type recommendations, allow customization of a sustainable bridge, illustrate forecasted footprint levels, present the conceptual design in AutoCAD’s 3-dimensional (3D) mode, facilitate a recalculation process when the bridge’s dimensions are altered, and generate a conceptual cost estimate.

Acceleration of Evaluation, Synthesis and Selection of Engineering Solutions by Morphological Approaches

The article discussed the issues of creating a decision support system in the design process. It is based on morphological methods and approaches. These approaches can be successfully used to accelerate the evaluation, synthesis and selection of engineering solutions at the conceptual design stage. With the help of combinatorics, it is possible to generate a significant number of different engineering solutions. The conceptual stage is the most important in determining the future parameters and characteristics of the designed systems. The article also considers the possibility of computerization of the morphological approach. All this leads to an increase in the efficiency of the design process as a whole.

A Machine-Learning Approach to Assess Aircraft Engine System Performance

Machine learning and big data have become the most disruptive technologies for organizations to improve workplace efficiency and productivity. This work explored the application of machine learning-based predictive analytics that would enable aircraft engine designers to estimate engine system performance quickly during the conceptual design stage. Supervised machine-learning algorithm was employed to study patterns in an open-source database of one-hundred-eighty-three production and research turbofan engines, and built predictive analytics for use in predicting system performance of new turbofan designs. Specifically, the author developed deep-learning analytics to predict turbofan system weight, using turbofan design parameters as the input. The predictive analytics were trained and deployed in Keras, an open-source neural networks API (application program interface) written in Python, with TensorFlow (an open-source Google machine learning library) serving as the backend engine. The current engine-weight prediction results, together with those for the TSFC (thrust specific fuel consumption) and core-size predictions that were studied previously by the author, show that machine learning-based predictive analytics can be an effective, time-saving tool for assessing aircraft engine system performance (TSFC, weight, and core size) during the conceptual design stage. It would enable expeditious identification of the best engine design amongst several candidates.