Research reports: 1990 NASA/ASEE Summer Faculty Fellowship Program

Reports on the research projects performed under the NASA/ASEE Summer Faculty Fellowship Program are presented. The program was conducted by The University of Alabama and MSFC during the period from June 4, 1990 through August 10, 1990. Some of the topics covered include: (1) Space Shuttles; (2) Space Station Freedom; (3) information systems; (4) materials and processes; (4) Space Shuttle main engine; (5) aerospace sciences; (6) mathematical models; (7) mission operations; (8) systems analysis and integration; (9) systems control; (10) structures and dynamics; (11) aerospace safety; and (12) remote sensin

1992 NASA/ASEE Summer Faculty Fellowship Program

For the 28th consecutive year, a NASA/ASEE Summer Faculty Fellowship Program was conducted at the Marshall Space Flight Center (MSFC). The program was conducted by the University of Alabama and MSFC during the period June 1, 1992 through August 7, 1992. Operated under the auspices of the American Society for Engineering Education, the MSFC program, was well as those at other centers, was sponsored by the Office of Educational Affairs, NASA Headquarters, Washington, DC. The basic objectives of the programs, which are the 29th year of operation nationally, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate and exchange ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA centers

Numerical Simulations

This book will interest researchers, scientists, engineers and graduate students in many disciplines, who make use of mathematical modeling and computer simulation. Although it represents only a small sample of the research activity on numerical simulations, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. It will be useful to encourage further experimental and theoretical researches in the above mentioned areas of numerical simulation

NASA Tech Briefs, September 1990

Topics covered include: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

Applied Mathematics and Computational Physics

As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications

Prediction of electron beam welding penetration depth using machine learning-enhanced computational fluid dynamics modelling

The necessity for precise prediction of penetration depth in the context of electron beam welding (EBW) cannot be overstated. Traditional statistical methodologies, including regression analysis and neural networks, often necessitate a considerable investment of both time and financial resources to produce results that meet acceptable standards. To address these challenges, this study introduces a novel approach for predicting EBW penetration depth that synergistically combines computational fluid dynamics (CFD) modelling with artificial neural networks (ANN). The CFD modelling technique was proven to be highly effective, yielding predictions with an average absolute percentage deviation of around 8%. This level of accuracy is consistent across a linear electron beam (EB) power range spanning from 86 J/mm to 324 J/mm. One of the most compelling advantages of this integrated approach is its efficiency. By leveraging the capabilities of CFD and ANN, the need for extensive and costly preliminary testing is effectively eliminated, thereby reducing both the time and financial outlay typically associated with such predictive modelling. Furthermore, the versatility of this approach is demonstrated by its adaptability to other types of EB machines, made possible through the application of the beam characterisation method outlined in the research. With the implementation of the models introduced in this study, practitioners can exert effective control over the quality of EBW welds. This is achieved by fine-tuning key variables, including but not limited to the beam power, beam radius, and the speed of travel during the welding process.Lloyds Register Foundation; Joining 4.0 Innovation Centre (J4IC); Cranfield Universit

Development of a high-performance artificial neural network model integrated with finite element analysis for residual stress simulation of the direct metal deposition process

Additive manufacturing (AM) processes are among the manufacturing methods implemented in various industries. Direct metal deposition (DMD) is part of AM processes that uses the laser heat source to deposit the metallic material in the form of powder or wire onto a substrate and build a component in a layer-by-layer scheme. The DMD process is known to be cost-effective and easily adaptable for building complex structures. During a DMD process, material experiences several heating and cooling cycles which lead to the formation of residual stresses and distortions of the fabricated part. There are several experimental-based methods and techniques for measuring the residual stresses of metallic components. However, the application of these methods can damage the fabricated parts or may require considerable time and tooling expenses for the experiment. Alternative solutions such as finite element (FE) analysis were developed to predict the residual stresses without damaging the part. The application of the FE in assessing the residual stress distribution is time-efficient and cost-effective. The FE analysis of DMD process includes thermal and mechanical analyses; the temperature history of the elements is obtained by performing a pure heat transfer analysis, then it is applied to the mechanical model to calculate the structural response of the part. One of the shortcomings of the FE analysis of DMD process corresponds to the high computational time of the mechanical analysis. Therefore, several techniques and approaches were developed in the literature to address this issue and improve the computational efficiency of the FE method. Throughout this thesis, a novel approach of integrating the FE analysis with artificial neural networks (ANNs) is presented as an efficient method for improving the computational time of predicting the residual stresses in DMD fabricated parts. ANNs are part of machine learning (ML) algorithms that tries to determine the logical relationship between the given inputs and the associated output(s). A feed-forward ANN with gradient descent backpropagation developed in Keras was implemented. The ANN is trained by feeding the dataset into the network and minimizing the error function. In the present study, several structures made from AISI 304L with 12-layers and 18-layers deposition were considered. and a detailed thermomechanical FE analysis was performed on them. Temperature history of the elements along with their dimensional features of 12-layers structures were extracted as the inputs and the corresponding residual stress components were recorded as the outputs to train the ANN. On the other hand, the temperature history of the elements and their geometrical features extracted from 18-layers structures were fed into the trained ANN for making predictions. The results of the integrated ANN-FE are compared with the results of the residual stresses of 18-layers obtained from the detailed thermomechanical analysis. The prediction errors were calculated and shown in the form of 3D contours and scattered errors. Moreover, the histogram analysis was performed for each 18-layers structures to better present the fraction of the elements with the associated error ranges. Finally, the computational times are recorded and compared with the results of the detailed FE analysis to evaluate the efficiency and performance of the proposed novel ANN-FE method. The results showed that for almost all of the structures and all the stress components, the predicted pattern and magnitude of the residual stress were consistent with the detailed FE analysis. For some of the structures, very high errors were observed which were associated with the low-stress state zones in which the actual stress magnitude was low and the high errors pose no critical condition. Although there are some predictions showing higher errors in some regions, the majority of the elements in the structures showed prediction errors of less than 15% supported by the histogram analysis. Significant improvement in the computational time of the 18-layers structures was also achieved (6 times as an average). The computational time of predicting the residual stresses in the DMD parts was improved substantially with low loss in the accuracy of the predicted results. Therefore, the proposed method can be implemented for investigating the effects of the hyperparameters on the residual stresses in DMD process

NASA Tech Briefs, December 1990

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences