Engineering Papers⌕ Search

Engineering topics

Peng, Jian

Publications and source records attributed to Peng, Jian.

26 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Coupling physics in machine learning to predict properties of high-temperatures alloys

Abstract High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9–12 wt% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As a part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Solute-vacancy clustering in aluminum

In this study, we present an extensive first-principles database of solute-vacancy, homoatomic, heteroatomic solute-solute, and solute-solute-vacancy binding energies of relevant alloying elements in aluminum. We particularly focus on the systems with major alloying elements in aluminum, i.e., Cu, Mg, and Si. The computed binding energies of solute-vacancy, solute-solute pairs, and solute-solute-vacancy triplets agree with available experiments and theoretical results in literature. We consider physical factors such as solute size and formation energies of intermetallic compounds to correlate with binding energies. Systematic studies of the homoatomic solute-solute-vacancy and heteroatomic (Cu, Mg, or Si)-solute-vacancy complexes reveal the overarching effect of the vacancy in stabilizing solute-solute pairs. The binding energy database presented here elucidates the interaction between solute cluster and vacancy in aluminum, and it is expected to provide insight into the design of advanced Al alloys with tailored properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

ASCENDS: Advanced data SCiENce toolkit for Non-Data Scientists

Recently, advances in machine learning and artificial intelligence have been playing more and more critical roles in a wide range of areas. For the last several years, industries have shown that how learning from data, identifying patterns and making decisions with minimal human intervention can be extremely useful to their business (e.g., image classification, recommending a product to a customer, finding friends in a social network, predicting customers actions, etc.). These success stories have been motivating scientists who study physics, chemistry, materials, medicine, and many other subjects, to explore a new pathway of utilizing machine learning techniques like regression and classification for their scientific activities. However, most existing machine learning tools, systems, and methodologies have been developed for programming experts but not for scientists (or any users) who have no or little knowledge of programming. ASCENDS is a toolkit that is developed to assist scientists (or any persons) who want to use their data for machine learning tasks, more specifically, correlation analysis, regression, and classification. ASCENDS does not require programming skills. Instead, it provides a set of simple but powerful CLI (Command Line Interface) and GUI (Graphic User Interface) tools for non-data scientists to be able to intuitively perform advanced data analysis and machine learning techniques. ASCENDS has been implemented by wrapping around opensource software including Keras, TensorFlow, and scikit-learn.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1993

Advanced electromagnetic methods for aerospace vehicles

The Advanced Helicopter Electromagnetics (AHE) Industrial Associates Program has continuously progressed with its research effort focused on subjects identified and recommended by the Advisory Task Force of the program. The research activities in this reporting period have been steered toward practical helicopter electromagnetic problems, such as HF antenna problems and antenna efficiencies, recommended by the AHE members at the annual conference held at Arizona State University on 28-29 Oct. 1992 and the last biannual meeting held at the Boeing Helicopter on 19-20 May 1993. The main topics addressed include the following: Composite Materials and Antenna Technology. The research work on each topic is closely tied with the AHE Consortium members' interests. Significant progress in each subject is reported. Special attention in the area of Composite Materials has been given to the following: modeling of material discontinuity and their effects on towel-bar antenna patterns; guidelines for composite material modeling by using the Green's function approach in the NEC code; measurements of towel-bar antennas grounded with a partially material-coated plate; development of 3-D volume mesh generator for modeling thick and volumetric dielectrics by using FD-TD method; FDTD modeling of horn antennas with composite E-plane walls; and antenna efficiency analysis for a horn antenna loaded with composite dielectric materials.

Balanis, Constantine A.↗
NASA NTRS1993

Advanced electromagnetic methods for aerospace vehicles

The Advanced Helicopter Electromagnetics (AHE) Industrial Associates Program has fruitfully completed its fourth year. Under the support of the AHE members and the joint effort of the research team, new and significant progress has been achieved in the year. Following the recommendations by the Advisory Task Force, the research effort is placed on more practical helicopter electromagnetic problems, such as HF antennas, composite materials, and antenna efficiencies. In this annual report, the main topics to be addressed include composite materials and antenna technology. The research work on each topic has been driven by the AHE consortium members' interests and needs. The remarkable achievements and progresses in each subject is reported respectively in individual sections of the report. The work in the area of composite materials includes: modeling of low conductivity composite materials by using Green's function approach; guidelines for composite material modeling by using the Green's function approach in the NEC code; development of 3-D volume mesh generator for modeling thick and volumetric dielectrics by using FD-TD method; modeling antenna elements mounted on a composite Comanche tail stabilizer; and antenna pattern control and efficiency estimate for a horn antenna loaded with composite dielectric materials.

Balanis, Constantine A.↗
NASA NTRS1992

Advanced electromagnetic methods for aerospace vehicles

The Advanced Helicopter Electromagnetics (AHE) Industrial Associates Program continues its research on variety of main topics identified and recommended by the Advisory Task Force of the program. The research activities center on issues that advance technology related to helicopter electromagnetics. While most of the topics are a continuation of previous works, special effort has been focused on some of the areas due to recommendations from the last annual conference. The main topics addressed in this report are: composite materials, and antenna technology. The area of composite materials continues getting special attention in this period. The research has focused on: (1) measurements of the electrical properties of low-conductivity materials; (2) modeling of material discontinuity and their effects on the scattering patterns; (3) preliminary analysis on interaction of electromagnetic fields with multi-layered graphite fiberglass plates; and (4) finite difference time domain (FDTD) modeling of fields penetration through composite panels of a helicopter.

Balanis, Constantine A.↗