Engineering topics

Ouyang, Qi

Publications and source records attributed to Ouyang, Qi.

5 records

DOE OSTI2019

Microstructure and Mechanical Properties of B{sub 4}C-Blended M3:2 High-Speed Steel Powders Consolidated by Sintering and Heat Treatment

Water-atomized powders of M3:2 high-speed steel (HSS) were blended with B{sub 4}C and consolidated by cold press sintering. With the addition of B{sub 4}C (~ 0.2 vol.%), satisfactory mechanical properties were achieved for HSS by sintering at a temperature as low as 1190 °C [e.g., 53.2 ± 0.2 in the Rockwell hardness (HRC) and 3074 ± 56 MPa in bending strength]. High relative density (98.5%) and good strength were attributed to the mechanism described as follows: First, the atoms of boron and carbon escaped after the reaction between B{sub 4}C and iron matrix. Then, the sintered HSS was strengthened by boron atoms while they were distributed in M{sub 6}C or MC carbides and iron matrix. Second, a plenty of M{sub 6}C carbides were formed due to the release of carbon atoms. As a result, the value of solidus temperature was reduced. In addition, mechanical properties were greatly improved by heat treatment (quenching and tempering) (e.g., 61.2 ± 0.3 in HRC, 4356 ± 64 MPa in bending strength, and 42.3 ± 0.5 MPa m{sup 1/2} in fracture toughness, respectively).

36 MATERIALS SCIENCE↗

DOE OSTI2019

A deep learning approach to anomaly detection in geological carbon sequestration sites using pressure measurements

Carbon capture and storage (CCS) has been extensively investigated as a potential engineering measure to reduce anthropogenic carbon emission to the atmosphere. Real-time monitoring of the safety and integrity of carbon storage reservoirs is a critical aspect of any commercial-scale CCS deployment. Pressure-based sensing is cost effective, suitable for real-time monitoring, and scalable to large monitoring networks. However, questions remain on how to best harness intelligent information from the high-frequency pressure monitoring sensors to support real-time decisions. Here this work presents a deep-learning-based framework for analyzing and detecting anomalies in pressure data streams by using a convolutional long short-term memory (ConvLSTM) neural network model, which allows for the fusion of both static and dynamic reservoir data. In ConvLSTM, the convolutional neural network (CNN) is used for spatial pattern mining and the LSTM is used for temporal pattern recognition. The performance of the ConvLSTM model for real-time anomaly detection is demonstrated using a set of pressure monitoring data collected from Cranfield, Mississippi, an active enhanced-oil-recovery field. The anomaly detection model is trained using bottom-hole pressure data acquired from the base experiment (without leak event) and then tested on pressure data collected during a series of controlled CO 2 release experiments (with artificially created leak events). Results show that the ConvLSTM neural network model successfully detected anomalies in the pressure time series obtained from the controlled release experiments. Inclusion of static information into the model further improves the robustness of ConvLSTM.

13 HYDRO ENERGY↗

DOE OSTI2018

Analyzing the Effect of CeB{sub 6} on Microstructure and Mechanical Properties of High-Speed Steel Consolidated by Powder Metallurgy

By adding various amounts of CeB{sub 6}, high-speed steel (M3:2) was consolidated by powder metallurgy in the form of sintering. The addition of CeB{sub 6} improved the density of the sintered steel and the formation of M{sub 6}C phase in microstructure. It is speculated that CeB{sub 6} was decomposed into boron and cerium. Boron atoms were enriched in M{sub 6}C carbide phases, but cerium atoms were mainly clustered on grain boundary while they were converted to oxides, whereby the mechanical properties were improved. For example, upon the sintering at 1210 °C, relative density ~ 98.5% and average grain size ~ 18 μm were obtained with CeB{sub 6} content at 0.3 wt.%. Excellent mechanical properties, e.g., the Rockwell hardness ~ 52 HRC, flexural strength ~ 3.05 GPa, and fracture toughness ~ 40.92 MPa m{sup 1/2}, were achieved in the sample containing 0.3 wt.% CeB{sub 6}, which implies accordingly remarkable increases by 23.5, 38.1, and 23.7%, of the properties compared with those in the sample free of CeB{sub 6}.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Designing a stochastic genetic switch by coupling chaos and bistability

In stem cell differentiation, a pluripotent stem cell becomes progressively specialized and generates specific cell types through a series of epigenetic processes. How cells can precisely determine their fate in a fluctuating environment is a currently unsolved problem. In this paper, we suggest an abstract gene regulatory network to describe mathematically the differentiation phenomenon featuring stochasticity, divergent cell fates, and robustness. The network consists of three functional motifs: an upstream chaotic motif, a buffering motif of incoherent feed forward loop capable of generating a pulse, and a downstream motif which is bistable. The dynamic behavior is typically a transient chaos with fractal basin boundaries. The trajectories take transiently chaotic journeys before divergently settling down to the bistable states. The ratio of the probability that the high state is achieved to the probability that the low state is reached can maintain a constant in a population of cells with varied molecular fluctuations. The ratio can be turned up or down when proper parameters are adjusted. The model suggests a possible mechanism for the robustness against fluctuations that is prominently featured in pluripotent cell differentiations and developmental phenomena.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2014

Square Turing patterns in reaction-diffusion systems with coupled layers

Square Turing patterns are usually unstable in reaction-diffusion systems and are rarely observed in corresponding experiments and simulations. We report here an example of spontaneous formation of square Turing patterns with the Lengyel-Epstein model of two coupled layers. The squares are found to be a result of the resonance between two supercritical Turing modes with an appropriate ratio. Besides, the spatiotemporal resonance of Turing modes resembles to the mode-locking phenomenon. Analysis of the general amplitude equations for square patterns reveals that the fixed point corresponding to square Turing patterns is stationary when the parameters adopt appropriate values.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗