Showing papers by "General Electric published in 2022"

Clinical translation of hyperpolarized 13 C pyruvate and urea MRI for simultaneous metabolic and perfusion imaging.

Abstract: PURPOSE The combined hyperpolarized (HP) 13 C pyruvate and urea MRI has provided a simultaneous assessment of glycolytic metabolism and tissue perfusion for improved cancer diagnosis and therapeutic evaluation in preclinical studies. This work aims to translate this dual-probe HP imaging technique to clinical research. METHODS A co-polarization system was developed where [1-13 C]pyruvic acid (PA) and [13 C, 15 N2 ]urea in water solution were homogeneously mixed and polarized on a 5T SPINlab system. Physical and chemical characterizations and toxicology studies of the combined probe were performed. Simultaneous metabolic and perfusion imaging was performed on a 3T clinical MR scanner by alternatively applying a multi-slice 2D spiral sequence for [1-13 C]pyruvate and its downstream metabolites and a 3D balanced steady-state free precession (bSSFP) sequence for [13 C, 15 N2 ]urea. RESULTS The combined PA/urea probe has a glass-formation ability similar to neat PA and can generate nearly 40% liquid-state 13 C polarization for both pyruvate and urea in 3-4 h. A standard operating procedure for routine on-site production was developed and validated to produce 40 mL injection product of approximately 150 mM pyruvate and 35 mM urea. The toxicology study demonstrated the safety profile of the combined probe. Dynamic metabolite-specific imaging of [1-13 C]pyruvate, [1-13 C]lactate, [1-13 C]alanine, and [13 C, 15 N2 ]urea was achieved with adequate spatial (2.6 mm × 2.6 mm) and temporal resolution (4.2 s), and urea images showed reduced off-resonance artifacts due to the JCN coupling. CONCLUSION The reported technical development and translational studies will lead to the first-in-human dual-agent HP MRI study and mark the clinical translation of the first HP 13 C MRI probe after pyruvate.

Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion

Abstract: Accurate identification and modeling of process maps in additive manufacturing remains a pertinent challenge. To ensure high quality and reliability of the finished product researchers, rely on models that entail the physics of the process as a computer code or conduct laboratory experiments, which are expensive and oftentimes demand significant logistic and overheads. Physics-based computational modeling has shown promise in alleviating the aforementioned challenge, albeit with limitations like physical approximations, model-form uncertainty, and limited experimental data. This calls for modeling methods that can combine limited experimental and simulation data in a computationally efficient manner, in order to achieve the desired properties in the manufactured parts. In this paper, we focus on demonstrating the impact of probabilistic modeling and uncertainty quantification on powder-bed fusion (PBF) additive manufacturing by focusing on the following three milieu: (a) accelerating the parameter development processes associated with laser powder bed fusion additive manufacturing process of metals, (b) quantifying uncertainty and identifying missing physical correlations in the computational model, and (c) transferring learned process maps from a source to a target process. These tasks demonstrate the application of multifidelity modeling, global sensitivity analysis, intelligent design of experiments, and deep transfer learning for a meso-scale meltpool model of the additive manufacturing process.

Effect of Mo on the microstructure and mechanical properties of (Hf0.73Ta0.27)100-XMoX (X = 0, 5, 16, 21 and 30 at.%) alloys

Abstract: Microstructure and compression properties of (Hf0.73Ta0.27)100-XMoX alloys, where X = 0, 5, 16, 21 and 30 at. %, are reported. The alloys were prepared by vacuum arc melting and hot isostatically pressed at 207 MPa for 3 h at 1400 °C. The alloys with 0, 5, 16 and 21 at. % Mo contained two phases, BCC and HCP, while the alloy with 30 at. % Mo contained three phases, BCC, HCP and cubic Laves (C15). The Hf73Ta27, Hf69Ta26Mo5 and Hf61Ta23Mo16 alloys experienced eutectoid transformation with the formation of a mixture of Hf-rich HCP and Ta-rich BCC phases. The volume fraction of the eutectoid regions decreased from 55% to 0% with increasing Mo from 0 to 21 at.%. The alloy with 30% Mo (Hf51Ta19Mo30) experienced eutectoid transformation with the formation of a mixture of the Mo-rich cubic Laves (C15) phase and Hf-rich HCP phase. The volume fraction of the eutectoidally transformed regions in Hf51Ta19Mo30 was ~20%. Among the studied alloys, Hf73Ta27 had the highest room temperature yield stress of 1738 MPa. The yield stress continuously decreased with increasing Mo concentration to 1468 MPa at 16% Mo and then increased to 1672 MPa at 30% Mo. The room temperature ductility strongly depended on the amount of Mo. The maximum true fracture strain of 0.137 was achieved in Hf69Ta26Mo5, but it rapidly decreased to 0.027 in Hf51Ta19Mo30. All the studied alloys were ductile at 1000–1400 °C and no fracture was observed after 0.7 true strain. The yield stress increased almost linearly with increasing the amount of Mo at these high temperatures.

Accelerating Additive Design With Probabilistic Machine Learning

Abstract: Additive manufacturing (AM) has been growing rapidly to transform industrial applications. However the fundamental mechanism of AM hasn't been fully understood which resulted in low success rate of building. A remedy is to introduce surrogate modeling based on experimental dataset to assist additive design and increase design efficiency. As one of the first papers for predictive modeling of AM especially Direct Energy Deposition (DED), this paper discusses a bidirectional modeling framework and its application to multiple DED benchmark designs including (1) Forward Prediction with Cross Validation, (2) Global Sensitivity Analyses, (3) Backward Prediction and Optimization, (4) Intelligent Data Addition. Approximately 1,150 mechanical tensile test samples were extracted and tested with input variables from machine parameters, post-process, output variables from mechanical, microstructure and physical properties.

Recognition of 2021 TRANSACTIONS and Magazine Paper Reviewers

Abstract: Special Feature that lists the names of people involved in the review of technical papers for the IEEE Transactions on Industry Applications and Industry Applications Magazine.

IEEE CIS VP Industrial and Governmental Activities Vision Statement [Society Briefs]

Abstract: Presents the CIS society VP Industrial and Governmental Activities Vision Statement.

Fourier transforms and integer homology cobordism

Abstract: We prove that connected sums of lens spaces are integer homology cobordant if and only if they are oriented diffeomorphic. The main tool is the Fourier transform of the $d$-invariants, which is particularly well-behaved with respect to connected sum: the $d$-invariants of lens spaces have a certain non-vanishing property which allows one to canonically extract them from the $d$-invariants of the connected sum. On the way, we show how the Fourier transform can be used to reprove a lemma of Gonz\'alez-Acu\~na$\unicode{x2013}$Short on Alexander polynomials of knots with reducible surgeries.

FERC Order 881 - A Step Towards Dynamic Line Ratings for Improved Market Efficiency

Abstract: Issued on December 16, 2021, FERC Order 881 requires transmission providers to implement ambient-adjusted ratings (AAR) to determine the maximum transfer capability of their transmission lines for near-term transmission services. Traditionally, static line rating (SLR) of a line is conservatively calculated under the almost worst-case operating conditions and are updated infrequently. These conservative assumptions may restrict the line capacity whenever the real weather condition is less stressful. More accurate assessment of transmission flow limits like AAR which is a limited form of dynamic line ratings (DLR) will positively impact the efficiency of market and system operations. DLR has the potential to expand practical line capacity, improve line utilization, reduce transmission congestion, and enhance market efficiency. In North America, independent system operators are heavily counting on mathematical optimization to dispatch generation resources and serve the net demand in their corresponding market footprints. This paper discusses AAR within the framework of transmission constraint management and its application to market clearing. Simulations of case studies are conducted on a large power network. The benefits of DLR/AAR are illustrated to demonstrate the effectiveness of improving social welfare.