Electronic and magnetic properties of V-doped anatase TiO2_{2} from first principles

We report a first-principles study on the geometric, electronic and magnetic properties of V-doped anatase TiO$_{2}$. The DFT+U (Hubbard coefficient) approach predicts semiconductor band structures for Ti$_{1-x}$V$_{x}$O$_{2}$ (x=6.25 and 12.5%), in good agreement with the poor conductivity of samples, while the standard calculation within generalized gradient approximation fails. Theoretical results show that V atoms tend to stay close and result in strong ferromagnetism through superexchange interactions. Oxygen vacancy induced magnetic polaron could produce long-range ferromagnetic interaction between largely separated magnetic impurities. The experimentally observed ferromagnetism in V-doped anatase TiO$_{2}$ at room temperature may originate from a combination of short-range superexchange coupling and long-range bound magnetic polaron percolation.Comment: 12 pages and 4 figures (to be appeared in PRB as a brief report

The linear dependence problem for power linear maps

AbstractLet Bl, l=1,…,k, be m×nl complex matrices and let x[l]∈Cnl,l=1,…,k, be complex vector variables. We show that the components of the map H=(B1x[1])(d1)∘⋯∘(Bkx[k])(dk) are linearly dependent over C if and only if det(B1B1∗)(d1)∘⋯∘(BkBk∗)(dk)=0, where ∘ means the Hadamard product, X∗ and X(d) denote the conjugate transpose and the dth Hadamard power of a matrix X respectively. Connections are established between the Homogenous Dependence Problem (HDP(n,d)), which arises in the study of the Jacobian Conjecture, and the dependence problem for power linear maps (PLDP(n,d)). An algorithm is given to compute counterexamples to PLDP(n,d) from those to HDP(n,d), and counterexamples to PLDP(n,3) are obtained for all n⩾67

Role of Self-Assembled Monolayers on Improved Electrical Stability of Amorphous In-Ga-Zn-O Thin-Film Transistors

Self-assembled monolayers (SAMs) have been used to improve both the positive and negative bias-stress stability of amorphous indium gallium zinc oxide (IGZO) bottom gate thin film transistors (TFTs). N-hexylphosphonic acid (HPA) and fluorinated hexylphosphonic acid (FPA) SAMs adsorbed on IGZO back channel surfaces were shown to significantly reduce bias stress turn-on voltage shifts compared to IGZO back channel surfaces with no SAMs. FPA was found to have a lower surface energy and lower packing density than HPA, as well as lower bias stress turn-on voltage shifts. The improved stability of IGZO TFTs with SAMs can be primarily attributed to a reduction in molecular adsorption of contaminants on the IGZO back channel surface and minimal trapping states present with phosphonic acid binding to the IGZO surface.Comment: 27 pages, 6 figure

Surrogate-Based Multidisciplinary Optimization for the Takeoff Trajectory Design of Electric Drones

Electric vertical takeoff and landing (eVTOL) aircraft attract attention due to their unique characteristics of reduced noise, moderate pollutant emission, and lowered operating cost. However, the benefits of electric vehicles, including eVTOL aircraft, are critically challenged by the energy density of batteries, which prohibit long-distance tasks and broader applications. Since the takeoff process of eVTOL aircraft demands excessive energy and couples multiple subsystems (such as aerodynamics and propulsion), multidisciplinary analysis and optimization (MDAO) become essential. Conventional MDAO, however, iteratively evaluates high-fidelity simulation models, making the whole process computationally intensive. Surrogates, in lieu of simulation models, empower efficient MDAO with the premise of sufficient accuracy, but naive surrogate modeling could result in an enormous training cost. Thus, this work develops a twin-generator generative adversarial network (twinGAN) model to intelligently parameterize takeoff power and wing angle profiles of an eVTOL aircraft. The twinGAN-enabled surrogate-based takeoff trajectory design framework was demonstrated on the Airbus (Formula presented.) Vahana aircraft. The twinGAN provisioned two-fold dimensionality reductions. First, twinGAN generated only realistic trajectory profiles of power and wing angle, which implicitly reduced the design space. Second, twinGAN with three variables represented the takeoff trajectory profiles originally parameterized using 40 B-spline control points, which explicitly reduced the design space while maintaining sufficient variability, as verified by fitting optimization. Moreover, surrogate modeling with respect to the three twinGAN variables, total takeoff time, mass, and power efficiency, reached around 99% accuracy for all the quantities of interest (such as vertical displacement). Surrogate-based, derivative-free optimizations obtained over 95% accuracy and reduced the required computational time by around 26 times compared with simulation-based, gradient-based optimization. Thus, the novelty of this work lies in the fact that the twinGAN model intelligently parameterized trajectory designs, which achieved implicit and explicit dimensionality reductions. Additionally, twinGAN-enabled surrogate modeling enabled the efficient takeoff trajectory design with high accuracy and computational cost reduction

Multifidelity Modeling by Polynomial Chaos-Based Cokriging to Enable Efficient Model-Based Reliability Analysis of NDT Systems

This work proposes a novel multifidelity metamodeling approach, the polynomial chaos-based Cokriging (PC-Cokriging). The proposed approach is used for fast uncertainty propagation in a reliability analysis of nondestructive testing systems using model-assisted probability of detection (MAPOD). In particular, PC-Cokriging is a multivariate version of polynomial chaos-based Kriging (PC-Kriging), which aims at combining the advantages of the regression-based polynomial chaos expansions and the interpolation-based Kriging metamodeling methods. Following a similar process as Cokriging, the PC-Cokriging advances PC-Kriging by enabling the incorporation of multifidelity physics information. The proposed PC-Cokriging is demonstrated on two analytical functions and three ultrasonic testing MAPOD cases. The results show that PC-Cokriging outperforms the state-of-the-art metamodeling approaches when providing the same number of training points. Specifically, PC-Cokriging reduces the high-fidelity training sample cost of the Kriging and PCE metamodels by over one order of magnitude, and the PC-Kriging and conventional Cokriging multifidelity metamodeling by up to 50 % to reach the same accuracy level (defined by the root mean squared error being no greater than 1 % of the standard deviation of the testing points). The accuracy and robustness of the proposed method of the key MAPOD metrics versus various detection thresholds are investigated and satisfactory results are obtained

Optimal Tilt-Wing EVTOL Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks

Electric vertical takeoff and landing (eVTOL) aircraft have attracted tremendous attention nowadays due to their flexible maneuverability, precise control, cost efficiency, and low noise. The optimal takeoff trajectory design is a key component of cost-effective and passenger-friendly eVTOL systems. However, conventional design optimization is typically computationally prohibitive due to the adoption of high-fidelity simulation models in an iterative manner. Machine learning (ML) allows rapid decision making; however, new ML surrogate modeling architectures and strategies are still desired to address large-scale problems. Therefore, we showcase a novel regression generative adversarial network (regGAN) surrogate for fast interactive optimal takeoff trajectory predictions of eVTOL aircraft. The regGAN leverages generative adversarial network architectures for regression tasks with a combined loss function of a mean squared error (MSE) loss and an adversarial binary cross-entropy (BC) loss. Moreover, we introduce a surrogate-based inverse mapping concept into eVTOL optimal trajectory designs for the first time. In particular, an inverse-mapping surrogate takes design requirements (including design constraints and flight condition parameters) as input and directly predicts optimal trajectory designs, with no need to run design optimizations once trained. We demonstrated the regGAN on optimal takeoff trajectory designs for the Airbus (Formula presented.) Vahana. The results revealed that regGAN outperformed reference surrogate strategies, including multi-output Gaussian processes and conditional generative adversarial network surrogates, by matching simulation-based ground truth with 99.6% relative testing accuracy using 1000 training samples. A parametric study showed that a regGAN surrogate with an MSE weight of one and a BC weight of 0.01 consistently achieved over 99.5% accuracy (denoting negligible predictive errors) using 400 training samples, while other regGAN models require at least 800 samples