Efficient stochastic finite element methods for flow in heterogeneous porous media. Part 2: random lognormal permeability

Efficient and robust iterative methods are developed for solving the linear systems of equations arising from stochastic finite element methods for single phase fluid flow in porous media. Permeability is assumed to vary randomly in space according to some given correlation function. In the companion paper, herein referred to as Part 1, permeability was approximated using a truncated Karhunen‐Loève expansion (KLE). The stochastic variability of permeability is modeled using lognormal random fields and the truncated KLE is projected onto a polynomial chaos basis. This results in a stochastic nonlinear problem since the random fields are represented using polynomial chaos containing terms that are generally nonlinear in the random variables. Symmetric block Gauss‐Seidel used as a preconditioner for CG is shown to be efficient and robust for stochastic finite element method

Data-driven modeling for drop size distributions

The prediction of the drop size distribution (DSD) resulting from liquid atomization is key to the optimization of multiphase flows from gas-turbine propulsion through agriculture to healthcare. Obtaining high-fidelity data of liquid atomization, either experimentally or numerically, is expensive, which makes the exploration of the design space difficult. First, to tackle these challenges, we propose a framework to predict the DSD of a liquid spray based on data as a function of the spray angle, the Reynolds number, and the Weber number. Second, to guide the design of liquid atomizers, the model accurately predicts the volume of fluid contained in drops of specific sizes while providing uncertainty estimation. To do so, we propose a Gaussian process regression (GPR) model, which infers the DSD and its uncertainty form the knowledge of its integrals and of its first moment, i.e., the mean drop diameter. Third, we deploy multiple GPR models to estimate these quantities at arbitrary points of the design space from data obtained from a large number of numerical simulations of a flat fan spray. The kernel used for reconstructing the DSD incorporates prior physical knowledge, which enables the prediction of sharply peaked and heavy-tailed distributions. Fourth, we compare our method with a benchmark approach, which estimates the DSD by interpolating the frequency polygon of the binned drops with a GPR. We show that our integral approach is significantly more accurate, especially in the tail of the distribution (i.e., large, rare drops), and it reduces the bias of the density estimator by up to 10 times. Finally, we discuss physical aspects of the model's predictions and interpret them against experimental results from the literature. This work opens opportunities for modeling drop size distribution in multiphase flows from data

Experimental and numerical study of error fields in the CNT stellarator

Sources of error fields were indirectly inferred in a stellarator by reconciling computed and numerical flux surfaces. Sources considered so far include the displacements and tilts (but not the deformations, yet) of the four circular coils featured in the simple CNT stellarator. The flux surfaces were measured by means of an electron beam and phosphor rod, and were computed by means of a Biot-Savart field-line tracing code. If the ideal coil locations and orientations are used in the computation, agreement with measurements is poor. Discrepancies are ascribed to errors in the positioning and orientation of the in-vessel interlocked coils. To that end, an iterative numerical method was developed. A Newton-Raphson algorithm searches for the coils' displacements and tilts that minimize the discrepancy between the measured and computed flux surfaces. This method was verified by misplacing and tilting the coils in a numerical model of CNT, calculating the flux surfaces that they generated, and testing the algorithm's ability to deduce the coils' displacements and tilts. Subsequently, the numerical method was applied to the experimental data, arriving at a set of coil displacements whose resulting field errors exhibited significantly improved quantitative and qualitative agreement with experimental results.Comment: Special Issue on the 20th International Stellarator-Heliotron Worksho

Inelastic and elastic collision rates for triplet states of ultracold strontium

We report measurement of the inelastic and elastic collision rates for ^{88}Sr atoms in the (5s5p)^3P_0 state in a crossed-beam optical dipole trap. This is the first measurement of ultracold collision properties of a ^3P_0 level in an alkaline-earth atom or atom with similar electronic structure. Since the (5s5p)^3P_0 state is the lowest level of the triplet manifold, large loss rates indicate the importance of principle-quantum-number-changing collisions at short range. We also provide an estimate of the collisional loss rates for the (5s5p){^3P_2} state.Comment: 4 pages 5 figure

Repumping and spectroscopy of laser-cooled Sr atoms using the (5s5p)3P2 - (5s4d)3D2 transition

We describe repumping and spectroscopy of laser-cooled strontium (Sr) atoms using the (5s5p)3P2 - (5s4d)3D2 transition. Atom number in a magneto-optical trap is enhanced by driving this transition because Sr atoms that have decayed into the (5s5p)3P2 dark state are repumped back into the (5s2)1S0 ground state. Spectroscopy of 84Sr, 86Sr, 87Sr, and 88Sr improves the value of the (5s5p)3P2 - (5s4d)3D2 transition frequency for 88Sr and determines the isotope shifts for the transition.Comment: 4 pages, 5 figure

Modeling Web Services by Iterative Reformulation of Functional and Non-Functional Requirements

Abstract. We propose an approach for incremental modeling of composite Web services. The technique takes into consideration both the functional and nonfunctional requirements of the composition. While the functional requirements are described using symbolic transition systems—transition systems augmented with state variables, function invocations, and guards; non-functional requirements are quantified using thresholds. The approach allows users to specify an abstract and possibly incomplete specification of the desired service (goal) that can be realized by selecting and composing a set of pre-existing services. In the event that such a composition is unrealizable, i.e. the composition is not functionally equivalent to the goal or the non-functional requirements are violated, our system provides the user with the causes for the failure, that can be used to appropriately reformulate the functional and/or non-functional requirements of the goal specification.

Glutathione is key to the synergistic enhancement of doxorubicin and etoposide by polyphenols in leukaemia cell lines

Recently published in Nature: Cell Death and Discovery, Mahbub et al.1 have demonstrated that polyphenols can synergistically enhance the action of the topoisomerase II inhibitors: doxorubicin and etoposide in leukaemia cells. A reduction of glutathione (GSH) was strongly associated with sensitising cells to the pro-apoptotic effects of polyphenols when used in combination with doxorubicin or etoposide. Importantly, when polyphenols and topoisomerase II inhibitors were combined, it was possible to induce a synergistic decrease in cell proliferation (measured as ATP levels), cell-cycle arrest and induction of apoptosis in leukaemia cell lines

Two-photon photoassociative spectroscopy of ultracold 88-Sr

We present results from two-photon photoassociative spectroscopy of the least-bound vibrational level of the X$^1\Sigma_g^+$ state of the $^{88}$Sr$_2$ dimer. Measurement of the binding energy allows us to determine the s-wave scattering length, $a_{88}=-1.4(6) a_0$. For the intermediate state, we use a bound level on the metastable $^1S_0$-$^3P_1$ potential, which provides large Franck-Condon transition factors and narrow one-photon photoassociative lines that are advantageous for observing quantum-optical effects such as Autler-Townes resonance splittings.Comment: 9 pages, 9 figure

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy