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Molnupiravir Form I crystallizes in space group C2 (#5) with a = 6.48110(17), b = 8.71848(19), c = 27.0607(19) Å, β = 91.920(4)°, V = 1528.22(12) Å3, and Z = 4 at 295 K. The crystal structure consists of supramolecular double layers of molecules parallel to the ab-plane. The layer centers consist of hydrogen-bonded rings forming a 2D network and the outer surfaces of isopropyl groups, with van der Waals interactions between the layers. Each O atom acts as an acceptor in at least one hydrogen bond. A strong O–H⋯O hydrogen bond forms between the hydroxyl group of the oxolane ring and the carbonyl group of the oxopyrimidine ring. The other oxolane hydroxyl group forms bifurcated intra- and intermolecular hydrogen bonds. The hydroxylamino group forms an intramolecular O–H⋯N hydrogen bond with an N atom of the oxopyrimidine ring. The amino group forms an intermolecular N–H⋯N hydrogen bond to the same N atom of the ring. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).
Mathematical optimization models are mathematical means to find the best possible solutions to real-life optimization problems. They consist of three parts: decision variables that describe possible solutions, constraints that define conditions that these solutions need to satisfy, and an objective function that assigns a value to each solution, expressing how “good” it is.
In all the optimization problems discussed so far, we treated the quantities in the problem description as exact, but, in reality, they cannot always be trusted or assumed to be what we think. Uncertainty might negatively affect solutions to an optimization problem in the following forms:
Estimation/forecast errors (increasingly important in an ML-driven world):
– in a production planning problem, future customer demand is a forecast;
– in a vehicle routing problem, travel times along various roads are real-time updated forecasts;
– in a wind farm layout problem, power production levels are based on wind forecasts.
Measurement errors:
– a warehouse manager might have errors in the data records regarding current stock levels;
– the concentration level of a given chemical substance is different from expected.
Implementation errors:
– a given quantity of an ingredient is sent to production in a chemical company, but due to device errors, a slightly smaller amount is actually received;
– electrical power sent to an antenna is subject to the generator’s errors.
Poiseuille flow is a fundamental flow in fluid mechanics and is driven by a pressure gradient in a channel. Although the rheology of active particle suspensions has been investigated extensively, knowledge of the Poiseuille flow of such suspensions is lacking. In this study, dynamic simulations of a suspension of active particles in Poiseuille flow, situated between two parallel walls, were conducted by Stokesian dynamics assuming negligible inertia. Active particles were modelled as spherical squirmers. In the case of inert spheres in Poiseuille flow, the distribution of spheres between the walls was layered. In the case of non-bottom-heavy squirmers, on the other hand, the layers collapsed and the distribution became more uniform. This led to a much larger pressure drop for the squirmers than for the inert spheres. The effects of volume fraction, swimming mode, swimming speed and the wall separation on the pressure drop were investigated. When the squirmers were bottom heavy, they accumulated at the channel centre in downflow, whereas they accumulated near the walls in upflow, as observed in former experiments. The difference in squirmer configuration alters the hydrodynamic force on the wall and hence the pressure drop and effective viscosity. In upflow, pusher squirmers induced a considerably larger pressure drop, while neutral and puller squirmers could even generate negative pressure drops, i.e. spontaneous flow could occur. While previous studies have reported negative viscosity of pusher suspensions, this study shows that the effective viscosity of bottom-heavy puller suspensions can be negative for Poiseuille upflow, which is a new finding. The knowledge obtained is important for understanding channel flow of active suspensions.
In this chapter, compared to Chapter 8 we assume that data or expert knowledge can tell us not only something about the possible values of the problem’s parameters but also about their relative likelihood, that is, the probability distribution.
We now consider problems in which the situation is not as simple as “first we make the decisions, then we observe the uncertainty and compute the costs”
We used the PW high-repetition laser facility VEGA-3 at Centro de Láseres Pulsados in Salamanca, with the goal of studying the generation of radioisotopes using laser-driven proton beams. Various types of targets have been irradiated, including in particular several targets containing boron to generate α-particles through the hydrogen–boron fusion reaction. We have successfully identified γ-ray lines from several radioisotopes created by irradiation using laser-generated α-particles or protons including 43Sc, 44Sc, 48Sc, 7Be, 11C and 18F. We show that radioisotope generation can be used as a diagnostic tool to evaluate α-particle generation in laser-driven proton–boron fusion experiments. We also show the production of 11C radioisotopes, $\approx 6 \times 10^{6}$, and of 44Sc radioisotopes, $\approx 5 \times 10^{4}$ per laser shot. This result can open the way to develop laser-driven radiation sources of radioisotopes for medical applications.
We seek the conditions in which Alfvén waves (AW) can be produced in laboratory-scale liquid metal experiments, i.e. at low magnetic Reynolds Number ($Rm$). Alfvén waves are incompressible waves propagating along magnetic fields typically found in geophysical and astrophysical systems. Despite the high values of $Rm$ in these flows, AW can undergo high dissipation in thin regions, for example in the solar corona where anomalous heating occurs (Davila, Astrophys. J., vol. 317, 1987, p. 514; Singh & Subramanian, Sol. Phys., vol. 243, 2007, pp. 163–169). Understanding how AW dissipate energy and studying their nonlinear regime in controlled laboratory conditions may thus offer a convenient alternative to observations to understand these mechanisms at a fundamental level. Until now, however, only linear waves have been experimentally produced in liquid metals because of the large magnetic dissipation they undergo when $Rm\ll 1$ and the conditions of their existence at low $Rm$ are not understood. To address these questions, we force AW with an alternating electric current in a liquid metal in a transverse magnetic field. We provide the first mathematical derivation of a wave-bearing extension of the usual low-$Rm$ magnetohydrodynamics (MHD) approximation to identify two linear regimes: the purely diffusive regime exists when $N_{\omega }$, the ratio of the oscillation period to the time scale of diffusive two-dimensionalisation by the Lorentz force, is small; the propagative regime is governed by the ratio of the forcing period to the AW propagation time scale, which we call the Jameson number $Ja$ after (Jameson, J. Fluid Mech., vol. 19, issue 4, 1964, pp. 513–527). In this regime, AW are dissipative and dispersive as they propagate more slowly where transverse velocity gradients are higher. Both regimes are recovered in the FlowCube experiment (Pothérat & Klein, J. Fluid Mech., vol. 761, 2014, pp. 168–205), in excellent agreement with the model up to $Ja \lesssim 0.85$ but near the $Ja=1$ resonance, high amplitude waves become clearly nonlinear. Hence, in electrically driving AW, we identified the purely diffusive MHD regime, the regime where linear, dispersive AW propagate, and the regime of nonlinear propagation.
In the previous chapters, we dealt with general problems by first formulating all necessary constraints and then passing the problem to an LO or MILO solver, but in a way, we have been oblivious to the problem’s structure. However, it is often advantageous to analyze this structure, as it can enable us to find better solution methods. In this chapter, we consider a very general class of problems with special structure – the network problems. In the following example, we illustrate the key ideas.
The double-cone ignition scheme is a promising novel ignition method, which is expected to greatly save the driver energy and enhance the robustness of the implosion process. In this paper, ablation of the inner surface of the cone by the hard X-ray from coronal Au plasma is studied via radiation hydrodynamics simulations. It is found that the X-ray ablation of the inner wall will form strong pre-plasma, which will significantly affect the implosion process and cause the Au plasma to mix with the fuel, leading to ignition failure. The radiation and pre-ablation intensities in the system are estimated, and the evolutions of areal density, ion temperature and the distribution of Au ions are analysed. In addition, the mixing of Au in CH at collision is quantified. Then, a scheme to reduce the X-ray pre-ablation by replacing the gold cone with a tungsten cone is proposed, showing that it is effective in reducing high-Z mixing and improving collision results.
In Chapter 5, we claimed that the watershed between easy and difficult problems is their convexity status. Convex optimization problems are, however, a very broad class and one of their downsides is that the dual problem is not always readily available; see the discussion in Section 5.3. In view of the computational benefits of concurrently solving the primal and dual problems, a natural question arises: Is there a subclass of convex optimization problems that are expressive enough to model relevant real-life problems and, at the same time, allow us for a systematic derivation of the dual akin to linear optimization?
Direct numerical simulations of temporally developing compressible mixing layers have been performed to investigate the effects of large-scale structures (LSSs) on turbulent kinetic energy (TKE) budgets at convective Mach numbers ranging from $M_c=0.2$ to $1.8$ and at Taylor Reynolds numbers up to 290. In the core region of mixing layers, the volume fraction of low-speed LSSs decreases linearly with respect to the vertical distance at a Mach-number-independent rate. The contributions of low-speed LSSs to TKE, and its budget, including production, dissipation, pressure-strain and spatial diffusion terms, are primarily concentrated in the upper region of mixing layer. The streamwise and vertical mass flux coupling terms mainly transport TKE downwards in low-speed LSSs, and their magnitudes are comparable to the other dominant terms. Near the edges of LSSs, the sources and losses of all three components of TKE are completely different to each other, and dominated by turbulent diffusion, pressure diffusion, pressure-strain and dissipation terms. The TKE, their total variation and dissipation are significantly amplified at edges of low-speed LSSs, especially at the upper edge. This observation supports the existence of amplitude modulation exerted by the LSSs onto the near-edge small-scale structures in mixing layers. The level of amplitude modulation is strongest for the vertical velocity, followed by the streamwise velocity, and weakest for the spanwise velocity. Additionally, the amplitude modulation effect decreases significantly with increasing convective Mach number. The results on the amplitude modulation effect is helpful for developing predictive models of budget terms of TKE in mixing layers.
Active suspensions encompass a wide range of complex fluids containing microscale energy-injecting particles, such as cells, bacteria or artificially powered active colloids. Because they are intrinsically non-equilibrium, active suspensions can display a number of fascinating phenomena, including turbulent-like large-scale coherent motion and enhanced diffusion. Here, using a recently developed active fast Stokesian dynamics method, we present a detailed numerical study of the hydrodynamic diffusion in apolar active suspensions of squirmers. Specifically, we simulate suspensions of active but non-self-propelling spherical squirmers (or ‘shakers’), of either puller type or pusher type, at volume fractions from 0.5 % to 55 %. Our results show little difference between pulling and pushing shakers in their instantaneous and long-time dynamics, where the translational dynamics varies non-monotonically with the volume fraction, with a peak diffusivity at around 10 % to 20 %, in stark contrast to suspensions of self-propelling particles. On the other hand, the rotational dynamics tends to increase with the volume fraction as is the case for self-propelling particles. To explain these dynamics, we provide detailed scaling and statistical analyses based on the activity-induced hydrodynamic interactions and the observed microstructural correlations, which display a weak local order. Overall, these results elucidate and highlight the different effects of particle activity versus motility on the collective dynamics and transport phenomena in active fluids.
This study investigates experimentally the pressure fluctuations of liquids in a column under short-time acceleration. It demonstrates that the Strouhal number $St=L/(c\,\Delta t)$, where $L$, $c$ and $\Delta t$ are the liquid column length, speed of sound, and acceleration duration, respectively, provides a measure of the pressure fluctuations for intermediate $St$ values. On the one hand, the incompressible fluid theory implies that the magnitude of the averaged pressure fluctuation $\bar {P}$ becomes negligible for $St\ll 1$. On the other hand, the water hammer theory predicts that the pressure tends to $\rho cu_0$ (where $u_0$ is the change in the liquid velocity) for $St\geq O(1)$. For intermediate $St$ values, there is no consensus on the value of $\bar {P}$. In our experiments, $L$, $c$ and $\Delta t$ are varied so that $0.02 \leq St \leq 2.2$. The results suggest that the incompressible fluid theory holds only up to $St\sim 0.2$, and that $St$ governs the pressure fluctuations under different experimental conditions for higher $St$ values. The data relating to a hydrogel also tend to collapse to a unified trend. The inception of cavitation in the liquid starts at $St\sim 0.2$ for various $\Delta t$, indicating that the liquid pressure goes lower than the liquid vapour pressure. To understand this mechanism, we employ a one-dimensional wave propagation model with a pressure wavefront of finite thickness that scales with $\Delta t$. The model provides a reasonable description of the experimental results as a function of $St$.
The particular feature of linear optimization problems is that as long as the decision variables satisfy all the constraints, they can take any value. However, there are many situations in which it makes sense to restrict the solution space in a way that cannot be expressed using linear (in)equality constraints. For example, some numbers might need to be integers, such as the number of people to be assigned to a task. Another situation is when certain constraints need to hold only if another constraint holds. For example, the amount of power generated by a power plant must not be less than a certain minimum threshold only if that generator is turned on. Neither of these two examples can be expressed using only linear constraints, as we have seen up to this point. In these cases, it is often still possible to formulate the problem as an LO problem, although some additional restrictions may be needed on certain variables, requiring them to take integer values only. We will refer to this type of LO problem in which some variables are constrained to be integers as mixed-integer linear optimization (MILO) problems.