Central Density and Low-Mode Perturbation Control of Inertial Confinement Fusion Dynamic-Shell Targets

The dynamic-shell target is a new class of design for inertial confinement fusion (ICF). These targets address some of the target fabrication challenges prevalent in current ICF targets and take advantage of advances in manufacturing technologies. This study first examines how the dynamic-shell design can be used to control the density of the central region and therefore convergence ratio, thus expanding the design space for ICF. Additionally, the concern of low-mode perturbation growth is considered. A new class of high-performing beam configurations, based on icosahedral polyhedra and charged-particle simulations is proposed. These configurations achieve low levels of irradiation nonuniformity through selection of beam shapes that suppress the dominant symmetrical mode.

Richtmyer–Meshkov instability on two-dimensional multi-mode interfaces

Shock-tube experiments on eight kinds of two-dimensional multi-mode air–SF $_6$ interface with controllable initial conditions are performed to examine the dependence of perturbation growth on initial spectra. We deduce and demonstrate experimentally that the amplitude development of each mode is influenced by the mode-competition effect from quasi-linear stages. It is confirmed that the mode-competition effect is closely related to initial spectra, including the wavenumber, the phase and the initial amplitude of constituent modes. By considering both the mode-competition effect and the high-order harmonics effect, a nonlinear model is established based on initial spectra to predict the amplitude growth of each individual mode. The nonlinear model is validated by the present experiments and data in the literature by considering diverse initial spectra, shock intensities and density ratios. Moreover, the nonlinear model is successfully extended based on the superposition principle to predict the growths of the total perturbation width and the bubble/spike width from quasi-linear to nonlinear stages.

Ensemble Transform Kalman Incremental Smoother and Its Application to Data Assimilation and Prediction

The analysis correction made by data assimilation (DA) can introduce model shock or artificial signal, leading to degradation in forecast. In this study, we propose an Ensemble Transform Kalman Incremental Smoother (ETKIS) as an incremental update solution for ETKF-based algorithms. ETKIS not only has the advantages as other incremental update schemes to improve the balance in the analysis but also provides effective incremental correction, even under strong nonlinear dynamics. Results with the shallow-water model show that ETKIS can smooth out the imbalance associated with the use of covariance localization. More importantly, ETKIS preserves the moving signal better than the overly smoothed corrections derived by other incremental update schemes. Results from the Lorenz 3-variable model show that ETKIS and ETKF achieve similar accuracy at the end of the assimilation window, while the time-varying increment of ETKIS allows the ensemble to avoid strong corrections during strong nonlinearity. ETKIS shows benefits over 4DIAU by better capturing the evolving error and constraining the over-dispersive spread under conditions of long assimilation windows or a high perturbation growth rate.

Using GAMA to probe the impact of small-scale galaxy physics on nonlinear redshift-space distortions

We present improved modelling of the redshift-space distortions of galaxy clustering that arise from peculiar velocities. We create mock galaxy catalogues in the framework of the halo model, using data from the Bolshoi project. These mock galaxy populations are inserted into the haloes with additional degrees of freedom that govern spatial and kinematical biases of the galaxy populations relative to the dark matter. We explore this generalised halo model with an MCMC algorithm, comparing the predictions to data from the Galaxy And Mass Assembly (GAMA) survey, and thus derive one of the first constraints on the detailed kinematic degrees of freedom for satellite galaxies within haloes. With this approach, the distortions of the redshift-space galaxy autocorrelations can be accounted for down to spatial separations close to 10 kpc, opening the prospect of improved RSD measurements of the perturbation growth rate by the inclusion of data from nonlinear scales.

Non-Modal Three-Dimensional Optimal Perturbation Growth in Thermally Stratified Mixing Layers

A non-modal transient disturbances growth in a stably stratified mixing layer flow is studied numerically. The model accounts for a density gradient within a shear region, implying a heavier layer at the bottom. Numerical analysis of non-modal stability is followed by a full three-dimensional direct numerical simulation (DNS) with the optimally perturbed base flow. It is found that the transient growth of two-dimensional disturbances diminishes with the strengthening of stratification, while three-dimensional disturbances cause significant non-modal growth, even for a strong, stable stratification. This non-modal growth is governed mainly by the Holmboe modes and does not necessarily weaken with the increase of the Richardson number. The optimal perturbation consists of two waves traveling in opposite directions. Compared to the two-dimensional transient growth, the three-dimensional growth is found to be larger, taking place at shorter times. The non-modal growth is observed in linearly stable regimes and, in slightly linearly supercritical regimes, is steeper than that defined by the most unstable eigenmode. The DNS analysis confirms the presence of the structures determined by the transient growth analysis.

Quantification of wake unsteadiness for low-Re flow across two staggered cylinders

This work performs a numerical investigation on the two-dimensional flow across two circular cylinders in staggered arrangement at Re = 100. The seaparting distances between the centers of the cylinders are D/ d = 4–10 with Δ D/ d = 2 and T/ d = 0.0–2.0 with Δ T/ d = 0.5 in the streamwise and transverse directions, respectively, in which d is the cylinder diameter. Although the low- Re flow across staggered cylinders has been studied in a number of works, the authors mainly concerned about the identification and transition of various flow patterns. In this work, our objective is to quantitatively reveal the characteristics of flow unsteadiness as affected by the two separating distances. The flow unsteadiness is assessed from several aspects, including the spatial distributions and temporal variations of instantaneous flow patterns, fluctuating characteristic quantities, and fluctuating flow in the gap and in the near- and far-wake regions. To investigate the inherent instability of the flow, the global linear stability and sensitivity analysis is further carried out to demonstrate the unstable mode of perturbation growth and the critical flow patterns that destabilize the flow. The numerical results reveal that the wake flow between the two centerlines and beside the upstream cylinder is the most intensely perturbed. The flow around the downstream cylinder exhibits great fluctuation as perturbed by the destabilized shear layer of the upstream cylinder. The flow downstream of both cylinders shows multiple peak fluctuation of velocity because of the complex interactions between the destabilized shear layer and the wake vortices, resulting in the bidirectional transverse propagation of fluctuation. The stability analysis demonstrates that the unstable mode of perturbation growth is more significant in the far-wake region as the two cylinders are placed in proximity; the sensitivity analysis shows that the gap flow is crucial for the flow destabilization at small D, while the wake flow of cylinder- B is more significant for large D.

Adjoint Sensitivity of North Pacific Atmospheric River Forecasts

The initial-state sensitivity and optimal perturbation growth for 24- and 36-h forecasts of low-level kinetic energy and precipitation over California during a series of atmospheric river (AR) events that took place in early 2017 are explored using adjoint-based tools from the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). This time period was part of the record-breaking winter of 2016–17 in which several high-impact ARs made landfall in California. The adjoint sensitivity indicates that both low-level winds and precipitation are most sensitive to mid- to lower-tropospheric perturbations in the initial state in and near the ARs. A case study indicates that the optimal moist perturbations occur most typically along the subsaturated edges of the ARs, in a warm conveyor belt region. The sensitivity to moisture is largest, followed by temperature and winds. A 1 g kg−1 perturbation to moisture may elicit twice as large a response in kinetic energy and precipitation as a 1 m s−1 perturbation to the zonal or meridional wind. In an average sense, the sensitivity and related optimal perturbations are very similar for the kinetic energy and precipitation response functions. However, on a case-by-case basis, differences in the sensitivity magnitude and optimal perturbation structures result in substantially different forecast perturbations, suggesting that optimal adaptive observing strategies should be metric dependent. While the nonlinear evolved perturbations are usually smaller (by about 20%, on average) than the expected linear perturbations, the optimal perturbations are still capable of producing rapid nonlinear perturbation growth. The positive correlation between sensitivity magnitude and wind speed forecast error or precipitation forecast differences supports the relevance of adjoint-based calculations for predictability studies.