Testing and evaluation are expensive but critical steps in the development of connected and automated vehicles (CAVs). In this paper, we develop an adaptive sampling framework to efficiently evaluate the accident rate of CAVs, particularly for scenario-based tests where the probability distribution of input parameters is known from the Naturalistic Driving Data. Our framework relies on a surrogate model to approximate the CAV performance and a novel acquisition function to maximize the benefit (information to accident rate) of the next sample formulated through an information-theoretic consideration. In addition to the standard application with only a single high-fidelity model of CAV performance, we also extend our approach to the bi-fidelity context where an additional low-fidelity model can be used at a lower computational cost to approximate the CAV performance. Accordingly, for the second case, our approach is formulated such that it allows the choice of the next sample in terms of both fidelity level (i.e., which model to use) and sampling location to maximize the benefit per cost. Our framework is tested in a widely-considered two-dimensional cut-in problem for CAVs, where Intelligent Driving Model (IDM) with different time resolutions are used to construct the high and low-fidelity models. We show that our single-fidelity method outperforms the existing approach for the same problem, and the bi-fidelity method can further save half of the computational cost to reach a similar accuracy in estimating the accident rate.
While the primary origin of ocean diapycnal diffusivity is commonly attributed to stratified turbulence induced by breaking internal waves (IWs), verifying diffusivity values in ocean circulation models within specific geographical regions remains challenging due to limited microstructure measurements. Recent analyses of a downscaled global ocean simulation into higher-resolution regional setups northeast of Hawaii, reveal a notably enhanced fit between simulated IW spectra and in-situ profiler measurements like the Garrett-Munk spectrum [Nelson et al. (2020), Pan et al. (2020), Thakur et al. (2022)]. In this study, we utilize this dynamically downscaled ocean simulation to scrutinize the dynamics of IW-breaking and the wave-turbulence cascade in this region explicitly. Employing a modified version of the Kappa Profile Parameterization (KPP), we infer the horizontally-averaged vertical profile of diapycnal diffusivity. Comparing this inferred profile to the background profile used in low-resolution coupled climate models—such as the Community Earth System Model (CESM) by the US National Center for Atmospheric Research (NCAR)—is a central aspect of our investigation. Our exploration reveals that the wavefield in the high-resolution regional domain is dominated by a well-resolved spectrum of low-mode IWs, predictable through appropriate eigenvalue computations for stratified flow. Finally, we propose a novel tentative approach to enhance the KPP parameterization. This approach holds promise for refining our understanding of diapycnal diffusivity, offering valuable insights for improving ocean circulation models.   References: AD Nelson, BK Arbic, D Menemenlis, WR Peltier, MH Alford, N Grisouard, and JM Klymak. Improved internal wave spectral continuum in a regional ocean model. Journal of Geophysical Research: Oceans, 125(5):e2019JC015974, 2020. Yulin Pan, Brian K Arbic, Arin D Nelson, Dimitris Menemenlis, WR Peltier, Wentao Xu, and Ye Li. Numerical investigation of mechanisms underlying oceanic internal gravity wave power-law spectra. Journal of Physical Oceanography, 50(9):2713–2733, 2020. Ritabrata Thakur, Brian K Arbic, Dimitris Menemenlis, Kayhan Momeni, Yulin Pan, W Richard Peltier, Joseph Skitka, Matthew H Alford, and Yuchen Ma. Impact of vertical mixing parameterizations on internal gravity wave spectra in regional ocean models. Geophysical Research Letters, 49(16): e2022GL099614, 2022.
Abstract The internal wave (IW) continuum of a regional ocean model is studied in terms of the vertical spectral kinetic energy (KE) fluxes and transfers at high vertical wavenumbers. Previous work has shown that this model permits a partial representation of the IW cascade. In this work, vertical spectral KE flux is decomposed into catalyst, source, and destination vertical modes and frequency bands of nonlinear scattering, a framework that allows for the discernment of different types of nonlinear interactions involving both waves and eddies. Energy transfer within the supertidal IW continuum is found to be strongly dependent on resolution. Specifically, at a horizontal grid spacing of 1/48°, most KE in the supertidal continuum arrives there from lower-frequency modes through a single nonlinear interaction, whereas at 1/384° and with sufficient vertical resolution KE transfers within the supertidal IW continuum are comparable in size to KE transfer from lower-frequency modes. Additionally, comparisons are made with existing theoretical and observational work on energy pathways in the IW continuum. Induced diffusion (ID) is found to be associated with a weak forward frequency transfer within the supertidal IW continuum. ID is also limited to the highest vertical wavenumbers and is more sensitive to resolution relative to spectrally local interactions. At the same time, ID-like processes involving high-vertical-wavenumber near-inertial and tidal waves as well as low-vertical-wavenumber eddy fields are substantial, suggesting that the processes giving rise to a Garrett–Munk-like spectra in the present numerical simulation and perhaps the real ocean may be more varied than in idealized or wave-only frameworks.
Using the 1D Majda-McLaughlin-Tabak model as an example, we develop numerical experiments to study the validity of the wave kinetic equation (WKE) at the kinetic limit (i.e., small nonlinearity and large domain). We show that the dynamics converge to the WKE prediction, in terms of the closure model and energy flux, when the kinetic limit is approached. When the kinetic limit is combined with a process of widening the inertial range, the theoretical Kolmogorov constant can be recovered numerically to a very high precision. Published by the American Physical Society 2024
It is well known that wave collapses can emerge from the focusing one-dimensional (1-D) Majda-McLaughlin-Tabak (MMT) model as a result of modulational instability. However, how these wave collapses affect the spectral properties and statistics of the wave field has not been adequately studied. We undertake this task by simulating the forced-dissipated 1-D MMT model over a range of forcing amplitudes. Our results show that when the forcing is weak, the spectrum agrees well with the prediction by wave turbulence theory with few collapses in the field. As the forcing strength increases, we see an increase in the occurrence of collapses, together with a transition from a power-law spectrum to an exponentially decaying spectrum. Through a spectral decomposition, we find that the exponential spectrum is dominated by the wave collapse component in the non-integrable MMT model, which is in analogy to a soliton gas in integrable turbulence.
Abstract We study the flow physics underlying the recently developed remote sensing capability of detecting oceanic microplastics, which is based on the measurable surface roughness reduction induced by the presence of microplastics on the ocean surface. In particular, we are interested in whether this roughness reduction is caused by the microplastics as floating particles, or by surfactants which follow similar transport paths as microplastics. For this purpose, we experimentally test the effects of floating particles and surfactants on surface roughness, quantified by the mean square slope (MSS), with waves generated by a mechanical wave maker or by wind. For microplastics, we find that their effect on MSS critically depends on the surface area fraction of coverage. The damping by particles is observed only for fractions above O (5–10%), much higher than the realistic ocean condition. For surfactants, their damping effects on both mechanically generated waves and wind waves are quantified, which are shown to be much more significant than that by microplastics. Several new mechanisms/relations for roughness damping by surfactants are also identified. The implications of these experimental results to remote sensing are discussed.
With ever-tightening emission regulations, particulate filters are critical for internal combustion engines to meet the stringent particulate matter emission standards. A fast way to predict the filter performance, instead of numerically solving the governing differential equations, is needed for filter design and selection, real-time control, malfunction detection, and deposit load sensing. Approximate analytical solutions for wall flow filters, considering asymmetric channels and arbitrary deposit amounts, are derived by a technique of successive approximation. The analytical predictions of filter pressure drop have been validated against both steady state and transient experimental measurements. Moreover, over a broad range of filter operating conditions, the accuracy of the second-order analytical solution is validated by comparisons with the numerical predictions. The derivation also provides analytical expressions for channel and wall velocity profiles along the filter length. This study reveals the necessity of considering the nonlinear term of the governing equations when the actual open widths of inlet and outlet channels are quite different.
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