Multi-objective optimization of a wing fence on an unmanned aerial vehicle using surrogate-derived gradients

In this paper, the multi-objective, multifidelity optimization of a wing fence on an unmanned aerial vehicle (UAV) near stall is presented. The UAV under consideration is characterized by a blended wing body (BWB), which increases its efficiency, and a tailless design, which leads to a swept wing to ensure longitudinal static stability. The consequence is a possible appearance of a nose-up moment, loss of lift initiating at the tips, and reduced controllability during landing, commonly referred to as tip stall. A possible solution to counter this phenomenon is wing fences: planes placed on top of the wing aligned with the flow and developed from the idea of stopping the transverse component of the boundary layer flow. These are optimized to obtain the design that would fence off the appearance of a pitch-up moment at high angles of attack, without a significant loss of lift and controllability. This brings forth a constrained multi-objective optimization problem. The evaluations are performed through unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations. However, since controllability cannot be directly assessed through computational fluid dynamics (CFD), surrogate-derived gradients are used. An efficient global optimization framework is developed employing surrogate modeling, namely regressive co-Kriging, updated using a multi-objective formulation of the expected improvement. The result is a wing fence design that extends the flight envelope of the aircraft, obtained with a feasible computational budget

A Generative Model of Natural Texture Surrogates

Natural images can be viewed as patchworks of different textures, where the local image statistics is roughly stationary within a small neighborhood but otherwise varies from region to region. In order to model this variability, we first applied the parametric texture algorithm of Portilla and Simoncelli to image patches of 64X64 pixels in a large database of natural images such that each image patch is then described by 655 texture parameters which specify certain statistics, such as variances and covariances of wavelet coefficients or coefficient magnitudes within that patch. To model the statistics of these texture parameters, we then developed suitable nonlinear transformations of the parameters that allowed us to fit their joint statistics with a multivariate Gaussian distribution. We find that the first 200 principal components contain more than 99% of the variance and are sufficient to generate textures that are perceptually extremely close to those generated with all 655 components. We demonstrate the usefulness of the model in several ways: (1) We sample ensembles of texture patches that can be directly compared to samples of patches from the natural image database and can to a high degree reproduce their perceptual appearance. (2) We further developed an image compression algorithm which generates surprisingly accurate images at bit rates as low as 0.14 bits/pixel. Finally, (3) We demonstrate how our approach can be used for an efficient and objective evaluation of samples generated with probabilistic models of natural images.Comment: 34 pages, 9 figure

Transcritical mixing of sprays for multi-component fuel mixtures

[EN] The mixing of fuels with oxidizer has been an increasingly interesting area of research with new engine technologies and the need to reduce emissions, while leveraging efficiency. High-efficiency combustion systems such as diesel engines rely on elevated chamber pressures to maximize power density, producing higher output. In such systems, the fuel is injected under liquid state in a chamber filled with pressurized air at high temperatures. Theoretical calculations on the thermodynamics of fuel mixing processes under these conditions suggest that the injected liquid can undergo a transcritical change of state. Our previous experimental efforts in that regard showed through highspeed imaging that spray droplets transition to fluid parcels mixing without notable surface tension forces, supporting a transcritical process. Only mono-component fuels were used in these studies to provide full control over boundary conditions, which prevented extrapolation of the findings to real systems in which multi-component fuels are injected. Multi-component fuels add another layer of complexity, especially when detailed experiments serve model development, requiring the fuels to be well characterized. In this work, we performed high-speed microscopy in the near-field of high-pressure sprays injected into elevated temperature and pressure environments. A reference diesel fuel and several multi-component surrogates were studied and compared to single component fuels. The results support that a transition occurs under certain thermodynamic conditions for all fuels. As anticipated, the transition from classical evaporation to diffusive mixing is affected by ambient conditions, fuel properties, droplet size and velocity, as well as time scales. Analogous to previous observations made with the normal alkane sprays, the behavior of the multi-component fuels correlate well with their bulk critical properties.This work was supported by the UK’s Engineering and Physical Science Research Council [grant number EP/K020528/1]. The authors gratefully acknowledge Coordinating Research Council Project AVFL-18a for formulating, characterizing, and providing the target and surrogate fuels used in this study. This study was performed at the Combustion Research Facility, Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.Manin, J.; Crua, C.; Pickett, LM. (2017). Transcritical mixing of sprays for multi-component fuel mixtures. En Ilass Europe. 28th european conference on Liquid Atomization and Spray Systems. Editorial Universitat Politècnica de València. 553-560. https://doi.org/10.4995/ILASS2017.2017.5065OCS55356

Gaussian process hyper-parameter estimation using parallel asymptotically independent Markov sampling

Gaussian process emulators of computationally expensive computer codes provide fast statistical approximations to model physical processes. The training of these surrogates depends on the set of design points chosen to run the simulator. Due to computational cost, such training set is bound to be limited and quantifying the resulting uncertainty in the hyper-parameters of the emulator by uni-modal distributions is likely to induce bias. In order to quantify this uncertainty, this paper proposes a computationally efficient sampler based on an extension of Asymptotically Independent Markov Sampling, a recently developed algorithm for Bayesian inference. Structural uncertainty of the emulator is obtained as a by-product of the Bayesian treatment of the hyper-parameters. Additionally, the user can choose to perform stochastic optimisation to sample from a neighbourhood of the Maximum a Posteriori estimate, even in the presence of multimodality. Model uncertainty is also acknowledged through numerical stabilisation measures by including a nugget term in the formulation of the probability model. The efficiency of the proposed sampler is illustrated in examples where multi-modal distributions are encountered. For the purpose of reproducibility, further development, and use in other applications the code used to generate the examples is freely available for download at https://github.com/agarbuno/paims_codesComment: Computational Statistics \& Data Analysis, Volume 103, November 201

ROV's Video Recordings as a Tool to Estimate Variation in Megabenthic Epifauna Diversity and Community Composition in the Guaymas Basin

Patterns in benthic megafauna diversity in littoral and intertidal zones in the Gulf of California have been associated with both habitat heterogeneity and substrate type. Current knowledge of invertebrate communities in hard bottom habitats at depths > 200 m in the Gulf is poor due to the methodological limitations inherent in sampling deep habitats. Using video imagery of benthic habitats coupled with environmental data from the Remotely Operated Vehicle Doc Ricketts, we documented variation in the diversity and community composition of the benthos from 849 to 990 m depth in the NW limit of the Guaymas Basin, in relation to dissolved oxygen and substrate characteristics. This depth range overlaps an oxygen minimum zone where oxygen drops to levels < 0.5 ml L-1 and strong gradients in a narrow depth range occur. Dissolved oxygen varied along our benthic survey from 0.200 to 0.135 ml L-1. We observed high taxonomic richness across an area of rocky outcrops through the lower transition zone. This megafaunal pattern differs from reports from other oxygen minimum zones characterized by a great abundance of a few species. Taxonomic richness diminished at depths with reduced dissolved oxygen in the lower boundary of the oxygen minimum zone with increasing soft sediment cover. We found that rocky outcrops and structure-forming organisms such as corals, sponges, and oyster aggregations supported a higher diversity (H' = 0.8) than soft sediment (H' = 0.7) as have been observed in other habitats such as seamounts. Environmental variables that explained most of the megafaunal variation were substrate type (18.4%), depth (1.14%) and temperature (0.9%). Salinity (0.45%) and dissolved oxygen (0.3%) were less important factors to explain the megafaunal composition variance. Substrate type played a key role in the diversity and composition of benthic megafauna. These results broaden our understanding concerning the potential roles of substrate characteristics in the community composition of the deep-sea benthic megafaunal assemblages in the Gulf of California and oxygen minimum zones in general

Benthic studies in LTER sites: the use of taxonomy surrogates in the detection of long-term changes in lagoonal benthic assemblages

In benthic studies, the identification of organisms at the species level is known to be the best source for ecological and biological information even if time-consuming and expensive. However, taxonomic sufficiency (TS) has been proposed as a short-cut method for quantifying changes in biological assemblages in environmental monitoring. In this paper, we set out to determine whether and how the taxonomic complexity of a benthic assemblage influences the results of TS at two different long-term ecological research (LTER) sites in the Po delta region (north-eastern Italy). Specifically, we investigated whether TS can be used to detect natural and human-driven patterns of variation in benthic assemblages from lagoonal soft bottoms. The first benthic dataset was collected from 1996 to 2015 in a “choked” lagoon, the Valli di Comacchio, a lagoon characterised by long water residence times and heavy eutrophication, while the second was collected from 2004 to 2010 in a “leaky” lagoon, the Sacca di Goro, a coastal area with human pressure limited to aquaculture. Univariate and multivariate statistical analyses were used to assess differences in the taxonomic structure of benthic assemblages and to test TS on the two different datasets. TS seemed to work from species to family level at both sites, despite a higher natural variability of environmental conditions combined with multiple anthropogenic stressors. Therefore, TS at the family level may represent effective taxonomic surrogates across a range of environmental contexts in lagoon environments. Since the structure of the community and the magnitude of changes could influence the efficiency of taxonomic surrogates and data transformations in long-term monitoring, we also suggest periodic analyses at finer taxonomic levels in order to check the efficiency of the application of taxonomic substitutes in routine monitoring programmes in lagoon systems

Surrogate model for an aligned-spin effective one body waveform model of binary neutron star inspirals using Gaussian process regression

Fast and accurate waveform models are necessary for measuring the properties of inspiraling binary neutron star systems such as GW170817. We present a frequency-domain surrogate version of the aligned-spin binary neutron star waveform model using the effective one body formalism known as SEOBNRv4T. This model includes the quadrupolar and octopolar adiabatic and dynamical tides. The version presented here is improved by the inclusion of the spin-induced quadrupole moment effect, and completed by a prescription for tapering the end of the waveform to qualitatively reproduce numerical relativity simulations. The resulting model has 14 intrinsic parameters. We reduce its dimensionality by using universal relations that approximate all matter effects in terms of the leading quadrupolar tidal parameters. The implementation of the time-domain model can take up to an hour to evaluate using a starting frequency of 20Hz, and this is too slow for many parameter estimation codes that require $O(10^7)$ sequential waveform evaluations. We therefore construct a fast and faithful frequency-domain surrogate of this model using Gaussian process regression. The resulting surrogate has a maximum mismatch of $4.5\times 10^{-4}$ for the Advanced LIGO detector, and requires 0.13s to evaluate for a waveform with a starting frequency of 20Hz. Finally, we perform an end-to-end test of the surrogate with a set of parameter estimation runs, and find that the surrogate accurately recovers the parameters of injected waveforms.Comment: 19 pages, 10 figures, submitted to PR