Non-intrusive double-greedy parametric model reduction by interpolation of frequency-domain rational surrogates

We propose a model order reduction approach for non-intrusive surrogate modeling of parametric dynamical systems. The reduced model over the whole parameter space is built by combining surrogates in frequency only, built at few selected values of the parameters. This, in particular, requires matching the respective poles by solving an optimization problem. If the frequency surrogates are constructed by a suitable rational interpolation strategy, frequency and parameters can both be sampled in an adaptive fashion. This, in general, yields frequency surrogates with different numbers of poles, a situation addressed by our proposed algorithm. Moreover, we explain how our method can be applied even in high-dimensional settings, by employing locally-refined sparse grids in parameter space to weaken the curse of dimensionality. Numerical examples are used to showcase the effectiveness of the method, and to highlight some of its limitations in dealing with unbalanced pole matching, as well as with a large number of parameters.

Accurate prediction of machining feedrate and cycle times considering interpolator dynamics

This paper presents an accurate machining feedrate prediction technique by modelling the trajectory generation behaviour of modern CNC machine tools. Typically, CAM systems simulate machines’ motion based on the commanded feedrate and the path geometry. Such approach does not consider the feed planning and interpolation strategy of the machine’s numerical control (NC) system. In this study, trajectory generation behaviour of the NC system is modelled and accurate cycle time prediction for complex machining toolpaths is realised. NC system’s linear interpolation dynamics and commanded axis kinematic profiles are predicted by using finite impulse response (FIR)–based low-pass filters. The corner blending behaviour during non-stop interpolation of linear segments is modelled, and for the first time, the minimum cornering feedrate that satisfies both the tolerance and machining constraints has been calculated analytically for 3-axis toolpaths of any geometry. The proposed method is applied to 4 different case studies including complex machining toolpaths. Experimental validations show actual cycle times can be estimated with > 90% accuracy, greatly outperforming CAM-based predictions. It is expected that the proposed approach will help improve the accuracy of virtual machining models and support businesses’ decision-making when costing machining processes.

Up and down deconvolution in complex geological scenarios

The redatuming approach, often referred to as up-down deconvolution, is well-known and applied to remove water-layer and source-signature effects in seabed seismic surveys. The upgoing wavefield can be expressed as the multidimensional convolution of the downgoing wavefield with the earth’s reflectivity. Consequently, deconvolving the downgoing wavefield from the upgoing wavefield, gives us the earth’s reflectivity response. The deconvolution process requires solving a multidimensional integral equation but, in a laterally invariant medium, after that wavefields are decomposed into plane-wave components, deconvolution can be enormously simplified if performed as a spectral division in the Fourier or Radon domain. It has been experimentally observed that deconvolution carried out one plane-wave component at a time gives good results, even in the presence of complex subsurface structures, provided that the seabed is relatively flat. When this geological condition is not satisfied, the same problem can be formulated in terms of interferometric redatuming using multidimensional deconvolution, where the integral equation solution is achieved by introducing the point-spread function concept. We present a methodology based on numerical simulations to determine when the integral equations associated with the problem of up-down deconvolution can be solved under the assumption of shift-invariant wavefields and when it requires multidimensional deconvolution. In the latter case, we propose a regularized inverse procedure that mitigates the numerical problems due to the typically ill-posed nature of the inversion and that, combined with an interpolation strategy for the downgoing, enables the application of multidimensional deconvolution within the range of sampling scenarios considered so far. We apply this methodology to synthetic data, and we discuss on the potential to extend up-down deconvolution to a broader range of geological conditions.

Integral performance optimization for the two-stage-to-orbit RBCC-RKT launch vehicle based on GPM

ABSTRACTThe takeoff-mass of a two-stage-to-orbit Rocket-Based Combined Cycle Engine-Rocket (RBCC-RKT) launch vehicle is a crucial factor in its comprehensive performance. This paper optimizes the takeoff-mass together with the trajectory by reformulating it to a nonlinear optimal control problem. The range of the second stage rocket mass is considered as a process constraint. When the scopes of initial and terminal states are specified, the problem can be solved by using the Gauss pseudo-spectral method (GPM). In order to reduce the convergent difficulty caused by using table data, the data in different stages are utilized by employing an integrated interpolation strategy through the optimization. Simulation results show that the mass can be effectively optimized to meet the inertia mass ratio constraint of the first-stage, and the separation of Mach number and altitude can be optimized at the same time.

Sun-Induced Chlorophyll Fluorescence III: Benchmarking Retrieval Methods and Sensor Characteristics for Proximal Sensing

The interest of the scientific community on the remote observation of sun‐induced chlorophyll fluorescence (SIF) has increased in the recent years. In this context, hyperspectral ground measurements play a crucial role in the calibration and validation of future satellite missions. For this reason, the European cooperation in science and technology (COST) Action ES1309 OPTIMISE has compiled three papers on instrument characterization, measurement setups and protocols, and retrieval methods (current paper). This study is divided in two sections; first, we evaluated the uncertainties in SIF retrieval methods (e.g., Fraunhofer line depth (FLD) approaches and spectral fitting method (SFM)) for a combination of off-the-shelf commercial spectrometers. Secondly, we evaluated how an erroneous implementation of the retrieval methods increases the uncertainty in the estimated SIF values. Results show that the SFM approach applied to high-resolution spectra provided the most reliable SIF retrieval with a relative error (RE) ≤6% and <5% for F687 and F760, respectively. Furthermore, although the SFM was the least affected by an inaccurate definition of the absorption spectral window (RE = 5%) and/or interpolation strategy (RE = 15%–30%), we observed a sensitivity of the SIF retrieval for the simulated training data underlying the SFM model implementation.