Shared low-dimensional subspaces for propagating kinetic uncertainty to multiple outputs

Gradient free active subspace construction using Morris screening elementary effects

Quantifying kinetic uncertainty in turbulent combustion simulations using active subspaces

Reliability analysis and design of a single diode solar cell model using polynomial chaos and active subspace

Global sensitivity analysis and uncertainty quantification of soot formation in an n-dodecane spray flame

We present a distributed active subspace method for training surrogate models of complex physical processes with high-dimensional inputs and function valued outputs. Specifically, we represent the model output with a truncated Karhunen–Loeve (KL) expansion, screen the structure of the input space with respect to each KL mode via the active subspace method, and finally form an overall surrogate model of the output by combining surrogates of individual output KL modes. To ensure scalable computation of the gradients of the output KL modes, needed in active subspace discovery, we rely on adjoint-based gradient computation. The proposed method combines benefits of active subspace methods for input dimension reduction and KL expansions used for spectral representation of the output field. We provide a mathematical framework for the proposed method and conduct an error analysis of the mixed KL active subspace approach. Specifically, we provide an error estimate that quantifies errors due to active subspace projection and truncated KL expansion of the output. We demonstrate the numerical performance of the surrogate modeling approach with an application example from biotransport.

We present a distributed active subspace method for training surrogate models of complex physical processes with high-dimensional inputs and function valued outputs. Specifically, we represent the model output with a truncated Karhunen-Lo\`eve (KL) expansion, screen the structure of the input space with respect to each KL mode via the active subspace method, and finally form an overall surrogate model of the output by combining surrogates of individual output KL modes. To ensure scalable computation of the gradients of the output KL modes, needed in active subspace discovery, we rely on adjoint-based gradient computation. The proposed method combines benefits of active subspace methods for input dimension reduction and KL expansions used for spectral representation of the output field. We provide a mathematical framework for the proposed method and conduct an error analysis of the mixed KL active subspace approach. Specifically, we provide an error estimate that quantifies errors due to active subspace projection and truncated KL expansion of the output. We demonstrate the numerical performance of the surrogate modeling approach with an application example from biotransport.

Active subspaces for sensitivity analysis and dimension reduction of an integrated hydrologic model

Aerodynamic shape optimization plays a fundamental role in aircraft design. However, useful parameterizations of shapes for engineering models often result in high-dimensional design spaces which can create challenges for both local and global optimizers. In this paper, we employ an active subspace method (ASM) to discover and exploit low-dimensional, monotonic trends in the quantity of interest as a function of the design variables. The trend enables us to eciently and eectively nd an optimal design in appropriate areas of the design space. We apply this approach to the ONERA-M6 transonic wing, parameterized with 50 Free-Form Deformation (FFD) design variables. Given an initial set of 300 designs, the ASM discovered a low-dimensional linear subspace of the input space that explained the majority of the variability in the drag and lift coecients. This revealed a global trend that we exploited to nd an optimal design with reduced computational cost.

Design and optimization benefit from understanding the dependence of a quantity of interest (e.g., a design objective or constraint function) on the design variables. A low-dimensional active subspace, when present, identifies important directions in the space of design variables; perturbing a design along the active subspace associated with a particular quantity of interest changes that quantity more, on average, than perturbing the design orthogonally to the active subspace. This low-dimensional structure provides insights that characterize the dependence of quantities of interest on design variables. Airfoil design in a transonic flowfield with a parameterized geometry is a popular test problem for design methodologies. Two particular airfoil shape parameterizations are examined (parametric sections and class-shape transformation), and the active subspaces present in two common design quantities of interest (transonic lift and wave-drag coefficients) are studied under each shape parameterization. The two parameterizations are mathematically related by a simple change of variable and subsequent odd-power polynomial series. The active subspaces enable low-dimensional approximations of lift and drag that relate to physical perturbations of the airfoil. In particular, a two-dimensional approximation of both transonic lift and drag is obtained and interpreted, where 1) each dimension is a particular linear combination of the design variables resulting in intuitive airfoil perturbations, and 2) the linear combination weights are related between lift and drag. It is shown how these approximations along with similarities between the lift and drag active subspaces inform a multi-objective design problem.

Groundwater models always involve high‐dimensional parameters, which makes computationally tractable data assimilation using surrogate models very challenging. To address this issue, one common practice is to employ dimension reduction (DR) techniques. Nevertheless, traditional DR methods are usually implemented based on prior parameter statistics, that is, without considering the inherent system dynamics. Here, we show that when significant difference in parameter sensitivity exists, further efficiency can be achieved by adopting a supervised DR method, that is, the active subspace (AS) method. To avoid non‐trivial efforts in calculating the gradient information needed in the standard AS method, a cluster‐based gradient‐free AS (GFAS) method is developed in this study. By combining GFAS with Gaussian process regression, a surrogate model for the CPU‐demanding groundwater model can be adaptively constructed to accelerate data assimilation. Furthermore, a compensation scheme is proposed to cope with uncertainty underestimation caused by DR. The developed approach is tested with numerical experiments and field cases, which illustrated that the new approach is more efficient than the previously developed unsupervised ones by incorporating sensitivity information. Although an iterative ensemble smoother is employed in this study, the proposed method can also be used in other data assimilation approaches, such as Markov chain Monte Carlo and ensemble Kalman filter.

The fluid dynamic preliminary design of unconventional turbomachinery is customary done with meanline design procedures coupled with gradient-free optimizers. This method features various drawbacks, since it might become computationally expensive, and it does not provide design insights or guidelines to the designer. This work proposes a strategy to abate this disadvantages, namely, the construction of a reduced-order model by means of active sub-spaces, and the use of the surrogate combined with a gradient-based optimizer. The case study is the design optimization of a Organic Rankine Cycle radial inflow turbine. The results show that active subspaces exist for this application, and that it is possible to construct a surrogate with an approximate error of ±1% for the total-to-static efficiency. Additionally, the optimization using the surrogate leads to accurate results and a computational cost at least four times faster. Furthermore, the results reveal that the models for unconventional turbomachinery feature multiple regions containing constrained optima. Active subspace methods thus prove to be a promising alternative for optimization of unconventional turbomachinery.

Fuel octane number prediction based on topological indices and active subspace method

Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation

Advances in high-performance computational capabilities enable scientific simulations with increasingly realistic physical representations. This situation is especially true of turbulent combustion involving multiscale interactions between turbulent flow, complex chemical reaction, and scalar transport. A fundamental understanding of combustion processes is crucial to the development and optimization of next-generation combustion technologies operating with alternative fuels, at higher pressures, and under less stable operating conditions, such as highly dilute, stratified mixtures. Direct numerical simulations (DNS) of turbulent combustion resolving all flow and chemical features in canonical configurations are used to improve fundamental understanding of complex flow processes and to provide a database for the development and validation of combustion models. A description of the DNS solver and its optimization for use in massively parallel simulations is presented. Recent DNS results from a series of three combustion configurations are presented: soot formation and transport in a nonpremixed ethylene jet flame, the effect of fuel stratification in methane Bunsen flames, and extinction and reignition processes in nonpremixed ethylene jet flames.

This paper is the first application of the tensor-train (TT) cross approximation procedure for potential energy surface fitting. In order to reduce the complexity, we combine the TT-approach with another technique recently introduced in the field of numerical analysis: an affine transformation of Cartesian coordinates into the active subspaces where the PES function has the most variability. The numerical experiments for the water molecule and for the nitrous acid molecule confirm the efficiency of this approach.