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The purpose of this paper is to propose the use of the continuous adjoint method as a tool to identify the appropriate location and “type” (suction or blowing) of steady jets used in active flow control systems.

The method is based on continuous adjoint and covers both internal and external aerodynamics. The adjoint equations, including the adjoint to the SpalartAllmaras turbulence model and their boundary conditions are formulated. At the cost of solving the flow and adjoint equations just once, the sensitivity derivatives of the objective function with respect to hypothetical (normal) jet velocities at all wall nodes are computed. Comparisons of the computed sensitivities with finite differences and parametric studies to assess the present method are included.

Though the sensitivities are computed for zero jet velocities, they adequately support decision making on: the recommended location of jet(s), at boundary nodes with high absolute valued sensitivities; and the selection between suction or blowing jets, based on the sign of the computed sensitivities. Regarding adjoint methods, two important findings of this work are: the role of the adjoint pressure which proves to be an excellent sensor in flow control problems; and the prediction accuracy of the proposed adjoint method compared to the commonly made assumption of “frozen turbulence”.

First use of the continuous adjoint method using full differentiation of the turbulence model, in flow control optimization. A low‐cost design tool for recommending some of the most important jet characteristics.

Topology optimization is a method used for developing optimized geometric designs by distributing material pixels in a given design space that maximizes a chosen quantity of interest (QoI) subject to constraints. The purpose of this study is to develop a problem-agnostic automatic differentiation (AD) framework to compute sensitivities of the QoI required for density distribution-based topology optimization in an unstructured co-located cell-centered finite volume framework. Using this AD framework, the authors develop and demonstrate the topology optimization procedure for multi-dimensional steady-state heat conduction problems.

Topology optimization is performed using the well-established solid isotropic material with penalization approach. The method of moving asymptotes, a gradient-based optimization algorithm, is used to perform the optimization. The sensitivities of the QoI with respect to design variables, required for optimization algorithm, are computed using a discrete adjoint method with a novel AD library named residual automatic partial differentiator (Rapid).

Topologies that maximize or minimize relevant quantities of interest in heat conduction applications are presented. The efficacy of the technique is demonstrated using a variety of realistic heat transfer applications in both two and three dimensions, in conjugate heat transfer problems with finite conductivity ratios and in non-rectangular/non-cuboidal domains.

In contrast to most published work which has either used finite element methods or Cartesian finite volume methods for transport applications, the topology optimization procedure is developed in a general unstructured finite volume framework. This permits topology optimization for flow and heat transfer applications in complex design domains such as those encountered in industry. In addition, the Rapid library is designed to provide a problem-agnostic pathway to automatically compute all required derivatives to machine accuracy. This obviates the necessity to write new code for finding sensitivities when new physics are added or new cost functions are considered and permits general-purpose implementations of topology optimization for complex industrial applications.

The purpose of this paper is to present a method to calculate the derivative of a functional that depends on the shape of an object. This functional depends on the solution of a linear acoustic problem posed in an unbounded domain. We rewrite this problem in terms of another one posed in a bounded domain using the Dirichlet‐to‐Neumann (DtN) map or the modified DtN map. Using a classical method in shape sensitivity analysis, called the adjoint method, we are able to calculate the derivative of the functional using the solution of an auxiliary problem. This method is particularly efficient because the cost of calculating the derivatives is independent of the number of parameters used to approximate the shape of the domain. The resulting variational problems are discretized using the finite‐element method and solved using an efficient Krylov‐subspace iterative scheme. Numerical examples that illustrate the efficacy of our approach are presented.

The present contribution deals with the sensitivity analysis and optimization of structures for path‐dependent structural response. Geometrically as well as materially non‐linear behavior with hardening and softening is taken into account. Prandtl‐Reuss‐plasticity is adopted so that not only the state variables but also their sensitivities are path‐dependent. Because of this the variational direct approach is preferred for the sensitivity analysis. For accuracy reasons the sensitivity analysis has to be consistent with the analysis method evaluating the structural response. The proposed sensitivity analysis as well as its application in structural optimization is demonstrated by several examples.

Vector autoregressions (VAR) combined with Minnesota-type priors are widely used for macroeconomic forecasting. The fact that strong but sensible priors can substantially improve forecast performance implies VAR forecasts are sensitive to prior hyperparameters. But the nature of this sensitivity is seldom investigated. We develop a general method based on Automatic Differentiation to systematically compute the sensitivities of forecasts – both points and intervals – with respect to any prior hyperparameters. In a forecasting exercise using US data, we find that forecasts are relatively sensitive to the strength of shrinkage for the VAR coefficients, but they are not much affected by the prior mean of the error covariance matrix or the strength of shrinkage for the intercepts.

The general problem of sizing, material and loading parameter sensitivity of non‐linear systems is presented. Both kinematic and path‐dependent material non‐linearities are considered; non‐linear sensitivity path is traced by an incremental solution strategy. The variational approach employed is quite general and can be employed for studying sensitivity of various path‐dependent highly non‐linear phenomena. Both the direct differentiation method (DDM) and adjoint system method (ASM) are discussed in the context of continuum and finite element mechanics. The merits of using the consistent tangent matrix and the necessity of accumulation of design derivatives of stresses and internal parameters are indicated. Aspects of sensitivity problems in metal forming are also discussed. A number of examples illustrate the paper.

Transient climate sensitivity relates total climate forcings from anthropogenic and other sources to surface temperature. Global transient climate sensitivity is well studied, as are the related concepts of equilibrium climate sensitivity (ECS) and transient climate response (TCR), but spatially disaggregated local climate sensitivity (LCS) is less so. An energy balance model (EBM) and an easily implemented semiparametric statistical approach are proposed to estimate LCS using the historical record and to assess its contribution to global transient climate sensitivity. Results suggest that areas dominated by ocean tend to import energy, they are relatively more sensitive to forcings, but they warm more slowly than areas dominated by land. Economic implications are discussed.

This paper is devoted to the theoretical and numerical study of a new topological sensitivity concerning the insertion of a small bolt connecting two parts in a mechanical structure. First, an idealized model of bolt is proposed which relies on a non-local interaction between the two ends of the bolt (head and threads) and possibly featuring a pre-stressed state. Second, a formula for the topological sensitivity of such an idealized bolt is rigorously derived for a large class of objective functions. Third, numerical tests are performed in 2D and 3D to assess the efficiency of the bolt topological sensitivity in the case of no pre-stress. In particular, the placement of bolts (acting then as springs) is coupled to the further optimization of their location and to the shape and topology of the structure for volume minimization under compliance constraint.

The methodology relies on the adjoint method and the variational formulation of the linearized elasticity equations in order to establish the topological sensitivity.

The numerical results prove the influence of the number and locations of the bolts which strongly influence the final optimized design of the structure.

This paper is the first one to study the topology optimization of bolted systems without a fixed prescribed number of bolts.

In this paper, the authors revisit the computation of closed-form expressions of the topological indicator function for a one step imaging algorithm of two- and three-dimensional sound-soft (Dirichlet condition), sound-hard (Neumann condition) and isotropic inclusions (transmission conditions) in the free space.

From the addition theorem for translated harmonics, explicit expressions of the scattered waves by infinitesimal circular (and spherical) holes subject to an incident plane wave or a compactly supported distribution of point sources are available. Then the authors derive the first-order term in the asymptotic expansion of the Dirichlet and Neumann traces and their surface derivatives on the boundary of the singular medium perturbation.

As the shape gradient of shape functionals are expressed in terms of boundary integrals involving the boundary traces of the state and the associated adjoint field, then the topological gradient formulae follow readily.

The authors exhibit singular perturbation asymptotics that can be reused in the derivation of the topological gradient function that generates initial guesses in the iterated numerical solution of any shape optimization problem or imaging problems relying on time-harmonic acoustic wave propagation.