Relevant bibliographies by topics / Discrete adjoints

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Discrete adjoints'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Discrete adjoints"

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Zhao, Shunliu, Matthew G. Russell, Amir Hakami, Shannon L. Capps, Matthew D. Turner, Daven K. Henze, Peter B. Percell, et al. "A multiphase CMAQ version 5.0 adjoint." Geoscientific Model Development 13, no. 7 (July 2, 2020): 2925–44. http://dx.doi.org/10.5194/gmd-13-2925-2020.

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Abstract. We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint model provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation, and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosol dynamics and thermodynamics, cloud chemistry and dynamics, diffusion, and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with algorithmic differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools are used for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or finite-difference method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the complex variable method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM and CVM. In an example application of the full multiphase adjoint model, we provide the first estimates of how emissions of particulate matter (PM2.5) affect public health across the US.
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Bixby, Robert E., and Collette R. Coullard. "Adjoints of Binary Matroids." European Journal of Combinatorics 9, no. 2 (March 1988): 139–47. http://dx.doi.org/10.1016/s0195-6698(88)80038-6.

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Foniok, Jan, Jaroslav Nešetřil, and Claude Tardif. "Interleaved adjoints of directed graphs." European Journal of Combinatorics 32, no. 7 (October 2011): 1018–24. http://dx.doi.org/10.1016/j.ejc.2011.03.013.

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Bachem, A., and W. Kern. "Adjoints of oriented matroids." Combinatorica 6, no. 4 (December 1986): 299–308. http://dx.doi.org/10.1007/bf02579255.

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Sandu, Adrian, and Lin Zhang. "Discrete second order adjoints in atmospheric chemical transport modeling." Journal of Computational Physics 227, no. 12 (June 2008): 5949–83. http://dx.doi.org/10.1016/j.jcp.2008.02.011.

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Alexe, Mihai, and Adrian Sandu. "On the discrete adjoints of adaptive time stepping algorithms." Journal of Computational and Applied Mathematics 233, no. 4 (December 2009): 1005–20. http://dx.doi.org/10.1016/j.cam.2009.08.109.

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Szenicer, Alexandre, Kuangdai Leng, and Tarje Nissen-Meyer. "A complexity-driven framework for waveform tomography with discrete adjoints." Geophysical Journal International 223, no. 2 (July 20, 2020): 1247–64. http://dx.doi.org/10.1093/gji/ggaa349.

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Summary We develop a new approach for computing Fréchet sensitivity kernels in full waveform inversion by using the discrete adjoint approach in addition to the widely used continuous adjoint approach for seismic waveform inversion. This method is particularly well suited for the forward solver AxiSEM3D, a combination of the spectral-element method (SEM) and a Fourier pseudo-spectral method, which allows for a sparse azimuthal wavefield parametrization adaptive to wavefield complexity, leading to lower computational costs and better frequency scaling than conventional 3-D solvers. We implement the continuous adjoint method to serve as a benchmark, additionally allowing for simulating off-axis sources in axisymmetric or 3-D models. The kernels generated by both methods are compared to each other, and benchmarked against theoretical predictions based on linearized Born theory, providing an excellent fit to this independent reference solution. Our verification benchmarks show that the discrete adjoint method can produce exact kernels, largely identical to continuous kernels. While using the continuous adjoint method we lose the computational advantage and fall back on a full-3-D frequency scaling, using the discrete adjoint retains the speedup offered by AxiSEM3D. We also discuss the creation of a data-coverage based mesh to run the simulations on during the inversion process, which would allow to exploit the flexibility of the Fourier parametrization and thus the speedup offered by our method.
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Faure, Christèle, and Isabelle Charpentier. "Comparing Global Strategies for Coding Adjoints." Scientific Programming 9, no. 1 (2001): 1–10. http://dx.doi.org/10.1155/2001/485915.

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From a computational point of view, sensitivity analysis, calibration of a model, or variational data assimilation may be tackled after the differentiation of the numerical code representing the model into an adjoint code. This paper presents and compares methodologies to generate discrete adjoint codes. These methods can be implemented when hand writing adjoint codes, or within Automatic Differentiation (AD) tools. AD has been successfully applied to industrial codes that were large and general enough to fully validate this new technology. We compare these methodologies in terms of execution time and memory requirement on a one dimensional thermal-hydraulic module for two-phase flow modeling. With regard to this experiment, some development axes for AD tools are extracted as well as methods for AD tool users to get efficient adjoint codes semi-automatically. The next objective is to generate automatically adjoint codes as efficient as hand written ones.
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Larour, Eric, Jean Utke, Anton Bovin, Mathieu Morlighem, and Gilberto Perez. "An approach to computing discrete adjoints for MPI-parallelized models applied to Ice Sheet System Model 4.11." Geoscientific Model Development 9, no. 11 (November 1, 2016): 3907–18. http://dx.doi.org/10.5194/gmd-9-3907-2016.

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Abstract. Within the framework of sea-level rise projections, there is a strong need for hindcast validation of the evolution of polar ice sheets in a way that tightly matches observational records (from radar, gravity, and altimetry observations mainly). However, the computational requirements for making hindcast reconstructions possible are severe and rely mainly on the evaluation of the adjoint state of transient ice-flow models. Here, we look at the computation of adjoints in the context of the NASA/JPL/UCI Ice Sheet System Model (ISSM), written in C++ and designed for parallel execution with MPI. We present the adaptations required in the way the software is designed and written, but also generic adaptations in the tools facilitating the adjoint computations. We concentrate on the use of operator overloading coupled with the AdjoinableMPI library to achieve the adjoint computation of the ISSM. We present a comprehensive approach to (1) carry out type changing through the ISSM, hence facilitating operator overloading, (2) bind to external solvers such as MUMPS and GSL-LU, and (3) handle MPI-based parallelism to scale the capability. We demonstrate the success of the approach by computing sensitivities of hindcast metrics such as the misfit to observed records of surface altimetry on the northeastern Greenland Ice Stream, or the misfit to observed records of surface velocities on Upernavik Glacier, central West Greenland. We also provide metrics for the scalability of the approach, and the expected performance. This approach has the potential to enable a new generation of hindcast-validated projections that make full use of the wealth of datasets currently being collected, or already collected, in Greenland and Antarctica.
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Gou, Tianyi, and Adrian Sandu. "Continuous versus discrete advection adjoints in chemical data assimilation with CMAQ." Atmospheric Environment 45, no. 28 (September 2011): 4868–81. http://dx.doi.org/10.1016/j.atmosenv.2011.06.015.

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Dissertations / Theses on the topic "Discrete adjoints"

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Walther, Andrea. "Discrete Adjoints: Theoretical Analysis, Efficient Computation, and Applications." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1214221752009-12115.

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The technique of automatic differentiation provides directional derivatives and discrete adjoints with working accuracy. A complete complexity analysis of the basic modes of automatic differentiation is available. Therefore, the research activities are focused now on different aspects of the derivative calculation, as for example the efficient implementation by exploitation of structural information, studies of the theoretical properties of the provided derivatives in the context of optimization problems, and the development and analysis of new mathematical algorithms based on discrete adjoint information. According to this motivation, this habilitation presents an analysis of different checkpointing strategies to reduce the memory requirement of the discrete adjoint computation. Additionally, a new algorithm for computing sparse Hessian matrices is presented including a complexity analysis and a report on practical experiments. Hence, the first two contributions of this thesis are dedicated to an efficient computation of discrete adjoints. The analysis of discrete adjoints with respect to their theoretical properties is another important research topic. The third and fourth contribution of this thesis focus on the relation of discrete adjoint information and continuous adjoint information for optimal control problems. Here, differences resulting from different discretization strategies as well as convergence properties of the discrete adjoints are analyzed comprehensively. In the fifth contribution, checkpointing approaches that are successfully applied for the computation of discrete adjoints, are adapted such that they can be used also for the computation of continuous adjoints. Additionally, the fifth contributions presents a new proof of optimality for the binomial checkpointing that is based on new theoretical results. Discrete adjoint information can be applied for example for the approximation of dense Jacobian matrices. The development and analysis of new mathematical algorithms based on these approximate Jacobians is the topic of the sixth contribution. Is was possible to show global convergence to first-order critical points for a whole class of trust-region methods. Here, the usage of inexact Jacobian matrices allows a considerable reduction of the computational complexity.
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Walther, Andrea. "Discrete Adjoints: Theoretical Analysis, Efficient Computation, and Applications." Doctoral thesis, Technische Universität Dresden, 2007. https://tud.qucosa.de/id/qucosa%3A23715.

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The technique of automatic differentiation provides directional derivatives and discrete adjoints with working accuracy. A complete complexity analysis of the basic modes of automatic differentiation is available. Therefore, the research activities are focused now on different aspects of the derivative calculation, as for example the efficient implementation by exploitation of structural information, studies of the theoretical properties of the provided derivatives in the context of optimization problems, and the development and analysis of new mathematical algorithms based on discrete adjoint information. According to this motivation, this habilitation presents an analysis of different checkpointing strategies to reduce the memory requirement of the discrete adjoint computation. Additionally, a new algorithm for computing sparse Hessian matrices is presented including a complexity analysis and a report on practical experiments. Hence, the first two contributions of this thesis are dedicated to an efficient computation of discrete adjoints. The analysis of discrete adjoints with respect to their theoretical properties is another important research topic. The third and fourth contribution of this thesis focus on the relation of discrete adjoint information and continuous adjoint information for optimal control problems. Here, differences resulting from different discretization strategies as well as convergence properties of the discrete adjoints are analyzed comprehensively. In the fifth contribution, checkpointing approaches that are successfully applied for the computation of discrete adjoints, are adapted such that they can be used also for the computation of continuous adjoints. Additionally, the fifth contributions presents a new proof of optimality for the binomial checkpointing that is based on new theoretical results. Discrete adjoint information can be applied for example for the approximation of dense Jacobian matrices. The development and analysis of new mathematical algorithms based on these approximate Jacobians is the topic of the sixth contribution. Is was possible to show global convergence to first-order critical points for a whole class of trust-region methods. Here, the usage of inexact Jacobian matrices allows a considerable reduction of the computational complexity.
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Hückelheim, Jan Christian. "Discrete adjoints on many cores : algorithmic differentiation of accelerated fluid simulations." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/24644.

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Simulations are used in science and industry to predict the performance of technical systems. Adjoint derivatives of these simulations can reveal the sensitivity of the system performance to changes in design or operating conditions, and are increasingly used in shape optimisation and uncertainty quantification. Algorithmic differentiation (AD) by source-transformation is an efficient method to compute such derivatives. AD requires an analysis of the computation and its data flow to produce efficient adjoint code. One important step is the activity analysis that detects operations that need to be differentiated. An improved activity analysis is investigated in this thesis that simplifies build procedures for certain adjoint programs, and is demonstrated to improve the speed of an adjoint fluid dynamics solver. The method works by allowing a context-dependent analysis of routines. The ongoing trend towards multi- and many-core architectures such as the Intel XeonPhi is creating challenges for AD. Two novel approaches are presented that replicate the parallelisation of a program in its corresponding adjoint program. The first approach detects loops that naturally result in a parallelisable adjoint loop, while the second approach uses loop transformation and the aforementioned context-dependent analysis to enforce parallelisable data access in the adjoint loop. A case study shows that both approaches yield adjoints that are as scalable as their underlying primal programs. Adjoint computations are limited by their memory footprint, particularly in unsteady simulations, for which this work presents incomplete checkpointing as a method to reduce memory usage at the cost of a slight reduction in accuracy. Finally, convergence of iterative linear solvers is discussed, which is especially relevant on accelerator cards, where single precision floating point numbers are frequently used and the choice of solvers is limited by the small memory size. Some problems that are particular to adjoint computations are discussed.
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Beigel, Dörte [Verfasser], and Hans Georg [Akademischer Betreuer] Bock. "Efficient goal-oriented global error estimation for BDF-type methods using discrete adjoints / Dörte Beigel ; Betreuer: Hans Georg Bock." Heidelberg : Universitätsbibliothek Heidelberg, 2012. http://d-nb.info/1177148099/34.

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Jando, Dörte [Verfasser], and Hans Georg [Akademischer Betreuer] Bock. "Efficient goal-oriented global error estimation for BDF-type methods using discrete adjoints / Dörte Beigel ; Betreuer: Hans Georg Bock." Heidelberg : Universitätsbibliothek Heidelberg, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:16-heidok-143177.

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Mura, Gabriele Luigi. "Mesh sensitivity investigation in the discrete adjoint framework." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/17384/.

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Aerodynamic optimisation using gradient-based methods has found a wide range of academic applications in the last 30 years. This framework is also becoming more and more popular in the industrial world where, most of the time, unstructured grids are largely used. In this framework, apart from the need to solve the flow field, there is the need to quickly map the aerodynamic surface in terms of some aerodynamic figure of merits such as the drag coefficient, without being limited by the computational expense related to the grid size. This is a concrete industrial need which requires the efficient computation of the grid sensitivity. A novel method based on the DGM (Delaunay Graph Mapping) mesh movement is proposed to efficiently compute the grid sensitivity required in the discrete adjoint optimisation framework. The method makes use of a one-to-one explicit algebraic mapping between the volume mesh and the solid boundary nodes. This procedure results in a straightforward computation of the gradient without the need to invert a large, sparse and stiff matrix generally associated with implicit mesh movements such as the spring or LE (Linear Elastic) analogy. The method is verified using FDs (Finite Difference) and a thorough comparison in terms of CPU time, formulation against the LE-based mesh movement and adjoint gradient is presented. The DGM-based gradient chain allows to comfortably obtain the gradient with respect to each surface mesh point. Unfortunately, these gradients cannot be used directly because of their inherent poor smoothness feature. In order to address this issue one has to use a parameterisation technique which inevitably sacrifices the design space explorablity. To bridge the gap between the free-nodes and the parameterisation approaches, a novel formulation of the CST (Class Shape Transformation) was developed and termed l-CST (local-CST). The method is based on a simple trigonometric function which works as a cut-off filter on the BPs (Bernstein Polynomials) which are used to enforce a strong on-demand local control. The method is tested on an inverse geometric fitting and its effect on the resulting aerodynamic coefficients and the pressure distribution is also analysed. The DGM-based chain allows the efficient mapping of the entire surface while the l-CST allows the combination of excellent explorablity and surface smoothness. The former is tested within the non-consistent mesh movement and sensitivity framework because there are situations where one method may be preferred over the other based on the grounds that mesh movement is a very different task than mesh sensitivity although strongly related to each other. The latter is instead tested against the free-nodes approach which offers a similar advantage in terms of discrete control although without maintaining a C2 curve unless properly smoothed.
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Schneider, Rene. "Applications of the discrete adjoint method in computational fluid dynamics." Thesis, University of Leeds, 2006. http://etheses.whiterose.ac.uk/1343/.

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The discrete adjoint method allows efficient evaluation of the derivative of a function I(s) with respect to parameters s in situations where I depends on s indirectly, via an intermediate variable w(s), which is computationally expensive to evaluate. In this thesis two applications of this method in the context of computational fluid dynamics are considered. The first is shape optimisation, where the discrete adjoint approach is employed to compute the derivatives with respect to shape parameters for a performance functional depending on the solution of a mathematical flow model which has the form of a discretised system of partial differential equations. In this context particular emphasis is given to efficient solution strategies for the linear systems arising in the discretisation of the flow models. Numerical results for two example problems are presented, demonstrating the utility of the approach. The second application, in adaptive mesh design, allows efficient evaluation of the derivatives of an a posteriori error estimate with respect to the positions of the nodes in a finite element mesh. This novel approach makes additional information available which may be utilised to guide the automatic design of adaptive meshes. Special emphasis is given to problems with anisotropic solution features, for which adaptive anisotropic mesh refinement can deliver significant performance improvements over existing adaptive hrefinement approaches. Two adaptive solution algorithms are presented and compared to existing approaches by applying them to a reaction-diffusion model problem.
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Rothauge, Kai. "The discrete adjoint method for high-order time-stepping methods." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/60285.

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This thesis examines the derivation and implementation of the discrete adjoint method for several time-stepping methods. Our results are important for gradient-based numerical optimization in the context of large-scale model calibration problems that are constrained by nonlinear time-dependent PDEs. To this end, we discuss finding the gradient and the action of the Hessian of the data misfit function with respect to three sets of parameters: model parameters, source parameters and the initial condition. We also discuss the closely related topic of computing the action of the sensitivity matrix on a vector, which is required when performing a sensitivity analysis. The gradient and Hessian of the data misfit function with respect to these parameters requires the derivatives of the misfit with respect to the simulated data, and we give the procedures for computing these derivatives for several data misfit functions that are of use in seismic imaging and elsewhere. The methods we consider can be divided into two categories, linear multistep (LM) methods and Runge-Kutta (RK) methods, and several variants of these are discussed. Regular LM and RK methods can be used for ODE systems arising from the semi-discretization of general nonlinear time-dependent PDEs, whereas implicit-explicit and staggered variants can be applied when the PDE has a more specialized form. Exponential time-differencing RK methods are also discussed. The implementation of the associated adjoint time-stepping methods is discussed in detail. Our motivation is the application of the discrete adjoint method to high-order time-stepping methods, but the approach taken here does not exclude lower-order methods. All of the algorithms have been implemented in MATLAB using an object-oriented design and are written with extensibility in mind. For exponential RK methods it is illustrated numerically that the adjoint methods have the same order of accuracy as their corresponding forward methods, and for linear PDEs we give a simple proof that this must always be the case. The applicability of some of the methods developed here to pattern formation problems is demonstrated using the Swift-Hohenberg model.
Science, Faculty of
Mathematics, Department of
Graduate
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Roth, Rolf [Verfasser]. "Multilevel Optimization of Turbulent Flows by Discrete Adjoint Techniques / Rolf Roth." München : Verlag Dr. Hut, 2012. http://d-nb.info/1025821424/34.

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Towara, Markus [Verfasser], Uwe [Akademischer Betreuer] Naumann, and Wolfgang [Akademischer Betreuer] Schröder. "Discrete adjoint optimization with OpenFOAM / Markus Towara ; Uwe Naumann, Wolfgang Schröder." Aachen : Universitätsbibliothek der RWTH Aachen, 2018. http://d-nb.info/1187346942/34.

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Books on the topic "Discrete adjoints"

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Edmunds, D. E., and W. D. Evans. Capacity and Compactness Criteria. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198812050.003.0008.

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In this chapter, necessary and sufficient conditions are derived for the Poincaré inequality to hold, for the embedding of W01,p(Ω) in Lp(Ω‎) to be compact, and for a self-adjoint realization of − aijDiDj + q to have a wholly discrete spectrum when q is real and bounded below. The results are proved using a method of Maz’ya.
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Book chapters on the topic "Discrete adjoints"

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Catlin, Donald E. "Adjoints, Projections, Pseudoinverses." In Estimation, Control, and the Discrete Kalman Filter, 92–113. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-4528-5_4.

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Lotz, Johannes, Uwe Naumann, Max Sagebaum, and Michel Schanen. "Discrete Adjoints of PETSc through dco/c++ and Adjoint MPI." In Euro-Par 2013 Parallel Processing, 497–507. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40047-6_51.

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Deussen, Jens, and Uwe Naumann. "Discrete Interval Adjoints in Unconstrained Global Optimization." In Advances in Intelligent Systems and Computing, 78–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21803-4_8.

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Liu, Zheng, and Adrian Sandu. "Analysis of Discrete Adjoints for Upwind Numerical Schemes." In Lecture Notes in Computer Science, 829–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428848_106.

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Sandu, A. "Solution of Inverse Problems using Discrete ODE Adjoints." In Large-Scale Inverse Problems and Quantification of Uncertainty, 345–65. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470685853.ch16.

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Sandu, Adrian. "On the Properties of Runge-Kutta Discrete Adjoints." In Computational Science – ICCS 2006, 550–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758549_76.

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Gou, Tianyi, Kumaresh Singh, and Adrian Sandu. "Chemical Data Assimilation with CMAQ: Continuous vs. Discrete Advection Adjoints." In Lecture Notes in Computer Science, 312–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01973-9_35.

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Burghardt, Ole, and Nicolas R. Gauger. "Accurate Gradient Computations for Shape Optimization via Discrete Adjoints in CFD-Related Multiphysics Problems." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 27–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25253-3_3.

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Wong, M. W. "Self-Adjoint Operators." In Discrete Fourier Analysis, 113–16. Basel: Springer Basel, 2011. http://dx.doi.org/10.1007/978-3-0348-0116-4_16.

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Giles, M. B. "Discrete Adjoint Approximations with Shocks." In Hyperbolic Problems: Theory, Numerics, Applications, 185–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55711-8_16.

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Conference papers on the topic "Discrete adjoints"

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Burghardt, Ole, Nicolas R. Gauger, Pedro Gomes, Rafael Palacios, Tobias Kattmann, and Thomas D. Economon. "Coupled Discrete Adjoints for Multiphysics in SU2." In AIAA AVIATION 2020 FORUM. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2020. http://dx.doi.org/10.2514/6.2020-3139.

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Nemili, Anil, Emre Özkaya, Nicolas Gauger, Frank Thiele, and Angelo Carnarius. "Optimal Control of Unsteady Flows Using Discrete Adjoints." In 41st AIAA Fluid Dynamics Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-3720.

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Alexe, Mihai, and Adrian Sandu. "An investigation of discrete adjoints for flux-limited numerical schemes." In the 45th annual southeast regional conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1233341.1233409.

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Duraisamy, Karthikeyan, Juan Alonso, Praveen Chandrasekhar, and Francisco Palacios. "Error Estimation for High Speed Flows Using Continuous and Discrete Adjoints." In 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-128.

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Makhija, David, Richard D. Snyder, and Philip S. Beran. "Towards Cross-Language and Distributed Coupled-Model Design Optimization with Discrete Adjoints." In 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-0568.

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Nimmagadda, Sravya, Thomas D. Economon, Juan J. Alonso, Carlos Silva, Beckett Yx Zhou, and Tim Albring. "Low-cost unsteady discrete adjoints for aeroacoustic optimization using temporal and spatial coarsening techniques." In 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-1911.

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Biava, Massimo, Mark Woodgate, and George N. Barakos. "Fully Implicit Discrete Adjoint Methods." In 53rd AIAA Aerospace Sciences Meeting. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-1491.

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Martins, Joaquim R. R. A., Charles Mader, and Juan Alonso. "ADjoint: An Approach for Rapid Development of Discrete Adjoint Solvers." In 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-7121.

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Frey, Christian, Hans-Peter Kersken, and Dirk Nu¨rnberger. "The Discrete Adjoint of a Turbomachinery RANS Solver." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-59062.

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Since adjoint flow solvers allow for the computation of sensitivities of global flow parameters under geometric variations in an amount of time which is nearly independent of the number of geometric parameters, automatic shape optimization can be accelerated considerably by the use of an adjoint solver. In this article, a systematic approach for the development of an exact discrete adjoint of a turbomachinery flow solver is described. By using finite differences to differentiate the numerical fluxes, the problems associated with automatic and hand differentiation are circumvented. Moreover, a general treatment of the adjoint numerical boundary conditions is presented. As a result, an exact adjoint boundary condition for the conservative mixing planes is obtained. In combination with nonreflecting boundary conditions the latter are crucial for accurate flow simulations in turbomachinery. The adjoint is validated on the basis of a transonic compressor stage.
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10

Schäfer, Fellcitas, Luca Magri, and Wolfgang Polifke. "A Hybrid Adjoint Network Model for Thermoacoustic Optimization." In ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-59866.

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Abstract A method is proposed that allows the computation of the continuous adjoint of a thermoacoustic network model based on the discretized direct equations. This hybrid approach exploits the self-adjoint character of the duct element, which allows all jump conditions to be derived from the direct scattering matrix. In this way, the need to derive the adjoint equations for every element of the network model is eliminated. This methodology combines the advantages of the discrete and continuous adjoint, as the accuracy of the continuous adjoint is achieved whilst maintaining the flexibility of the discrete adjoint. It is demonstrated how the obtained adjoint system may be utilized to optimize a thermoacoustic configuration by determining the optimal damper setting for an annular combustor.
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Reports on the topic "Discrete adjoints"

1

Slater, C. O. DRC2: A code with specialized applications for coupling localized Monte Carlo adjoint calculations with fluences from two-dimensional R-Z discrete ordinates air-over-ground calculations. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/5973682.

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2

Slater, C. O. DRC2: A code with specialized applications for coupling localized Monte Carlo adjoint calculations with fluences from two-dimensional R-Z discrete ordinates air-over-ground calculations. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/10110196.

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