Center for Design Optimization (CDO)
Our Projects
The CDO works with clients to design systems for the nation’s ever-changing energy, environment, and security missions. CDO staff use high performance computing (HPC) and advanced manufacturing (AM) tools to meet and exceed design requirements, paving the way for technological breakthroughs and setting new standards in engineering excellence. Some examples of our work are highlighted below.
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Optimizing Stimuli-Responsive Materials
CDO researchers developed the first gradient-based shape and topology optimization framework for stimuli-responsive or “active” materials, which can change shape, color, or mechanical properties when exposed to stimuli like light, heat, or electricity. Read more coverage
Optimizing Stimuli-Responsive Materials
Stimuli-responsive or “active” materials like liquid crystal elastomers (LCEs) can change shape, color, or mechanical properties when exposed to external stimuli like light, heat, or electricity. With additive manufacturing, researchers can design, fabricate, and validate multifunctional devices that actuate in programmed ways to select stimuli. This could lead to “smart” materials that autonomously move, sense, adapt, and even make decisions.
Working with the LiDO software, CDO researchers developed a gradient-based shape and topology optimization framework to design complex multimaterial lattice active structures with precise and predictable stimuli responses. The framework is the first of its kind for designing this new class of devices and lays the foundation for future advancements in this emerging field.
The team is improving the framework by using experimental data to develop more accurate models specific to the stimuli-responsive materials of interest, which will help increase the predictability of the optimized designs.
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Relevant publications:
Controlling Richtmyer-Meshkov Instabilities
CDO researchers developed an AI-driven inverse design framework to identify and control Richtmyer-Meshokov instabilities (RMI), fluid dynamics phenomena that occur in multiple Lab-relevant scenarios like inertial confinement fusion. Read more coverage
Controlling Richtmyer-Meshkov Instabilities
Richtmyer-Meshkov instabilities (RMI) are fluid dynamics phenomena that occur when materials of different densities experience shock loading from a high-velocity impact and begin to mix. RMIs can lead to “jetting,” where one material propagates into the other, forming narrow regions with high kinetic energy density that may be helpful or harmful, depending on the application, such as inertial confinement fusion (ICF) implosions or explosively driven linear shaped charges.
CDO researchers used black box optimization to control RMI as part of the Lab’s DarkStar project. Given a performance goal, the AI-driven framework identifies the variables that have the most influence on the RMI and uses those as the design parameters in a subsequent optimization to maximize performance.
Thanks to the Lab’s advanced manufacturing capabilities, the team developed, manufactured, experimentally validated designs in less than three months. Not only did the designs demonstrate near-perfect control of the RMI, but they also shed light on the materials’ phase transition behavior, which will help researchers better understand and control the phenomena in the future.
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Relevant publications:
- "Explosively driven Richtmyer–Meshkov instability jet suppression and enhancement via coupling machine learning and additive manufacturing"
- "Linear shaped-charge jet optimization using machine learning methods"
- "Design optimization for Richtmyer-Meshkov instability suppression at shock-compressed material interfaces"
Modular Fischer-Troph Reactors
In collaboration with an industry partner, CDO researchers redesigned a small, modular Fisher-Troph (FT) reactor—used to convert atmospheric CO2 into liquid hydrocarbons—using both shape and topology optimization, which could make small, modular FT reactors viable and easier to deploy.
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Modular Fischer-Troph Reactors
Fischer-Troph (FT) reactors are used to convert atmospheric CO2 into liquid hydrocarbons for synthetic fuels and feedstocks. The reactors are widely used at large power plants and refineries that emit huge quantities of CO2, but they are prohibitively expensive to deploy on smaller and more distributed emissions sources, thus limiting their potential CO2 reduction capabilities
In collaboration with industry partner OxEon Energy, LLC, CDO researchers redesigned a small, modular FT reactor using both shape and topology optimization. The approach optimized the shape and layout of highly conductive cooling “fin” inserts that are located in a containment tube infilled with the catalyst bed. The team’s designs maximized overall CO2 conversion under a variety of operating conditions, while limiting the high temperatures caused by exothermic reactions.
The optimized designs featured a series of branch-like fins, with more material toward the tube’s inner edge and multiple sub-branches closer to the center of the catalyst bed. These designs may make smaller FT reactors viable and easier to deploy.
Relevant publications:
Photonic Bandgap Materials
The CDO developed a topology optimization framework to design photonic bandgap materals, which block the transmission of electromagnetic waves within certain frequency ranges, resulting in designs that were superior to state-of-the-art photonic devices. Read more coverage
Photonic Bandgap Materials
Photonic bandgap materials block the transmission of electromagnetic waves within certain frequency ranges, making them integral components in waveguides, filters, and communication networks. CDO researchers designed metamaterials with both two-dimensional and three-dimensional photonic bandgaps that consider both mechanical stiffness and manufacturability constraints so it is possible to fabricate them and incorporate them in real-world devices.
The team developed a topology optimization framework to design these devices, which used an efficient plane-wave expansion modeling technique to hasten computational modeling. The framework also addresses the non-smooth design space due to the presence of degenerate modes, making it possible to use a traditional gradient-based optimization solver. The team’s designs were superior to state-of-the-art photonic devices, and the framework is generalizable to design photonic crystals with any unit cell symmetry.
Relevant publications:
Metal Lattice Mount
As part of an NNSA challenge, CDO researchers developed a functionally-graded torus-shaped structure, designed so its unit cells that experienced greater loads had larger rod diameters. Read more coverage
Metal Lattice Mount
To support metal lattice research, the NNSA challenged national laboratories to design a torus-shaped component made from octet truss unit cells that can withstand compressive loads. CDO researchers used machine learning to develop a surrogate model for mapping the unit cell’s density relative to its mass, stiffness and stress, and integrated it into LiDO to optimize the component.
The result was a functionally-graded structure where the unit cell struts that experienced greater loads had larger rod diameters—increasing stiffness and reducing stress where it mattered most. As part of the project, the team also developed a design-to-print workflow, which translates the optimized designs into instructions a 3D printer can use to fabricate the part. The intricacy of the design, which included millions of struts, made this translation a significant task.
Materials with Negative Thermal Expansion
CDO researchers helped design a class of metamaterials with negative thermal expansion, meaning they shrink when they are heated. These systems can potentially be used to fasten parts that move out of alignment under thermal loads, such as microchips, optical mounts, and precision devices.
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Materials with Negative Thermal Expansion
CDO researchers designed a class of metamaterials and structural systems with negative thermal expansion, meaning they shrink when they are heated. The systems contain both a stiff, low thermal expansion material and a more flexible, high thermal expansion material. Although both materials expand when heated, their layout is optimized so that the whole system contracts when heated. Researchers can also optimize the layout to achieve a desired range of thermal expansion behavior.
CDO researchers were able to generate these non-intuitive designs in hours and worked with the Center for Engineered Materials and Manufacturing (CEMM) to fabricate and validate them. The metmaterials can potentially be used to fasten parts that tend to move out of alignment under varying thermal loads, including microchips, optical mounts, and precision devices like atomic-force microscopes.
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Our Publications
Our research has been published in a variety of high-impact journals in the field and our work has been cited hundreds of times. Check out some of our publications below:
Granlund G., Wallin M., Günther-Hanssen O., Tortorelli D., Watts S., "Topology optimization of compliant mechanisms under transient thermal conditions. “Computer Methods in Applied Mechanics and Engineering.” (2024): 418, DOI: 10.1016/j.cma.2023.116478
Armstrong, M., Nguyen, J., Sterbentz, D., White, D., Benedict, L., Rieben, R., Hoff, A., Lorenzana, H., La Lone, B., D. Staska, M., Belof, J., “Suppression of Richtmyer-Meshkov instability via special pairs of shocks and phase transitions,” Physical Review Letters (2024): 132.2, DOI:10.1103/physrevlet.132.024001
Kline D.J., Hennessey M.P., Amondson D.K., Lin S., Grapes M.D., Ferrucci M., Li P., Springer H.K., Reeves R.V., Sullivan K.T., Belof J.L., “Reducing Richtmyer-Meshkov instability jet velocity via inverse design.” Journal of Applied Physics (2024): 135.7, DOI: 10.1063/5.0180712
Dalklint A., Wallin M., Tortorelli D., “Simultaneous shape and topology optimization of inflatable soft robots,” Computer Methods in Applied Mechanics and Engineering (2024): 420, DOI: 10.1016/j.cma.2024.116751
Barrera, J.-L., Cook, C., Lee, E., Swartz, K., Tortorelli, D.A. ,"Liquid Crystal Orientation and Shape Optimization for the Active Response of Liquid Crystal Elastomers." Polymers (2024): 16.10, DOI: 10.3390/polym16101425
Chung, S., Choi, Y., Roy, P., Moore, T., Roy, T., Lin, T., Nguyen, D., Hahn, C., Duoss, E., Baker, S., "Train small, model big: Scalable physics simulators via reduced order modeling and domain decomposition." Computer Methods in Applied Mechanics and Engineering (2024): 427, DOI: 10.1016/j.cma.2024.117041
Sterbentz, D., Kline, D., White, D.A., Jekel, C., Hennessey, M., Amondson, D., Wilson, A., Sevcik, M., Cillena, M., Lin, S., Grapes, M., Sullivan, K., Belof, J., "Explosively driven Richtmyer–Meshkov instability jet suppression and enhancement via coupling machine learning and additive manufacturing." Journal of Applied Physics (2024): 136, DOI: 10.1063/5.0213123
Sjövall, F, Wallin, M., Tortorelli, D.A., "Shape optimization of hyperelastic structures subject to frictionless contact." Computers & Structures (2024): 301, DOI: 10.1016/j.compstruc.2024.107426
Lin, T., Li, H., Brady, N., Cross, N., Ehlinger, V., Roy, T., Tortorelli, D.A., Orme, C., Worsley, M., Bucci, G., "Shape Matters: Understanding the Effect of Electrode Geometry on Cell Resistance and Chemo-Mechanical Stress." Journal of the Electrochemistry Society (2024): 171, DOI: 10.1149/1945-7111/ad81b4
Schmidt, M., Barrera, J.-L., Mittal, K., Swartz, K., Tortorelli, D.A., "Level-set topology optimization with PDE generated conformal meshes." Structural and Multidisciplinary Optimization (2024): 67, DOI: 10.1007/s00158-024-03870-3
Li, H., Bucci, G., Brady, N., Cross, N., Ehlinger, V., Lin, T., Salazar de Troya, M., Tortorelli, D.A., Worsley, M., Roy, T., "Topology optimization for the full-cell design of porous electrodes in electrochemical energy storage devices." Structural and Multidisciplinary Optimization (2024): 67, DOI: 10.1007/s00158-024-03901-z
Jekel, C., Sterbentz, D., Stitt, T. M., Mocz, P., Rieben, R., White, D.A., Belof, J., "Machine learning visualization tool for exploring parameterized hydrodynamics." Machine Learning: Science and Technology (2024): 5, DOI: 10.1088/2632-2153/ad8daa
Schmidt, M., Noël, L., Wunsch, N., Doble, K., Evans, J., Maute, K., "Adaptive immersed isogeometric level-set topology optimization." Structural and Multidisciplinary Optimization (2024): 68, DOI: 10.1007/s00158-024-03921-9
Kim, D., Lazarov, B., Surowiec, T., Keith, B., "A simple introduction to the SiMPL method for density-based topology optimization." Structural and Multidisciplinary Optimization (2025): 68, DOI: 10.1007/s00158-025-04008-9
Wallin, M., Watts, S., Sigmund, O., Tortorelli, D.A., "Response tailoring of elasto-plastic trusses." Structural and Multidisciplinary Optimization (2025): 68, DOI: 10.1007/s00158-025-03993-1
Reale Batista M.D., Chandrasekaran S., Moran B.D., Salazar de Troya M., Pinongcos A., Wang Z., Hensleigh R., Carleton A., Zeng M., Roy T., Lin D., Xue X., Beck V.A., Tortorelli D.A., Stadermann M., Zheng R., Li Y., Worsley M.A., “Design and additive manufacturing of optimized electrodes for energy storage applications.” Carbon (2023): 205, DOI: 10.1016/j.carbon.2023.01.044
White D.A., Kudo J., Swartz K., Tortorelli D.A., Watts S., “A reduced order model approach for finite element analysis of cellular structures” Finite Elements in Analysis and Design (2023): 214.1, DOI: 10.1016/jfinel.2022.103855
Granlund G., Wallin M., Tortorelli D., Watts S., “Stress-constrained topology optimization of structures subjected to nonproportional loading.” International Journal for Numerical Methods in Engineering (2023): 124.12, DOE:10.1002/nme.7230
Sterbentz, D., Jekel, C., White, D., Rieben, R., Belof, J., “Linear shaped-charge jet optimization using machine learning methods.” Journal of Applied Physics (2023): 134.4, DOI:10.1063/5.0156373
Knueven B., Mildebrath D., Muir C., Siirola J.D., Watson J.-P., Woodruff D.L., “A parallel hub-and-spoke system for large-scale scenario-based optimization under uncertainty.” Mathematical Programming Computation (2023): 15.4, DOI: 10.1007/s12532-023-00247-3
Bollapragada R., Karamanli C., Keith B., Lazarov B., Petrides S., Wang J., “An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints.” Computers and Mathematics with Applications (2023): 149, DOI: 10.1016/j.camwa.2023.09.014
Swartz K.E., Mittal K., Schmidt M., Barrera J.-L., Watts S., Tortorelli D.A., “Yet another parameter-free shape optimization method.” Structural and Multidisciplinary Optimization (2023): 66.12, DOI: 10.1007/s00158-023-03684-9
Dalklint A., Sjövall F., Wallin M., Watts S., Tortorelli D., “Computational design of metamaterials with self contact.” Computer Methods in Applied Mechanics and Engineering (2023): 417, DOI: 10.1016/j.cma.2023.116424
Brust J.J., Marcia R.F., Petra C.G., Saunders M.A., "Large-Scale Optimization with Linear Equality Constraints Using Reduced Compact Representation.” SIAM Journal on Scientific Computing (2022): 44.1, DOI: 10.1137/21M1393819
Barrera J.L., Geiss M.J., Maute K., "Minimum feature size control in level set topology optimization via density fields." Structural and Multidisciplinary Optimization (2022): 65.3, DOI: 10.1007/s00158-021-03096-7
Dalklint A., Wallin M., Bertoldi K., Tortorelli D., “Tunable phononic bandgap materials designed via topology optimization.” Journal of the Mechanics and Physics of Solids (2022): 163, DOI: 10.1016/j.jmps.2022.104849
Swartz K.E., Tortorelli D.A., White D.A., James K.A., “Manufacturing and stiffness constraints for topology optimized periodic structures,” Structural and Multidisciplinary Optimization (2022): 65.4, DOI: 10.1007/s00158-022-03222-z
Roy T., Salazar de Troya M.A., Worsley M.A., Beck V.A., “Topology optimization for the design of porous electrodes.” Structural and Multidisciplinary Optimization (2022): 65.6, DOI: 10.1007/s00158-022-03249-2
Zhang Z.J., Butscher A., Watts S., Tortorelli D.A., “Anisotropic yield models for lattice unit cell structures exploiting orthotropic symmetry.” Computer Methods in Applied Mechanics and Engineering (2022): 394, DOI: 10.1016/j.cma.2022.114935
Alidoost K., Fernandez F., Geubelle P.H., Tortorelli D.A., “Fracture-based shape optimization built upon the topological derivative.” Computer Methods in Applied Mechanics and Engineering (2022): 395, DOI: 10.1016/j.cma.2022.114994
Lin T.Y., Baker S.E., Duoss E.B., Beck V.A., “Topology Optimization of 3D Flow Fields for Flow Batteries.” Journal of the Electrochemical Society, (2022): 169.4, DOI: 10.1149/1945-7111/ac716d
Sterbentz D.M., Jekel C.F., White D.A., Aubry S., Lorenzana H.E., Belof J.L., "Design optimization for Richtmyer-Meshkov instability suppression at shock-compressed material interfaces.” (2022) Physics of Fluids (2022): 34.8, DOI: 10.1063/5.0100100
Humbird K.D., Peterson J.L., “Transfer learning driven design optimization for inertial confinement fusion.” Physics of Plasmas (2022): 29.10, DOI: 10.1063/5.0100364
McBane S., Choi Y., Willcox K., “Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models.” Computer Methods in Applied Mechanics and Engineering (2022): 400, DOI: 10.1016/j.cma.2022.115525
Petra C.G., Salazar De Troya M., Petra N., Choi Y., Oxberry G.M., Tortorelli D.A., “On the implementation of a quasi-Newton interior-point method for PDE-constrained optimization using finite element discretizations.” Optimization Methods and Software (2023): 38.1, DOI: 10.1080/10556788.2022.2117354
Najafi, A.R., Safdari, M., Tortorelli, D.A., Geubelle, P.H., “Multiscale design of nonlinear materials using a Eulerian shape optimization scheme.” International Journal for Numerical Methods in Engineering (2021): 122.12, DOI: 10.1002/nme.6650
Fernandez, F., Lewicki, J.P., Tortorelli, D.A., “Optimal toolpath design of additive manufactured composite cylindrical structures.” Computer Methods in Applied Mechanics and Engineering (2021): 376, DOI: 10.1016/j.cma.2021.113673
Ivarsson, N., Wallin, M., Amir, O., Tortorelli, D.A., “Plastic work constrained elastoplastic topology optimization.” International Journal for Numerical Methods in Engineering (2021): 122.16, DOI: 10.1002/nme.6706
Zambrano, M., Serrano, S., Lazarov, B.S., Galvis, J., “Fast multiscale contrast independent preconditioners for linear elastic topology optimization problems.” Journal of Computational and Applied Mathematics (2021): 389, DOI: 10.1016/j.cam.2020.113366
Swartz, K.E., White, D.A., Tortorelli, D.A., James, K.A., “Topology optimization of 3D photonic crystals with complete bandgaps,” Optics Express (2021): 29.14, DOI: 10.1364/OE.427702
Brust J.J., Di Z.W., Leyffer S., Petra C.G., “Compact representations of structured BFGS matrices.” Computational Optimization and Applications (2021): 80.1, DOI: 10.1007/s10589-021-00297-0
McBane, S., Choi, Y., “Component-wise reduced order model lattice-type structure design.” Computer Methods in Applied Mechanics and Engineering (2021): 381, DOI: 10.1016/j.cma.2021.113813
Dalklint, A., Wallin, M., Tortorelli, D.A., “Structural stability and artificial buckling modes in topology optimization.” Structural and Multidisciplinary Optimization (2021): 64.4, DOI: 10.1007/s00158-021-03012-z
Wallin, M., Dalklint, A., Tortorelli, D., “Topology optimization of bistable elastic structures — An application to logic gates,” Computer Methods in Applied Mechanics and Engineering (2021): 383, DOI: 10.1016/j.cma.2021.113912
Beck, V.A., Wong, J.J., Jekel, C.F., Tortorelli, D.A., Baker, S.E., Duoss, E.B., Worsley, M.A., “Computational design of microarchitected porous electrodes for redox flow batteries.” Journal of Power Sources (2021): 512, DOI: 10.1016/j.jpowsour.2021.230453
Kang Z., Tortorelli D.A., James K.A., “Parallel projection—An improved return mapping algorithm for finite element modeling of shape memory alloys,” Computer Methods in Applied Mechanics and Engineering (2021): 389, DOI: 10.1016/j.cma.2021.114364
Choi, Y., Coombs, R., Anderson, R., “SNS: A Solution-based Nonlinear Subspace method for time-dependent nonlinear model order reduction.” SIAM journal on Scientific Computing (2020): 42.2, DOI: 10.1137/19M1242963
Wallin, M., Ivarsson, N., Amirand, O., Tortorelli, D.A., "Consistent boundary conditions for PDE filter regularization in topology optimization." Structural and Multidisciplinary Optimization (2020): 62, DOI: 10.1007/s00158-020-02556-w
Dalklint, A., Wallin, M., Tortorelli, D.A., “Eigenfrequency constrained topology optimization of finite strain hyperelastic structures.” Structural and Multidisciplinary Optimization (2020): 61, DOI: 10.1007/s00158-020-02557-9
Ivarsson, N., Wallin, M., Tortorelli, D.A., "Topology optimization for designing periodic microstructures based on finite strain viscoplasticity.” Structural and Multidisciplinary Optimization (2020): 61, DOI: 10.1007/s00158-020-02555-x
Wallin, M., Tortorelli, D.A., “Nonlinear homogenization for topology optimization.” Mechanics of Materials (2020): 145, DOI: 10.1016/j.mechmat.2020.103324
Salazar De Troya, M., Tortorelli, D.A., "Three dimensional adaptive mesh refinement in stress constrained topology optimization." Structural and Multidisciplinary Optimization (2020): 62, DOI: 10.1007/s00158-020-02618-z
Fernandez, F., Puso, M.A., Solberg, J., Tortorelli, D.A., "Topology optimization of multiple deformable bodies in contact with large deformations." Computer Methods in Applied Mechanics and Engineering (2020): 371, DOI: 10.1016/j.cma.2020.113288
Fernandez, F., Barker, A.T., Kudo, J., Lewicki, J.P., Swartz, K., Tortorelli, D.A., Watts, S., White, D.A., Wong, J., "Simultaneous Material, Shape and Topology Optimization." Computer Methods in Applied Mechanics and Engineering (2020): 371, DOI: 10.1016/j.cma.2020.113321
Watts, S., “Elastic response of hollow truss lattice micro-architectures.” International Journal of Solids and Structures (2020): 206, DOI: 10.1016/j.ijsolstr.2020.08.018
Choi, Y., Boncoraglio, G., Anderson, S., Amsallem, D., Farhat, C., “Gradient-based constrained optimization using a database of linear reduced-order models.” Journal of Computational Physics (2020): 423, DOI: 10.1016/j.jcp.2020.109787
Choi, Y., Carlberg, K., “Space–time least-squares Petrov–Galerkin projection for nonlinear model reduction.” SIAM journal on Scientific Computing (2019): 41.1, DOI: 10.1137/17M1120531
White, D., Arrighi, B., Kudo, J., Watts, S., “Multiscale Topology Optimization using Neural Network Surrogate Models.” Computer Applications Applied Mechanics and Engineering (2019): 346, DOI: 10.1016/j.cma.2018.09.007
Petra, C.G., Chiang, N., Anitescu, M., “A structured quasi-Newton algorithm for optimizing with incomplete Hessian information.” SIAM Journal on Optimization (2019): 29.2, DOI: 10.1137/18M1167942
Saito, Y., Fernandez, F., Tortorelli, D.A., Compel, W.S., Lewicki, J.P., Lambros, J., “Experimental Validation of an Additively Manufactured Stiffness-Optimized Short-Fiber Reinforced Composite Clevis Joint.” Experimental Mechanics (2019): 59.6, DOI: 10.1007/s11340-019-00514-2
Watts, S., Arrighi, W., Kudo, J., Tortorelli, D.A., White, D.A., “Simple, accurate surrogate models of the elastic response of three-dimensional open truss micro-architectures with applications to multiscale topology design.” Structural and Multidisciplinary Optimization (2019): 60.5, DOI: 10.1007/s00158-019-02297-5
Fernandez, F., Compel, W.S., Lewicki, J.P., Tortorelli, D.A., “Optimal design of fiber reinforced composite structures and their direct ink write fabrication.” Computer Methods in Applied Mechanics and Engineering (2019): 353, DOI: 10.1016/j.cma.2019.05.010
Keshavarzzadeh, V., Ghanem, R.G., Tortorelli, D.A., “Shape optimization under uncertainty for rotor blades of horizontal axis wind turbines.” Computer Methods in Applied Mechanics and Engineering (2019): 354, DOI: 10.1016/j.cma.2019.05.015
Chen, W., Watts, S., Jackson, J.A., Smith, W.L., Tortorelli, D.A., Spadaccini, C.M., "Stiff isotropic lattices beyond the Maxwell criterion," Science Advances (2019): 5.9, DOI: 10.1126/sciadv.aaw1937
Petra C.G., “A memory-distributed quasi-Newton solver for nonlinear programming problems with a small number of general constraints.” Journal of Parallel and Distributed Computing (2019): 133, DOI: 10.1016/j.jpdc.2018.10.009
White, D.A., Choi, Y., Kudo, J., "A dual mesh method with adaptivity for stress-constrained topology optimization," Structural and Multidisciplinary Optimization (2019): 61.2, DOI: 10.1007/s00158-019-02393-6
Lian, H., Christiansen, A., Tortorelli, D.A., Sigmund, O., Aage, N., “Combined shape and topology optimization for minimization of maximal von Mises stress, Structural and Multidisciplinary Optimization (2017): 55.5, DOI: 10.1007/s00158-017-1656-x
Watts, S., Tortorelli, D.A., “Optimality of thermal expansion bounds in three dimensions.” Extreme Mechanics Letters (2017): 12, DOI: 10.1016/j.eml.2016.06.007
Keshavarzzadeh, V., Fernandez, F., Tortorelli, D.A., “Topology optimization under uncertainty via non-intrusive polynomial chaos expansion.” Computer Methods in Applied Mechanics and Engineering (2017): 318.1, DOI: 10.1016/j.cma.2017.01.019
Watts, S., Tortorelli, D.A., “A geometric projection method for designing three-dimensional open lattices with inverse homogenization.” International Journal for Numerical Methods in Engineering (2017): 112.11, DOI: 10.1002/nme.5569
Barbarosie, C., Tortorelli, D.A., Watts, S., “On domain symmetry and its use in homogenization.” Computer Methods in Applied Mechanics and Engineering (2017): 320, DOI: 10.1016/j.cma.2017.01.009
Najafi, A., Safdari, M., Tortorelli, D.A., Geubelle, P., “Shape optimization using a NURBS-based interface-enriched generalized FEM.” International Journal for Numerical Methods in Engineering (2017): 111.10, DOI: 10.1002/nme.5482
Alidoost, K., Geubelle, P., Tortorelli, D.A., “Energy release rate approximation for edge cracks using higher-order topological derivatives.” International Journal of Fracture (2018): 210, DOI: 10.1007/s10704-018-0271-1
Ivarsson, N., Wallin, M., Tortorelli, D.A., “Topology optimization of finite strain viscoplastic systems under transient loads.” International Journal for Numerical Methods in Engineering (2018): 58, DOI: 10.1002/nme.5789
Ivarsson, N., Wallin, M., Tortorelli, D.A., “Stiffness optimization of non-linear elastic structures.” Computer Methods in Applied Mechanics and Engineering (2018): 330, DOI: 10.1016/j.cma.2017.11.004
Dilgen, S.B. and Dilgen, C.B. and Fuhrman, D.R. and Sigmund, O., Lazarov, B.S., “Density based topology optimization of turbulent flow heat transfer systems.” Structural and Multidisciplinary Optimization (2018): 57, DOI: 10.1007/s00158-018-1967-6
White, D.A., Stowell, M., Tortorelli, D.A., “Topological optimization of structures using Fourier representations.” Structural and Multidisciplinary Optimization (2018): 58, DOI: 10.1007/s00158-018-1962-y
Fernandez, F., Tortorelli, D.A., “Semi-analytical sensitivity analysis for nonlinear transient problems.” Structural and Multidisciplinary Optimization (2018): 58, DOI: 10.1007/s00158-018-2096-yv
Dan White, D.A., Alexey Voronin, A., “A Computational Study of Symmetry and Well-Posedness of Structural Topology Optimization.” Structural and Multidisciplinary Optimization (2018): 59.3, DOI: 10.1007/s00158-018-2098-9
Salazar De Troya, M., Tortorelli, D.A., “Adaptive mesh refinement in stress-constrained topology optimization.” Structural and Multidisciplinary Optimization (2018): 58, DOI: 10.1007/s00158-018-2084-2
Carlberg, K., Choi, Y., Sargsyan, S., "Conservative model reduction for finite-volume models," Journal of Computational Physics (2018): 371, DOI: 10.1016/j.jcp.2018.05.019
Petra, C.G., “A memory-distributed quasi-Newton solver for nonlinear programming problems with a small number of general constraints.” Journal of Parallel and Distributed Computing (2018): 133, DOI: 10.1016/j.jpdc.2018.10.009
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