Inverse design of anisotropic microstructures using physics-augmented neural networks
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The energy footprint of modern information processing and communications systems is immense and utilizes a significant fraction of total global energy usage. Data centers alone consume over 70 billion kilowatt-hours per year. Much of this energy usage is intrinsic to the use of electronic wiring, making optical-based technologies a necessary and promising route to mitigating energy consumption in short- to medium-distance communication links. In this project, we developed and implemented a framework for the design of optical components, based on machine learning, which enables optical components relevant to optical information processing to be realized at their physical performance limits.
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A numerical technique to generate cascades is presented. The basic prescribed parameters are: inlet angle, exit pressure, and distribution of blade thickness and lift along a blade. Other sets of parameters are also discussed. The technique is based on the lambda scheme. The problem of stability of the computation as a function of the prescribed set of parameters and the treatment of boundary conditions is discussed. A one dimensional analysis to indicate a possible way for assuring stability for any two dimensional calculation is provided.
A set of sample cases was produced to test an automated design procedure developed at the NASA Lewis Research Center for the design of controlled diffusion blades. The range of application of the automated design procedure is documented. The results presented include characteristic compressor and turbine blade sections produced with the automated design code as well as various other airfoils produced with the base design method prior to the incorporation of the automated procedure.
This paper describes a practical method for the design of an infinite cascade in incompressible flow. The method is based on conformal mapping, and as a result it allows for multi-point design. The cascade blade to be determined is divided into a desired number of segments. Over each segment, the velocity distribution is prescribed together with an inlet or outlet flow angle at which this velocity distribution is to be achieved. In this way multi-point design requirements can be met. It is necessary to satisfy several conditions that arise to guarantee compatibility with the inlet and outlet flow as well as closure of the cascade blade. Satisfaction of these conditions does not necessarily result in a cascade with all the desired characteristics. For example, the cascade blades may be bulbous or crossed. Through Newton iteration, however, the desired characteristics may be prescribed by allowing for the adjustment of the design parameters that define the mathematical problem through conformal mapping. Several examples will be illustrated to demonstrate the capability of the method. It will be shown that the method is limited to the design of cascades with solidities of up to one.
A numerical design methodology for multistage turbomachinery is being developed that minimizes the use of experimental data required in the design process. This approach is resulting in an efficient method of enlarging the design database for turbomachinery blading. This capability has been demonstrated for blade sections, and the goal is to extend it to fully three dimensional, multistage configurations.
This paper describes a method for the design of an infinite cascade in incompressible flow. The method is based on conformal mapping and does not allow for multipoint design. The cascade blade is to determined is divided into a number of segments. Over each segment, the velocity distribution is prescribed together with an inlet or outlet flow angle at which this velocity distributions is to be achieved. In this way multipoint design requirements can be met. It is necessary to satisfy several conditions that arise to guarantee compatibility with the inlet and outlet flow as well as closure of the cascade blade. Satisfaction of these conditions does not necessarily result in a cascade with all of the desired characteristucs. For example, the cascade blades may be bulbous or crossed. Through Newtonian iteration, however, the desired characteristics may be prescribed directly. Four examples will be illustrated to demonstrate the capability of the method.
The objective of this work is to reshape a nacelle to achieve a specified nacelle pressure distribution. The nacelle may be either isolated or installed on an airplane. There are no restrictions on the attitude (toe, incidence, and roll) and position of the nacelle. The design algorithm is coupled to two different multi-block 3-D Navier Stokes flow solvers. The coupling between design and analysis is automated to the point where the design proceeds with minimal user input.
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The research activities in Italy on inverse design and optimization are reviewed. The review is focused on aerodynamic aspects in turbomachinery and wing section design. Inverse design of blade rows and ducts of turbomachinery in subsonic and transonic regime are illustrated by the Politecnico di Torino and turbomachinery industry (FIAT AVIO).
Inverse material design is an extremely challenging optimization task made difficult by, in part, the highly nonlinear relationship linking performance with composition. Quantitative approaches have improved significantly owing to advances in high throughput experimentation and computational thermodynamics. However, existing physics-based tools are mostly forward models; input a chemistry and obtain a prediction. More recently the materials community has leveraged advances in the machine learning community to establish novel inverse design frameworks. Very recently denoising diffusion probabilistic models have been shown to be extremely powerful generators producing synthetic data of various modalities e.g. images, text, audio, tables, etc.. In this work a novel framework for alloy design and optimization is proposed leveraging these class of models. Five key generative tasks are demonstrated (1) unconditional generation (2) composition conditioned generation (3) property conditioned generation (4) multi-feedstock conditioned generation and (5) generative optimization. These methods were tested on three case studies: high entropy alloy design, superalloy binder jet additive manufacturing, and in-situ dual-feedstock wire-arc additive manufacturing. Results indicate that the established models are extremely flexible, expressive, and robust. The architecture’s flexibility and training procedure empower the model to learn complex intra-compositional and composition-property relationships. Furthermore, the probabilistic nature of these models makes them well suited for addressing solution non-uniqueness and tackling uncertainty quantification tasks. While the fidelity and quantity of the underlying training data is paramount, we envision that future alloy design frameworks will make extensive use of these kinds of machine learning models as “search” tools bolstering the utility of experimental and computational approaches.
A simple lumped parameter based inverse design tool has been developed which provides flow path geometry and entrainment estimates subject to operational, acoustic, and design constraints. These constraints are manifested through specification of primary mass flow rate or ejector thrust, fully-mixed exit velocity, and static pressure matching. Fundamentally, integral forms of the conservation equations coupled with the specified design constraints are combined to yield an easily invertible linear system in terms of the flow path cross-sectional areas. Entrainment is computed by back substitution. Initial comparison with experimental and analogous one-dimensional methods show good agreement. Thus, this simple inverse design code provides an analytically based, preliminary design tool with direct application to High Speed Civil Transport (HSCT) design studies.
An inverse wing design method has been developed around an existing transonic wing analysis code. The original analysis code, TAWFIVE, has as its core the numerical potential flow solver, FLO30, developed by Jameson and Caughey. Features of the analysis code include a finite-volume formulation; wing and fuselage fitted, curvilinear grid mesh; and a viscous boundary layer correction that also accounts for viscous wake thickness and curvature. The development of the inverse methods as an extension of previous methods existing for design in Cartesian coordinates is presented. Results are shown for inviscid wing design cases in super-critical flow regimes. The test cases selected also demonstrate the versatility of the design method in designing an entire wing or discontinuous sections of a wing.
In a rather general sense, inverse airfoil design can be taken to mean the problem of specifying a desired set of airfoil characteristics, such as the airfoil maximum thickness ratio, pitching moment, part of the velocity distribution or boundary-layer development, etc., then from this information determine the corresponding airfoil shape. This paper presents a method which approaches the design problem from this perspective. In particular, the airfoil is divided into segments along which, together with the design conditions, either the velocity distribution or boundary-layer development may be prescribed. In addition to these local desired distributions, single parameters like the airfoil thickness can be specified. The problem of finding the airfoil shape is determined by coupling an incompressible, inviscid, inverse airfoil design method with a direct integral boundary-layer analysis method and solving the resulting nonlinear equations via a multidimensional Newton iteration technique. The approach is fast and easily allows for interactive design. It is also flexible and could be adapted to solving compressible, inverse airfoil design problems.
An exact method of multipoint inverse airfoil design for incompressible flow is presented. Multipoint design is handled by dividing the airfoil into a number of desired segments. For each segment the velocity distribution is prescribed together with an angle of attack at which the prescribed velocity distribution is to be achieved. In this manner, multipoint design objectives can be taken into account in the initial specification of the velocity distribution. In order for the multipoint inverse airfoil design problem to be well posed, three integral constraints and several conditions arise which must be satisfied. Further restrictions are imposed if the airfoil is to have a specified pitching moment, thickness ratio, or other constraints. The system of equations is solved partly as a linear system and partly through multidimensional Newton iteration. Since the velocity distribution is prescribed about the circle in the angular coordinate, specification of the velocity in terms of arc length is handled through the multidimensional Newton iteration as well. The current formulation sets the stage for a more general multipoint inverse airfoil design method in which it will be possible to specify the velocity distribution, some boundary-layer development, or the surface geometry along a segment.
The traditional goal of inverse self-assembly is to design interactions that drive particles toward a desired target structure. However, achieving successful self-assembly also requires tuning the thermodynamic conditions under which the structure is stable. In this work, we extend the inverse design paradigm to explicitly address this challenge by developing a framework for inverse thermodynamics, i.e., the design of interaction potentials that realize specific thermodynamic behavior. As a step in this direction, using patchy particle mixtures as a model system, we demonstrate how precise control over both bonding topology and bond energetics enables the programming of targeted phase behavior. In particular, we establish design principles for azeotropic demixing and show how to create mixtures that exhibit azeotropy at any prescribed composition. Our predictions are validated through Gibbs-ensemble simulations [Panagiotopoulos, Mol. Phys. 1987, 61, 813−826]. These results highlight the necessity of coupling structural design with thermodynamic engineering, and provide a blueprint for controlling complex phase behavior in multicomponent systems.