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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Raytracing the long trace profiler

The optical design of the Long Trace Profiler optical system is explored with a commercial raytrace program, Zemax OpticStudio™ (ZOS)1, with the intent of finding and correcting sources of systematic error. ZOS provides both geometric raytracing tools and physical optics Gaussian beam propagation and diffraction image calculation tools, and two design modes, sequential component (SC) and non-sequential component (NSC) that are optimized for different aspects of the design process. The original LTP-II system employs a singlet lens with a 1250 mm focal length. It is optimized to provide minimum distortion over a surface slope angle range of ±5 mrad. Using the ZOS tools, we are able to simulate ghost rays that produce distortion in the beam spot image and can minimize the distortion by deliberate misalignment of the beamsplitter (BS) components. Unfortunately, the reference beam is compromised because of the component tilts. The most recent LTP500 system design simplifies the optical system and makes the reference beam usable again, even with misalignment of the polarizing beamsplitter (PBS). Two lenses are designed for the LTP500 - a cemented doublet that has been fabricated, and a singlet with one aspheric surface. Both have focal distances of 500 mm with an expanded angular measurement range of ±10 mrad. The aspheric singlet provides superior performance. ZOS allows the import of wavefront measurement data produced by commercial interferometer software. We apply the wavefront error measurement from the cemented doublet to the model to show that the 19nm RMS wavefront error needs to be improved by at least a factor of ten in order to reduce the systematic error to a level that will allow the LTP to approach its design limit of a few tens of nanoradians.

Takacs, Peter Z.↗
DOE OSTI2023

A Multi-Dimensional Benefit Assessment of Automated Mobility Platforms (AMP) for Large Facilities: Mobility, Energy, Equity, and Facility Management & Design

The goal of the automated mobility platforms (AMPs) initiative is to raise the bar of service regarding equity and sustainability for public mobility systems that are crucial to large facilities, and doing so using electrified, energy efficient technology. Using airports as an example, the rapid growth in air travel demand has led to facility expansions and congested terminals, which directly impacts equity (e.g., increased challenges for Passengers with Reduced Mobility [PRMs]) and sustainability - both of which are important metrics often overlooked during the engineering design process. Therefore, to evaluate systems and inform critical near- and long-term decisions more effectively, a holistic evaluation framework is proposed focused on four key areas: (1) mobility, with emphasis on travel time and accessibility within an airport, (2) environment, focused on energy consumption and greenhouse gas (GHG) emissions associated with intra-airport mobility, (3) equity, specifically to the PRM community, but with an eye to the whole of society, and (4) built environment, or the fundamental changes in building design enabled by different mobility systems for larger and more flexible, functional, and energy-efficient structures. Below, AMPs are defined, and each metric is discussed further, all with a focus on airport mobility.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Book Chapter: Small Modular Reactors

Small Modular Reactors (SMRs) have been a very promising development in nuclear power over the last two decades. SMRs are defined as nuclear reactors with a power output of less than 300 MWe. This is in comparison to gigawatt-size reactors, which can have electrical output of 1000–1500 MWe or more. This chapter will consist of two major sections. The first will be a detailed summary of the small modular reactor designs being proposed around the world. This section will focus on those that are the furthest along in their development, but will also include some information about the wide variety of proposed designs that require significant research and development. The second part will be a discussion of the remaining challenges to the adoption of SMRs as a major energy source. SMRs are not a new concept, but they do represent a new vision for an older concept. These reactors have the potential to become a major source of energy in the near future. The development of small, modular designs can help promote the adoption of nuclear energy by reducing upfront costs, reducing the financial risk associated with nuclear power, and the barriers to entry. However, the adoption of SMRs is not without challenges. Regulatory and licensing changes to address the unique benefits and concerns associated with SMRs will continue to be a challenge as regulators adapt to the unique features emerging from the design process. The development of new instrumentation and control systems is an ongoing issue. And economics is possibly the most significant challenge, with high construction costs, cheap natural gas, and government subsidies, combining to result in significant financial risk associated with adopting nuclear generation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Comments on Control of Wave Energy Converters

The main objective of this letter is to consolidate the knowledge about the dynamics and control of oscillating-body wave energy converters (WECs). A number of studies have shown that control systems strongly affect power absorption; however, there remains a need for a concise and integrated explanation of the theoretical and practical implications that control can have on both performance and the broader WEC design process. This short letter attempts to fill this gap by presenting a discussion on the key practical aspects concerning the dynamics and control of oscillating-body WEC. In particular, the focus is on the choice of control models and a simple causal control scheme suitable for real-time implementation. To conclude, consideration is given to the effect of the power takeoff (PTO) on the maximization of electrical power, thus leading to the derivation of useful conditions for the control co-design of the PTO system.

42 ENGINEERING↗
NASA NTRS2013

Influence of Computational Drop Representation in LES of a Droplet-Laden Mixing Layer

Multiphase turbulent flows are encountered in many practical applications including turbine engines or natural phenomena involving particle dispersion. Numerical computations of multiphase turbulent flows are important because they provide a cheaper alternative to performing experiments during an engine design process or because they can provide predictions of pollutant dispersion, etc. Two-phase flows contain millions and sometimes billions of particles. For flows with volumetrically dilute particle loading, the most accurate method of numerically simulating the flow is based on direct numerical simulation (DNS) of the governing equations in which all scales of the flow including the small scales that are responsible for the overwhelming amount of dissipation are resolved. DNS, however, requires high computational cost and cannot be used in engineering design applications where iterations among several design conditions are necessary. Because of high computational cost, numerical simulations of such flows cannot track all these drops. The objective of this work is to quantify the influence of the number of computational drops and grid spacing on the accuracy of predicted flow statistics, and to possibly identify the minimum number, or, if not possible, the optimal number of computational drops that provide minimal error in flow prediction. For this purpose, several Large Eddy Simulation (LES) of a mixing layer with evaporating drops have been performed by using coarse, medium, and fine grid spacings and computational drops, rather than physical drops. To define computational drops, an integer NR is introduced that represents the ratio of the number of existing physical drops to the desired number of computational drops; for example, if NR=8, this means that a computational drop represents 8 physical drops in the flow field. The desired number of computational drops is determined by the available computational resources; the larger NR is, the less computationally intensive is the simulation. A set of first order and second order flow statistics, and of drop statistics are extracted from LES predictions and are compared to results obtained by filtering a DNS database. First order statistics such as Favre averaged stream-wise velocity, Favre averaged vapor mass fraction, and the drop stream-wise velocity, are predicted accurately independent of the number of computational drops and grid spacing. Second order flow statistics depend both on the number of computational drops and on grid spacing. The scalar variance and turbulent vapor flux are predicted accurately by the fine mesh LES only when NR is less than 32, and by the coarse mesh LES reasonably accurately for all NR values. This is attributed to the fact that when the grid spacing is coarsened, the number of drops in a computational cell must not be significantly lower than that in the DNS.

Bellan, Josette↗
NASA NTRS2000

Development of a Composite Tailoring Procedure for Airplane Wings

The quest for finding optimum solutions to engineering problems has existed for a long time. In modern times, the development of optimization as a branch of applied mathematics is regarded to have originated in the works of Newton, Bernoulli and Euler. Venkayya has presented a historical perspective on optimization in [1]. The term 'optimization' is defined by Ashley [2] as a procedure "...which attempts to choose the variables in a design process so as formally to achieve the best value of some performance index while not violating any of the associated conditions or constraints". Ashley presented an extensive review of practical applications of optimization in the aeronautical field till about 1980 [2]. It was noted that there existed an enormous amount of published literature in the field of optimization, but its practical applications in industry were very limited. Over the past 15 years, though, optimization has been widely applied to address practical problems in aerospace design [3-5]. The design of high performance aerospace systems is a complex task. It involves the integration of several disciplines such as aerodynamics, structural analysis, dynamics, and aeroelasticity. The problem involves multiple objectives and constraints pertaining to the design criteria associated with each of these disciplines. Many important trade-offs exist between the parameters involved which are used to define the different disciplines. Therefore, the development of multidisciplinary design optimization (MDO) techniques, in which different disciplines and design parameters are coupled into a closed loop numerical procedure, seems appropriate to address such a complex problem. The importance of MDO in successful design of aerospace systems has been long recognized. Recent developments in this field have been surveyed by Sobieszczanski-Sobieski and Haftka [6].

Chattopadhyay, Aditi↗
DOE OSTI2023

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗
NASA NTRS2006

G2 Flywheel Module Design

Design of a flywheel module, designated the G2 module, is described. The G2 flywheel is a 60,000 RPM, 525 W-hr, 1 kW system designed for a laboratory environment; it will be used for component testing and system demonstrations, with the goal of applying flywheels to aerospace energy storage and integrated power and attitude control (IPACS) applications. G2 has a modular design, which allows for new motors, magnetic bearings, touchdown bearings, and rotors to be installed without a complete redesign of the system. This design process involves several engineering disciplines, and requirements are developed for the speed, energy storage, power level, and operating environment. The G2 rotor system consists of a multilayer carbon fiber rim with a titanium hub on which the other components mount, and rotordynamics analysis is conducted to ensure rigid and flexible rotor modes are controllable or outside of the operating speed range. Magnetic bearings are sized using 1-D magnetic circuit analysis and refined using 3-D finite element analysis. The G2 magnetic bearing system was designed by Texas A&M and has redundancy which allows derated operation after the loss of some components, and an existing liquid cooled two pole permanent magnet motor/generator is used. The touchdown bearing system is designed with a squeeze film damper system allowing spin down from full operating speed in case of a magnetic bearing failure. The G2 flywheel will enable module level demonstrations of component technology, and will be a key building block in system level attitude control and IPACS demonstrations.

Jensen, Ralph H.↗
NASA NTRSDate not supplied

Using Systems Engineering to Develop an Integrated Crew Health and Performance System to Mitigate Risk for Human Exploration Missions

New space exploration missions are currently being designed to take humanity beyond Low EarthOrbit (LEO) to cis-lunar space, the lunar surface, and eventually to Mars. These missions carryincreased risks due to a number of factors, including distance from Earth, exposure to deep spacehazards, reduced capacity and ability to resupply and evacuate, and increased communication delays.As distance from Earth grows and mission length increases, a growing proportion of overall missionrisk can be attributed to the “human system.” Almost twenty years ago, the Institute of Medicine in theUnited States recommended the early and complete integration of the human system into thespacecraft and mission design process to mitigate this increased risk inherent in exploration missions.Exploration missions will require increasing levels of crew self-sufficiency that will challenge thecurrent operational paradigms established in LEO. Enabling progressive Earth independence requiresmanagement of the increasingly complex interactions among spacecraft systems and integration of allthe data and functions that affect human health and performance into one coordinated system – theCrew Health and Performance (CHP) system. This system is a critical spacecraft system that isanalogous to other systems such as propulsion, guidance and navigation, or avionics. Design andintegration of the CHP system requires the evidence-based merger of typically disparate disciplinessuch as medicine, human factors, and physiological support system design, using systems engineering(SE) practices. The approach described here enables spacecraft and mission designers to align thescope of a CHP system with mission specific requirements, decrease risk to the crew, and increase theprobability of mission success.

Kerry Mcguire↗
DOE OSTI2021

Valence Effects of Fe Impurity for Recovered LiNi 0.6 Co 0.2 Mn 0.2 O 2 Cathode Materials

Iron impurities are generally included in the obtained leaching liquor solution during the hydrometallurgical recycling method of spent lithium-ion batteries (LIBs) due to the usage of iron in battery casings and machinery parts of recycling equipment, which would definitely affect the physical and electrochemical features of the recovered active materials. In this paper, the effects of iron impurity with different valence states (Fe 2+ and Fe 3+ ) and gradient concentrations (0.2, 1.0, and 5.0 at. %) for the obtained LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) cathodes are fully studied. It is found that Fe3+ impurity could easily lower the tap density and average size of NCM622 particles and even introduce some impurity phases in the NCM622 structure at high concentration (5.0 at. %), leading to much lower specific capacity, worse rate capability, and cycling performance of the Fe 3+ -based NCM622 cathode. On contrast, with certain concentrations of Fe 2+ impurity (0.2 and 1.0 at. %), the NCM622 cathode material exhibits comparable and much better electrochemical properties compared with the virgin NCM622 materials. Based on these results, the valence of Fe impurity should be considered and controlled as well as its concentration during the recycling process design for spent LIBs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Using Co-Optimized Machine Learned Manifolds for Modeling Chemically Reacting Flows

Chemically reacting flows play a key role in a wide range of engineered systems, from chemical and polymer processing to combustion-based energy conversion technologies. Simulations of these flows involve solving a coupled set of partial differential equations for mass, momentum, energy, and all relevant chemical species in the system. Chemical reaction pathways may be extremely complex and involve hundreds or more intermediate species, with reactions that occur over timescales varying by several orders of magnitude - presenting a significant numerical stiffness challenge. The combination of these factors makes simulation of chemically reacting flows vastly more expensive than nonreactive simulations, and often makes direct solution of the governing equations intractable. It is necessary to apply lower-fidelity models in place of the detailed governing equations in order to reduce computational cost to enable reacting flow simulation tools to be used in the engineering design process. Many of the models employed for this purpose are based on reducing the dimension of the thermochemical state, motivated by the observation that the observed thermochemical states in a system lie on a low-dimensional manifold in thermochemical state space. This behavior occurs due to the fast equilibration of certain reactive and transport processes, and physics-based manifold models rely on idealized assumptions about the balance of timescales and the way in which chemistry and transport are coupled. In this work, we apply a novel method for data-driven manifold-based modeling that can leverage data from high-fidelity reacting flow simulations to improve model accuracy in cases where the physics-based modeling assumptions break down. The approach is designed to be broadly applicable across chemically reacting flow systems but is applied here to turbulent combustion modeling.

machine learning↗
DOE OSTI2023

Physics-informed machine-learning model of temperature evolution under solid phase processes

We model temperature dynamics during Shear Assisted Proccess Extrusion (ShAPE), a solid phase process that plasticizes feedstock with a rotating tool and subsequently extrudes it into a consolidated tube, rod, or wire. Control of temperature is critical during ShAPE processing to avoid liquefaction, ensure smooth extrusion, and develop desired material properties in the extruded products. Accurate modeling of the complicated thermo-mechanical feedbacks between process inputs, material temperature, and heat generation presents a significant barrier to predictive modeling and process design. In particular, connecting micro-structural scale mechanisms of heat generation to macro-scale predictions of temperature can become computationally intractable. In this work we use a neural network (NN) model of heat generation to bridge this gap, by combining it with a simplified model of the temperature dynamics due to conduction and convection to capture the macro scale evolution of temperature. We inform the construction of the NN heat generation model using crystal plasticity simulations at the micro-structural scale to model the effects of process inputs on generation of heat. We achieved close fits of the temperature dynamics model to a diverse experimental data-set. Further, the relationships learned by the NN model between process inputs and heat generation showed qualitative agreement with those predicted by crystal plasticity simulations.

36 MATERIALS SCIENCE↗
NASA NTRS2010

General Pressurization Model in Simscape

System integration is an essential part of the engineering design process. The Ares I Upper Stage (US) is a complex system which is made up of thousands of components assembled into subsystems including a J2-X engine, liquid hydrogen (LH2) and liquid oxygen (LO2) tanks, avionics, thrust vector control, motors, etc. System integration is the task of connecting together all of the subsystems into one large system. To ensure that all the components will "fit together" as well as safety and, quality, integration analysis is required. Integration analysis verifies that, as an integrated system, the system will behave as designed. Models that represent the actual subsystems are built for more comprehensive analysis. Matlab has been an instrument widely use by engineers to construct mathematical models of systems. Simulink, one of the tools offered by Matlab, provides multi-domain graphical environment to simulate and design time-varying systems. Simulink is a powerful tool to analyze the dynamic behavior of systems over time. Furthermore, Simscape, a tool provided by Simulink, allows users to model physical (such as mechanical, thermal and hydraulic) systems using physical networks. Using Simscape, a model representing an inflow of gas to a pressurized tank was created where the temperature and pressure of the tank are measured over time to show the behavior of the gas. By further incorporation of Simscape into model building, the full potential of this software can be discovered and it hopefully can become a more utilized tool.

Servin, Mario↗
DOE OSTI2021

Economic and Environmental Assessment of Biological Conversions of Agile BioFoundry (ABF) Bio-Derived Chemicals

Bio-derived chemicals are an essential part of the growing bioeconomy. They possess the potential to boost a new domestic bioproduct industry, improve the sustainability of integrated biomanufacturing, and reduce U.S. dependence on fossil energy. The Agile BioFoundry (ABF) consortium, a Department of Energy (DOE)-sponsored collaboration, is investigating biobased pathways to produce advantaged products including advanced biofuels, fuel intermediates, and bioproducts. ABF is integrating advanced computational tools for biological engineering, process design and data analysis, and economic/sustainability modeling tools into a comprehensive and dynamic platform for biomanufacturing of microbes and using them to produce key metabolic intermediates or beachhead molecules which may be derivatized into several distinct bioproducts of industrial interest. In this presentation, we first discuss a methodology to select a single exemplar product molecule to represent each beachhead pathway based on similarities with other end-molecule options (yields/titers/rates, fermentation operation mode, oxygen requirements, general separations challenges, etc.), in order to maintain a reasonable number of cases for rigorous process simulation. We then use techno-economic analysis (TEA) and life-cycle analysis (LCA) to highlight sensitivities and trends around economic performance and environmental footprint, reflecting examples for two selected ABF technology pathways to bio-derived chemicals: 1) adipic acid production via muconic acid fermentation from mixed sugars with Pseudomonas putida and 2) cineole via geranyl diphosphate with Rhodosporidium toruloides. We present multi-variable scan plots highlighting key drivers on costs and greenhouse gas emissions in order to identify the parameter space in which each pathway could ultimately achieve economic and environmental sustainability meeting or exceeding commodity product benchmarks, thus prioritizing future R&D focus areas.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRSDate not supplied

Using Systems Engineering to Develop an Integrated Crew Health and Performance System to Mitigate Risk for Human Exploration Missions

New space exploration missions are currently being designed to take humanity beyond low Earth orbit (LEO) to cis-lunar space, the lunar surface, and eventually to Mars. These missions carry increased risks due to a number of factors, including distance from Earth, exposure to deep space hazards, reduced capacity and ability to resupply and evacuate, and increased communication delays. As distance from Earth grows and mission length increases, a growing proportion of mission risk can be attributed to the “human system.” Almost twenty years ago, the Institute of Medicine in the United States recommended the early and complete integration of the human system into the spacecraft and mission design process to mitigate this increased risk inherent in exploration missions. Exploration missions will require increasing levels of crew self-sufficiency that will challenge the current operational paradigms established in LEO. Enabling progressive Earth independence requires management of the increasingly complex interactions among spacecraft systems and integration of all the data and functions that affect human health and performance into one coordinated system –the Crew Health and Performance (CHP) system. This system is a critical spacecraft system that is analogous to other systems such as propulsion, guidance and navigation, or avionics. Design and integration of the CHP system requires the evidence-based merger of typically disparate disciplines such as medicine, human factors, and life support system design, using systems engineering (SE) practices. SE provides traceability of spacecraft requirements and enables reliable and repeatable trade space analysis of the many competing options for hardware and software to be included in the mission architecture. The approach described here enables spacecraft and mission designers to align the scope of a CHP system with mission specific requirements, decrease risk to the crew, and increase the probability of mission success.

Kerry McGuire↗
NASA NTRS1990

Mars mission gravity profile simulation

A flight experiment designed to determine the need for artificial gravity for Mars mission architectures at earlier stages of the design process is proposed. The Soviet Mir space station, the NASA Space Shuttle, and the resources of NASA Ames Research Center would be used to duplicate in the terrestrial environment the complete Mars-mission gravity profile in order to assess the need for artificial gravity. All mission phases of 1 G would be on earth; all mission phases of zero or micro G would be in space aboard Mir; and all launch, ascent, orbit, deorbit, approach, departure, and descent G loads would be provided by actual spacecraft in operations that could be designed to simulate the actual G loads, while the Mars stay time would be simulated on earth or in a variable-gravity research facility in space. Methods of simulating activities on the Martian surface are outlined along with data monitoring, countermeasures, and launch site and vehicle selection criteria.

Kuznetz, Lawrence H.↗
NASA NTRS1992

The NASA/Industry Design Analysis Methods for Vibrations (DAMVIBS) Program - Boeing helicopters airframe finite element modeling

Finite-element modeling of the airframe vibration of the Army/Boeing CH-47D helicopter is conducted with comparisons to experimental data in an effort to improve the design process. A NASTRAN FEM is developed that is fully representative of the test configuration and includes the support fixture, shakers, and the aircraft/shaker suspension system. The analysis is conducted with specific attention given to the prediction of reasonable forced amplitudes throughout the airframe. Reasonable correlation is noted between the FEM and experimental results, although improved correlation can be obtained by including more accurate damping values and secondary effects such as stringer shear loading. It is shown that the general stress model does not provide an adequate dynamic analysis on which to base design improvements. A more detailed model is required that emphasizes highly detailed helicopter elements and employs a finer mesh particularly in the description of the mass distribution.

Gabel, R.↗
DOE OSTI2025

A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching

Acoustic waves play a crucial role in various applications, including medical imaging, non-destructive testing, and sonar systems. One of the significant challenges in these applications is impedance matching, which is essential for minimizing reflections and maximizing the transfer of acoustic energy between different media. Acoustic metamaterials offer a promising solution to this challenge. In addition to impedance control, gradient stiffness can enhance structural efficiency and enable spatial control of wave propagation, making it a valuable feature in acoustic metamaterial design. In this pa- per, we present our developed computational method to design 3D stiffness gradient acoustic metamaterials for impedance matching. The key steps in our approach include generating initial designs using a periodic covariance function to provide unit cells that are both periodic on the boundaries and randomly formed inside the unit cell. Furthermore, we integrated manufacturing constraints into the design process, ensuring that the structures are interconnected for fabrication. We propose two computational optimization algorithms: GenUnit, based on a non-dominated sorting genetic algorithm (NSGA-II), and MLMatch, which leverages differentiable machine learning. The two approaches are not separate contributions but complementary com- ponents of a unified framework. GenUnit requires no training data and directly interfaces with physics-based simulations, making it highly accurate but slower for large-scale exploration. In contrast, MLMatch is data-hungry during training but, once trained, enables near-instantaneous inference and broad design-space coverage. Together, they form a hybrid strategy: ML- Match rapidly explores the global design space, and GenUnit provides local refinement with high-fidelity accuracy. This balance between training cost, inference time, and precision is the motivation for including both methods in the same study. We applied this dual-algorithm framework to generate two metallic-based metamaterial designs that match the acoustic impedance of water while exhibiting a controlled gradient in stiffness (from stiff to soft). The stiffness gradient is particularly advantageous in applications where one side of the structure must interface with soft or sensitive surfaces, such as human tissue or delicate components. Here, this work paves the way for improved materials in various acoustic applications, particularly in ultrasound devices, by providing better impedance.

Metamaterial↗