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DOE OSTI2022

Model-based Co-Simulation of Heat Pump Water Heater with Phase Change Materials Thermal Energy Storage

The study analysis the behavior of a new developed heat pump water heater technology which integrates a phase change materials storage with a standard heat pump water heater to maximize the performance parameters of the Unified Energy Factor (UEF) and First Hour Rating (FHR). A model-based control development co-simulation platform is developed to include equipment models, such as heat pump, standard water tank, phase change materials storage tank, and integrate them with control model to support controls design, analysis, verification, and validation. Simulation results are compared with lab test results to validate the accuracy of developed co-simulation platform

Sun, Jian↗

DOE OSTI2022

Quantifying Movement Motivations, Demand, and Inflow-Outflow Dynamics in Four Cities (New York, Chicago, Austin, and San Diego) During COVID-19: Preprint

COVID-19 has impacted human activities ranging from food delivery habits to major moving and travel decisions. It’s clear that multiple pandemic-related factors have influenced millions of relocation decisions by Americans (e.g. health risk, financial pressures, more space, employment). There are positive economic and social outcomes of this influence (e.g. remote work and education) enabling more affordable living and opportunity. This paper addresses COVID-19 impacts on mobility, especially the mobility that involves permanent relocation from place to place. Survey design and data analysis with U-Haul targeted customers in Austin, New York, San Diego, and Chicago to understand mobility, new moving dynamics, and motivations.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2022

Quantifying Movement Motivations, Demand, and Inflow-Outflow Dynamics in Four Cities (New York, Chicago, Austin, and San Diego) During COVID-19

The COVID-19 pandemic has impacted a wide range of human activities, from food delivery habits to major moving and travel decisions. Results indicate multiple pandemic-related factors have influenced millions of relocation decisions by Americans (e.g., health risk, financial pressures, more space, and employment), and there are various positive economic and social outcomes of this influence (e.g., remote work and education), enabling more affordable living and opportunity. This paper addresses COVID-19 impacts on mobility, especially involving permanent relocations. Survey design and data analysis with U-Haul targeted customers in Austin, New York, San Diego, and Chicago to understand mobility, new moving dynamics, and motivations.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2025

Efficient analysis of magnetic field line behavior in toroidal plasmas

The confinement of plasmas in tokamaks and stellarators depends on magnetic field lines lying in nested toroidal surfaces. The transition near the plasma edge away from the lines lying in magnetic surfaces defines properties of divertors. The transition in time defines properties of disruptions. Divertor design and disruption analyses require a detailed understanding of these transitions. The use of a Fourier transform coupled with a Gaussian window function allows far more information to be extracted about these transitions using far shorter field line integrations than can be obtained using traditional methods based on Poincaré plots. The physics of divertors and disruptions is reviewed to clarify why the type of information that can be gained from more efficient methods of analysis is of central importance to the fusion program based on magnetic confinement.

Boozer, Allen H. [Columbia Univ., New York, NY (Un↗

DOE OSTI2024

Monitoring the impact of confinement on hyphal penetration and fungal behavior

Through their expansive mycelium network, soil fungi alter the physical arrangement and chemical composition of their local environment. This can significantly impact bacterial distribution and nutrient transport and can play a dramatic role in shaping the rhizosphere around a developing plant. However, direct observation and quantitation of such behaviors is extremely difficult due to the opacity and complex porosity of the soil microenvironment. In this study, we demonstrate the development and use of an engineered microhabitat to visualize fungal growth in response to varied levels of confinement. Microfluidics were fabricated using photolithography and conventional soft lithography, assembled onto glass slides, and prepared to accommodate fungal cultures. Selected fungal strains across three phyla (Ascomycota: Morchella sextalata , Fusarium falciforme ; Mucoromycota: Linnemannia elongata , Podila minutissima , Benniella ; Basidiomycota: Laccaria bicolor , and Serendipita sp.) were cultured within microhabitats and imaged using time-lapse microscopy to visualize development at the mycelial level. Fungal hyphae of each strain were imaged as they penetrated through microchannels with well-defined pore dimensions. The hyphal penetration rates through the microchannels were quantified via image analysis. Other behaviors, including differences in the degree of branching, peer movement, and tip strength were also recorded for each strain. Our results provide a repeatable and easy-to-use approach for culturing fungi within a microfluidics platform and for visualizing the impact of confinement on hyphal growth and other fungal behaviors pertinent to their remodeling of the underground environment.

Guo, Yi-Syuan (ORCID:0000000340142225)↗

DOE OSTI2020

Dynamic Behavior of Natural Seep Vents: Analysis of Field and Laboratory Observations and Modeling (Final Scientific/Technical Report)

In this project, we have analyzed data collected by the U.S. Department of Energy (DOE), National Energy Technology Laboratory (NETL) in a high pressure water tunnel (HPWT) and data from two research cruises to natural seeps in the Gulf of Mexico to adapt and validate a numerical model to predict the dynamics of natural seeps in the deep oceans. The HPWT data include video observations of the shrinkage rate of individual methane and natural gas bubbles under simulated deep-water conditions. Field data were collected during two cruises by the Gulf Integrated Spill Research (GISR) Consortium led by Texas A&M University and funded by the Gulf of Mexico Research Initiative (GoMRI). These data included in situ observations from a remotely operated vehicle (ROV) of gas bubbles at two natural seep sites in the Gulf and acoustic observations of the natural seep bubble flares in the ocean water column. The acoustic data were from multibeam echosounders, one mounted in a forward-looking orientation on the ROV and another mounted down-looking in the haul of the ship. All of these laboratory and field data were focused on the dynamics of natural gas bubbles at temperatures and pressures favorable for clathrate hydrate formation between the gas and water. Our analyses of this data focused on understanding the mechanisms responsible for gas bubble dissolution within the hydrate stability zone (HSZ) of the oceans. Ice-like hydrate shells may form on the bubble-water interface under these conditions, and it was unknown how this might affect the mass transfer of gas into the ocean. We were able to extract bubble shrinkage rates from the HPWT datasets. Using this data we determined that mass transfer coefficients with and without a hydrate shell match empirical values for bubbles in contaminated systems (so-called dirty bubbles contaminated by naturally occurring surfactants). We also showed that free gas, and not gas hydrate, is the dominant dissolving phase when the hydrate sub-cooling is below 11 degree Celsius (temperature difference between hydrate the hydrate formation temperature and ambient temperator) or the pressure is reducing as bubbles rise through the ocean water column. Using this mass transfer model, our numerical model of bubble dissolution matched the over 200 HPWT experiments with an average error of 10% for predicting the bubble size at the end of an experiment. From field data in the literature, we also observed that gas bubbles dissolve faster when they are initially released, following mass transfer coefficients for so-called clean-bubbles (those not yet contaminated by surfactants). Shortly after release within the HSZ, a hydrate shell forms on the bubble-water interface, and the mass transfer reduces to rates matching those of dirty bubbles. We correlated this transition time from clean to dirty bubble behavior with the initial bubble surface area and the hydrate sub-cooling. With this model for hydrate formation time and using the mass transfer coefficients deduced from the HPWT data, we validated our numerical model for predicting the rise heights of natural seep flares in the oceans. Flare heights are commonly observed in haul-mounted acoustic multibeam data. The numerical model predicts bubbles to rise high in the ocean water column owing to the slower mass transfer rates for dirty bubbles that accompany the majority of their rise time. We found that the numerical model predictions matched the observed flare heights within 5% to 10% accuracy when we compared the rise heights of the largest bubbles released from the seafloor with the bubbles acoustically visible in the multibeam data. Bubbles become acoustically transparent as they shrink to sizes of order 1 mm in diameter for the multibeam frequencies used in the field. The forward-looking multibeam on the ROV also provided data on the lateral spreading of bubbles in natural seep flares. Our analysis of this data showed that spreading follows a diffusion process, with the effective diffusivity correlating with the wobbling length scale of these ellipsoidal bubbles. When we apply this diffusivity in a random displacement model of bubble spreading, our numerical simulations match closely the lateral spread observed by the M3 in the ocean water column. Finally, we compared the seep model predictions for the acoustic properties of these natural seep plumes with that observed by the acoustic instruments in the field. The M3 and EM 302 observations were converted to relative values of target strength using a calibration we obtained in the laboratory for the M3 and using an algorithm from the manufacturer for the EM 302. Comparing the numerical seep model to these data, we obtain good agreement over the whole height of rise of these bubble flares. This further validates the numerical model. Overall, our validated seep model captures the key dynamics of gas bubbles released from natural seeps in the oceans and helps to predict the fate of methane in the water column.

03 NATURAL GAS↗

DOE OSTI2025

Sm 2 Ru 3 Sn 5 : A Noncentrosymmetric Cubic Member of the Ln 2 M 3 X 5 Family

An optimized synthetic method is presented for Sm 2 Ru 3 Sn 5 and investigate its physical properties and electronic structure. Sm 2 Ru 3 Sn 5 is prepared by arc-melting stoichiometric ratios of the elements and is confirmed by single crystal and powder X-ray diffraction. An antiferromagnetic transition is observed at T N = 3.8 K. A modified Curie-Weiss fit to the data in the range 50–150 K yields a Curie-Weiss temperature: θ CW = −36.6 K and an effective magnetic moment: μ eff = 0.83 μ B , in agreement with a Sm 3+ oxidation state. Field-dependent magnetization up to H = 7 T at 2 K shows a maximum response of 0.06 μ B , which is significantly lower than the expected Sm 3+ saturation moment (0.71 μ B ). Resistivity measurements indicate metallic behavior, and analysis of the magnetic entropy from the heat capacity reveals a doublet ground state due to crystal electric field splitting. The electronic structure and density of states are calculated with density function theory and further supported by the local density approximation with dynamical mean-field theory. Finally, the experimental and computational results highlight localized Sm 3+ moments and suggest a possible interplay between Ruddelman–Kitel–Kasuya–Yosida and Kondo interactions, positioning Sm 2 Ru 3 Sn 5 as a promising material for studying topology and complex physical phenomena.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

INSPIRED: Inelastic neutron scattering prediction for instantaneous results and experimental design

Inelastic neutron scattering (INS) has unique advantages in probing how atoms vibrate and how the vibrations propagate and interact. Such dynamic information is crucial in understanding various material properties, from heat capacity, thermal conductivity, phase transitions, and chemical reactions to more exotic quantum behavior. The analysis and interpretation of the INS spectra often start from a model structure of the sample, followed by a series of calculations to obtain the simulated spectra to compare with experiments. The conventional way to perform such calculations usually requires significant time, computing resources, and specialized expertise. Here, we present a new program named INSPIRED (Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design), which enables users to perform rapid INS simulations in several different ways on their personal computers in just a few clicks, with the crystal structure as the only input file. Specifically, the users can choose a pre-trained symmetry-aware neural network (coupled with an autoencoder) to predict the phonon density of states (DOS), 1D S(E) and 2D S(|Q|,E) spectra for any given structure. One can also choose an existing density functional theory (DFT) calculation from a database (containing over 12,000 crystals), and quickly obtain the simulated INS spectra for single crystals and powders. It is also possible to use pre-trained universal machine learning force fields to relax a given crystal structure, calculate the phonon dispersion and DOS, and, subsequently, the INS spectra. All these functions are implemented with a PyQt graphic user interface. Finally, we expect these new tools will benefit broad user communities and significantly improve the efficiency of experiment design, execution, and data analysis for INS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques

Here, the US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increasingly valuable for understanding detailed patterns in freight logistics. In this study, we used the 2017 Commodity Flow Survey Public Use File data set to explore building a high-performance freight mode choice model, considering three main improvements: (1) constructing local models for each separate commodity/industry category; (2) extracting useful geographical features, particularly the derived distance of each freight mode between origin/destination zones; and (3) applying additional ensemble learning methods such as stacking or voting to combine results from local and unified models for improved performance. The proposed method achieved over 92% accuracy without incorporating external information, an over 19% increase compared to directly fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely Additive Explanations) values were computed to explain the outputs and major patterns obtained from the proposed model. The model framework could enhance the performance and interpretability of existing freight mode choice models.

42 ENGINEERING↗

DOE OSTI2025

Deep learning model for fast, science-based forecasting of fluid migration along faults in geologic carbon storage scenarios

Effective long-term geologic storage depends on robust site selection and credible, science-based forecasting of subsurface behavior to ensure storage integrity. For this work, we develop a deep learning–based reduced-order model (ROM) to quantify potential carbon dioxide (CO₂) and brine migration through geological faults. The ROM combines a Transformer model for binary classification and a Stacked Ensemble for regression, trained on a comprehensive dataset generated from 1400 physics-based reservoir simulations. Key geologic and operational parameters—including fault geometry, reservoir structure, and injection conditions—were systematically varied to capture a wide range of fluid migration scenarios. The ROM accurately predicts the onset of migration, cumulative migration volumes of both CO₂ and brine, and associated migration rates, as compared to an independent set of validation simulations, while significantly reducing computational cost compared to traditional simulation methods. Model performance was evaluated across diverse fault configurations, revealing that shallow reservoir geometry and fault angle are among the most influential factors governing migration behavior. Sensitivity analysis using SHapley Additive exPlanations (SHAP) provided interpretability, revealing distinct patterns in how geological and operational features drive transient versus cumulative migration outcomes. The ROM’s ability to rapidly simulate fault migration scenarios enables efficient sensitivity analyses, scenario evaluations, and decision support for site selection and monitoring design. This approach enhances the safety, scalability, and long-term operational performance of geologic carbon storage (GCS) systems by providing a robust, interpretable tool for predicting subsurface fluid migration and assessing fault-related migration potential.

42 ENGINEERING↗

DOE OSTI2025

Analysis of Degradation and Electrochemical Behavior of Lithium–Oxygen Batteries under Lean Electrolyte Conditions

Lithium-oxygen batteries have garnered much attention in the past decades due to their high energy density and active material abundancy. Decreasing the electrolyte volume is critical to achieving a high energy density for practical applications. Recent works have demonstrated a serious performance limitation at lower electrolyte fillings. Here, in this work, we study specifically the difference between flooded and lean electrolyte lithium-oxygen batteries by systematically tracing the source of performance degradation. We find that the key contributor to the decrease in performance under lean electrolyte conditions stems from the lithium metal anode rather than the cathode. Solid electrolyte interphase (SEI) compositionally appears to be the same from X-ray photoelectron spectroscopy but the coverage is less uniform, and impedance is much higher under lean electrolyte conditions. Such an effect likely produced the observed poorer cycle performance at 10 μL cm -2 (similar to 28% cathode's pore volume filled) vs 100 μL cm -2 (278% of cathode's pore volume) at a capacity of 1.0 mAh cm -2 (0.1 mAh cm -2 ) using an electrolyte composition of 1 M LiTFSI in TEGDME.

Córdoba, Daniel [Argonne National Laboratory (ANL)↗

DOE OSTI2020

Improved Single-Ion Conductivity of Polymer Electrolyte via Accelerated Segmental Dynamics

Single-ion conducting polymer electrolytes (SICPEs) have many advantages for solid-state battery applications, while low conductivities remain the bottleneck for their practical applications. In this paper, a strategy that can significantly improve the ionic conductivity of SICPEs based on the concept of accelerated segmental dynamics was demonstrated by the evaluation of the thermal property, rheological behavior, morphology analysis, molecular dynamics simulation, and conductivity performance. With both “soft” poly(dimethylsiloxane) (PDMS) backbone and poly(ethylene glycol) (PEG) side chains, the obtained SICPE possesses faster segmental relaxation and higher lithium-ion conductivity. With lithium “transference” number close to unity, the obtained SICPE also shows excellent electrochemical stability against lithium metal electrodes. The clear relationship established between the segmental dynamics of polymer electrolyte and its ionic conductivity should contribute to achieve a solid electrolyte with improved ionic conductivity toward the next-generation solid-state battery.

Segmental Dynamics↗

DOE OSTI2023

Symmetry and scaling in one-dimensional compressible two-phase flow

Investigations of shock compression of heterogeneous materials often focus on the shock front width and overall profile. The number of experiments required to fully characterize the dynamic response of a material often belie the structure–property relationships governing these aspects of a shock wave. Recent observations measured a pronounced shock-front width on the order of 10 s of ns in particulate composites. We focus on particulate composites with disparate densities and investigate whether the mechanical interactions between the phases are adequate to describe this emergent behavior. The analysis proceeds with a general Mie–Grüneisen equation of state for the matrix material, a general drag force law with general power-law scaling for the particle-matrix coupling of the phases, and a volume fraction-dependent viscosity. Lie group analysis is applied to one-dimensional hydrodynamic flow equations for the self-consistent interaction of particles embedded in a matrix material. The particle phase is characterized by a particle size and volume fraction. The Lie group analysis results in self-similar solutions reflecting the symmetries of the flow. The symmetries lead to well-defined scaling laws, which may be used to characterize the propagation of shock waves in particle composites. An example of the derived scaling laws for shock attenuation and rise time is shown for experimental data on shock-driven tungsten-loaded polymers. A key result of the Lie analysis is that there is a relationship between the exponents characterizing the form of the drag force and the exponent characterizing the shock velocity and its attenuation in a particulate composite. Comparison to recent experiments results in a single exponent that corresponds to a conventional drag force.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Fingerprinting triangular-lattice antiferromagnet by excitation gaps

CeCd 3 As 3 is a rare-earth triangular-lattice antiferromagnet with large inter-layer separation. Here, our field-dependent heat capacity measurements at dilution fridge temperatures allow us to trace the field-evolution of the spin-excitation gaps throughout the antiferromagnetic and paramagnetic regions. The distinct gap evolution places strong constraints on the microscopic pseudo-spin model, which, in return, yields a close quantitative description of the gap behavior. This analysis provides crucial insights into the nature of the magnetic state of CeCd 3 As 3 , with a certainty regarding its stripe order and low-energy model parameters that sets a compelling paradigm for exploring and understanding the rapidly growing family of the rare-earth-based triangular-lattice systems

36 MATERIALS SCIENCE↗

DOE OSTI2020

Open-Source High-Fidelity Aggregate Composite Load Models of Emerging Load Behaviors for Large-Sale Analysis

The goal of this project is to accurately model the dynamic loads for power system planning and operation and to address the challenges inherent in such modeling. Existing load models are inadequate to handle the increasing proliferation of residential air conditioner loads and distributed energy resources, the emerging trends of price-responsive demand, and the growing importance of protective devices in equipment and buildings. A need has emerged to develop a fundamentally new class of load models and next-generation data tools. This work proposes to develop a set of regional–level, scalable open source load models and tools, including large-scale aggregate load protection, concept of price responsive demand, advanced load composition data, next-generation load model data tools, and advanced composite load model calibration and validation frameworks to address new challenges in the representation of next-generation load models.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Signal Decomposition for Intrusion Detection in Reliability Assessment in Cyber Resilience (Summary Report)

The complexity of assuring cyber resilience for physical process interactions in connected systems such as energy grids increases dramatically as the coupling between processes becomes more direct and responsive. An example of this growing complexity is provided by Integrated Energy Systems (IES), in which various processes such as nuclear heat generation and commodity production are being directly coupled for increased responsiveness to highly variable signals such as market pricing or electricity demand. As such, the potential attack surface of the coupled processes is larger than the two processes independently. Securing these complex systems requires two-fold monitoring: cybersecure monitoring for potential malicious incursion, and physics monitoring for system tampering. Physics monitoring includes analyzing the behavior of the signals within the system for anomalous behavior. This analysis has been shown to be insufficient if approached by only data-driven machine learning and artificial intelligence (MLAI) techniques or only low-level model comparison. Previous efforts at Purdue University suggested combining high-fidelity models with MLAI algorithms as a basis for a software tool for detecting anomalies in physical processes. This work built on that suggestion, developing an advanced library for signal decomposition and analysis using both MLAI and high-fidelity physics algorithms for greatly improved anomaly detection, especially false data injection. This software can be used as part of a secure imbedded intelligence (SEI) system designed under Consequence-driven Cyber-informed Engineering (CCE) for complex coupled systems. This library established a foundation for online and posteriori analysis of digital signals for the purpose of detecting potential malicious tampering in digital signals representing physical processes. Demonstrations carried out throughout the development highlight the effective use of characterization algorithms to detect signal perturbations, particularly triangle attack-style perturbations, in three wide-ranging applications: seismic monitoring, nuclear thermal hydraulics system simulation, and custom manufacturing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Impacts of Irradiation Structural Behavior on Thermal Hydraulics Safety Analysis to Support MURR LEU Conversion

The University of Missouri Research Reactor (MURR) located in Columbia, Missouri is one of six U.S. High Performance Research Reactors (USHPRR), including one critical facility, that is actively collaborating with U. S. Department of Energy (DOE) National Nuclear Security Administration (NNSA) Material Management and Minimization (M3) Office of Reactor Conversion and Uranium Supply to convert from the use of highly enriched uranium (HEU; ≥20 wt% U-235) to low-enriched uranium (LEU; <20 wt% U-235) fuel. A new type of very high-density LEU fuel based on an alloy of uranium and 10 wt% molybdenum (U-10Mo) is expected to allow the conversion to LEU of MURR, as well as four other USHPRR. MURR has been working with the Reactor Conversion Pillar at Argonne to perform fuel element design and fuel cycle performance analyses, steady-state thermal hydraulics safety analysis, and accident safety analyses in preparation for the conversion of MURR and to support a preliminary safety analysis report for conversion to LEU fuels. Subsequent analyses have also been performed, including transition cycles where all-fresh LEU fuel elements are introduced upon conversion and progressing through reactor operations the core is brought to equilibrium. Thermal hydraulics safety analyses performed as part of the above have employed an assumption on channel gap reduction due to burnup-related phenomena including fuel swelling, irradiation creep, and oxide layer buildup. Recently, a series of structural analyses have been performed on the MURR LEU fuel plates and an element due to significant differences between the plate and element designs of the MURR HEU and LEU fuels. In addition, NUREG-1537 indicates that structural phenomena are to be evaluated. Two separate types of structural analyses were performed for the MURR LEU fuel element: fluid-structure interaction (FSI) and irradiation thermo-mechanical. The FSI analysis evaluated the effects of hydraulic forces on the MURR LEU fuel element to quantify the flow-induced plate deflection, and a minimal impact to the channel gap thickness was predicted under prototypic and bounding conditions. The irradiation thermo-mechanical analysis evaluated the effects of fuel swelling, irradiation creep, and thermal expansion for the MURR LEU plates and the element for prototypic thermal and irradiation conditions based on a high-fidelity approach multiphysics approach. Overall, this thermo-mechanical analysis predicts smaller gap thickness changes in previously limiting regions. Larger changes are predicted in the middle of channels, and for end channels where power density is not typically a maximum. An additional thermo-mechanical analysis was performed for the outermost HEU fuel plate, which showed a similar magnitude of deflection as the outermost LEU plate. Due to substantial differences between the channel gap reductions assumed for the previous safety analyses and those predicted by the irradiation thermo-mechanical analysis, a need to evaluate their impact on the thermal hydraulics safety analyses arose. This report presents the results from the steady-state safety analyses for normal operation as well as the accident analyses for the two most limiting accident scenarios.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Liquid Crystal Orientation and Shape Optimization for the Active Response of Liquid Crystal Elastomers

Liquid crystal elastomers (LCEs) are responsive materials that can undergo large reversible deformations upon exposure to external stimuli, such as electrical and thermal fields. Controlling the alignment of their liquid crystals mesogens to achieve desired shape changes unlocks a new design paradigm that is unavailable when using traditional materials. While experimental measurements can provide valuable insights into their behavior, computational analysis is essential to exploit their full potential. Accurate simulation is not, however, the end goal; rather, it is the means to achieve their optimal design. Such design optimization problems are best solved with algorithms that require gradients, i.e., sensitivities, of the cost and constraint functions with respect to the design parameters, to efficiently traverse the design space. In this work, a nonlinear LCE model and adjoint sensitivity analysis are implemented in a scalable and flexible finite element-based open source framework and integrated into a gradient-based design optimization tool. To display the versatility of the computational framework, LCE design problems that optimize both the material, i.e., liquid crystal orientation, and structural shape to reach a target actuated shapes or maximize energy absorption are solved. Multiple parameterizations, customized to address fabrication limitations, are investigated in both 2D and 3D. The case studies are followed by a discussion on the simulation and design optimization hurdles, as well as potential avenues for improving the robustness of similar computational frameworks for applications of interest.

42 ENGINEERING↗