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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Spatially Resolved Evaluation of Accelerated Environmental Aging on Emerging Polypropylene-Based Photovoltaic Backsheets Using Raman Spectroscopy

For this work, accelerated aging was used to assess environmental degradation in emerging co-extruded polypropylene (PP)-based backsheets under three different environmental conditions (65°C/20% relative humidity (RH), 75°C/20% RH, and 75°C/50% RH). Although differential scanning calorimetry did not measure crystallinity changes with exposure, spatially resolved Raman spectroscopy identified crystallinity increases in the core layer of aged samples, indicating a heterogeneous postcrystallization process. The Raman results were in agreement with synchrotron-based microfocused wide-angle X-ray scattering measurements. Cross-sectional nanoindentation was used to correlate localized crystallinity shifts with changes in Young's modulus. A similar trend was found where increased modulus was measured in the core layer, supporting the relationship between modulus and crystallinity. Finally, dielectric characterization was used to assess the impact of these material property changes on performance. While changes in the backsheet material properties and dielectric performance were observed with accelerated aging, these shifts generally equilibrated with time, indicating overall stability in response to environmental stressors. Additionally, the identified heterogeneous material property changes indicate that spatially resolved crystallinity measurements may be a valuable early failure indicator to be used in the assessment of PV backsheet long-term durability.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Capturing soil moisture and salinity changes in flooded coastal forests using electrical resistivity and induced polarization

Geophysical methods provide high-resolution spatial measurements of physical quantities sensitive to changes in soil moisture and salinity and can be used to monitor soil hydrological responses to flooding. However, extracting quantitative hydrological information from geophysical data remains challenging. In this study, we extended existing petrophysical models to estimate soil moisture and salinity from electrical measurements to address this challenge. We manipulated two hydrologically isolated 2000 m 2 experimental plots by simultaneously inundating them with 265 m 3 of either freshwater or estuarine water. Repeated electrical resistivity and induced polarization measurements were used to image the water and solute infiltration along two transects that are 100 and 42 m in length. Petrophysical models derived from laboratory multi-salinity electrical measurements were used to estimate changes in soil moisture and fluid salinity from field measurements of real and imaginary conductivity during the ecosystem-scale flooding experiment. The real conductivity increased by ∼100% in the freshwater plot and ∼570% in the saltwater plot. The change in imaginary conductivity in the freshwater plot was <1 mS/m, whereas that of the estuarine water plot was ∼5 mS/m. Real conductivity shows dependence on soil moisture content with a coefficient of determination (R 2 ) > 0.7, while the imaginary conductivity shows a dependence on soil salinity with R 2 > 0.6. The results validate the use of electrical resistivity for estimating changes in soil moisture content in response to flooding. Combining electrical resistivity imaging with induced polarization measurements provides the possibility to account for changes in pore fluid conductivity.

Adebayo, Moses B. [Univ. of Toledo, OH (United Sta↗
DOE OSTI2025

Efficient use of quantum computers for collider physics

Most observables at particle colliders involve physics at a wide variety of distance scales. Due to asymptotic freedom of the strong interaction, the physics at short distances can be calculated reliably using perturbative techniques, while long distance physics is non-perturbative in nature. Factorization theorems separate the contributions from different scales, allowing to identify the pieces that can be determined perturbatively from those that require non-perturbative information, and if the non-perturbative pieces can be reliably determined, one can use experimental measurements to extract the short distance effects, sensitive to possible new physics. Without the ability to compute the non-perturbative ingredients from first principles one typically identifies observables for which the non-perturbative information is universal in the sense that it can be extracted from some experimental observables and then used to predict other observables. In this paper we argue that the future ability to use quantum computers to calculate non-perturbative matrix elements from first principles will allow to make predictions for observables with non-universal non-perturbative long-distance physics.

Algorithms and Theoretical Developments↗
DOE OSTI2026

Attention-based 3D – convolutional neural network model for mechanical property predictions using visible light images in metal additive manufacturing

Additive manufacturing (AM), while commonly used for rapid prototyping and creating components with complex geometries, has not been widely adopted for critical applications across the aerospace, automotive, defense, energy, and medical industries. This is, in part, due to the challenges of controlling flaws and uncertainty in the mechanical behavior of additively manufactured components. In recent years, there has been an increase in research aimed at predicting the final mechanical properties of additively manufactured components during the printing process. To address these issues, a 3D-CNN model was trained using low-cost in situ visible-light camera data, anomaly classifications, and the chosen process parameters to predict the ultimate tensile strength (UTS), yield strength (YS), total elongation (TE), and uniform elongation (UE). The 3D-CNN layers of the model employed attention mechanisms to prioritize features in the data, thereby improving prediction accuracy. Furthermore, the effect of each process parameter and anomaly class is investigated using attention-based dynamic sigmoid weighted gates to interpret the influence each class has on the final prediction. Different combinations of the in situ data were fed into the 3D-CNN, with varying amounts of image layers, to determine the ideal combination for predicting mechanical properties in situ. Here, the 3D-CNN model achieved mean absolute percentage errors (MAPE) below 5% for both UTS and YS while using only a single camera input and under half of the available image layers.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Micro-cold Spray Deposition of YSZ Films from Ultrafine Powders Using a Pressure Relief Channel Nozzle

Abstract The use of ultrafine powders in the micro-cold spray (MCS) process, also referred to as the aerosol deposition method, typically results in porous and/or poorly adhering films because the particles do not impact at a high enough velocity for sufficient plastic deformation and interparticle bonding to occur. Under typical operating conditions, particles < 100 nm accelerate to high velocities but then are slowed by the stagnant gas in the bow shock that forms just upstream of the substrate. Using larger particles reduces particle slowing, but large particles can cause erosion of the film at high impact velocity, decreasing deposition efficiency. In this study, a pressure relief channel nozzle using helium as a carrier gas is proposed such that high-velocity deposition of yttria-stabilized zirconia particles as small as 10 nm in diameter is possible. This is well below the size range of powders previously used for MCS. The proposed nozzle design increases impact velocities for 10, 20, and 50 nm particles by ~ 880, 560, and 160 m/s, respectively, when compared to a conventional nozzle. Experimental deposition of ultrafine 8YSZ powder shows that the pressure relief channel nozzle results in lower porosity and more uniform deposits, with a ∼ 186% increase in deposition efficiency.

Materials Science↗
DOE OSTI2024

High-Throughput Microstructural Characterization and Process Correlation Using Automated Electron Backscatter Diffraction

The need to optimize the processing conditions of additively manufactured (AM) metals and alloys has driven advances in throughput capabilities for material property measurements such as tensile strength or hardness. High-throughput (HT) characterization of AM metal microstructure has fallen significantly behind the pace of property measurements due to intrinsic bottlenecks associated with the artisan and labor-intensive preparation methods required to produce highly polished surfaces. This inequality in data throughput has led to a reliance on heuristics to connect process to structure or structure to properties for AM structural materials. In this study, we show a transformative approach to achieve laser powder bed fusion (LPBF) printing, HT preparation using dry electropolishing and HT electron backscatter diffraction (EBSD). This approach was used to construct a library of > 600 experimental EBSD sample sets spanning a diverse range of LPBF process conditions for AM Kovar. This vast library is far more expansive in parameter space than most state-of-the-art studies, yet it required only approximately 10 labor hours to acquire. Build geometries, surface preparation methods, and microscopy details, as well as the entire library of >600 EBSD data sets over the two sample design versions, have been shared with intent for the materials community to leverage the data and further advance the approach. Using this library, we investigated process–structure relationships and uncovered an unexpected, strong dependence of microstructure on location within the build, when varied, using otherwise identical laser parameters.

Characterization and Analytical Technique↗
DOE OSTI2024

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling↗
DOE OSTI2025

Material extrusion with integrated compression molding of NdFeB/SmFeN nylon bonded magnets using small- and large-scale pellet-based 3D-printers

High-density bonded rare-earth magnets are manufactured using pellet-fed additive manufacturing (AM)/material extrusion and an integrated additive manufacturing-compression molding (AM-CM) process. Neodymium iron boron – samarium iron nitride in polyamide 12 (NdFeB-SmFeN/PA12) of 93 % weight fraction (65 % volume fraction) are used for the study. The mechanical properties (tensile strength and modulus), magnetic properties (maximum energy density, coercivity, remanence) are reported. Manufacturing parameters such as layer height, barrel temperatures, screw speed and gantry feed rate are optimized to obtain the highest possible density of the magnets using a small-scale desktop material extrusion printer. Large scale integrated additive manufacturing-compression molding (AM-CM) is then utilized to increase the density of the magnets by reducing porosity defects common in the material extrusion process. The density of as-printed magnets was 5.2 g/cm 3 with a BH max value of 124.14 kJ/m 3 , tensile strength of 20 MPa and a modulus of 2 GPa. AM-CM increased the density of the compound by 5.5 % (5.49 g/cm 3 ). The reduction in porosity was confirmed using X-ray tomography (XCT). Improvement in mechanical strength of the material was also observed, with an increase in tensile strength of 25 % (25.09 MPa) and increase in tensile modulus of 275 % (5.49 GPa). Scanning electron microscopy showed increased particle-matrix adhesion with the integrated AM-CM process.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. Here, this work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Reduction of baseplate distortion during directed energy deposition using compliant features

Distortion in additive manufacturing (AM) remains a barrier to its adoption in precision industries. Baseplate warpage is one such issue which compromises the feasibility of post-process precision machining. The restriction to thermal contraction of the deposited part, imposed by the baseplate, generates bending moments, that in turn causes warpage. A novel distortion mitigation strategy using baseplates with integrated compliant features, which enables thermal contraction of the build is presented. Six unique design concepts are evaluated, including a solid reference, using two deposition geometries. A laser, hot-wire, directed energy deposition (DED) process is used to deposit a symmetric cylindrical part (C-part) and a T-shaped asymmetric part (T-part). Flatness deviation of baseplates is measured using structured light 3D scanning. Measurements reveal a reduction in net flatness deviation of 58.8% for the C-part and 40.9% for the T-part, compared to the solid reference. While most designs yielded reductions exceeding 30% and 20% for the C- and T-parts, respectively, one configuration resulted in increased deviation. Finite element (FE) simulations are performed to elucidate the underlying mechanisms affecting distortion of compliant baseplates during DED. Despite variations between predictions and measurements, agreement in the general trend is observed. It also revealed that initial flatness errors in baseplates significantly affect its deviation during deposition. Predictions indicate that compliant features significantly affect the thermal distribution as well as the evolution of flatness deviation in the baseplate during deposition. Notably, one design exhibited a reduction in distortion during cooling, following its initial increase during deposition. FE predictions show a maximum reduction of 60.9% and 38.8% in net flatness deviation for the C- and T-parts, respectively. The performance of compliant baseplates is found to be governed by both its the thermal and mechanical characteristics, which are crucial factors to be considered during design.

Mathews, Ritin [ORNL] (ORCID:0000000301440828)↗
DOE OSTI2025

Butterfly valve performance factors using the multiphysics object oriented simulation environment

Butterfly valves are typically used in nuclear reactors to control incompressible fluid flow with high inlet velocities. Performance factors for butterfly valves include the pressure drop across the valve and the loss coefficient from which hydrodynamic torque and flow coefficients can be computed. This work explores a computational fluid dynamics approach for butterfly valve performance factors using the open-source Multiphysics Object Oriented Simulation Environment (MOOSE) framework. While MOOSE is often used in the nuclear energy modeling and simulation community for simulations ranging from fuel characterization to heat pipe simulation, this work employs the MOOSE open-source Navier–Stokes solver capability for simulating butterfly valve performance factors and compares those to experimentally measured results from the Advanced Test Reactor at Idaho National Laboratory at Reynolds numbers in the order of 10 6 for the partially opened configuration. The MOOSE framework results are compared against experimentally measured butterfly valve performance factors across five valve opening angles using meshes with order 10 4 – 10 5 elements. This validation serves to enable MOOSE-based multiphysics simulations incorporating the open-source Navier–Stokes module.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Using time reversal with long duration broadband noise signals to achieve high amplitude and a desired spectrum at a target location

Time Reversal (TR) is a signal processing technique that can be used to focus acoustic waves to a specific location in space, with most applications aiming to create an impulsive focus. Here, this study instead aims to focus long-duration noise signals using TR. This paper seeks to generate higher amplitude noise at a desired location over an existing method of broadcasting equalized noise. Additionally, this paper explores various characteristics associated with focusing long duration noise using TR. The dependence of the focal amplitude on the duration of the focused signal is explored as well as the implications of using multiple sources when focusing noise. The focal amplitude decreases with longer duration and then levels off when the duration exceeds a few seconds. Coherent addition of focused noise is observed if all loudspeakers have coherent noise signals convolved with their reversed impulse responses. Lastly, focusing noise with a desired spectrum is explored.

42 ENGINEERING↗
DOE OSTI2024

Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics

Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.

25 ENERGY STORAGE↗
DOE OSTI2025

Advancing Pumped Thermal Energy Storage Performance and Cost Using Silica Storage Media

Pumped Thermal Energy Storage (PTES) is an electricity storage system that is suitable for long-duration energy storage (10-1000 h) due to its low marginal cost of energy capacity. We present a techno-economic model of a PTES system that uses particle thermal energy storage. Particles have low costs and can be operated over a wide temperature range leading to increased efficiency and reduced costs compared to other PTES designs. We show how the round-trip efficiency, specific power output, capital cost, and levelized cost of storage (LCOS) depend on parameters such as the pressure ratio, heat exchanger approach temperature and pressure loss, and maximum temperature. We compare particle-PTES (P-PTES) performance to PTES which uses liquid thermal energy storage - i.e. molten salt hot storage and methanol cold storage (MS-PTES). We find that using silica particles for storage advances PTES technology: P-PTES can be operated at higher maximum temperatures than MS-PTES. Furthermore, P-PTES can achieve lower approach temperatures in the heat exchangers than MS-PTES without increasing capital costs, because P-PTES uses direct-contact heat transfer in fluidized bed heat exchangers. As a result, we find that P-PTES systems achieve higher round-trip efficiency than MS-PTES (66 % versus 57 %) and lower LCOS (e.g. 0.115 +- 0.03 $/kWhe versus 0.171 +- 0.04 $/kWhe for 10 h discharge). The low cost of particles and containment means that P-PTES can provide long-duration energy storage at low capital cost per unit energy capacity. For example, the total capital cost per unit energy reduces from 245 $/kWhe at 10 h to 38 $/kWhe at 100 h. These costs are considerably lower than MS-PTES (72 $/kWhe at 100 h) and also outcompete current and future Li-ion battery system projections (100-265 $/kWhe).

25 ENERGY STORAGE↗
DOE OSTI2024

Rapid and high-throughput determination of sorghum ( Sorghum bicolor ) biomass composition using near infrared spectroscopy and chemometrics

Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum (Sorghum bicolor) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models’ effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production.

09 BIOMASS FUELS↗
DOE OSTI2026

Analysis of strategies to meet ASHRAE S241 infectious aerosol control targets by space type and region using EnergyPlus™ simulations

Professional organizations, such as ASHRAE, have recently proposed new voluntary standards for controlling infectious aerosols. Specifically, ASHRAE Standard 241 (S241) defines equivalent clean air targets for a range of space types that can be met through combinations of mitigation measures. This paper seeks to inform the selection of measures by space type and climate zone through building simulations that quantify the impacts of increased outdoor air ventilation; increasing filtration and/or adding germicidal ultraviolet (GUV) in the central heating, ventilation, and air-conditioning (HVAC) systems; or using portable air cleaners (PACs), upper room GUV, or whole room GUV approaches to increase equivalent clean air delivery. The measures are assessed individually and in practical combinations for their ability to meet S241 in four space types (offices, classrooms, dining areas, and healthcare waiting rooms) using prototype buildings modeled using EnergyPlus. The mitigation measures are compared holistically against baseline building operations using metrics for equivalent clean air, energy, and comfort. The results in this study show that single measures can meet S241 for offices with minimal impacts on energy and comfort, while either upper room GUV systems or combinations of measures such as MERV 13 HVAC filtration with PACs are needed to meet S241 for classrooms. The dining area and healthcare waiting room targets cannot be met in this study when assuming design occupancy and MS2 as the challenge agent, even when combining multiple measures together. This paper provides valuable considerations when designing measures to meet S241 for a range of spaces and scenarios.

GUV↗
DOE OSTI2025

2,3-Butanediol recovery from fermentation broth using membrane pertraction

2,3-Butanediol (BDO) has gained immense interest for use as a platform chemical in the production of many important chemicals such as synthetic rubber, plasticizer, and octane boosters. Using BDO as a precursor for sustainable aviation fuel production may significantly reduce the carbon footprint of the airline industry. However, recovery of BDO is challenging due to its relatively low concentration (∼10 wt %) in fermentation broth and its high affinity for water. In this work, we developed a membrane pertraction process for BDO recovery from biomass derived fermentation broth. Different organic solvents such as hexanol and oleyl alcohol were investigated. With traditional solvent extraction, hexanol required a solvent to feed ratio of 10 to recover >90 % BDO, and oleyl alcohol could recover 35 % of BDO at the same ratio. BDO recovery of >90 % was demonstrated with pertraction; a BDO extraction rate of 15 g 1 m −2 h −1 was achieved using hexanol, and an extraction rate of 10 g 1 m −2 h −1 was achieved using oleyl alcohol. Impurities commonly found in fermentation broth did not affect the separation performance of the pertraction process. In conclusion, this work demonstrates pertraction as a low-footprint, scalable method for recovery of high-purity BDO from fermentation broth.

2,3 butanediol↗
DOE OSTI2024

Predicting non-linear stress–strain response of mesostructured cellular materials using supervised autoencoder

Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of how the topology of the unit cell structure affects the macroscopic behavior of the material in both the linear and the non-linear regimes encountered under large compression. Here, we have developed a machine learning (ML) approach capable of accelerating the prediction of the stress–strain response of a polymer-based cellular structure under uniaxial confined compression. As part of generating the training data for ML, 60,000 mesostructures were generated using a relatively novel approach based on cellular automata, and their corresponding stress–strain responses were obtained from the finite element simulations. Principal component analysis (PCA) was used to reduce the dimensionality of the stress–strain curves. With only 20 principal components, PCA captured 99.89% of the variance in the stress–strain curves while reducing the dimensionality by 5X. ML using supervised autoencoder was able to successfully speed up the prediction of the non-linear stress–strain response of a unit cell by up to 4600X. The proposed method can serve as an efficient data generation tool and a rapid means for predicting the structure–property relationship through accelerated forward modeling of cellular materials under compaction, in cases where the macroscopic stress–strain response is governed by the unit-cell topology.

36 MATERIALS SCIENCE↗