Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “3D fields”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Morphologies of dealloying corrosion attack at grain boundaries

Dealloying corrosion at grain boundaries severely compromises the performance of polycrystalline materials across a wide variety of technological applications. The impact of this phenomenon depends upon the morphology and rate of intergranular dealloying, which can range from planar to wormhole-like patterns that rapidly advance into the alloy. Using 2D and 3D multi-phase field simulations, we reveal how diverse microstructures result from a fundamental interplay between alloy composition and a grain boundary migration mechanism that alters diffusional pathways of dealloying. Inside intergranular dealloying channels, corrosion product buildup can spawn new channels that branch into grain interiors, such that alloys can be degraded from the inside out. These processes further lead to an atypical coarsening mode assisted by diffusion in the dealloying agent. We summarize a unifying explanation for dealloying morphology selection in polycrystalline alloys, which provides an important step towards their optimization for dealloying corrosion environments.

Corrosion↗
DOE OSTI2026

Learning turbulent flows with generative models for super resolution and sparse flow reconstruction

Neural operators are promising surrogates for dynamical systems but when trained with standard L 2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that combining operator learning with generative modeling overcomes this limitation. We consider three practical turbulent-flow challenges where conventional neural operators fail: spatio-temporal super-resolution, forecasting, and sparse flow reconstruction. For Schlieren jet super-resolution, an adversarially trained neural operator (adv-NO) reduces the energy-spectrum error by 15 × while preserving sharp gradients at neural operator-like inference cost. For 3D homogeneous isotropic turbulence, adv-NO trained on only 160 timesteps from a single trajectory forecasts accurately for five eddy-turnover times and offers 114 × wall-clock speed-up at inference than the baseline diffusion-based forecasters, enabling near-real-time rollouts. For reconstructing cylinder wake flows from highly sparse Particle Tracking Velocimetry-like inputs, a conditional generative model infers full 3D velocity and pressure fields with correct phase alignment and statistics. These advances enable accurate reconstruction and forecasting at low compute cost, bringing near-real-time analysis and control within reach in experimental and computational fluid mechanics.

Fluid dynamics↗
DOE OSTI2025

Pressure tuning of Kitaev spin liquid candidate Na 3 Co 2 SbO 6

The search for Kitaev’s quantum spin liquid in real materials has recently expanded with the prediction that honeycomb lattices of divalent, high-spin cobalt ions could host the dominant bond-dependent exchange interactions required to stabilize the elusive entangled quantum state. The layered honeycomb Na 3 Co 2 SbO 6 has been singled out as a leading candidate provided that the trigonal crystal field acting on Co 3d orbitals, which enhances non-Kitaev exchange interactions between $J$ eff = $\frac{1}{2}$ spin-orbital pseudospins, is reduced. Here we show that applied pressure leads to anisotropic compression of the layered structure, significantly reducing the trigonal distortion of CoO 6 octahedra. Ferromagnetic correlations between pseudospins are enhanced in the spin-polarized (3 Tesla) phase up to about 60 GPa. Higher pressures drive a high-spin to low-spin transition destroying the $J$ eff = $\frac{1}{2}$ moments required to map the spin Hamiltonian into Kitaev’s model. The spin transition strongly suppresses the low-temperature magnetic susceptibility and appears to stabilize a paramagnetic phase driven by frustration. The possible emergence of frustrated magnetism of localized $S$ = $\frac{1}{2}$ moments opens the door for exploration of novel magnetic quantum states in compressed honeycomb lattices of divalent cobaltates.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Multiscale and multidimensional modeling of particle acceleration and transport in solar flares

Multi-messenger, multi-viewpoint, and time-resolved observations of solar flares are now providing unprecedented constraints on particle acceleration sites, energy conversion, and energy transport. The interpretation of current observations, including microwave imaging spectroscopy from EOVSA, hard x-ray (HXR) imaging from Solar Orbiter/STIX, gamma-ray diagnostics from Fermi, and in situ measurements from Parker Solar Probe and Solar Orbiter, collectively demands modeling frameworks that go beyond traditional spatially unresolved, one-zone models or single-mechanism descriptions. This review surveys multiscale and multidimensional modeling approaches, including kinetic, magnetohydrodynamic (MHD), and macroscopic particle models, that are being developed to meet the need. Kinetic simulations reveal that three-dimensional (3D) effects, including field-line chaos and self-generated turbulence, are essential for sustained power-law particle acceleration. MHD simulations now capture flux-rope eruptions, plasmoid-unstable current sheets, and turbulent flare regions in realistic magnetic topologies. Macroscopic models coupling MHD with energetic-particle models produce spatially resolved electron distributions and synthetic HXR and microwave emissions for direct comparison with observations. Despite these advances, outstanding challenges remain in bridging kinetic and global scales, improving MHD simulations and macroscopic particle models, and achieving quantitative model-observation closure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Pressure tuning of Kitaev spin liquid candidate Na$_3$Co$_2$SbO$_6$goo

The search for Kitaev's quantum spin liquid (KQSL) state in real materials has recently expanded with the prediction that honeycomb lattices of divalent, high-spin cobalt ions could host the dominant bond-dependent exchange interactions required to stabilize the elusive entangled quantum state. The layered honeycomb Na$_3$Co$_2$SbO$_6$ has been singled out as a leading candidate provided that the trigonal crystal field acting on Co $3d$ orbitals, which enhances non-Kitaev exchange interactions between $J_{\rm eff}=\frac{1}{2}$ spin-orbital pseudospins, is reduced. We find that applied pressure leads to anisotropic compression of the layered structure, significantly reducing the trigonal distortion of CoO$_6$ octahedra. A strong enhancement of ferromagnetic correlations between pseudospins is observed in the spin-polarized (3 Tesla) phase up to about 60 GPa. Higher pressures drive a spin transition into a low-spin state destroying the $J_{\rm eff}=\frac{1}{2}$ local moments required to map the spin Hamiltonian into Kitaev's model. The spin transition strongly suppresses the low-temperature magnetic susceptibility and appears to stabilize a paramagnetic phase driven by frustration. Although applied pressure fails to realize a KQSL state, the possible emergence of frustrated magnetism of localized, low-spin $S=\frac{1}{2}$ moments opens the door for exploration of novel magnetic quantum states in compressed honeycomb lattices of divalent cobaltates.

FOS: Physical sciences↗
DOE OSTI2026

A tetrahedral probe constellation approach for measuring canonical momentum in self-organized laboratory plasma

To examine momentum redistribution processes and study generalized helicities during plasma relaxation in Madison Symmetric Torus, MST, reversed field pinch plasma, a new probe is being tested to measure the full 3D plasma ion flow and magnetic field vectors at four spatial locations arranged in a tetrahedral shape reminiscent of a satellite measurement constellation. These measurements permit calculation of ∇ x $\vec{u}$ and canonical momentum, where $\vec{u}$ is the plasma ion flow vector. The probe consists of four probe heads arranged in a tetrahedral pattern, with an overall probe diameter of ∼31.75 mm. The probe head diameter is ∼1.0 cm, which is of the order of the ion Larmor radius. Each head has four molybdenum electrodes, also arranged in tetrahedral geometry, which are biased relative to a common return electrode, using four power supplies (one for each head), to measure the local ion flow. Additionally, each head has three orthogonal magnetic pickup coils within it to measure equilibrium and fluctuating magnetic fields.

Physics - Plasma physics↗
DOE OSTI2026

Data for Clumping Index Estimation With 30°-tilted Cameras in Row Crops: Evaluation of Methods and Segment Size Effects

The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30°-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45° segment offered accurate estimation of CI (bias within ±0.1, RMSE < 0.2) and PAI (bias within ±0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15° segment (PAI bias within ±0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15° segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68°, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30°-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.

Modeling↗
DOE OSTI2025

Using 3D Measurements from MRI to Improve VVUQ

Magnetic resonance velocimetry (MRV) is a diagnostic that can measure 3D, three-component turbulent velocity fields in arbitrarily complex flow configurations. The approach uses magnetic resonance imaging (MRI), which is a system commonly available in radiology departments or medical research centers. MRV acquires signal from hydrogen protons in water channel flows. Despite the obvious utility of the measurements for investigating a variety of flows, comparisons with computational fluid dynamic (CFD) simulations largely retain traditional metrics, comparing velocity profiles and planes at discrete regions within the flow rather than using the 3D nature of the measured field. In this effort, MRV was conducted in a simple water channel with six spanwise-centered periodic obstacles roughly shaped as cubes. At a fully turbulent Reynolds number of 15,000, the channel includes two flows: a main streamwise flow and, in the wake of the second obstacle, an injected flow oriented perpendicular to the streamwise flow. The flow geometry includes partial obstacles on the side walls and is inherently 3D because of the interaction of the two streams and the wake features, making the flow challenging to completely measure with traditional optical techniques. Aside from line profiles and planar comparisons, two 3D metrics are used to compare the data with a steady Reynolds-averaged Navier–Stokes (RANS) simulation result. A brief discussion about the comparison is provided, including comments about uncertainty.

Benson, Mike [ORNL] (ORCID:000000023210116X)↗
DOE OSTI2025

Alaska's Rural Building Stock: a Validation Study Using ResStock and Field Data

The availability of accurate national data on demographics, building stock, and energy use is vital for modeling residential buildings and evaluating decarbonization strategies. However, rural and Indigenous populations, including those in rural Alaska, are typically underrepresented in these datasets. These communities face unique challenges due to their remote locations, severe weather conditions, and limited access to resources, resulting in high energy burden. This report examines how rural Alaskan communities are underrepresented in the ResStock housing model and highlights the need for improved data to address their unique housing and energy challenges. Thus, this report examines the representation of rural Alaskan communities within the national housing stock model, ResStock. A validation study was conducted, considering ResStock, Field Data and Aerial and 3D-view data collection (A3DDC) datasets. The validation process started by using the down selecting approach on the ResStock building stock dataset. For the purpose of this study, only the rural Alaska Boroughs and Census areas located in ASHRAE IECC Climate Zone 8 were considered to ensure a more accurate and fair comparison with the field data, which was collected in rural areas located in climate zone 8, specifically within the Nome Census area. While ResStock may accurately represent several characteristics of the building stock for rural Alaska, some differences between modeled, field data, and aerial and 3D-view data collection datasets were identified. The following building characteristics have a high impact on modeled energy consumption and demonstrated large differences: Revisit heating setpoints and consider a substantially higher setpoint distribution, it could potentially address "missing loads" if this is the case. Develop and include Toyo heating in future modeling for ResStock and EnergyPlus. Remove natural gas as a water heater fuel type outside of North Slope County. Foundation type updated to have more crawlspaces rather than basements. Infiltration rates need reexamination for a larger distribution toward higher infiltration rates. Include more vinyl and less brick in exterior wall type and revise wall color for greater proportion of light rather than dark color. Roof material revised from majority shingles to majority metal. Update number of occupants to higher number of occupant count. Building orientation represents a higher proportion of south facing buildings rather than relatively equal. The findings suggest that updating ResStock's probability logic could better represent rural Alaskan buildings. ResStock can be utilized to identify the best upgrades or energy efficiency and energy efficiency improvements, helping community leaders in making more informed decisions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Digital Light Process 3D Printing of Magnetically Aligned Liquid Crystalline Elastomer Free–forms

Liquid crystalline elastomers (LCEs) are anisotropic soft materials capable of large dimensional changes when subjected to a stimulus. The magnitude and directionality of the stimuli-induced thermomechanical response is associated with the alignment of the LCE. Recent reports detail the preparation of LCEs by additive manufacturing (AM) techniques, predominately using direct ink write printing. Another AM technique, digital light process (DLP) 3D printing, has generated significant interest as it affords LCE free-forms with high fidelity and resolution. However, one challenge of printing LCEs using vat polymerization methods such as DLP is enforcing alignment. Here, we document the preparation of aligned, main-chain LCEs via DLP 3D printing using a 100 mT magnetic field. Systematic examination isolates the contribution of magnetic field strength, alignment time, and build layer thickness on the degree of orientation in 3D printed LCEs. Informed by this fundamental understanding, DLP is used to print complex LCE free-forms with through-thickness variation in both spatial orientations. Further, the hierarchical variation in spatial orientation within LCE free-forms is used to produce objects that exhibit mechanical instabilities upon heating. DLP printing of aligned LCEs opens new opportunities to fabricate stimuli-responsive materials in form factors optimized for functional use in soft robotics and energy absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Exploring strain rate effects upon 3D materials using high speed in situ X-ray tomoscopy

Cellular materials are ubiquitous in our modern society. They may be stochastic gas-blown foams (e.g., polyurethane), foamed starches (e.g., cereals), or, in this case, 3D printed microlattices. Failure in these materials is often driven by surface or sub-surface defects, which may be nucleated at a surface roughness, an interior void, or inclusion interfaces that may not be typically observable. Obfuscating our understanding further, bulk materials are known to exhibit strain-rate-dependent mechanical response, making a subsurface understanding of damage even more critical. For the first time, an in situ uniaxial mechanical loading stage that simultaneously rotates specimens up to 18 Hz was fielded at a synchrotron for 3D tomographic imaging. This capability opens a plethora of materials science opportunities to explore strain rate effects in materials and examining deformation, fracture, and delamination’s (in composites) for a complete 3D picture (movie) of material response. We demonstrate the deformation of 3D printed polymer lattice structures, of three different material types, at 0.25, 1.1, and 2.2 s −1 strain rates. We successfully imaged the 3D deformation of these materials and can directly compare the same printed structure to the three material types at three strain rates, all in 3D. Material point method simulations were applied to one of the materials to better understand the role of voids on the 3D printed structure’s performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Digital bead modeling for wire-arc directed energy deposition

Prediction of 2D cross-section and full 3D geometry for stacked weld beads is critical for the outcome of wire-arc directed energy deposition (DED) parts; however, most additive path planning software packages model beads as extrusions of a rectangle. Weld beads are not rectangular, and the resulting shape is dependent upon physics effects at the moment of deposition. Physics phenomena such as the geometry of the underlying surface, the heat input of the welding mode, and the direction of gravity contribute to bead shape. Here, this paper presents a novel implicit modeling method that discretizes a 2D area or 3D volume of space into pixels or voxels and constructs fields based on these physics phenomena. The fields are combined using a weighting scheme trained on 3D scan measurements of welds and wire-arc DED prints. Pixels or voxels are added until the known amount of deposited volume has been achieved. Thereby, a strong conservation of mass principle is applied to the process. Utilizing machine learning techniques, the present model can be trained on a database of scans allowing for the representation of a wide variety of prints. Results show that this method can produce predictions with realistic bead morphology and sub-millimeter form error.

Bead geometry modeling↗
DOE OSTI2024

Plasma Dynamics and Nonthermal Particle Acceleration in 3D Nonrelativistic Magnetic Reconnection

Understanding plasma dynamics and nonthermal particle acceleration in 3D magnetic reconnection has been a long-standing challenge. In this paper, we explore these problems by performing large-scale fully kinetic simulations of multi-X-line plasmoid reconnection with various parameters in both the weak- and strong-guide-field regimes. In each regime, we have identified its unique 3D dynamics that lead to field-line chaos and efficient acceleration, and we have achieved nonthermal acceleration of both electrons and protons into power-law spectra. The spectral indices agree well with a simple Fermi acceleration theory that includes guide-field dependence. In the low-guide-field regime, the flux rope kink instability governs the 3D dynamics for efficient acceleration. The weak dependence of the spectra on the ion-to-electron mass ratio and β ($\ll$1) implies that the particles are sufficiently magnetized for Fermi acceleration in our simulations. While both electrons and protons are injected at reconnection exhausts, protons are primarily injected by perpendicular electric fields through Fermi reflections and electrons are injected by a combination of perpendicular and parallel electric fields. The magnetic power spectra agree with in situ magnetotail observations, and the spectral index may reflect a reconnection-driven size distribution of plasmoids instead of the Goldreich–Sridhar vortex cascade. As the guide field becomes stronger, the oblique flux ropes of large sizes capture the main 3D dynamics for efficient acceleration. Intriguingly, the oblique flux ropes can also experience flux rope kink instability, to drive extra 3D dynamics. This work has broad implications for 3D reconnection dynamics and particle acceleration in heliophysics and astrophysics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Multiscale aperture synthesis imager

Synthetic aperture imaging has enabled breakthrough observations from radar to astronomy. However, optical implementation remains challenging due to stringent wavefield synchronization requirements among multiple receivers. Here we present the multiscale aperture synthesis imager (MASI), which utilizes parallelism to break complex optical challenges into tractable sub-problems. MASI employs a distributed array of coded sensors that operate independently yet coherently to surpass the diffraction limit of single receiver. It combines the propagated wavefields from individual sensors through a computational phase synchronization scheme, eliminating the need for overlapping measurement regions to establish phase coherence. Light diffraction in MASI naturally expands the imaging field, generating phase-contrast visualizations that are substantially larger than sensor dimensions. Without using lenses, MASI resolves sub-micron features at ultralong working distances and reconstructs 3D shapes over centimeter-scale fields. MASI transforms the intractable optical synchronization problem into a computational one, enabling practical deployment of scalable synthetic aperture systems at optical wavelengths.

electrical and electronic engineering↗
DOE OSTI2025

Constraints on Local Primordial Non-Gaussianity with 3D Velocity Reconstruction from the Kinetic Sunyaev-Zeldovich Effect

The cosmic velocity field is an unbiased probe of the total matter distribution but is challenging to measure directly at intermediate and high redshifts. The large-scale velocity field imprints a signal in the cosmic microwave background (CMB) through the kinetic Sunyaev-Zeldovich (kSZ) effect. We perform the first 3D reconstruction of the large-scale velocity field from the kSZ effect by applying a quadratic estimator to CMB temperature maps and the 3D positions of galaxies. We do so by combining CMB data from the fifth data release of the Atacama Cosmology Telescope (in combination with Planck) and a spectroscopic galaxy sample from the Sloan Digital Sky Survey. We then measure the galaxy-velocity cross-power spectrum and detect the presence of the kSZ signal at a signal-to-noise ratio of 7.2⁢𝜎. Using this galaxy-velocity cross-correlation alone, we constrain the amplitude of local primordial non-Gaussianity finding 𝑓 NL =−9⁢0$^{+210}_{−350}$. In conclusion, this pathfinder measurement sets the stage for joint galaxy-CMB kSZ constraints to significantly enhance the 𝑓 NL information obtained from galaxy surveys through sample variance cancellation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Prediction of carbon nanostructure mechanical properties and the role of defects using machine learning

Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.

Winetrout, Jordan J.↗
DOE OSTI2025

Design of B2PF: A Large Aperture Nb-Ti Dipole Magnet for the Electron-Ion Collider

Here, we present the initial design of the matching dipole B2PF, a large aperture cosine-theta magnet for the Electron-Ion Collider (EIC). First we share an optimized 2D magnet cross-section based on a double-layer design with Nb-Ti Rutherford cable. This cross-section is optimized to meet the required field quality in the 120 mm coil aperture both at the nominal operating current and considering iron saturation effects during the ramp. Next, we share a first design of the 3D coil ends optimized for windability, field quality, and minimizing the conductor peak field. Based on the resulting peak field, we evaluate the short-sample margin of the design for an operating target of 6.3 T dipole field at 1.9 K. Finally, we share an initial mechanical design study which utilizes a key-and-bladder support structure capable of adjusting magnet preload.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Making Small-Volume Heat Pump Water Heaters Larger: A Design Framework for Integrated Phase Change Material Heat Exchangers

Those that use electricity for water heating, the majority use resistive elements rather than heat pump water heaters (HPWHs), the latter of which use 60-70% less energy than the former. One major barrier to wider HPWH adoption is the added equipment required, which prevents current 50-80-gallon tanks on the market from fitting into smaller utility closets sized for 30-40 gallons, such as those found in manufactured housing. Additionally, these smaller HPWHs tend to underperform relative to their larger counterparts. One solution that addresses both space and performance concerns is thermal energy storage, and in particular, phase change materials (PCMs). PCMs have been studied extensively in building envelope and HVAC systems, but remain a nascent technology in residential water heating. While water itself has a uniquely high energy storage capacity, PCMs have an even higher energy storage density, thus providing the potential to elevate the performance of 40-gallon HPWHs to that of 50-gallon or larger tanks. This research is part of a larger project that seeks to utilize thermal energy storage to enable decarbonized water heating in low-income communities. In this study, we outline the design process used to produce novel PCM heat exchangers for use in small-volume HPWH tanks, including the identification of design constraints and performance targets relevant to real-world applications. In order to ensure optimal PCM utilization and tank storage capacity, we focus here on co-maximizing surface area and PCM volume in the heat exchangers; therefore, this research targets triply periodic minimal surface (TPMS) lattices. TPMS lattices boast enhanced heat transfer capabilities compared to traditional heat exchanger geometries and offer highly tailorable designs; thus, they pair well with the growing field of additive manufacturing, or 3D printing. Starting with a suite of TPMS lattices, we demonstrate a systematic approach for narrowing down feasible designs that comply with identified constraints while meeting PCM performance objectives.

25 ENERGY STORAGE↗