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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Simulation of Solar-Induced Chlorophyll Fluorescence from 3D Canopies with the Dart Model

The potential of solar-induced chlorophyll fluorescence (SIF) to monitor photosynthesis and plant stress has attracted considerable interest in SIF remote sensing (RS). However, canopy SIF and RS observations are impacted by topography, vegetation three dimension (3D) structure, leaf orientation, non foliar elements (e.g., tree woody skeleton), ... Physically based downscaling of canopy SIF RS data to leaf-level (i.e., to leaf photosynthesis) requires 3D radiative transfer (RT) models simulating canopy SIF and its observation. These models are necessary to better exploit the potential of SIF, by linking leaf SIF and SIF in RS observations as a function of canopy 3D architecture and experimental configurations (sun and viewing directions, etc.). The Discrete Anisotropic Radiative Transfer (DART) model is a comprehensive 3D radiative transfer (RT) model for urban and natural landscapes. This paper presents its SIF modeling for vegetation simulated with facets, its validation with the SCOPE/mSCOPE 1D models, and its recent extension to SIF modelling for landscapes simulated with 3D turbid medium.

Radiative transfer↗
NASA NTRS2021

3D Cloud Tomography and Droplet Size Retrieval from Multi-Angle Polarimetric Imaging of Scattered Sunlight from Above

Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging,it is extensively used for diagnostics via X-ray computed tomography (CT). We define and derive a tomographyof cloud droplet distributions via passive remote sensing. We use multi-view polarimetric images to fit a 3Dpolarized radiative transfer (RT) forward model. Our motivation is 3D volumetric probing of vertically-developedconvectively-driven clouds that are ill-served by current methods in operational passive remote sensing. Currenttechniques are indeed based on strictly 1D RT modeling and applied to a single cloudy pixel, where cloud geometrydefaults to that of a plane-parallel slab. Incident unpolarized sunlight, once scattered by cloud droplets, changesits polarization state according to droplet size. Therefore, polarimetric measurements in the rainbow and gloryangular regions can be used to infer the droplet size distribution. This work defines and derives a framework for afull 3D tomography of cloud droplets for both their mass concentration in space and their distribution across arange of sizes. This gridded 3D retrieval of key microphysical properties is made tractable by our novel approachthat involves a restructuring and partial linearization of an open-source polarized 3D RT code to accommodate aspecial two-step iterative optimization technique. Physically-realistic synthetic clouds are used to demonstrate themethodology with rigorous uncertainty quantification, while a real-world cloud imaged by AirMSPI is processedto illustrate the new remote sensing capability

Schechner, Yoav Y.↗
DOE OSTI2025

Morphogenic Growth 3D Printing

Inspired by nature's morphogenesis, a new 3D printing process –growth printing (GP)– takes advantage of a self‐propagating curing front to produce 3D polymeric parts following a growth‐like development plan. The propagation of the curing front is driven by the exothermic polymerization of dicyclopentadiene (DCPD), which transforms the liquid resin into a stiff polymer as it propagates at 1 mm s −1 . GP is triggered when a heated initiator contacts the uncured liquid resin in an open container. The initiator nucleates the frontal polymerization reaction and the isotropic radial propagation of the growth front. Simultaneously, the initiator is moved up across the free surface of the resin, pulling the cured object out of the uncured resin. The motion trajectory of the initiator with respect to the free resin surface controls the growth morphology of the 3D part. An inverse design algorithm is developed to produce 3D parts by modeling the reaction‐diffusion‐driven solidification process. This process has substantial energy savings and high printing speeds.

3D printing↗
DOE OSTI2025

Coaxial Direct Ink Writing of Cholesteric Liquid Crystal Elastomers in 3D Architectures

Abstract Cholesteric liquid crystal elastomers (CLCEs) hold great promise for mechanochromic applications in anti‐counterfeiting, smart textiles, and soft robotics, thanks to the structural color and elasticity. While CLCEs are printed via direct ink writing (DIW) to fabricate free‐standing films, complex 3D structures are not fabricated due to the opposing rheological properties necessary for cholesteric alignment and multilayer stacking. Here, 3D CLCE structures are realized by utilizing coaxial DIW to print a CLC ink within a silicone ink. By tailoring the ink compositions, and thus, the rheological properties, the cholesteric phase rapidly forms without an annealing step, while the silicone shell provides encapsulation and support to the CLCE core, allowing for layer‐by‐layer printing of self‐supported 3D structures. As a demonstration, free‐standing bistable thin‐shell domes are printed. Color changes due to compressive and tensile stresses can be witnessed from the top and bottom of the inverted domes, respectively. When the domes are arranged in an array and inverted, they can snap back to their base state by uniaxial stretching, thereby functioning as mechanical sensors with memory. The additive manufacturing platform enables the rapid fabrication of 3D mechanochromic sensors thereby expanding the realm of potential applications for CLCEs.

36 MATERIALS SCIENCE↗
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

Combining 3D printing of copper current collectors and electrophoretic deposition of electrode materials for structural lithium-ion batteries

Serving as a proof of concept, additive manufacturing and electrophoretic deposition are leveraged in this work to enable structural lithium-ion batteries with load-bearing and energy storage dual functionality. The preparation steps of a complex 3D printed copper current collector, involving the formulation of a photocurable resin formulation, as well as the vat photopolymerization process followed by a precursors-based solution soaking step and thermal post-processing are presented. Compression and microhardness testing onto the resulting 3D printed copper current collector are shown to demonstrate adequate mechanical performance. Electrophoretic deposition of graphite as a negative electrode active material and other additives was then performed onto the 3D printed copper collector, with the intention to demonstrate energy storage functionality. Half-cell electrochemical cycling of the 3D multi-material current collector/negative electrode versus lithium metal finally demonstrates that structural battery components can be successfully obtained through this approach.

25 ENERGY STORAGE↗
DOE OSTI2025

Physics augmented machine learning discovery of composition-dependent constitutive laws for 3D printed digital materials

Multi-material 3D printing, particularly through polymer jetting, enables the fabrication of digital materials by mixing distinct photopolymers at the micron scale within a single build to create a composite with tunable mechanical properties. Here, this work presents an integrated experimental and computational investigation into the composition-dependent mechanical behavior of 3D printed digital materials. We experimentally characterize five formulations, combining soft and rigid UV-cured polymers under uniaxial tension and torsion across three strain and twist rates. The results reveal nonlinear and rate-dependent responses that strongly depend on composition. To model this behavior, we develop a physics-augmented neural network (PANN) that combines a partially input convex neural network (pICNN) for learning the composition-dependent hyperelastic strain energy function with a quasi-linear viscoelastic (QLV) formulation for time-dependent response. The pICNN ensures convexity with respect to strain invariants while allowing non-convex dependence on composition. To enhance interpretability, we apply $L_0$ sparsification. For the time-dependent response, we introduce a multilayer perceptron (MLP) to predict viscoelastic relaxation parameters from composition. The proposed model accurately captures the nonlinear, rate-dependent behavior of 3D printed digital materials in both uniaxial tension and torsion, achieving high predictive accuracy for interpolated material compositions. This approach provides a scalable framework for automated, composition-aware constitutive model discovery for multi-material 3D printing.

Constitutive modeling↗
DOE OSTI2025

Mineral Scaling in 3D Interfacial Solar Evaporators–A Challenge for Brine Treatment and Lithium Recovery

In this work, we analyzed the effects of mineral scaling on the performance of a 3D interfacial solar evaporator, with a focus on the cations relevant to lithium recovery from brackish water. The field has been rapidly moving toward resource recovery applications from brines with higher cation concentrations. However, the potential complications caused by common minerals in these brines other than NaCl have been largely overlooked. Therefore, in this study, we thoroughly examined the effects of two common cations (calcium and magnesium) on the long-term solar evaporation performance of a 3D graphene oxide stalk. The 3D stalk can achieve an evaporation flux as high as 17.8 kg m –2 h –1 under one-sun illumination, and accumulation of NaCl on the stalk surface has no impact. However, the presence of CaCl 2 and MgCl 2 significantly reduces the evaporative flux even in solutions lacking scale-forming anions. A close examination of scale formation during long-term evaporation experiments revealed that CaCl 2 and MgCl 2 tend to precipitate out within the stalk, thus blocking water transport through the stalk and significantly dropping the evaporation rates. These findings imply that research attention is needed to modify and optimize the internal water transport channels for 3D evaporators. Additionally, we emphasize the importance of testing realistic mixtures–including prominent divalent cations– and testing long-term operations in interfacial solar evaporation research and investigating approaches to mitigate the negative impacts of divalent cations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

3D Printing of Functional Hydrogel Devices for Screenings of Membrane Permeability and Selectivity

Developing a fundamental understanding of the effects of varying ligand chemistries on mass transport rates is key to designing membranes with solute-specific selectivity. While permeation cells offer a robust method to characterize membrane performance, they are limited to assessing a single membrane chemistry or salt solution per test. As a result, investigating the effects of varying ligand chemistries on membrane performance can be a tedious process, involving both the preparation of multiple samples and numerous, time-consuming permeation tests. This study uses digital light processing (DLP) 3D printing to fabricate a millifluidic flow-based permeation device made from a hydrogel active ester network that can be easily functionalized with ion-selective ligands. Without the need for bonding or assembly steps, ligands can be introduced and tested in the permeation device by simply injecting a small volume of a ligand solution. Various salt concentrations and molecular species can be cycled through a single device by switching the solution feeding into the salt reservoir, thereby reducing the number of samples needed for permeability and selectivity screenings. This research sets the groundwork for formulation development and postprocessing methods to 3D-print functional millifluidic devices capable of assessing solute selectivity in membranes and polymer adsorbents for aqueous separations. In this work, comparable salt permeability trends were observed with both 3D-printed devices and traditional assays. Devices were functionalized with an imidazole ligand to investigate salt permeability and selectivity of monovalent and divalent salts. Measurements showed increasing permeability for monovalent salts (NaCl) relative to divalent salts (MgCl 2 , CuCl 2 ) in functionalized membranes, with higher monovalent/divalent selectivity at increasing imidazole grafting densities. Here, the methods and findings described here represent a step toward developing higher-throughput methods with 3D-printed devices for screening the effects of ligand chemistry on mass transport rates in membrane materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

3D pattern formation of a protein–membrane suspension

Many essential cellular processes, including cell division and the establishment of cell polarity during embryogenesis, are regulated by pattern-forming proteins. These proteins often need to bind to a substrate, such as the cell membrane, onto which they interact and form two-dimensional (2D) patterns. It is unclear how the membrane’s continuity and dimensionality impact pattern formation. Here, we address this gap using the MinDE system, a prototypical example of pattern-forming membrane proteins. We show that when the lipid substrate is fragmented into submicrometer-sized diffusive liposomes, adenosine triphosphate-driven protein–protein interactions generate three-dimensional (3D) spatially extended patterns, despite the complete loss of membrane continuity. Remarkably, these 3D patterns emerge at scales four orders of magnitude larger than the individual liposomes. By systematically varying protein concentration, liposome size, and density, we observed and characterized a variety of 3D dynamical patterns not seen on continuous 2D membranes, including traveling waves, dynamical spirals, and a coexistence phase. Simulations and linear stability analysis of a coarse-grained model revealed that the physical properties of the dispersed membrane effectively rescale both the protein–membrane binding rates and diffusion, two key parameters governing pattern formation and wavelength selection. These findings highlight the robustness of Min’s pattern-forming ability, suggesting that protein–membrane suspensions could serve as an adaptable template for studying out-of-equilibrium self-organization in 3D, beyond in vivo contexts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Assessment of RELAP5-3D Code with Molten Salt Heat Transfer Experiments

The accurate thermal-hydraulic assessment of thermal storage systems is crucial for addressing the integrity and performance of the storage system design. This is particularly important for thermal storage systems using molten salts as a heat transport and storage medium, as the chemical corrosion and erosion by the high-temperature molten salts can cause serious damage to the structural components of the storage system. To understand these effects for TerraPower’s Natrium® Demonstration Reactor design, the system thermal-hydraulic analysis code, RELAP5-3D, is utilized to analyze the thermal storage system. This study evaluates two heat transfer correlations implemented in the RELAP5-3D code—the Dittus-Boelter and Gnielinski correlations—using selected benchmark cases from two molten salt heat transfer experiments conducted at Xi’an Jiao Tong University and the German Aerospace Center. The Nusselt numbers calculated using the Gnielinski correlation agree with the experimental data within ±5% of the relative deviations at 300°C and 400°C. With the increasing Reynolds numbers, the Dittus-Boelter correlation underestimates the Nusselt numbers by up to 22% compared to the Gnielinski correlation. Comparison of the RELAP5-3D calculation results with experimental data at a high temperature of 550°C revealed that the thermo-chemical characteristics of solar salt at high temperatures surpassing the decomposition temperatures of nitrate salts reduce the accuracy of heat transfer model of the RELAP5-3D code. Specifically, it has been observed that the temperatures of the wall surface and liquid near the wall can surpass the decomposition temperatures, even though the bulk temperature of molten salts remains significantly lower than these decomposition temperatures. In summary, this study recommends the use of the Gnielinski correlation for the heat transfer analysis of molten salt, rather than the Dittus-Boelter correlation.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2026

An overview of 3D field optimization for control of transport and edge instabilities on KSTAR

An international team from several laboratories and universities has made key advances over the last few years in the control of plasma transport and edge instabilities with applied 3D fields in the KSTAR tokamak to optimize long pulse operation scenarios. This overview begins with the optimization of both core and edge resonant magnetic perturbations (RMP) to improve fast ion confinement to avoid excessive limiter heat loads due to fast ion losses and successful modeling of the experimental results. Integrated and advanced plasma control techniques with machine learning (ML) and adaptive control were then used to optimize the 3D field spectrum in real-time to control edge localized modes (ELMs) while avoiding core locked modes that could disrupt the plasma. Accelerating the offline model of 3D fields with a surrogate ML model can optimize ELM suppression in the edge while limiting the impact of the applied RMP fields deeper in the plasma core in real-time. In addition, the impact of the 3D fields on the divertor heat load has been modeled and compared with experimental measurements. An analysis of a multi-machine database including KSTAR has been performed to better understand the metrics for the observed RMP thresholds for ELM suppression and the resulting plasma performance. Predictive modeling of the operational space for ELM suppression and density pumpout due to RMP has shown the importance of magnetic islands in the plasma edge and their impact on plasma turbulence. This research has culminated in the development of successful long pulse operational scenarios on KSTAR while attempting to overcome challenges of the new tungsten divertor.

3D fields↗
DOE OSTI2026

Improving 3D reconstruction quality for root phenotyping: assessing the impact of camera calibration and imaging parameters

Arate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracyccu remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding in downstream analysis.

3D reconstruction↗
DOE OSTI2025

Preliminary modeling of triply periodic minimal surface (TPMS) structures using RELAP5-3D

With the United States Department of Energy (DOE)’s goal of quadrupling the nation’s nuclear energy supply by 2050, and with the Advanced Fuels Campaign pushing for new types of advanced reactor fuels and geometries, the need has arisen for new nuclear fuel designs. One such design is to swap out current nuclear fuel geometries in exchange for another type of geometry, called a Triply Periodic Minimal Surface (TPMS). TPMSs are self-supporting, infinitely repeating lattices—attributes that lend themselves well to additive manufacturing. These surfaces also possess enhanced heat transfer properties thanks to their internal area changes and large surface-area-to-volume ratios. Their drawback, however, is an increased pressure drop. Given the small amount of correlations and data (Reynolds numbers in the 2,000–8,000 range), and the minimal amount of experience so far obtained by modeling TPMS structures using 1D systems codes such as the Reactor Excursion and Leak Analysis Program (RELAP5-3D), further research into this topic was needed. Using data from the University of Wisconsin - Madison (UW), curve fits were created for both a Heat Transfer Coefficient (HTC) correlation and a Darcy friction factor empirical coefficient correlation. The curves’ coefficients and multipliers were then output and utilized in RELAP5-3D models of two upcoming experiments—Flow Loop for INFLUX Pressure drop (FLIP) and Microreactor Agile Non-nuclear Experimental Test (MAGNET)—aimed at increasing the available data for Reynolds numbers to the 16,000–36,000 range for TPMS structures. The models were run under the conditions utilized by a Computational Fluid Dynamics (CFD) analysis performed by another group at Idaho National Laboratory. Only CFD pressure drop values were obtained from the FLIP test, and those values showed that the RELAP5-3D models had a lower rate of pressure increase in comparison to the CFD values. In addition, there seemed to be a vertical shift upward in the pressure drop for both models whenever the TPMS porosity decreased, and the RELAP5-3D models showed a higher vertical shift in comparison to the CFD values. The MAGNET results did not correspond to any CFD or experimental results against which they could be compared, so they were instead compared against the proposed CFD input conditions. These values were then compared with each other to make sure the model seemed to be performing as expected, paving the way for future tests that can be run for the purpose of further analyses and comparisons. The pressure drop increased with temperature and mass flow rate independently. The temperature change would decrease with increasing mass flow rate and temperature, which was just as we expected based on the fact that the lower viscosity and decreased density would result in higher friction and churning losses. The last metric that was assessed was the enthalpy flow change, which increased with increasing mass flow rate and decreasing temperature.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Understanding Fracture Aperture and Permeability Evolution due to Carbonate Mineralization Utilizing 3D Printing

Caprock formations are a crucial part of subsurface-engineered systems. Composed largely of shale, caprocks act as natural barriers that prevent the upward migration of fluids, thereby ensuring the containment of stored substances in subsurface formations. Fractures in these formations are potential leakage pathways for stored fluids. Mineral precipitation reactions in these fractures, particularly calcite, can significantly restrict the fluid permeability, reducing leakage potential. However, predictive capabilities of mineral precipitation in fractures and associated permeability evolution are limited due to a lack of fundamental understanding of such reactions in natural samples, complicated by mineral heterogeneity and the complexity of the fracture structure. In this study, 3D-printed fracture samples are used to understand the impact of carbonate mineralization on fracture aperture and permeability evolution. Samples were printed using a digital light processing (DLP) 3D printer and commercial liquid resin. Calcite precipitation was first tested on printed 2D films before conducting plug flow column experiments aimed to understand fracture permeability changes due to mineral precipitation. Contact angle measurement and Fourier transform infrared (FTIR) spectroscopy on printed 2D films show evidence of a substantial amount of surface energy for calcite nucleation and precipitation. Surface topography analysis of printed fractured surfaces reveals comparable values, highlighting the high replicability of the printed samples. During the column experiments, the permeability reduces exponentially due to a decrease in fracture aperture. Reductions in fracture aperture estimated from effluent concentration and 3D X-ray computed tomography (CT) show comparable results. Moreover, 3D X-ray CT images suggest the impact of local flow velocities on precipitation. The insights gained from this research contribute to a deeper understanding of the permeability evolution due to carbonate mineralization in caprock formations.

3D printing↗
NASA NTRS2014

NASA VERVE: Interactive 3D Visualization Within Eclipse

At NASA, we develop myriad Eclipse RCP applications to provide situational awareness for remote systems. The Intelligent Robotics Group at NASA Ames Research Center has developed VERVE - a high-performance, robot user interface that provides scientists, robot operators, and mission planners with powerful, interactive 3D displays of remote environments.VERVE includes a 3D Eclipse view with an embedded Java Ardor3D scenario, including SWT and mouse controls which interact with the Ardor3D camera and objects in the scene. VERVE also includes Eclipse views for exploring and editing objects in the Ardor3D scene graph, and a HUD (Heads Up Display) framework allows Growl-style notifications and other textual information to be overlayed onto the 3D scene. We use VERVE to listen to telemetry from robots and display the robots and associated scientific data along the terrain they are exploring; VERVE can be used for any interactive 3D display of data.VERVE is now open source. VERVE derives from the prior Viz system, which was developed for Mars Polar Lander (2001) and used for the Mars Exploration Rover (2003) and the Phoenix Lander (2008). It has been used for ongoing research with IRG's K10 and KRex rovers in various locations. VERVE was used on the International Space Station during two experiments in 2013 - Surface Telerobotics, in which astronauts controlled robots on Earth from the ISS, and SPHERES, where astronauts control a free flying robot on board the ISS.We will show in detail how to code with VERVE, how to interact between SWT controls to the Ardor3D scenario, and share example code.

3D Visualization↗
NASA NTRSDate not supplied

3D Localization of Defects in Facility Inspection

Wind tunnels are crucial facilities that support the aerospace industry. However, these facilities are large, complex, and pose unique maintenance and inspection requirements. Manual inspections to identify defects such as cracks, missing fasteners, leaks, and foreign objects are important but labor and schedule intensive. Our goal is to utilize small Unmanned Aircraft Systems (sUAS) and computer vision-based analysis to automate the inspection of the interior and exterior of NASA’s critical wind tunnel facilities. We detect missing fasteners as our defect class, and detect existing fasteners to provide potential future missing fastener sites for preventative maintenance. These detections are done on both 2D raw images and in 3D space to provide a visual reference and real world location to facilitate repairs. A dataset was created consisting of images taken along a grid-like pattern of an interior tunnel section in the AEDC National Full-Scale Aerodynamics Complex (NFAC) at NASA Ames Research Center. Our method uses object detection to create image level bounding boxes of the fasteners and missing fasteners, then uses photogrammetry to create a mapping from 2D image locations to 3D real world locations. The image level bounding boxes and the 2D to 3D mapping are then combined to determine the 3D location of the defects. We describe the data collection, photogrammetry, and computer vision techniques used for object detection as well as a quantitative analysis of the method.

Small Unmanned Aircraft Systems (sUAS)↗
NASA NTRSDate not supplied

Modeling Methods for 3D Lightning Mapping from Space

Global lightning detection has advanced greatly over the past few decades both on ground and from orbit. Ground-based systems like the Lightning Mapping Array (LMA) excel at high-precision 3D reconstruction of local flashes, while spaceborne lightning sensors have much larger potential coverage but have been limited by coarser resolution and often rely on data from other instruments to determine flash location. The primary focus of this study is to examine the level of flash detail that can be expected from one or more low-Earth orbiting small satellites that combine VHF and optical measurements of lightning, which is a new mission concept called CubeSpark. The goal of CubeSpark is to map the 3D charge structure of thunderstorms at 1-2 km spatial resolution on a global scale, enabling a host of new atmospheric and space electricity studies. In order to maximize the potential data quality and minimize potential cost, flashes were simulated beneath single- and multi-satellite configurations. In the multi-satellite approach, RF detectors on each of the six satellites would measure the arrival times of impulsive VHF sources to collectively pinpoint their locations in 3D and reconstruct flashes with higher resolution than has been achieved from space. A single-station approach for 3D observation of lightning would follow the method of determining altitudes of strong VHF sources that produce trans-ionospheric pulse pairs (TIPPs) while relying on an on-board high-resolution optical day/night lightning mapper for approximate horizontal flash locations. Here we present preliminary results comparing the expected data fidelity and accuracy between these methods to inform potential satellite missions like CubeSpark.

lightning↗