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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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NASA NTRS1974

The structure and circulation of the deep Venus atmosphere

A simple model for the structure of a nonrotating Hadley regime in an atmosphere with large thermal inertia is developed. The radiative fluxes are estimated by using a linearization about the radiative equilibrium state, and the dynamical fluxes are estimated by using scaling analysis. The requirement that differential heating by these fluxes be in balance in both the meridional and vertical directions leads to two equations for the mean static stability and meridional temperature contrast. The solution depends on two parameters: the strength of the radiative heating, as measured by the static stability of the radiative equilibrium state; and the ratio of the time it takes an external gravity wave to traverse the atmosphere to the time it would take the atmosphere to cool off radiatively. It is shown that it is not necessary to invoke convection to explain the approximate adiabatic lapse rate in the Venus atmosphere, but a greenhouse effect is necessary to explain the high surface temperatures.

Stone, P. H.↗

NASA NTRS2011

Experimental Evaluation of Optically Polished Aluminum Panels on the Deep Space Network's 34 Meter Antenna

The potential development of large aperture ground?based "photon bucket" optical receivers for deep space communications has received considerable attention recently. One approach currently under investigation is to polish the aluminum reflector panels of 34?meter microwave antennas to high reflectance, and accept the relatively large spotsize generated by state of?the?art polished aluminum panels. Theoretical analyses of receiving antenna pointing, temporal synchronization and data detection have been addressed in previous papers. Here we describe the experimental effort currently underway at the Deep Space Network (DSN) Goldstone Communications Complex in California, to test and verify these concepts in a realistic operational environment. Two polished aluminum panels (a standard DSN panel polished to high reflectance, and a custom designed aluminum panel with much better surface quality) have been mounted on the 34 meter research antenna at Deep?Space Station 13 (DSS?13), and a remotely controlled CCD camera with a large CCD sensor in a weather?proof container has been installed next to the subreflector, pointed directly at the custom polished panel. The point?spread function (PSF) generated by the Vertex polished panel has been determined to be smaller than the sensor of the CCD camera, hence a detailed picture of the PSF can be obtained every few seconds, and the sensor array data processed to determine the center of the intensity distribution. In addition to estimating the center coordinates, expected communications performance can also been evaluated with the recorded data. The results of preliminary pointing experiments with the Vertex polished panel receiver using the planet Jupiter to simulate the PSF generated by a deep?space optical transmitter are presented and discussed in this paper.

optical receiver↗

NASA NTRSDate not supplied

The BioSensor Instrument Beyond LEIA: a Versatile Platform for Lunar Biology

Introduction: The BioSensor is a deep-space-compatible automated microfluidic culturing instrument. While originally designed to measure the effects of deep space radiation on yeast growth for the BioSentinel mission, it has the potential to host a diverse range of life science experiments with single- and/or multi-celled organisms and can be adapted to interface with a diverse range of platforms in both crewed and uncrewed settings. It is therefore a leading candidate for hosting life sciences experimentation associated with a lunar surface habitat. BioSensor design: The function of the BioSensor is to monitor the growth and metabolic activity of samples in batch fluidic culture, without the need for crew involvement. The current configuration houses organisms in 16 wells within 16 microfluidic cards, accommodating a total of 256 samples, replicates, and controls. Each well has an optical system including three LEDs and a photodiode detector to measure absorbance at three wavelengths, enabling measurement of optical density, color change in dyes such as alamarBlue, and bioproduction of pigmented compounds. Organisms are loaded into fluidic wells and air-dried for storage during integration, launch, and transit, then activated by the introduction of culture medium from storage bags via manifolds that fill one card at a time. Temperature is controlled by individual card heaters, and timing of all activities (fluidics fills, optical measurements, temperatures) is directed by an experiment script. The self-contained BioSensor payload is roughly 4U in volume; with electrical/mechanical/thermal interface, e.g. for operation on ISS or a lunar lander, as well as a linear energy-transfer (LET) charged-particle radiation spectrometer, the entire system is closer to 6U. BioSentinel and LEIA: Flown on the ISS and in a deep-space free flyer for BioSentinel, the BioSensor has been modified for use in the LEIA mission, including improvements to reduce the sensitivity to lengthy launch delays. LEIA will monitor yeast growth in the radiation and reduced-gravity environment of the lunar surface no earlier than 2026, on a CLPS lander [4]. Changes include accommodating additional culture media and an additional LED color for a new biological assay (bioproduction of carotenoids-- dietary antioxidants), as well as modifications to the housing to allow late-load biology changeout and improved isolation between fluidics and electronics. Future prospects: Future work with the BioSensor, beyond LEIA, will include expanding the range and diversity of organisms and assays accommodated. Preliminary work has demonstrated the growth of Arabidopsis seedlings in BioSensor fluidic cards, including optical measurements of growth rate over time. Minor modifications could allow measurement of phenotypes related to photosynthetic capacity in both plants and cyanobacteria. The experimental capabilities of the BioSensor could be dramatically increased by introducing the capability for fluorescence measurements, and/or the design of novel biological assays using luminescence. The BioSensor can also be adapted for new platforms and experiment settings; in addition to free-flyer, ISS, and CLPS lander, a preliminary design concept has been developed for crewed deployment directly to the lunar surface. The instrument could be accommodated inside a lunar habitat, where its automated operation would make it an excellent candidate for experiments from fundamental investigations into the response of organisms to lunar surface conditions to applied-science purposes such as screening engineered strains of various organisms for bioproduction capability.

J A Lee↗

DOE OSTI2025

jaxhps: An elliptic PDE solver built with machine learning in mind

Elliptic partial differential equations (PDEs) can model many physical phenomena, such as electrostatics, acoustics, wave propagation, and diffusion. In scientific machine learning settings, a high-throughput PDE solver may be required to generate a training dataset, run in the inner loop of an iterative algorithm, or interface directly with a deep neural network. To provide value to machine learning users, such a PDE solver must be compatible with standard automatic differentiation frameworks, scale efficiently when run on graphics processing units (GPUs), and maintain high accuracy for a large range of input parameters. We have designed the jaxhps package with these use-cases in mind by implementing a highly efficient and accurate solver for elliptic problems with native hardware acceleration and automatic differentiation support.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Comparative Life Cycle Analysis of Carbon Dioxide Utilization in Concrete Products

In this study, a comparative LCA of CO2U concrete processes is conducted, revealing promise in several research areas. The environmental impacts of replacing conventional binder and aggregates with carbonated steel slag and direct carbonation of concrete are investigated in ten different product systems, which include both ready-mix and pre-cast concretes. The results indicate that cement substitution, CO2 uptake, electricity consumption, and the electricity grid mix constitute critical levers for deep decarbonization of concrete building materials. This presentation applies LCA to inform the use of CO2U concrete technology pathways in the design of sustainable concrete while promoting transparency and rational assumptions in the presence of uncertainty. Broader themes in the work include LCA of emerging technologies and the sensitivity of LCA results to co-product management methods.

Clarke, James↗

DOE OSTI2021

Deep Learning Predicts Stress–Strain Relations of Granular Materials Based on Triaxial Testing Data

This study presents an AI-based constitutive modelling framework wherein the prediction model directly learns from triaxial testing data by combining discrete element modelling (DEM) and deep learning. A constitutive learning strategy is proposed based on the generally accepted frame-indifference assumption in constructing material constitutive models. The low-dimensional principal stress-strain sequence pairs, measured from discrete element modelling of triaxial testing, are used to train recurrent neural networks, and then the predicted principal stress sequence is augmented to other high-dimensional or general stress tensor via coordinate transformation. Through detailed hyperparameter investigations, it is found that long short-term memory (LSTM) and gated recurrent unit (GRU) networks have similar prediction performance in constitutive modelling problems, and both satisfactorily predict the stress responses of granular materials subjected to a given unseen strain path. Furthermore, the unique merits and ongoing challenges of data-driven constitutive models for granular materials are discussed.

42 ENGINEERING↗

NASA NTRS2018

Laboratory and On-Sky Validation of the Shaped Pupil Coronagraph's Sensitivity to Low-Order Aberrations with Active Wavefront Control

We present early laboratory simulations and extensive on-sky tests validating of the performance of a shaped pupil coronagraph (SPC) behind an extreme-AO (Adaptive Optics) corrected beam of the Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) system. In tests with the SCExAO internal source/wavefront error simulator, the normalized intensity profile for the SPC degrades more slowly than for the Lyot coronagraph as low-order aberrations reduce the Strehl ratio (S.R.) from extremely high values (S.R. approximately 0.93-0.99) to those characteristic of current ground-based extreme AO systems (S.R. approximately 0.74-0.93) and then slightly lower values down to S.R. approximately 0.57. On-sky SCExAO data taken with the SPC and other coronagraphs for brown dwarf/planet-hosting stars HD 1160 and HR 8799 provide further evidence for the SPC's robustness to low-order aberrations. From H-band Strehl ratios of 80 percent to 70 percent, the Lyot coronagraph's performance versus that of the SPC may degrade even faster on sky than is seen in our internal source simulations. The 5-sigma contrast also degrades faster (by a factor of two) for the Lyot than the SPC. The SPC we use was designed as a technology demonstrator only, with a contrast floor, throughput, and outer working angle poorly matched for SCExAO's current AO performance and poorly tuned for imaging the HR 8799 planets. Nevertheless, we detect HR 8799 cde with SCExAO/CHARIS using the SPC in broadband mode, where the signal-to-noise ratio for planet e is within 30 percent of that obtained using the vortex coronagraph. The shaped-pupil coronagraph is a promising design demonstrated to be robust in the presence of low-order aberrations and may be well-suited for future ground and space-based direct imaging observations, especially those focused on follow-up exoplanet characterization and technology demonstration of deep contrast within well-defined regions of the image plane.

Currie, Thayne↗

NASA NTRSDate not supplied

Mission Design Considerations for Robotic Lunar and Gateway Payload Return

A selection of lunar return trajectories is examined and assessed in terms of payload mass, vehicle mass, mission time, mission complexity, and total delta-V using a range of assumptions for the mission design based on historical precedence and near-future vehicle availability. Direct surface return trajectories using a single burn solution are compared with a range of Near-Rectilinear Halo Orbit return and Ballistic Return Trajectories. This study weighs anticipated mission needs, requirements and constraints with the aim of assessing the feasibility of commercial lunar cargo and/or sample return options utilizing NASA’s Gateway and Deep Space Logistics project.

lunar↗

DOE OSTI2023

Decarbonization of the Chemical Industry Through Electrification: Barriers and Opportunities

The chemical industry is a major source of economic productivity and employment globally and among the top 3 industrial sources of greenhouse gas (GHG) emissions, along with steel and cement. As global demand for chemical products continues to grow, there is an urgency to develop and deploy sustainable chemical production pathways and to reconsider continued investment in current emission-intensive production technologies. This perspective describes the challenges and opportunities to decarbonize the chemical industry via electrification powered by low-carbon electricity supply, both in the near term and long term, and it discusses four technological pathways ranging from the more mature direct substitution of heat with electricity and use of hydrogen to technologically less mature, yet potentially more selective, approaches based on electrochemistry and plasma. Finally, we highlight the key elements of integrating an electrified industrial process with the power sector to leverage process flexibility to reduce energy costs of chemical production and provide valuable power grid support services. Unlocking such plant-to-grid coordination and the four electrification pathways has significant potential to facilitate rapid and deep decarbonization of the chemical industry sector.

chemical synthesis↗

NASA NTRS2012

Effect of Rare Earth Elements (Er, Ho) on Semi-Metallic Materials (ScN) in an Applied Electric Field

The development of materials and fabrication technology for field-controlled spectrally active optics is essential for applications such as membrane optics, filters for LIDARs, windows for sensors, telescopes, spectroscopes, cameras and flat-panel displays. The dopants of rare earth elements, in a host of optical systems, create a number of absorption and emission band structures and can easily be incorporated into many high quality crystalline and amorphous hosts. In wide band-gap semiconductors like ScN, the existing deep levels can capture or emit the mobile charges, and can be ionized with the loss or capture of the carriers which are the fundamental basis of concept for smart optic materials. The band gap shrinkage or splitting with dopants supports the possibility of this concept. In the present work, a semi-metallic material (ScN) was doped with rare earth elements (Er, Ho) and tested under an applied electric field to characterize spectral and refractive index shifts by either Stark or Zeeman Effect. These effects can be verified using the UV-Vis spectroscopy, the Hall Effect measurement and the ellipsometric spectroscopy. The optical band gaps of ScN doped with Er and doped with Ho were experimentally estimated as 2.33eV and 2.24eV ( 0.2eV) respectively. This is less than that of undoped ScN (2.5 0.2eV). The red-shifted absorption onset is a direct evidence for the decrease of band gap energy (Eg), and the broadening of valence band states is attributable to the doping cases. A decrease in refractive index with an applied field was observed as a small shift in absorption coefficient using a variable angle spectroscopic ellipsometer. In the presence of an electric field, mobile carriers are redistributed within the space charge region (SCR) to produce this electro-refractive effect. The shift in refractive index is also affected by the density and location of deep potential wells within the SCR. In addition, the microstructure change was observed by a TEM analysis. These results give an insight for future applications for the field-controlled spectrally active material systems.

Kim, Hyunjung↗

NASA NTRS2001

So What's an RTG and Are They Safe?

When one considers space missions to the outer edges of our solar system and far beyond, our sun cannot be relied on to produce the required spacecraft (s/c) power. Solar energy diminishes as the square of the distance from the Sun. At Mars it is only 43% of that at earth. At Jupiter, it falls off to only 3.6% of Earth's. By the time we get out to Pluto, solar energy is only .066% what it is on Earth. Beyond the orbit of Mars, it is not practical to depend on solar power for a s/c. However, the farther out we go the more power we need to heat the s/c and to transmit data back to Earth over the long distances. On Earth, knowledge is power. In the outer solar system, power is knowledge. Solar arrays only operate at 19% efficiency, are very vulnerable to damage from radiation and temperature extremes, and cannot be used for even nearby missions that operate in extended darkness, or under the surface of a planet or moon. Twenty-six U.S. space missions, from the Transit to Cassini, have used radioisotope power systems and heater units to take s/c to the far reaches of our solar system and have demonstrated an outstanding record of safety and reliability. Radioisotope thermoelectric generators (RTG's) have proven to be safe, reliable, maintenance-free, and capable of providing both thermal and electrical power for decades under the harsh environments of deep space. RTG's have no problem operating in the high radiation belts of space, the extreme temperatures, or the severe dust storms of Mars, and they have proven to be the most reliable power source ever flown on U.S. s/c. For example, the two Pioneer s/c operated for more than two decades and the Voyager s/c may last for 40 years. RTG's are not nuclear reactors, they serve only as power generators and are not involved in the propulsion of the s/c. They operate on the principle of thermoelectric generation that converts heat directly into electricity, they have no moving parts, are extremely reliable, and have met or exceeded all safety and performance expectations. Federal laws and regulations require analysis and evaluation of the safety risks and any potential environmental impacts. Extensive safety testing of RTG's and RTG components has been performed by the U.S. Department of Energy (DOE) to demonstrate the ability to survive accidents related to Space Shuttle launches and assure that the systems would be safe under all accident conditions, including accidents at or near the launch pad or during orbital reentry. Many design improvements have been made over the four decades that RTG's have been flown on space missions. This paper outlines the operation and safety standards of RTG's and the advanced developments expected to be used on future deep space missions such as the Europa Orbiter, Pluto/Kuiper Express, Solar Probe, Europa Lander, and Titan Explorer missions.

Barret, Chris↗

DOE OSTI2023

Towards Geospatial Knowledge Graph Infused Neuro-Symbolic AI for Remote Sensing Scene Understanding

Deep learning has proven its effectiveness in numerous tasks for remote sensing scene understanding. However there is an increasing interest to explore fusion of domain-specific background information to the deep neural network to further improve its performance. Remote sensing researchers are also working towards developing models that generalize and adapt to multiple applications. Generalization challenges coupled with the scarcity of large corpora of high-quality noise-free labelled data, have together fueled an interest for leveraging background information. Knowledge graphs serve as excellent choice to represent domain-specific information in a structured, standardized and extensible manner. Integrating symbolic knowledge representations in the form of Knowledge Graph Embedding (KGE) to perform neuro-symbolic reasoning is an emerging research direction promising significant impacts. This vision paper seeks to position ideas and provoke early thoughts toward advancing neuro-symbolic artificial intelligence in the context of geospatial challenges. Specifically, it conceptualizes and elaborates on an architecture for infusing geospatial knowledge from knowledge graph in a deep neural network pipeline. As guiding case studies - land-use land-cover classification, object detection and instance segmentation can benefit from infusing spatio-contextual information with remote sensing imagery. The discussion further reflects on and articulates the challenges and explainable AI opportunities anticipated when scaling and maintaining large-scale geospatial knowledge graphs.

Potnis, Abhishek↗

DOE OSTI2020

A global DSCOVR/EPIC-based hourly/daily shortwave radiation/PAR dataset

Accurate characterization of the global downward shortwave (SW) and photosynthetically active radiation (PAR) is fundamental for Earth system modeling and global change research. Combined with a machine-learning method, we used the Earth Polychromatic Imaging Camera (EPIC) data onboard the Deep Space Climate Observatory (DSCOVR) to generate the global SW/PAR dataset at both hourly and daily temporal scales and 0.1degree×0.1degree spatial resolution. Covering a period from June, 2015 to June (about 4 years), 2019, the dataset consists of total SW/PAR and their direct and diffuse components. Our dataset will be valuable for understanding the diurnal cycles and seasonal patterns of ecosystem processes, as well as the solar energy utilizations

14 SOLAR ENERGY↗

DOE OSTI2025

Discovering the Higgsino at CTAO-North within the Decade

We demonstrate that higgsino dark matter (DM) could be discovered within the next few years using the Cherenkov Telescope Array Observatory's soon-to-be-operational northern site (CTAO-North). A 1.1 TeV thermal higgsino is a highly motivated yet untested model of DM. Despite its strong theoretical motivation in supersymmetry and beyond, the higgsino is notoriously difficult to detect; it lies deep within the neutrino fog of direct detection experiments and could pose a challenge even for a future muon collider. We show that, in contrast, higgsino detection could be possible within this decade with CTAO-North in La Palma, Spain. The Galactic Center is the region where the dominant DM annihilation signature emerges, but it only barely rises above the horizon at the CTAO-North site. However, we project that this challenge can be overcome with large-zenith-angle observations at the northern site, enabling the conclusive detection of a higgsino signal by 2030 for a range of DM density profiles in the inner Galaxy.

Cosmology and Nongalactic Astrophysics (astro-ph.C↗

DOE OSTI2020

Critical Infrastructure Classification via CNN-based Modeling and Image Analysis

With recent advances in the fields of satellite imagery and machine learning we now have the ability to develop explainable deep learning models that enhance critical infrastructure analysis. Funded through Idaho National Laboratory’s (INL) Laboratory Directed Research and Development (LDRD) office we are in the process of developing a deep learning model capable of identifying critical infrastructure facilities and embedded features within those facilities. Utilizing current limit of practice techniques in the machine learning areas of explainability and transfer learning our model, once complete, will have the capacity to be used on multiple different imagery data sets and produce results that not only classify critical infrastructure facilities, but also explain why a critical infrastructure facility was classified as a certain type of facility. These advancements eliminate the ‘black box’ approach deep learning models have had in the past, where a user will have to trust the conclusion of a model without understanding what reasoning when into the model’s classification process. They also expand a model’s usefulness, traditionally a deep learning model will have to use the same data set it was originally trained on. Given the long training times of deep learning models this is impractical in a number of scenarios. By utilizing transfer learning advancements, we are eliminating the need to train our model on the same data set it is then run on to classify critical infrastructure facilities. We are also enabling the analysis and classification of data sets that are potentially too small to be divided into a training and testing data set. Once completed this model can provide a foundation to enhanced critical infrastructure analysis, dependency analysis, and potential disaster relief efforts.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2005

System concepts for transmit arrays of parabolic antennas for deep space uplinks

Phased arrays of parabolic antennas are a potentially lower-cost way to provide uplink transmission to distant spacecraft, compared to the 34-m and 70-m antennas now used by the NASA Deep Space Network. A large transmit array could provide very high EIRP when needed for spacecraft emergencies, such as the equivalent of 1 MW radiated from a 70-m antenna. Cost-effectiveness is realized by dividing the array into smaller arrays to provide routine support to many spacecraft simultaneously. The antennas might be as small as 12-m in diameter, with as many as 100 antennas covering an area of 0.5 km to 1 km in extent. Such arrays present significant technical challenges in phase alignment, which must be maintained at close to 1 mm. The concept requires a very stable system with accurately known antenna phase center locations. The system is first calibrated by transmitting from all antennas, and observing the signals at a target located in the far fields of the individual antennas. The antennas are then pointed to the operational targets, with the signal phases and time delays set to reinforce in the target directions. This requires accurate knowledge of the target directions and calculation of the required phases. The system must be phase-stable for all directions and over the time between calibrations, which is desired to be at least one day. In this paper, a system concept is presented, the major error sources are identified, a rough error budget is established, and key elements of the system are discussed. A calibration method is recommended which uses satellites as radar targets. The performance goal is to achieve a combining loss of less than 0.2 dB in good weather, and of less than 1 dB in all but extremely bad weather.

Effective Isotropic Radiated Power (EIRP)↗

DOE OSTI2021

Using Machine Learning to Track Objects Across Cameras

Video surveillance is one of the most important technologies used by the International Atomic Energy Agency in international safeguards. At large, complicated facilities, multiple surveillance cameras are deployed to monitor the transfer of safeguards-relevant objects across the site. During inspections, all surveillance videos are reviewed to ensure the objects are not manipulated or diverted during transfer, a laborious, time-consuming task. This work describes using deep machine learning algorithms to track objects automatically across multiple cameras, greatly improving the efficiency of the review process. The fundamental problem in this object tracking task across multiple cameras is how to associate the same object, which may show extreme intra-class variations, such as viewpoints, occlusions, and various scales, in different and even non-overlapped cameras. Object re-identification (Re-ID) in nuclear facility video surveillance is even more challenging than classic person or vehicle Re-ID problems because different instances in the same category may display an identical appearance. One observation from nuclear facility surveillance videos is that all objects must be carted (e.g., via forklift) to move. Therefore, the spatial context information of an object, which provides the feature from the carrier, is critical for the object Re-ID task. This work proposes a two-stream convolutional neural networks model that takes features of objects and their surrounding regions into account. Moreover, the custom videos usually are gleaned from different scenes from the training data, which may have extreme variations in illumination changes and/or cluttered backgrounds. Directly applying the trained model to custom videos will dramatically decrease the performance. To tackle this problem, an advanced domain adaptation technique is proposed to mitigate the gap between the data taken from different scenes. The proposed framework will track objects of interest across a nuclear complex. The resulting tracks can be used in further analyses, such as event/activity recognition, anomaly detection, etc.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

NASA NTRS2024

Remote Science at Sea With Remotely Operated Vehicles

Conducting sea-going ocean science no longer needs to be limited to the number of berths on a ship given that telecommunications, computing, and networking technologies onboard ships have become familiar mechanisms for expanding scientists’ reach from onshore. The oceanographic community routinely works with remotely operated vehicles (ROVs) and pilots to access real-time video and data from the deep sea, while onboard a ship. The extension of using an ROV and its host vessel’s live-streaming capabilities has been popularized for almost 3 decades as a telepresence technology. Telepresence-enabled vessels with ROVs have been employed for science, education, and outreach, giving a greater number of communities viewing access to ocean science. However, the slower development of technologies and social processes enabling sustained real-time involvement between scientists on-ship and onshore undermines the potential for broader access, which limits the possibility of increasing inclusivity and discoveries through a diversity of knowledge and capabilities. This article reviews ocean scientists’ use of telepresence for ROV-based deep-sea research and funded studies of telepresence capabilities. The authors summarize these studies findings and conditions that lead to defining the use of telepresence-enabled vessels for “remote science at sea.” Authors define remote science at sea as a type of ocean expedition, an additional capability, not a replacement for all practices by which scientists conduct ocean research. Remote science for ocean research is an expedition at-sea directed by a distributed science team working together from at least two locations (on-ship and onshore) to complete their science objectives for which primary data is acquired by robotic technologies, with connectivity supported by a high-bandwidth satellite and the telepresence-enabled ship’s technologies to support the science team actively engaged before, during, and after dives across worksites. The growth of productive ocean expeditions with remote science is met with social, technical, and logistical challenges that impede the ability of remote scientists to succeed. In this article, authors review telepresence-enabled ocean science, define and situate the adjoined model of remote science at sea, and some infrastructural, technological and social considerations for conducting and further developing remote science at sea.

marine technology↗