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NASA NTRSDate not supplied

2024 Nasa Lunabotics University Competition: Site Preparation With Bulk Regolith

Introduction: Lunabotics provides accredited institutions of higher learning students (vocational-technical, college, university) an opportunity to apply the NASA systems engineering process to design and build a prototype robot. This robot would be capable of performing the proposed operations on the Lunar surface in support of future Artemis mission goals. Lunabotics features a systems engineering design challenge to engage students in the next phase of hu-man space exploration supporting the Artemis missions. This two-semester event encourages students to design and build an autonomous or telerobotic robot designed to traverse the simulated Lunar surface and complete the assigned construction tasks. The number of teams accepted into this challenge is not predetermined but is based on the scores and overall quality of the Project Management Plans received and other factors. The culmination of the Lunabotics virtual challenge will be the design, build and operation of a functional prototype Lunar robot. Teams are required to submit the following: (1) Project Management Plan, (2) Systems Engineering Paper, a (3) STEM Engage-ment Report, and a (4) Proof of Life Video. This is an optional item, but to qualify for the grand prize a team must also submit a: (5) Presentation and Demonstration. Background: The NASA Lunabotics University Competition was first held in 2010 as a follow on to the NASA Regolith Excavation Challenge [1]. The high level of interest and participation from over 50 universities each year has led to a sustained annual competi-ion cadence: it has been held every May for the past 14 years [2,3]. Over 6,000 students have participated and been inspired to pursue Science, Technology, En-gineering and Mathematics careers (STEM). 2024 Competition: The necessary lunar surface tasks are evolving to meet the NASA Artemis Mission requirements. In the past Lunabotics challenges we gathered data to support Lunar mining for consuma-bles in the Lunar regolith. Now, in 2024, the task is to gather data on Lunar site preparation and construction by designing and building a robot that will traverse the chaotic Lunar terrain and construct a regolith-based berm. The goal is to build a berm structure which would be useful to the Artemis Mission for blast and ejecta protection during lunar landings and launches, shading cryogenic propellant tank farms, providing radiation protection around a nuclear power plant and other mission critical uses. Lunabotics will consist of three separate events this year. The first event is NASA’s Lunabotics Project Development Challenge, where teams submit various deliverables to be scored by judges. The second event will be the University of Central Florida (UCF) Lunabotics Qualification challenge, in the Exolith laboratory, where teams will put their de-signs to the test. The top ten scoring teams from the Qualification challenge are then invited to the third and final event, NASA’s Lunabotics On-Site Challenge at Kennedy Space Center in Florida. This presentation will summarize the results and lessons learned from the NASA Lunabotics University Competition held in May 2024.

Lunabotics↗

NASA NTRS1988

Optimal solutions for complex design problems: Using isoperformance software for human factors trade offs

A major application of isoperformance is as a trade-off methodology of the three major drivers of system design; equipment, training variables, and user characteristics. The flexibility of isoperformance allows each of these three components to be nearly any rational variation. For example, aptitude may be military Armed Forces Qualification Testing (AFQT) categories, cutoff scores within a selection procedure, or simply dichotomizing high and low scorers (pass/fail). Equipment may be new versus old, 'smart' versus dumb, high versus low resolution, etc. Training may be short versus long or varieties of media types (lecture versus CAI/CBI versus self-paced workbooks). In its final computerized form isoperformance lets the user set an operational level of performance (e.g., a jet pilot in a simulated emergency must take prescribed corrective action and clear the plane in several seconds, pilot astronauts will check out all shuttle flight systems within 30 minutes, or Mission Specialists must handle sucdessfully a required number of job elements). At this point the computer program guides the user through any requested trade-offs of the three components while maintaining the specified operational level of performance through isoperformance curves. A demonstration of the computer program is currently available.

Kennedy, Robert S.↗

DOE OSTI2020

A Numerical Study of Hypoxia in Chesapeake Bay Using an Unstructured Grid Model: Validation and Sensitivity to Bathymetry Representation

A three-dimensional unstructured-grid hydrodynamic and water quality model (Semi-implicit Cross-scale Hydroscience Intergrated System Model-Integrated Compartment Model) is applied successfully for Chesapeake Bay. The model is validated with observations of salinity, chlorophyll-a, dissolved oxygen, nutrients, and phytoplankton productions from the year 1991 to 1995 for the mainstem and some major tributaries, based on multiple model skill scores. Model experiments are conducted to test the importance of having (1) an accurate representation of bathymetry to correctly predict hypoxia and other processes and (2) a high-resolution model grid for tributaries to correctly simulate water quality variables. Further, comparison with the model experiment results with bathymetry smoothing indicates that bathymetry smoothing, as commonly used for many systems, changes the stratification and lateral circulation pattern, resulting in more salt intrusion into shallow water regions, and an increase in the freshwater age. Consequently, a model with bathymetry smoothing can lead to an unrealistic prediction of the distribution of hypoxia and phytoplankton production. Local grid refinement shows significant improvement of model simulations on local stratification and water quality variables. Overall, the use of high-resolution unstructured grid model leads to a faithful representation of the complex geometry, and thus a seamless cross-scale capability for simulating water quality processes in the Bay including tributaries and tidal creeks.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2006

A Countermeasure for Space Motion Sickness

Overall, the results obtained in both the U.S. and the Russian space programs indicate that most space crews will experience some symptoms of motion sickness (MS) causing significant impact on the operational objectives that must be accomplished to assure mission success. At this time the primary countermeasure for MS requires the administration of Promethazine. Promethazine is not a benign drug, and is most frequently administered just prior to the sleep cycle to prevent its side effects from further compromising mission objectives. Clearly other countermeasures for SMS must be developed. Currently the primary focus is on two different technologies: (1) developing new and different pharmacological compounds with less significant side effects, (2) preflight training. The primary problem with all of these methods for controlling MS is time. New drugs that may be beneficial are years from testing and development, and preflight training requires a significant investment of crew time during an already intensive pre-launch schedule. Granted, motion sickness symptoms can be minimized with either of the two methods detailed above, however, it may be possible to develop a countermeasure that does not require either extensive adaptation time or exposure to motion sickness. Approximately 25 years ago Professor Geoffrey Melvill Jones presented his work on adaptation of the vestibuloocular reflex (VOR) using optically reversed vision (left-right prisms) during head rotations in the horizontal plane. It was of no surprise that most subjects experienced motion sickness while wearing the optically reversing prisms. However, a serendipitous finding emerged during this research showing that the same subjects did not experience motion sickness symptoms when wearing the reversing prisms under stroboscopic illumination. The mechanism, by which this side-effect was believed to have occurred, is not clearly understood. However, the fact that no motion sickness was ever noted, suggests the possibility of producing functionally useful adaptation during space flight without the penalty of disabling motion sickness by controlling the rate of the adaptive process by means of an appropriate stroboscopically presented environment. After several recent meetings with Professor Melvill Jones, we were encouraged to repeat the motion sickness portions of his and Mandl's 1981 stroboscopic experiment. In conducting this experiment we used a randomized cross-over design where subjects were randomly assigned to either a stroboscopic flash or no strobe for their first exposure in the experimental design. Twenty subjects (19 subjects completed the study) read a short passage from Treasure Island mounted on the wall approximately 1 m from their eyes while wearing left-right reversing prisms. The strobe on time of 3 microseconds and flash frequency of 4 Hz was set to equal that used in the original study. Motion sickness was scored using a modified Miller and Graybiel scale that we constructed to include symptoms that may be elicited under conditions where reversing prisms are worn. On this scale a score of 5 represented Malaise IIa (mild motion sickness) and a score of 8 or above is approaching frank sickness. Symptoms were tracked and recorded every 5 min during the task. Testing was limited to 30 min unless the subject had reached the MIIa score, at which time the test was terminated. Performance under stroboscopic illumination was significantly better than when the subjects read under normal room illumination while wearing the left-right reversing prisms. Based on these results we developed a goggle system using LCD material that can be strobed. To evaluate the effectiveness of stroboscopic goggles we tested an additional 9 subjects in addition to retesting 10 used in the stroboscopic pilot study described above. These 19 subjects wore a pair of strobing LCD goggles that could be cycled at 4 Hz. These subjects wore the goggles while also wearing left-right reversg prisms. Results while wearing the goggles showed that none of the 19 subjects scored at the MIIa level on the motion sickness rating scale. When the goggles did not flash (no strobe), 11 of the 19 developed symptoms above the MIIa criteria. As a countermeasure the goggles seem to be effective, even with an on time of 10 msec (time the goggles are clear). We have also collected anecdotal data, from our personnel in the Neuroscience Laboratory at the Johnson Space Center, suggesting that the goggles may effective in preventing carsickness.

Reschke, M. F.↗

DOE OSTI2020

Data-driven Cyberattack Detection for Photovoltaic (PV) Systems through Analyzing Micro-PMU Data

With increasing exposure to software-based sensing and control, Photovoltaic (PV) systems are facing higher risks of cyber attacks. Here, to ensure the system stability and minimize potential economic losses, it is imperative to monitor operating states and detect attacks at the early stage. To meet this demand, Micro-Phasor Measurement Units (μPMU) are increasingly popular in monitoring distribution networks. However, due to the relatively low sampling rate, μPMU has not yet been used to detect and classify cyber-attacks in power electronics enabled smart grid. To our knowledge, this is one of the first attempts to use μPMU to detect cyber attacks that degrade the performance of power electronics systems. We propose to apply data-driven methods on micro-PMU data to implement attack detection. We have evaluated data-driven methods, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), long short-term memory (LSTM) and convolutional neural network (CNN). The proposed CNN model achieves the required performances with the highest 99.23% accuracy and 0.9963 F 1 score.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition

Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, aviation incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts entities relevant to safety analysts. The custom NER model is built by fine-tuning an existing Bidirectional Encoder Representations from Transformers (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from failure-relevant text. This model performs passably, with a weighted average f1 score of 0.33 across entity types, indicating more labeled training data is needed. Extracted entities are used to form a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported using the SAFECOM system. Similar mishaps are manually clustered and reported as single rows within an FMEA. Foreach cluster, we compute frequency, severity, and overall riskin accordance with FAA standards. This methodology can beapplied as part of a broader safety management system totrack trends in mishaps (e.g., likelihood, severity) and discoverknowledge (e.g., causes, effects) that can be utilized to improvesafety outcomes and system performance.

Machine Learning↗

NASA NTRS2018

Trusted Communication: Utilizing Speech Communication to Enhance Human-Machine Teaming Success

An area of increasing interest for the next generation of aircraft is autonomy and the integration of increasingly autonomous systems into the national airspace. Such an integration requires humans to work closely with autonomous systems, forming teams. Our hypothesis is that a team composed of both humans and autonomous systems will operate better than either entity alone. We have existing procedures for certifying pilots to operate in the national airspace and are currently working on methods for validating the function of autonomous systems, however we have no method in place for assessing the interaction of these two disparate systems. Communication is one avenue. This paper will examine the use of language as a metric for ascertaining human-machine teaming effectiveness. A proof-of-concept of the application of two communication-based analysis techniques, Linguistic Inquiry and Word Count (LIWC) and Latent Semantic Analysis (LSA), for the prediction of success in human/chatbot teaming was conducted. By running these analyses over data from the 2014 and 2015 Loebner Prize competitions of human/chatbot teaming, numerical scores were obtained that can be associated with scores provided by human judges during the competition. Correlating their LIWC and LSA data with the scores provided by the judges, and using linear regression over this correlation, formulae were obtained that predict the score of human/chatbot interaction. These formulae were tested over the 2013 Loebner Prize transcripts, determining that, though there was strong correlation between predicted and actual scores, the predictive success of this method was not strong. However, with specialized topic spaces and lexica, as well as larger data sets, the predictive power of these metrics will improve. Given the importance of providing metrics for human-machine system team success and given the promise shown by the communication-basedLIWCand LSAmethods, continuing research in this area is necessary. After examining the potential for using communication and spoken language as a metric for the success of human/autonomous system teaming, this paper then examines aspects inherent to communication systems that may contribute to unreliability and reduced trust. Modern natural language processing tools rely on deep learning algorithms to create language rules that produce accurate results, but these rules are uninterpretable. The resulting blackbox system lacks transparency necessary for full validation and complete trust. Additionally, speech-based interfaces pose other difficulties to developing coordinated teamwork between humans and autonomous systems. Human communication is infrequently limited to speech only, instead usually relying on a combination of verbal, gestural, and general body language communication. Reducing an analysis of team effectiveness to a study of spoken language alone is problematic as it leaves these other equally important forms of communication out. This paper will examine these problems and the general deficiencies in speech-based metrics for human-machine teaming.

E L Meszaros↗

NASA NTRSDate not supplied

Results of a 3-day Pilot Study to Validate Planetary Prebreathe Protocols Using a 56.5 kPa 34% O2, 66% N2 Saturation Cabin Atmosphere

INTRODUCTION: Apollo missions used 100% O2 cabin atmospheres which effectively eliminated the risk of decompression sickness (DCS) during extravehicular activities (EVAs, ‘spacewalks’); however, this atmosphere presented a flammability risk that is no longer acceptable to NASA. Denitrogenation prebreathe protocols used to mitigate DCS risk for Space Shuttle and International Space Station EVAs are validated for the microgravity environment, but the significantly increased risk of DCS during equivalent ambulatory surface EVAs make these protocols inapplicable to planetary/Lunar missions. An “exploration atmosphere” of 56.5 kPa (8.2 psia), 34% O2, 66% N2 has been recommended by NASA for future Moon and Mars missions as a compromise that balances subsequent pre-EVA prebreathe duration, hypoxia, and flammability risk, assuming a 29.6 kPa (4.3 psi) spacesuit. Prebreathe validation studies was initiated utilizing a three-story 6m diameter hypobaric chamber at NASA’s Johnson Space Center. Here, we report the results of a 3-day human-in-the-loop system checkout. METHODS: Six volunteers acclimated to the 56.6kPa/34% O2 66% N2 environment for 48hrs prior to conducting a 20-minute prebreathe and a 6-hour simulated EVA at 34kPa/85% O2 / 15% N2. The EVA simulation was designed to include tasks that are physically and ergonomically representative of future planetary EVAs. Decompression stress was evaluated by serial doppler and echocardiographs, as well as by clinical features of DCS signs/symptoms. RESULTS AND DISCUSSION: Preliminary data analysis noted venous gas emboli (VGE) in 3 of 6 subjects, with peak Grade II VGE by Doppler and peak E-B score of 5 by cardiac ultrasonography. No volunteers were diagnosed with DCS during this initial test. No acute hypoxic symptoms were noted. Musculoskeletal and gastrointestinal complaints were noted, likely associated with the exercise load and the food system. Validation of exploration prebreathe protocols has since been initiated with an 11-day saturation test using the same facility and protocol.

Alejandro Garbino↗

DOE OSTI2024

A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

AI-based foundation models like FourCastNet, GraphCast are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions.

Lu, Dan↗

DOE OSTI2024

A multiobjective closed-loop approach towards autonomous discovery of electrocatalysts for nitrogen reduction

We propose a multiobjective scoring scheme toward closed-loop computational discovery of electrocatalysts. To demonstrate, we autonomously search over single-atom alloys for active, stable, and cost-effective systems for nitrogen reduction.

Kavalsky, Lance↗

NASA NTRS2003

A Global Aerosol Model Forecast for the ACE-Asia Field Experiment

We present the results of aerosol forecast during the Aerosol Characterization Experiment (ACE-Asia) field experiment in spring 2001, using the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model and the meteorological forecast fields from the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The aerosol model forecast provides direct information on aerosol optical thickness and concentrations, enabling effective flight planning, while feedbacks from measurements constantly evaluate the model, making successful model improvements. We verify the model forecast skill by comparing model predicted total aerosol extinction, dust, sulfate, and SO2 concentrations with those quantities measured by the C-130 aircraft during the ACE-Asia intensive operation period. The GEOS DAS meteorological forecast system shows excellent skills in predicting winds, relative humidity, and temperature for the ACE-Asia experiment area as well as for each individual flight, with skill scores usually above 0.7. The model is also skillful in forecast of pollution aerosols, with most scores above 0.5. The model correctly predicted the dust outbreak events and their trans-Pacific transport, but it constantly missed the high dust concentrations observed in the boundary layer. We attribute this missing dust source to the desertification regions in the Inner Mongolia Province in China, which have developed in recent years but were not included in the model during forecasting. After incorporating the desertification sources, the model is able to reproduce the observed high dust concentrations at low altitudes over the Yellow Sea. Two key elements for a successful aerosol model forecast are correct source locations that determine where the emissions take place, and realistic forecast winds and convection that determine where the aerosols are transported. We demonstrate that our global model can not only account for the large-scale intercontinental transport, but also produce the small-scale spatial and temporal variations that are adequate for aircraft measurements planning.

Chin, Mian↗

NASA NTRS2019

The Arctic-Boreal vulnerability experiment model benchmarking system

NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) integrates field and airborne data into modeling and synthesis activities for understanding Arctic and Boreal ecosystem dynamics. The ABoVE Benchmarking System (ABS) is an operational software package to evaluate terrestrial biosphere models against key indicators of Arctic and Boreal ecosystem dynamics, i.e.: carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance. The ABS utilizes satellite remote sensing data, airborne data, and field data from ABoVE as well as collaborating research networks in the region, e.g.: the Permafrost Carbon Network, the International Soil Carbon Network, the Northern Circumpolar Soil Carbon Database, AmeriFlux sites, the Moderate Resolution Imaging Spectroradiometer, the Orbiting Carbon Observatory 2, and the Soil Moisture Active Passive mission. The ABS is designed to be interactive for researchers interested in having their models accurately represent observations of key Arctic indicators: a user submits model results to the system, the system evaluates the model results against a set of Arctic-Boreal benchmarks outlined in the ABoVE Concise Experiment Plan, and the user then receives a quantitative scoring of model strengths and deficiencies through a web interface. This interactivity allows model developers to iteratively improve their model for the Arctic-Boreal Region by evaluating results from successive model versions. We show here, for illustration, the improvement of the Lund–Potsdam–Jena-Wald Schnee und Landschaft (LPJwsl) version model through the ABoVE ABS as a new permafrost module is coupled to the existing model framework. The ABS will continue to incorporate new benchmarks that address indicators of Arctic-Boreal ecosystem dynamics as they become available.

ABoVE↗

NASA NTRS2023

Usability Evaluation Technical Report: A Usability Evaluation of the Communication Tool Prototype Prepared in Adobe XD for High Density Vertiplex (HDV) Team

Purpose: As cities get more crowded, the roadway infrastructure cannot keep up with the travel demands. Aviation can be a solution. Organizations supporting NASA’s Urban Air Mobility (UAM) concept are conducting studies on feasible concepts of operations for the new air traffic management system required to implement UAM. NASA’s High Density Vertiplex (HDV) team conducts studies using both live flights and simulations. Significance: The goal of this study is to evaluate the effectiveness of a communication tool developed for this team to enhance team situation awareness. Specifically, the study aims to determine if the communication tool is effective in allowing NASA-Langley and NASA-Ames team members playing different roles to understand what others are doing so that completed steps can be communicated to the whole team. Methods: This study asks participants to conduct four distinct scenarios using an Adobe XD prototype. Participants included five members of the target audience, who each received up to 15 minutes of training on how to use the Adobe XD prototype. The participants were NASA-Ames or NASA-Langley researchers, developers or tech leads who had 0 to 15 years of experience. Their ages ranged from 20 to 40 years old, and they were of both genders. After training, each participant was asked to do 4 tasks of varying difficulty, using the prototype testbed. After each task they reported on task difficulty and after all tasks were completed they were asked to fill out the System Usability Survey (SUS). Performance will be assessed through task completion time and accuracy rating. Results: User perception was evaluated by the SUS which showed a mean score over the six participants of 84. All the participants were above the acceptable range which was 70 and nobody was in the marginal usability zone between 50 and 70. In addition, most of the participants did well in terms of time on task, success rate and task difficulty on all the tasks except for Task 3. Participants had two minutes to do the task correctly to succeed at the task, otherwise it was marked as a failure. The geomean for Task 1 for completion time was 22.20; Task 2 was 58.28; Task 3 was 84.68 and Task 4 was 19.61. In Task 3, three out of six people failed this task but all five scored this with an average score 4.2 out of 5 for self-rating on task difficulty.

human factors↗

NASA NTRSDate not supplied

Examining Performance and Usability of Situation Awareness Communication Tool for NASA's High Density Vertiplex (HDV) Team

human factors↗

DOE OSTI2023

Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder

Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics. In this paper, we present Set-VAE, a particle-based variational autoencoder (VAE) anomaly detection algorithm. We demonstrate a 2x signal efficiency gain compared with traditional subjettiness-based jet selection. Furthermore, with an eye to the future deployment to trigger systems, we propose the CLIP-VAE, which reduces the inference-time cost of anomaly detection by using the KL-divergence loss as the anomaly score, resulting in a 2x acceleration in latency and reducing the caching requirement.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Circular Economy for Photovoltaics in Service of Energy Transition

The challenge of energy transition is immediate and immense; current projections target 75 TW of photovoltaics (PV) capacity by 2050. While any transition to renewable energy technology is preferable to the current fossil-based system, it is ideal to improve the sustainability of PV to minimize negative environmental and social impacts. Circular economy (CE) has been proposed as a method to improve the sustainability of PV, especially for emerging materials like perovskites. CE is a set of actions, principles, and systems which aim to design out waste and keep products and materials in use, to reduce environmental impacts and enable sustainable development. At the most basic level, CE is "reduce, reuse, recycle", the R-actions, in ranked order. CE of a PV technology can be metricized in a variety of ways, such as the Material Circularity Indicator (Smith and Jones, Ellen MacArthur Foundation, 2019) or recycling rates. Unfortunately, standard CE metrics have several shortcomings for measuring renewable energy technologies in the context of deployment for energy transition (Figge 2018, Saidani 2019): 1) Only measure mass flows; 2) De-prioritization of the use phase in favor of mass circularity when scoring; and 3) Tight focus on a single product scale The use phase and energy flows of PV are key to energy transition, and therefore need to be quantified. Additionally, correlating product-scale to system-scale is necessary for quantifying the environmental impacts of energy transition. Life Cycle Assessment (LCA) can address some of these concerns, but also focuses on a single product scale and has trouble capturing the dynamics of system-scale energy transition, such as the interaction of module lifetime with manufacturing demands for energy transition deployment schedules. Therefore, we developed an open-source Python-based system dynamics model to quantify the mass, energy and carbon impacts of CE R-actions for PV technologies in the energy transition; PV in the CE (PV ICE) (Ovaitt & Mirletz 2021). The tool captures supply chains from material extraction through end of life, incorporating 5 circular end of life pathways. PV ICE takes in any evolving bill of materials, module properties and deployment schedule to support researchers and decision makers with data-backed insights. In this work, we quantify and compare proposed CE sustainable PV module designs and lifecycle management strategies, spanning currently commercialized technologies, government and industry technology targets, and several low Technology Readiness Level (TRL) emerging PV technologies, including perovskites. Our analyses capture the projected evolutions of lifetime, efficiency and material circularity of these PV technologies, as well as their material supply chains. Our analyses emphasize the importance of examining a suite of metrics to identify priorities and tradeoffs, and inform design or lifecycle management decisions holistically. Previous analyses have demonstrated the central importance of PV module lifetime to support energy transition while minimizing impacts. High levels of material circularity (>90%) enable minimizing lifecycle wastes, can reduce virgin material demands if paired with improving efficiency, but demonstrate tradeoffs in energy return on investment. In the fervor of new material and technology development, it is important to remember that CE is not the end goal; decarbonization and energy transition are the end goal. CE should be used in service to improve the sustainability of PV, and R-actions evaluated for their usefulness and efficacy to this end.

carbon↗

DOE OSTI2024

The seventh blind test of crystal structure prediction: structure ranking methods

A seventh blind test of crystal structure prediction has been organized by the Cambridge Crystallographic Data Centre. The results are presented in two parts, with this second part focusing on methods for ranking crystal structures in order of stability. The exercise involved standardized sets of structures seeded from a range of structure generation methods. Participants from 22 groups applied several periodic DFT-D methods, machine learned potentials, force fields derived from empirical data or quantum chemical calculations, and various combinations of the above. In addition, one non-energy-based scoring function was used. Results showed that periodic DFT-D methods overall agreed with experimental data within expected error margins, while one machine learned model, applying system-specific AIMnet potentials, agreed with experiment in many cases demonstrating promise as an efficient alternative to DFT-based methods. For target XXXII, a consensus was reached across periodic DFT methods, with consistently high predicted energies of experimental forms relative to the global minimum (above 4 kJ mol −1 at both low and ambient temperatures) suggesting a more stable polymorph is likely not yet observed. The calculation of free energies at ambient temperatures offered improvement of predictions only in some cases (for targets XXVII and XXXI). Several avenues for future research have been suggested, highlighting the need for greater efficiency considering the vast amounts of resources utilized in many cases.

Chemistry↗

DOE OSTI2021

A spatial evaluation of Arctic sea ice and regional limitations in CMIP6 historical simulations

The Arctic sea ice response to a warming climate is assessed in a subset of models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), using several metrics in comparison with satellite observations and results from the Pan-Arctic Ice Ocean Modeling and Assimilation System and the Regional Arctic System Model. Our study examines the historical representation of sea ice extent, volume, and thickness using spatial analysis metrics, such as the integrated ice-edge error, Brier score, and Spatial Probability Score. We find that the CMIP6 multi-model mean captures the mean annual cycle and 1979-2014 sea ice trends remarkably well. However, individual models experience a wide range of uncertainty in the spatial distribution of sea ice when compared against satellite measurements and reanalysis data. Our metrics expose common and individual regional model biases, which sea ice temporal analyses alone do not capture. We identify large ice edge and ice thickness errors in Arctic sub-regions, implying possible model specific limitations in or lack of representation of some key physical processes. We postulate that many of them could be related to the oceanic forcing, especially in the marginal and shelf seas, where seasonal sea ice changes are not adequately simulated. We therefore conclude that an individual model’s ability to represent the observed/reanalysis spatial distribution still remains a challenge. We propose the spatial analysis metrics as useful tools to diagnose model limitations, narrow down possible processes affecting them, and guide future model improvements critical to the representation and projections of Arctic climate change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗