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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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Navigating Uncertainty: Challenges in Visualizing Ensemble Data and Surrogate Models for Decision Systems
Uncertainty visualization plays a critical role in transforming ensemble simulation data into actionable insights by effectively communicating various dimensions of uncertainty within a system. The emergence of artificial intelligence-driven surrogate models trained on multirun ensemble data offers a transformative opportunity to replace computationally intensive simulations with fast estimates, enabling users to explore data spaces with unprecedented depth and interactivity. However, integrating ensemble data and surrogate models into decision-making workflows and tools introduces novel challenges for uncertainty visualization. These include reconciling and clearly communicating the unique uncertainties associated with ensembles and their surrogate model estimates, and leveraging these approximations to inform actionable decisions. This work explores these challenges in the context of high-dimensional data visualization, bridging discrete datasets with their continuous representations and addressing the complexities of systems that support iterative navigation between input and output spaces. We evaluate the role of uncertainty visualization in fostering intuitive, actionable interactions and identify critical hurdles in advancing this frontier of computational simulation.
Time and Frequency Analysis of Load Profile Data
Technology advancements and integration of modern advanced metering systems can monitor, forecast, inform, control, and operate the building's mechanical, electrical, and plumbing (MEP) systems. They offer a higher level of information, which can contribute to making smart buildings more energy efficient and to making them closer to becoming grid-interactive energy efficient buildings (GEB). This paper builds on the ongoing research on variability analysis of a case study building with a 1-minute load profile and examines the Discrete Wavelet Transform (DWT) process in the frequency domain to quantify the signal's energy in each bandwidth, with respect to each end-use category. Moreover, the amount of variability in the total variability is not similar among the end-use categories. This information is needed to understand the behavior of the variability in the frequency domain for future applications, such as generating synthetic load profiles with a similar frequency spectrum as the measured signal.
Impacts of Dispatch Strategies and Forecast Errors on the Economics of Behind-the-Meter PV-Battery Systems
To assess the economic value of batteries in hybrid PV-battery systems, one must create a dispatch profile for the battery. Many analyses of battery value assume perfect forecasts of PV generation and load, determining an upper limit on the value of the battery. Prior work that accounts for forecast uncer- tainty often does so in the context of a single dispatch algorithm, which does not provide a baseline for comparison. Furthermore, when multiple dispatch algorithms are assessed with uncertainty, the benefits considered are for diesel generation in a microgrid, not retail rate savings. This work addresses the gaps in the literature by comparing the performance of both heuristic and optimal dispatch algorithms for retail rate savings under forecast uncertainty, and provides comparisons of the robustness of these algorithms and their associated estimates of economic value. We find that using a perfect forecast can overestimate the value of hybrid PV-battery systems between 1% and 8% compared to the reality of using a day-ahead forecast, depending on the dispatch algorithm used. Thus, accounting for forecast uncertainty in system design and analysis will significantly improve the accuracy of modeled system values.
Power Grid Contingency Analysis with Machine Learning: A Brief Survey and Prospects
We briefly review previous applications of machine learning (ML) in power grid analyses and introduce our ongoing effort toward developing a generative-adversarial (GA) model for fast and reliable grid contingency analyses. According to our review, the persisting limitation of traditional ML techniques in grid analyses is the need for an exhaustive amount of training data for model generalization and accurate predictions. GA models overcome this limitation by first learning true data distribution from a small training set, from which new samples assimilating true data are generated with some variations. Subsequently, GA models can transfer learn or super-generalize with increased accuracy, that is, accurately predict n - (k + 2) contingencies from a small n - k training set and generated n - (k + 1) data. The joint effort between Idaho National Lab and Florida State University strives to develop a zero-shot and deep learning-based contingency analysis tool, named Smart Contingency Analysis Neural Network (SCANN), by leveraging the aforementioned advantages of GA models. The basic architecture of SCANN stems from the Latent Encoding of Atypical Perturbations network combined with an adversarial network, and it is designed to generate imbalanced power flow data from learned true data distributions for prediction purposes. Here we also introduce the abstract concept of resilience-chaos plots, a new resilience characterization tool proposed to complement SCANN by aiding in the assessment of large amounts of high-order contingency predictions.
Nb$_{3}$Sn SRF Photogun High Power Test at Cryogenic Temperatures
Superconducting RF (SRF) photoguns are emerging as promising candidates to produce highly stable electrons for Ultra fast Electron Diffraction/Microscopy (EUD/UEM) applications due to the ultrahigh shot-to-shot stability compared to room temperature RF photoguns. SRF technology was prohibitively expensive for industrial use until two recent advancements: Nb 3 Sn and conduction cooling. SRF gun can provide a CW operation capability while consuming only several Watts of RF power which eliminates the need of an expensive high power RF system and saves a facility footprint. Euclid is developing a continuous wave (CW), 1.5-cell, MeV-scale SRF conduction cooled photogun operating at 1.3 GHz. Here, in this paper, we present first high power results of the gun covered with Nb 3 Sn.
Microwave Vortex Motion Characterization of Nb$_{3}$ Sn Coatings for Applications in High Magnetic Fields
In this work, microwave measurements carried out in dielectric-loaded resonators exposed to high magnetic fields are exploited to yield the surface impedance of Nb$_3$ Sn superconducting coatings deposited via two different techniques: vapor tin diffusion, and DC magnetron sputtering. The obtained data lead to qualitative interpretations on both the Nb$_3$ Sn superconducting properties, and vortex-dynamics and pinning, of each coating separately, as well as simple distinctive features when comparing those. When examining the respective surface impedances at varying field, it is expected that the studied films perform at substantially diverse magnitudes of flux-flow resistivity, but also in well-differentiated pinning regimes, yet the obtained surface resistances of both samples are comparable, thus demonstrating that there is room for film optimization at the expense of certain compromise between the parameters involved.
A Typology of Decision-Making Tasks for Visualization
Despite decision-making being a vital goal of data visualization, little work has been done to differentiate decision-making tasks within the field. While visualization task taxonomies and typologies exist, they often focus on more granular analytical tasks that are too low-level to describe large complex decisions, which can make it difficult to reason about and design decision-support tools. In this paper, we contribute a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review. Our typology is concise and consists of only three tasks: CHOOSE, ACTIVATE, and CREATE. Although decision types originating in other disciplines exist, we provide definitions for these tasks that are suitable for the visualization community. Our proposed typology offers two benefits. First, the ability to compose and hierarchically organize the tasks enables flexible and clear descriptions of decisions with varying levels of complexities. Second, the typology encourages productive discourse between visualization designers and domain experts by abstracting the intricacies of data, thereby promoting clarity and rigorous analysis of decision-making processes. We demonstrate the benefits of our typology through four case studies, and present an evaluation of the typology from semi-structured interviews with experienced members of the visualization community who have contributed to developing or publishing decision support systems for domain experts. Our interviewees used our typology to delineate the decision-making processes supported by their systems, demonstrating its descriptive capacity and effectiveness. Finally, we present preliminary findings on the usefulness of our typology for visualization design.
Uncertainty Visualization Challenges in Decision Systems with Ensemble Data & Surrogate Models
Uncertainty visualization is a key component in translating important insights from ensemble simulation data into actionable decision-making by visually conveying various aspects of uncertainty within a system. With the recent advent of fast surrogate models trained on ensemble data, we can substitute computationally expensive simulations, which allows users to interact with more aspects of data spaces than ever before. However, the use of ensemble data with surrogate models in a decision-making tool brings up new challenges for uncertainty visualization, namely how to reconcile and communicate the new and different types of uncertainties brought in by surrogates and how to utilize these new data estimates in actionable ways. In this work, we examine these issues as they relate to high-dimensional data visualization, the integration of discrete datasets and the continuous representations of those datasets, and the unique difficulties associated with systems that allow users to iterate between input and output spaces. We assess the role of uncertainty visualization in facilitating intuitive and actionable interaction with ensemble data and surrogate models, and highlight key challenges in this new frontier of computational simulation.
Evaluation of a coastal acoustic buoy for cetacean detections, bearing accuracy and exclusion zone monitoring
Abstract There is strong socio‐political support for offshore wind development in US territorial waters and construction is planned off several east coast states. Some of the planned development sites coincide with important habitat for critically endangered North Atlantic right whales. Both exclusion zones and passive acoustic monitoring are important tools for managing interactions between marine mammals and human activities. Understanding where animals are with respect to exclusion zones is important to avoid costly construction delays while minimizing the potential for negative impacts. Impact piling from construction of hundreds of offshore wind turbines likely require exclusion zones as large as 10 km. We have developed a three‐hydrophone passive acoustic monitoring system that provides bearing information along with marine mammal detections to allow for informed management decisions in real‐time. Multiple units form a monitoring system designed to determine whether marine mammal calls originate from inside or outside of an exclusion zone. In October 2021, we undertook a full system validation, with a focus on evaluating the detection range and bearing accuracy of the system with respect to right whale upcalls. Five units were deployed in Mid‐Atlantic waters and we played more than 3500 simulated right whale upcalls at known locations to characterize the detection function and bearing accuracy of each unit. The modelled results of the detection function error were then used to compare the effectiveness of a bearing‐based system to a single sensor that can only detect a signal but not ascertain directivity. Field trials indicated maximum detection ranges from 4–7.3 km depending on source and ambient noise levels. Simulations showed that incorporating bearing detections provide a substantial improvement in false alarm rates (6 to 12 times depending on number of units, placement and signal to noise conditions) for a small increase in the risk of missed detections inside of an exclusion zone (1%–3%). We show that the system can be used for monitoring exclusion zones and clearly highlight the value of including bearing estimation into exclusion zone monitoring plans while noting that placement and configuration of units should reflect anticipated ambient noise conditions.
Tigers at a crossroads: Shedding light on the role of Bangladesh in the illegal trade of this iconic big cat
Abstract Unsustainable wildlife trade is a major threat to many species, but quantifying trade remains challenging, as seizure data provides an incomplete understanding. For this reason, integrating multiple types of information, including interviews with actors involved in trade, is crucial if we are to understand the problem better. Hence, in this study, we digitized Bangladesh Forest Department tiger seizure records to identify trade routes and interviewed 163 individuals involved in trafficking tigers through Bangladesh's air, sea and land ports, including poachers, smugglers, and traders. We identified six ports used to import tigers, 14 ports used for tiger export and three ports showing bi‐directional trade. Elite Bangladeshis were the most important consumer group, and tigers were sourced from populations in NE India, Myanmar and Bangladesh Sundarbans to supply domestic demand. Tiger products were exported to 14 countries, including seven G20 nations, with Bangladeshi expatriates as the consumer group in three countries (United Kingdom, Germany and Qatar). Rising economic development in Bangladesh over the last decade, combined with deep‐rooted cultural ties to tiger consumption, has led to a rise in domestic demand. Additionally, rapid growth in international transport links has increased smuggling and connected local traders with global markets, increasing the complexity of global trade. These findings suggest Bangladesh is poised to play a pivotal role in tiger conservation over the next decade, requiring strong national strategies to reduce trade opportunities, disrupt networks and weaken demand.
Mineralogy and petrology of fine‐grained samples recovered from the asteroid (162173) Ryugu
Abstract Samples returned from the carbonaceous asteroid (162173) Ryugu by the Hayabusa2 mission revealed that Ryugu is composed of materials consistent with CI chondrites and some types of space weathering. We report detailed mineralogy of the fine‐grained Ryugu samples allocated to our “Sand” team and report additional space weathering features found on the grains. The dominant mineralogy is composed of a fine‐grained mixture of Mg‐rich saponite and serpentine, magnetite, pyrrhotite, pentlandite, dolomite, and Fe‐bearing magnesite. These grains have mineralogy comparable to that of CI chondrites, showing severe aqueous alteration but lacking ferrihydrite and sulfate. These results are similar to previous works on large Ryugu grains. In addition to the major minerals, we also find many minerals that are rare or have not been reported among CI chondrites. Accessory minerals identified are hydroxyapatite, Mg‐Na phosphate, olivine, low‐Ca pyroxene, Mg‐Al spinel, chromite, manganochromite, eskolaite, ilmenite, cubanite, polydymite, transjordanite, schreibersite, calcite, moissanite, and poorly crystalline phyllosilicate. We also show scanning transmission electron microscope and scanning electron microscope compositional maps and images of some space‐weathered grains and severely heated and melted grains. Although our mineralogical results are consistent with that of millimeter‐sized grains, the fine‐grained fraction is best suited to investigate impact‐induced space weathering.
Multimodal correlative imaging and modelling of phosphorus uptake from soil by hyphae of mycorrhizal fungi
Phosphorus (P) is essential for plant growth. Arbuscular mycorrhizal fungi (AMF) aid its uptake by acquiring P from sources distant from roots in return for carbon. Little is known about how AMF colonise soil pore-space, and models of AMF-enhanced P-uptake are poorly validated. We used synchrotron X-ray computed tomography to visualize mycorrhizas in soil and synchrotron X-ray fluorescence/X-ray absorption near edge structure (XRF/XANES) elemental mapping for P, sulphur (S) and aluminium (Al) in combination with modelling. We found that AMF inoculation had a suppressive effect on colonisation by other soil fungi and identified differences in structure and growth rate between hyphae of AMF and nonmycorrhizal fungi. Our results showed that AMF co-locate with areas of high P and low Al, and preferentially associate with organic-type P species over Al-rich inorganic P. We discovered that AMF avoid Al-rich areas as a source of P. Sulphur-rich regions were found to be correlated with higher hyphal density and an increased organic-associated P-pool, whilst oxidized S-species were found close to AMF hyphae. Increased S oxidation close to AMF suggested the observed changes were microbiome-related. Our experimentally-validated model led to an estimate of P-uptake by AMF hyphae that is an order of magnitude lower than rates previously estimated – a result with significant implications for the modelling of plant–soil–AMF interactions.
Characterization of Properties of Laser Powder Bed Fusion 3D-Printed Inconel 718 for Centrifugal Turbomachinery Applications
Here, this paper presents the results of a comprehensive effort to characterize the properties of Inconel 718 produced by a form of laser powder bed fusion (LPBF) additive manufacturing (AM) or 3D-printing, subsequently subjected to hot isostatic pressing (HIP) and heat treatment according to standards F3055-14a and AMS 5663, respectively. Material property data, while broadly available for traditional Inconel 718 presentations (e.g. forgings or castings), is currently lacking for the 3D-printed material. It is expected that while limited in size, the experimental data sets presented provide sufficient information to glean the capability of LPBF Inconel 718. These include: 1) Chemical composition, electron backscatter diffraction (EBSD), and x-ray energy dispersive spectroscopy (XEDS) characterization of 3D-printed material structure; 2) Tensile properties — 0.2% yield stress, ultimate stress, modulus of elasticity, and elongation to failure — based on 108 samples, as functions of temperature and sample print orientation; 3) Creep rupture data including the Larson-Miller parameter, based on 21 samples; and 4) High cycle fatigue data based on 21 samples as a function of temperature. Results are compared to available standards and/or data for forged, cast, and other AM Inconel 718. A key observation of this study, based on the EBSD results, is that while the material appears to approach full recrystallization following heat treatment, there is a detectable fraction of the material that does not fully recrystallize, resulting in a material with mechanical properties (e.g. yield stress, creep rupture) measurably lower than those of forgings, but higher than those of castings.
Atomic layer deposited aluminum nitride for interface protection of lithium germanium thiophosphate solid electrolytes
Lithium germanium thiophosphate (LGPS) is an attractive solid-state electrolyte material due to its exceptionally high ionic conductivity (~1.2 × 10 –2 S cm –1 ), comparable to many organic liquid electrolytes commonly used in batteries. Despite the high conductivity of LGPS, the susceptibility of LGPS to deleterious degradation reactions has impeded its commercial adoption into solid-state batteries. In particular, the poor voltage stability of LGPS with high-voltage cathode or lithium metal potentials often results in dramatically increasing cell impedance during galvanic cycling. Here, we use aluminum nitride (AlN) as a protection layer for Li metal anode, applied directly to the LGPS at 250 °C using plasma-enhanced atomic layer deposition, to enhance cell performance by preventing LGPS-Li metal reactions. We compare the surface chemistry and electrochemical cycling performance of atomic layer-deposited AlN grown using both plasma N 2 and NH 3 precursors. Galvanostatic cycling and electrochemical impedance spectroscopy show that AlN-coated LGPS cells perform better than bare LGPS cells in contact with Li metal anodes, with the AlN able to improve cycling longevity by over a factor of 3 in certain cases. Finally, we utilize x-ray photoelectron spectroscopy (XPS) line scans to highlight the slow room-temperature reactivity between AlN and evaporated lithium metal, and a computational model is built to aid further XPS analysis.
Optical functions of crystalline and amorphous TeO2
The optical functions of crystalline paratellurite (α-TeO2) and amorphous TeO2 were determined using several optical techniques: (1) standard spectroscopic two-modulator generalized ellipsometry (2-MGE) of paratellurite (200–850 nm, 6.2–1.46 eV), (2) near-normal-incidence two-modulator generalized ellipsometry microscopy (2-MGEM) of paratellurite (577 nm, 2.15 eV), (3) Mueller matrix transmission of paratellurite (320–798 nm, 3.87–1.55 eV), and (4) polarized transmission of paratellurite (323.6–334.3 nm, 3.83–3.71 eV). The 2-MGE measurements yielded highly accurate values of the dielectric functions and error estimates from 1.46 to 6.2 eV for both paratellurite and amorphous TeO2, whereas the polarization-dependent transmission yielded more accurate values of the absorption coefficient below the band edge of paratellurite. The 2-MGEM measured the diattenuation of paratellurite, which is related to the birefringence. Mueller matrix transmission measurements of paratellurite of a (001) cut crystal as a function of angle of incidence were used to determine both the birefringence and the rotary power as a function of photon energy.
Performance and characterization of the SPT-3G digital frequency-domain multiplexed readout system using an improved noise and crosstalk model
The third-generation South Pole Telescope camera (SPT-3G) improves upon its predecessor (SPTpol) by an order of magnitude increase in detectors on the focal plane. The technology used to read out and control these detectors, digital frequency-domain multiplexing (DfMUX), is conceptually the same as used for SPTpol, but extended to accommodate more detectors. A nearly 5× expansion in the readout operating bandwidth has enabled the use of this large focal plane, and SPT-3G performance meets the forecasting targets relevant to its science objectives. However, the electrical dynamics of the higher-bandwidth readout differ from predictions based on models of the SPTpol system due to the higher frequencies used and parasitic impedances associated with new cryogenic electronic architecture. To address this, we present an updated derivation for electrical crosstalk in higher-bandwidth DfMUX systems and identify two previously uncharacterized contributions to readout noise, which become dominant at high bias frequency. The updated crosstalk and noise models successfully describe the measured crosstalk and readout noise performance of SPT-3G. These results also suggest specific changes to warm electronics component values, wire-harness properties, and SQUID parameters, to improve the readout system for future experiments using DfMUX, such as the LiteBIRD space telescope.