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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18

The EUSO Mission

The Extreme Universe Space Observatory (EUSO) experiment will use the Earth's atmosphere viewed from space as a detector of extreme energy cosmic rays (EECRs). EUSO s collecting power will exceed previous experiments by more than a factor of 10, making it possible to extend the cosmic ray spectrum beyond 100 EeV with high statistics. This will make it possible to investigate in detail the spectrum beyond the point were the Greisen-Zatsepin-Kuzmin effect is expected to strongly attenuate the proton flux and even beyond the energy where cosmic ray nuclei should be photo-disintegrated by interactions with the universal microwave background. EUSO makes its measurements by detecting the extensive air showers (EASs) that result from the interactions of the EECRs. Looking down from its birth on the International Space Station though wide-angle optics, EUSO records a high-speed movie clip of passage of each EAS though the atmosphere with the light from nitrogen fluorescence and Cherenkov light reflected from the EAS landing point. These movie clips are analyzed to determine the EECR arrival direction and energy. The data also allow neutrino-induced EASs to be discriminated from nuclear-induced showers.

Adams, J. H.↗

STS-111/Endeavour/ISS UF2 Pre-Launch Activities: Launch with Playbacks

This video of the preflight preparations for and launch of Space Shuttle Endeavour on STS-111 begins with a view of Endeavour on the launch pad. Additional launch pad views leading up to liftoff are interspersed with footage from the Firing Room at Kennedy Space Center, the crew's prelaunch activities, and inspection of the crew members in the White Room before boarding Endeavour. The crew is introduced by a narrator during the preflight banquet and suiting up, and a later clip shows them departing to the launch site. The crew consists of Commander Kenneth Cockrell, Pilot Paul Lockhart, Mission Specialists Philippe Perrin and Franklin Chang-Diaz, and the Expedition 5 crew of the International Space Station (ISS) (Commander Valery Korzun and Flight Engineers Peggy Whitsun and Sergei Treschev). The nozzles on Endeavour's Space Shuttle Main Engine (SSME) are swiveled before liftoff, and the launch is shown past the separation of the solid rocket boosters. After a brief clip from the Mission Control Center at Johnson Space Center, the following launch replays are shown: Beach Tracker, VAB, Pad A, Tower 1, UCS-15, Grandstand, Cocoa Beach DOAMS, Playalinda DOAMS, UCS-23, and OTV-070.

Source record↗

Comparison of Node-Centered and Cell-Centered Unstructured Finite-Volume Discretizations: Viscous Fluxes

Discretization of the viscous terms in current finite-volume unstructured-grid schemes are compared using node-centered and cell-centered approaches in two dimensions. Accuracy and complexity are studied for four nominally second-order accurate schemes: a node-centered scheme and three cell-centered schemes - a node-averaging scheme and two schemes with nearest-neighbor and adaptive compact stencils for least-square face gradient reconstruction. The grids considered range from structured (regular) grids to irregular grids composed of arbitrary mixtures of triangles and quadrilaterals, including random perturbations of the grid points to bring out the worst possible behavior of the solution. Two classes of tests are considered. The first class of tests involves smooth manufactured solutions on both isotropic and highly anisotropic grids with discontinuous metrics, typical of those encountered in grid adaptation. The second class concerns solutions and grids varying strongly anisotropically over a curved body, typical of those encountered in high-Reynolds number turbulent flow simulations. Tests from the first class indicate the face least-square methods, the node-averaging method without clipping, and the node-centered method demonstrate second-order convergence of discretization errors with very similar accuracies per degree of freedom. The tests of the second class are more discriminating. The node-centered scheme is always second order with an accuracy and complexity in linearization comparable to the best of the cell-centered schemes. In comparison, the cell-centered node-averaging schemes may degenerate on mixed grids, have a higher complexity in linearization, and can fail to converge to the exact solution when clipping of the node-averaged values is used. The cell-centered schemes using least-square face gradient reconstruction have more compact stencils with a complexity similar to that of the node-centered scheme. For simulations on highly anisotropic curved grids, the least-square methods have to be amended either by introducing a local mapping based on a distance function commonly available in practical schemes or modifying the scheme stencil to reflect the direction of strong coupling. The major conclusion is that accuracies of the node centered and the best cell-centered schemes are comparable at equivalent number of degrees of freedom.

Diskin, Boris↗

Development of Head Injury Assessment Reference Values Based on NASA Injury Modeling

NASA is developing a new capsule-based, crewed vehicle that will land in the ocean, and the space agency desires to reduce the risk of injury from impact during these landings. Because landing impact occurs for each flight and the crew might need to perform egress tasks, current injury assessment reference values (IARV) were deemed insufficient. Because NASCAR occupant restraint systems are more effective than the systems used to determine the current IARVs and are similar to NASA s proposed restraint system, an analysis of NASCAR impacts was performed to develop new IARVs that may be more relevant to NASA s context of vehicle landing operations. Head IARVs associated with race car impacts were investigated by completing a detailed analysis of all of the 2002-2008 NASCAR impact data. Specific inclusion and exclusion criteria were used to select 4071 impacts from the 4015 recorder files provided (each file could contain multiple impact events). Of the 4071 accepted impacts, 274 were selected for numerical simulation using a custom NASCAR restraint system and Humanetics Hybrid-III 50th percentile numerical dummy model in LS-DYNA. Injury had occurred in 32 of the 274 selected impacts, and 27 of those injuries involved the head. A majority of the head injuries were mild concussions with or without brief loss of consciousness. The 242 non-injury impacts were randomly selected and representative of the range of crash dynamics present in the total set of 4071 impacts. Head dynamics data (head translational acceleration, translational change in velocity, rotational acceleration, rotational velocity, HIC-15, HIC-36, and the Head 3ms clip) were filtered according to SAE J211 specifications and then transformed to a log scale. The probability of head injury was estimated using a separate logistic regression analysis for each log-transformed predictor candidate. Using the log transformation constrains the estimated probability of injury to become negligible as IARVs approach zero. For the parameters head translational acceleration, head translational velocity change, head rotational acceleration, HIC-15, and HIC-36, conservative values (in the lower 95% confidence interval) that gave rise to a 5% risk of any injury occurring were estimated as 40.0 G, 7.9 m/s, 2200 rad/s2, 98.4, and 77.4 respectively. Because NASA is interested in the consequence of any particular injury on the ability of the crew to perform egress tasks, the head injuries that occurred in the NASCAR dataset were classified according to a NASA-developed scale (Classes I - III) for operationally relevant injuries, which classifies injuries on the basis of their operational significance. Additional analysis of the data was performed to determine the probability of each injury class occurring, and this was estimated using an ordered probit model. For head translational acceleration, head translational velocity change, head rotational acceleration, head rotational velocity, HIC-36, and head 3ms clip, conservative values of IARVs that produced a 5% risk of Class II injury were estimated as 50.7 G, 9.5 m/s, 2863 rad/s2, 11.0 rad/s, 30.3, and 46.4 G respectively. The results indicate that head IARVs developed from the NASCAR dataset may be useful to protect crews during landing impact.

Somers, Jeffrey T.↗

Chamber for Growing and Observing Fungi

A chamber has been designed to enable growth and observation of microcolonies of fungi in isolation from the external environment. Unlike prior fungus-growing apparatuses, this chamber makes it possible to examine a fungus culture without disrupting it. Partly resembling a small picture frame, the chamber includes a metal plate having a rectangular through-thethickness opening with recesses for a top and a bottom cover glass, an inlet for air, and an inlet for water. The bottom cover glass is put in place and held there by clips, then a block of nutrient medium and a moisture pad are placed in the opening. The block is inoculated, then the top cover glass is put in place and held there by clips. Once growth is evident, the chamber can be sealed with tape. Little (if any) water evaporates past the edges of the cover glasses, and, hence there is little (if any) need to add water. A microscope can be used to observe the culture through either cover glass. Because the culture is sealed in the chamber, it is safe to examine the culture without risking contamination. The chamber can be sterilized and reused.

Pierson, Duane L.↗

The Effect of Drive Signal Limiting on High Cycle Fatigue Life Analysis

It is common practice to assume a Gaussian distribution of both the input acceleration and the response when modeling random vibration tests. In the laboratory, however, shaker controllers often limit the drive signal to prevent high amplitude peaks. The high amplitudes may either be truncated at a given level (socalled brick wall limiting or abrupt clipping), or compressed (soft limiting), resulting in drive signals which are no longer Gaussian. The paper first introduces several methods for limiting a drive signal, including brick wall limiting and compression. The limited signal is then passed through a linear time-invariant system representing a device under test. High cycle fatigue life predictions are subsequently made using spectral fatigue and rainflow cycle counting schemes. The life predictions are compared with those obtained from unclipped input signals. Some guidelines are provided to help the test engineer decide how clipping should be applied under different test scenarios.

Kihm, Frederic↗

Threaded Fastener Positive Anti-Rotation Locking Device

An apparatus includes a shaft, a device, a fastener, and an anti-rotation clip. The shaft is configured for rotation. The device is mounted on, and surrounds at least a portion of, the shaft, and has first and second protrusions that are formed on one side and are spaced apart to define a tab space. The fastener is rotationally mounted relative to the shaft, and includes a tab slot formed in its outer surface that extends radially inwardly and is disposed radially inwardly of the tab space. The anti-rotation clip includes a main body portion and a head portion that has a first tab portion and a second tab portion. At least a portion the main body portion is disposed between the device and the fastener, the first tab is disposed in the tab space, and the second tab is disposed in the tab slot.

Hartman, Timm E.↗

Novel Estimation of Pilot Performance Characteristics

Two mechanisms internal to the pilot that affect performance during a tracking task are: 1) Pilot equalization (i.e. lead/lag); and 2) Pilot gain (i.e. sensitivity to the error signal). For some applications McRuer's Crossover Model can be used to anticipate what equalization will be employed to control a vehicle's dynamics. McRuer also established approximate time delays associated with different types of equalization - the more cognitive processing that is required due to equalization difficulty, the larger the time delay. However, the Crossover Model does not predict what the pilot gain will be. A nonlinear pilot control technique, observed and coined by the authors as 'amplitude clipping', is shown to improve stability, performance, and reduce workload when employed with vehicle dynamics that require high lead compensation by the pilot. Combining linear and nonlinear methods a novel approach is used to measure the pilot control parameters when amplitude clipping is present, allowing precise measurement in real time of key pilot control parameters. Based on the results of an experiment which was designed to probe workload primary drivers, a method is developed that estimates pilot spare capacity from readily observable measures and is tested for generality using multi-axis flight data. This paper documents the initial steps to developing a novel, simple objective metric for assessing pilot workload and its variation over time across a wide variety of tasks. Additionally, it offers a tangible, easily implementable methodology for anticipating a pilot's operating parameters and workload, and an effective design tool. The model shows promise in being able to precisely predict the actual pilot settings and workload, and observed tolerance of pilot parameter variation over the course of operation. Finally, an approach is proposed for generating Cooper-Harper ratings based on the workload and parameter estimation methodology.

Human pilot models↗

Robust Metric-Aligned Quad-Dominant Meshing Using L(sub p) Centroidal Voronoi Tessellation

We introduce a meshing algorithm that can be used to both generate and adapt meshes for bounded domains in an anisotropic manner. This is particularly beneficial when anisotropic flow features like shock waves or contact discontinuities are present in the computational domain. The algorithm presented in this paper is based upon meshing under the imposed Riemannian metric tensor, which controls the orientation and size of the mesh elements. In this way there is no need for user intervention to recognize these features. We demonstrate that the method indeed aligns the elements with the underlying metric and produces right-angled simplices that can be recombined into quadrilateral elements. The aim is to eventually incorporate this meshing strategy in the monolithic high-order spectral element solver that is currently being developed at NASA Ames. This paper has two main contributions: First, we demonstrate that we can generate quad-dominant metric-aligned meshes for bounded domains using a generalized form of L(sub p)-Centroidal Voronoi Tessellation (L(sub p)-CVT). Unlike previous works, we do not rely on a background mesh and discretize the bounded domain in a hierarchical way by first discretizing the boundaries and then the volume using the underlying metric. Second, we present an alternative for clipping the Voronoi cells on the boundary, which is common practice in CVT-based meshing algorithms, by reconstructing the Voronoi cells using the defined metric field. In this way we avoid the geometrical complexity of the clipping procedure and we show that we evaluate the energy and its gradient correctly. We show that the reconstruction of the computational domain is consistent with the Lloyds’ algorithm that is used to compute the L(sub p)-CVT.

Centroidal↗

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. This was expected to accelerate development and provide more cost-effective, time-saving solutions. This work was selected for a NASA Crowdsourcing project through an agency-wide solicitation. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation and an execution crowdsourcing platform partner to solicit framework developments from external contenders. NASA provided contenders with video clips of spacesuits and simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy (weighted combination of scoring metrics). Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA simulation environments such as the Neutral Buoyancy Lab (NBL). However, 3D joint identification is less reliable when parts of the suit were obstructed in the image. After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗

Novel Crash Sled with a Translating Support Mass

Background: A novel crash sled has been developed with a translating support, incorporating transducers that allow multiple methods of measuring energy absorption to fully characterize the dynamic crush response of composite components. Objective: The main goal of the current investigation was to demonstrate functionality, repeatability, and accuracy of crush testing using a crash sled with a translating support mass. Methods: A semi-automated algorithm for data reduction was developed based on impact mechanics principles. A preliminary set of tests was initially conducted using aluminum honeycomb specimens with a specified stable crushing force to quantify the accuracy and repeatability of the crush data. Following the success of these tests, triaxially-braided fiber-reinforced polymer (FRP) specimens were evaluated. Results: Crush tests with the aluminum honeycomb specimens showed excellent outcomes for all three specimens. These data provided close agreement with cumulative energy absorption between individual instruments and stable crushing forces at expected values. For the FRP specimens, specific energy absorption (SEA) and force-displacement curves were successfully measured; however, data from the translating support mass accelerometer was excluded from the dataset due to clipping. The SEA of the corrugated specimens was greater than the SEA for the C-channel specimens at both test speeds. Conclusions: The crash sled functionality was verified, the specimen geometry was found to contribute more to SEA than the impact speed in the speed range tested, and the support mass accelerometer will be upgraded to prevent clipping in future tests.

Dynamic Crush↗

Machine-learning Solution for Automatic Spacesuit Motion Recognition and Measurement from Conventional Video

Extravehicular Activity (EVA) spacesuits exhibit unique movement patterns due to their design characteristics. Mobility assessments using traditional motion capture systems are cost prohibitive and not feasible for some training conditions (e.g., simulated lunar outdoor terrain). This paper aims to present the ongoing development of machine learning solutions to quantify suit motions from conventional videos without special sensors or hardware. Preliminary work into this field was promising but given the fast growth in deep/machine learning technologies, external expertise was sought from open-source communities. Partnerships were formed with the NASA JSC Center of Excellence for Collaborative Innovation (CoCEI) and an execution crowdsourcing platform partner to solicit machine learning framework developments from external contenders. NASA provided contenders with images and video clips of spacesuits with simultaneously measured motion capture data during EVA simulation tasks. The contenders used this data to train and develop generalized algorithms to predict motions. At the end of the crowdsourcing event, the top five solutions were selected from 250 submissions. Each submission was tested and scored using video clips not previously disclosed to the contenders. The weighted scoring metrics measured how well the algorithm detected the suit shape, the 2D suit joint detection accuracy, and 3D joint detection accuracy. The winning solution was able to achieve roughly 85% prediction accuracy. Overall, the algorithms could efficiently detect various types of spacesuits and motions across different EVA environments such as the NASA Active Response Gravity Offload System (ARGOS). After continued improvements and validation, the fully developed system will enable EVA stakeholders to quantify suit kinematic patterns, which can help optimize suit, hardware, and task designs.

Linh Vu↗

Video Summarization Using Deep Action Recognition Features and Robust Principal Components Analysis

In an instance where desired pre-defined actions, behaviors, or other categories are known a priori, various video classification and recognition models can be trained to discover those classifications and their location within the video. Absent that information, one might still be tasked with identifying interesting portions within a video, a process which—if done manually—is onerous and time-consuming as it requires manual inspection of the video itself. Recognizing high-level interesting segments within a whole video has been a general area of interest due to the ubiquity of video data. However the size of the data makes storage, retrieval, and inspection of large collections of videos cumbersome. This problem motivates the task of generating shortened clips highlighting the primary content of a video, relieving the burden of having to watch the entire video. This paper presents an unsupervised method of creating shortened clips of videos, enabling the rapid review of the most interesting content within a video. Our method uses features extracted from pre-trained action recognition models as input to online moving window robust principal component analysis to generate summaries. The procedure is tested on a publicly available video summarization dataset and demonstrates comparable performance to state-of-the-art in an un-augmented setting while requiring no training.

Claborne, Daniel M.↗

Distribution Function Instead of Steady-State Assumption in Time-Series Simulation

The quasi-steady-state assumption in time-series simulation is inadequate to model phenomena of interest to energy system integration such as inverter clipping, sell-back of excess electricity to a utility and utility hosting capacity, and battery throughput. Researchers are working on stochastic modeling, higher-resolution time series data, and machine learning approaches to address this need. This poster describes a distribution function that allows a maximum value, minimum value, and shape to the curve that can vary within each time-step based only on data inputs at the resolution of that time step (eg. Hourly). The distribution function is not used to synthesize high-resolution data, rather scalar integrals are used to calculate the quantities of interest within each time step. The form of the distribution function shows significant reduction in error when compared to 1 minute data and commercial software employing the distribution function (HOMER Pro version 14.3 and higher) shows a much improved estimate of inverter clipping in an example.

battery↗

Non-permeable substrate carrier for electroplating

One embodiment relates to a substrate carrier for use in electroplating a plurality of substrates. The substrate carrier comprises a non-conductive carrier body on which the substrates are to be held. Electrically-conductive lines are embedded within the carrier body, and a plurality of contact clips are coupled to the electrically-conductive lines embedded within the carrier body. The contact clips hold the substrates in place and electrically couple the substrates to the electrically-conductive lines. The non-conductive carrier body is continuous so as to be impermeable to flow of electroplating solution through the non-conductive carrier body. Other embodiments, aspects and features are also disclosed.

42 ENGINEERING↗

Macroalgae farming including attaching seaweed to a line

An apparatus for attaching vegetative propagules to a longline that may include a clip portion having a curved area sized to receive a stem of a vegetative propagule and having an opening to the curved area smaller than the curved area to secure the vegetative propagule in the curved area; a tapered barrel that is inserted into a longline; and a rigid connection by a tower between the clip portion and the tapered barrel. Also disclosed is a macroalgae farm including the apparatus for attaching vegetative propagules to a longline.

09 BIOMASS FUELS↗

Probabilistic Solar Power Forecasting Using Bayesian Model Averaging

There is rising interest in probabilistic forecasting to mitigate risks from solar power uncertainty, but the numerical weather prediction (NWP) ensembles readily available to system operators are often biased and underdispersed. We propose a Bayesian model averaging (BMA) post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead timescale. BMA is a kernel dressing technique for NWP ensembles in which the forecast is a weighted sum of member-specific probability density functions. We tailor BMA for utility-scale PV forecasting by modeling power clipping at the AC inverter rating and advance the theory of BMA with a new beta kernel parameterization that accommodates theoretical constraints not previously addressed. BMA is demonstrated for a case study of 11 utility-scale PV plants in Texas, forecasting at hourly resolution for the complete year 2018. BMA's mixture-model approach mitigates underdispersion of the raw ensemble to significantly improve forecast calibration, while consistently outperforming an ensemble model output statistics (EMOS) parametric approach from the literature. At 4-hour lead time, the BMA post-processing achieves continuous ranked probability skill scores of 2--36% over the raw ensemble, with consistent performance at multiple lead times suitable for power system operations.

14 SOLAR ENERGY↗

Concept-based Analysis of Neural Networks via Vision-Language Models

The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very challenging due to the difficulty of expressing formal specifications for vision tasks and the lack of efficient verification procedures. In this paper, we propose to leverage emerging multimodal, vision-language, foundation models (VLMs) as a lens through which we can reason about vision models. VLMs have been trained on a large body of images accompanied by their textual description, and are thus implicitly aware of high-level, human-understandable concepts describing the images. We describe a logical specification language Con spec designed to facilitate writing specifications in terms of these concepts. To define and formally check Con spec specifications, we build a map between the internal representations of a given vision model and a VLM, leading to an efficient verification procedure of natural-language properties for vision models. We demonstrate our techniques on a ResNet-based classifier trained on the RIVAL-10 dataset using CLIP as the multimodal model.

Large Vision Language Models↗