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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 1,639 records · Page 91

Tiny Device Mimics Human Lung Function

Scientists at Los Alamos National Laboratory are developing a miniature, tissue-engineered artificial lung that mimics the response of the human lung to drugs, toxins and other agents. “We breathe in and out thousands of times every day. And while we have control over what we eat or drink, we don’t always have control over what we breathe in,” said Jennifer Harris of Biosecurity and Public Health at Los Alamos, "and so we’re making this miniature lung to be able to test on actual human cells whether something in the environment, or a drug, is toxic or harmful to us." Nicknamed “PuLMo” for Pulmonary Lung Model (Pulmo is also the Latin word for "lung")the device consists of two major parts, the bronchiolar unit and the alveolar unit—just like the human lung. The units are primarily made from various polymers and are connected by a microfluidic “circuit board” that manages fluid and air flow. “When we build our lung, we not only take into account the aspects of different cell types, the tissues that are involved, we also take into account that a lung is supposed to breathe, so PuLMo actually breathes,” said Pulak Nath of Applied Modern Physics, who leads engineering efforts for the project. The most exciting application of PuLMo is a potentially revolutionary improvement in the reliability of drug-toxicity assessments and the prediction of new pharmaceutical success in humans, according to Harris. The PuLMo may also be designed to mimic lung disease conditions, such as Chronic Obstructive Pulmonary Disease (COPD) and asthma, and may be used to study lung air-flow dynamics to better understand the mechanisms of toxins and drug delivery and the effects of smoking, particularly the less-understood effects of e-cigarettes.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Mapping tree height in complex terrain of northern China using ultra-high-resolution images

Tree height is a key parameter for estimating forest biomass and carbon sequestration. In recent years, notable progress has been made in mapping tree height using satellite imagery. However, existing tree height products show low accuracy in mountainous and complex terrains, and few studies typically addressed tree height estimations in mountain areas. This study examines the Mentougou district of Beijing, China, characterized by complex terrain and mountainous landscapes. We analyzed two methods for estimating tree height: one using only spectral features and another combining spectral features with topographic factors (elevation, slope, aspect). We used 3-m resolution PlanetScope 8-band multispectral imagery, with 710 field-measured individual tree heights averaged to obtain 471 pixel-level tree height values as ground-truth, to develop tree height prediction models using eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Gradient Boosting Machine (GBM) models. The results show that the XGBoost model consistently presented the highest accuracy for both methods evaluated. Specifically, the XGBoost model that combined spectral data with elevation and slope variables with an R² of 0.75 and an RMSE of 2.69 m. Using the XGBoost model, we generated the tree height map for the Mentougou area at 3 m resolution, showing tree heights ranging from 0.5 to 30.4 m, and the model’s prediction error standard deviations ranged from 2.50 to 4.71 m, indicating reliable performance across varied terrain. Additionally, we compared and evaluated the global tree height products, identifying limitations in the accuracy within complex terrains. This study demonstrates the potential for accurately predicting tree heights by combining high-resolution multispectral satellites with a terrain factor modeling approach.

Complex terrain↗

Porous Enzymatic Membrane for Nanotextured Glucose Sweat Sensors with High Stability toward Reliable Noninvasive Health Monitoring

Development of reliable glucose sensors for noninvasive monitoring without interruption or limiting users' mobility is highly desirable, especially for diabetes diagnostics, which requires routine/long-term monitoring. However, their applications are largely limited by the relatively poor stability. In this paper, a porous membrane is synthesized for effective enzyme immobilization and it is robustly anchored to the modified nanotextured electrode solid contacts, so as to realize glucose sensors with significantly enhanced sensing stability and mechanical robustness. To the best of our knowledge, this is the first report of utilizing such nanoporous membranes for electrochemical sensor applications, which eliminates enzyme escape and provides a sufficient surface area for molecular/ion diffusion and interactions, thus ensuring the sustainable catalytic activities of the sensors and generating reliable measureable signals during noninvasive monitoring. The as-assembled nanostructured glucose sensors demonstrate reliable long-term stable monitoring with a minimal response drift for up to 20 h, which delivers a remarkable enhancement. Moreover, they can be integrated into a microfluidic sensing patch for noninvasive sweat glucose monitoring. The as-synthesized nanostructured glucose sensors with remarkable stability can inspire developments of various enzymatic biosensors for reliable noninvasive composition analysis and their ultimate applications in predictive clinical diagnostics, personalized health-care monitoring, and chronic diseases management.

36 MATERIALS SCIENCE↗

Stochastic Reconstruction of Thermal Protection Material Properties from Arc-Jet Experiments

Material response models are used to assess reliability using variances in the bond-line temperature predictions based on uncertainties in trajectory, aerothermal environment, and material properties. A key deficiency in the current approach is that input uncertainties are too often subjective, empirical, or ad-hoc, and are not rigorously linked to the arc-jet test data used to develop the TPS material model. While materials such as PICA are well understood, future missions may require more novel materials such as HEEET where unknown uncertainties have real consequences on the ability to assess reliability. A quantifiable estimate of reliability requires an iterative methodology where the parameters driving the variance in bond-line temperature (for example) are systematically identified. A test campaign to collect data or develop new models can then be identified to reduce those input uncertainties. A Bayesian inference loop defines these connections mathematically, i.e., prior knowledge about uncertainty is updated based on observation. While these concepts are well known (and often applied intuitively in a non-rigorous approach), only recent advances in reduced-order modelling have made them computationally viable methods for engineering. By replacing deterministic inverse methods with stochastic approaches, the hope is new materials proposed for future missions can more rapidly be developed with a greater understanding of the TPS material reliability. Two additional steps for the analysis of arc jet test data are discussed. The first is ability to construct a reduced-order model using material response simulations (Icarus/US3D) of the arc-jet test articles, and the second is the inclusion of this surrogate model in the Bayesian inversion process. Both capabilities will be demonstrated using prior PICA arc-jet test data. The quality of a surrogate model will be investigated and the variances on the calibrated material properties will be compared to our current understanding of the PICA material model.

Material response↗

Effects of Mistuning on the Forced Response of Turbomachinery Rotors

The basic purpose of this research has been to develop a fundamental understanding of the effects of blade-to-blade dissimilarities, or mistuning, on the dynamics of nearly cyclic bladed-disk assemblies. This topic is of importance as mistuning has been shown to increase the forced response amplitudes of some blades significantly, and even to lead to blade failure. Furthermore, the current trend toward high performance propulsion turbomachinery designed for finite service life demands an accurate prediction of system performance and dynamics at the design stage. This objective has been achieved by carrying out the following tasks. First, the investigation of the free and forced responses of representative, yet sufficiently simple blade assembly models that capture all the important characteristics of typical turbomachinery rotors. Second, the development of computational methods that predict the effects of mistuning in a systematic and reliable way, along with the development of a systematic reduced-order modeling procedure for mistuned bladed disks. Third, the application of these findings and the tools developed to an industrial rotor, namely the first stage of turbine blades of the oxidizer turbopump in the space shuttle main rocket engine (SSME). The research supported by NASA has led to the development of a coherent theory for mistuned blade assemblies. The further implementation of these computational tools into the forced response prediction system currently under development in the Structural Dynamics Branch-FREPS-would enable the designer and the analyst to: (1) identify types of blade assemblies highly sensitive to mistuning in various frequency and other parameter ranges and (2) characterize mistuning effects on their forced response by predicting true response amplitudes and fatigue life estimates.

Pierre, Christophe↗

Effects of Mistuning on the Forced Response of Turbomachinery Rotors

The basic purpose of this research has been to develop a fundamental understanding of the effects of blade-to-blade dissimilarities, or mistuning, on the dynamics of nearly cyclic bladed-disk assemblies. This topic is of importance as mistuning has been shown to increase the forced response amplitudes of some blades significantly, and even to lead to blade failure. Furthermore, the current trend toward high performance propulsion turbomachinery designed for finite service life demands an accurate prediction of system performance and dynamics at the design stage. This objective has been achieved by carrying out the following tasks. First, the investigation of the free and forced responses of representative, yet sufficiently simple blade assembly models that capture all the important characteristics of typical turbomachinery rotors. Second, the development of computational methods that predict the effects of mistuning in a systematic and reliable way, along with the development, of a systematic reduced-order modeling procedure for mistuned bladed disks. Third, the application of these findings and the tools developed to an industrial rotor, namely the first stage of turbine blades of the oxidizer turbopump in the space shuttle main rocket engine (SSME). The research supported by NASA has led to the development of a coherent theory for mistuned blade assemblies. The further implementation of these computational tools into the forced response prediction system currently under development in the Structural Dynamics Branch Forced Response Prediction System (FREPS) would enable the designer and the analyst (1) to identify types of blade assemblies highly sensitive to mistuning in various frequency and other parameter ranges and (2), to characterize mistuning effects on their forced response by predicting true response amplitudes and fatigue life estimates.

Stefko, George L.↗

Reliability Analysis in the Presence of Aleatory Uncertainty

This paper proposes a method for modeling a system’s response using data. In contrast to approaches that identify a limit state function, we focus on the case in which not all uncertain parameters affecting the response are observable and the measured response is corrupted by noise. To this end, the system response is not characterized by a limit state function but instead by a Random Predictor Model (RPM) having a nonparametric structure. Consequently, the resulting failure probability is not a scalar but a random variable. This variable accounts for the aleatory contributions of the model-form uncertainty and the measurement noise into the response. Furthermore, we propose a framework that enables trading off the predicted range of failure probabilities resulting from such an analysis with a measure of risk. In this context, risk is the percentage of all predicted outcomes the analyst is willing to ignore. The reliability analysis of an aeroelastic structure subject to flutter is used to illustrate the ideas proposed.

Crespo, L. G.↗

Kinetics of solid–liquid interface motion in molecular dynamics and phase-field models: crystallization of chromium and silicon

The results of molecular dynamics (MD) simulations of the crystallization process in one-component materials and solid solution alloys reveal a complex temperature dependence of the velocity of the crystal–liquid interface featuring an increase up to a maximum at 10–30% undercooling below the equilibrium melting temperature followed by a gradual decrease of the velocity at deeper levels of undercooling. At the qualitative level, such non-monotonous behaviour of the crystallization front velocity is consistent with the diffusion-controlled crystallization process described by the Wilson–Frenkel model, where the almost linear increase of the interface velocity in the vicinity of melting temperature is defined by the growth of the thermodynamic driving force for the phase transformation, while the decrease in atomic mobility with further increase of the undercooling drives the velocity through the maximum and into a gradual decrease at lower temperatures. At the quantitative level, however, the diffusional model fails to describe the results of MD simulations in the whole range of temperatures with a single set of parameters for some of the model materials. The limited ability of the existing theoretical models to adequately describe the MD results is illustrated in the present work for two materials, chromium and silicon. It is also demonstrated that the MD results can be well described by the solution following from the hodograph equation, previously found from the kinetic phase-field model (kinetic PFM) in the sharp interface limit. The ability of the hodograph equation to describe the predictions of MD simulation in the whole range of temperatures is related to the introduction of slow (phase field) and fast (gradient flow) variables into the original kinetic PFM from which the hodograph equation is obtained. The slow phase-field variable is responsible for the description of data at small undercoolings and the fast gradient flow variable accounts for local non-equilibrium effects at high undercoolings. The introduction of these two types of variables makes the solution of the hodograph equation sufficiently flexible for a reliable description of all nonlinearities of the kinetic curves predicted in MD simulations of Cr and Si. This article is part of the theme issue ‘Transport phenomena in complex systems (part 1)’.

Science & Technology - Other Topics↗

Software reliability - Measures and effects in flight critical digital avionics systems

The paper discusses software reliability as it applies particularly to design and evaluation of flight-critical digital avionics systems. Measures of software reliability, measurement methods and reliability (macro-) models are discussed. Recent work assessing their accuracy in predicting software errors in 'fly-by-wire' Newtonian applications is presented. Additional, detailed topics are discussed including software error distributions (e.g. catastrophic vs. noncatastrophic) and the effects of system growth/maturity on reliability improvement. In practical flight-critical digital applications, software reliability improvement is sought through use of parallel, redundant software (i.e. N-version programming) or backup software that can be invoked in the event of (primary) software failure. Achievable reliability levels are however highly sensitive to common-mode specification and programming errors. Recent data correlating these errors with net software reliability are discussed.

Dunn, William R.↗

Mission Reliability Estimation for Repairable Robot Teams

A mission reliability estimation method has been designed to translate mission requirements into choices of robot modules in order to configure a multi-robot team to have high reliability at minimal cost. In order to build cost-effective robot teams for long-term missions, one must be able to compare alternative design paradigms in a principled way by comparing the reliability of different robot models and robot team configurations. Core modules have been created including: a probabilistic module with reliability-cost characteristics, a method for combining the characteristics of multiple modules to determine an overall reliability-cost characteristic, and a method for the generation of legitimate module combinations based on mission specifications and the selection of the best of the resulting combinations from a cost-reliability standpoint. The developed methodology can be used to predict the probability of a mission being completed, given information about the components used to build the robots, as well as information about the mission tasks. In the research for this innovation, sample robot missions were examined and compared to the performance of robot teams with different numbers of robots and different numbers of spare components. Data that a mission designer would need was factored in, such as whether it would be better to have a spare robot versus an equivalent number of spare parts, or if mission cost can be reduced while maintaining reliability using spares. This analytical model was applied to an example robot mission, examining the cost-reliability tradeoffs among different team configurations. Particularly scrutinized were teams using either redundancy (spare robots) or repairability (spare components). Using conservative estimates of the cost-reliability relationship, results show that it is possible to significantly reduce the cost of a robotic mission by using cheaper, lower-reliability components and providing spares. This suggests that the current design paradigm of building a minimal number of highly robust robots may not be the best way to design robots for extended missions.

Trebi-Ollennu, Ashitey↗

Extension of a noninteractive reliability model for ceramic matrix composites

Developments in the processing of high temperature ceramic composites demand innovative and progressive design protocols that adequately predict the thermal and mechanical behavior of these materials. The focus here is the extension of a reliability model for orthotropic ceramic composites. The approach chosen to describe failures leads to a noninteractive formulation of reliability that is phenomenological. This particular criterion, which was constructed using tensorial invariant theory, allows six potential failure modes to emerge.

Duffy, Stephen F.↗

Some practical observations on the accelerated testing of Nickel-Cadmium Cells

A large scale test of 6.0 Ah Nickel-Cadmium Cells conducted at the Naval Weapons Support Center, Crane, Indiana has demonstrated a methodology for predicting battery life based on failure data from cells cycled in an accelerated mode. After examining eight variables used to accelerate failure, it was determined that temperature and depth of discharge were the most reliable and efficient parameters for use in accelerating failure and for predicting life.

Mcdermott, P. P.↗

Probabilistic Prediction of Lifetimes of Ceramic Parts

ANSYS/CARES/PDS is a software system that combines the ANSYS Probabilistic Design System (PDS) software with a modified version of the Ceramics Analysis and Reliability Evaluation of Structures Life (CARES/Life) Version 6.0 software. [A prior version of CARES/Life was reported in Program for Evaluation of Reliability of Ceramic Parts (LEW-16018), NASA Tech Briefs, Vol. 20, No. 3 (March 1996), page 28.] CARES/Life models effects of stochastic strength, slow crack growth, and stress distribution on the overall reliability of a ceramic component. The essence of the enhancement in CARES/Life 6.0 is the capability to predict the probability of failure using results from transient finite-element analysis. ANSYS PDS models the effects of uncertainty in material properties, dimensions, and loading on the stress distribution and deformation. ANSYS/CARES/PDS accounts for the effects of probabilistic strength, probabilistic loads, probabilistic material properties, and probabilistic tolerances on the lifetime and reliability of the component. Even failure probability becomes a stochastic quantity that can be tracked as a response variable. ANSYS/CARES/PDS enables tracking of all stochastic quantities in the design space, thereby enabling more precise probabilistic prediction of lifetimes of ceramic components.

Nemeth, Noel N.↗

Reliability of Strength Testing using the Advanced Resistive Exercise Device and Free Weights

The Advanced Resistive Exercise Device (ARED) was developed for use on the International Space Station as a countermeasure against muscle atrophy and decreased strength. This investigation examined the reliability of one-repetition maximum (1RM) strength testing using ARED and traditional free weight (FW) exercise. Methods: Six males (180.8 +/- 4.3 cm, 83.6 +/- 6.4 kg, 36 +/- 8 y, mean +/- SD) who had not engaged in resistive exercise for at least six months volunteered to participate in this project. Subjects completed four 1RM testing sessions each for FW and ARED (eight total sessions) using a balanced, randomized, crossover design. All testing using one device was completed before progressing to the other. During each session, 1RM was measured for the squat, heel raise, and deadlift exercises. Generalizability (G) and intraclass correlation coefficients (ICC) were calculated for each exercise on each device and were used to predict the number of sessions needed to obtain a reliable 1RM measurement (G . 0.90). Interclass reliability coefficients and Pearson's correlation coefficients (R) also were calculated for the highest 1RM value (1RM9sub peak)) obtained for each exercise on each device to quantify 1RM relationships between devices.

English, Kirk L.↗

Evidence of Kramer extrapolation inaccuracy for predicting high field Nb 3 Sn properties

Future applications requiring high magnetic fields, such as the proposed Future Circular Collider, demand a substantially higher critical current density, J c , at fields ≥16 T than is presently available in any commercial strand, so there is a strong effort to develop new routes to higher J c Nb 3 Sn. As a consequence, evaluating the irreversibility field (H irr ) of any new conductor to ensure reliable performance at these higher magnetic fields becomes essential. To predict the irreversibility field for Nb 3 Sn wires, critical current measurements, I c , are commonly performed in the 12-15 T range and the Kramer extrapolation is used to predict higher field properties. Here, the Kramer extrapolation typically models the contribution only for sparse grain boundary pinning, yet Nb 3 Sn wires rely on a high density of grain boundaries to provide the flux pinning that enables their high critical current density. However, whole-field range VSM measurements up to 30 T recently showed for Nb 3 Sn RRP® wires that the field dependence of the pinning force curve significantly deviates from the typical grain boundary shape, leading to a 1-2 T overestimation of Hirr when extrapolated from the typical mid-field data taken only up to about 15 T. In this work we characterized a variety of both RRP® and PIT Nb 3 Sn wires by transport measurements up to 29 T at the Laboratoire National des Champs Magnétiques Intenses (LNCMI), part of the European Magnetic Field Laboratory in Grenoble, to verify whether or not such overestimation is related to the measurement technique and whether or not it is a common feature across different designs. Indeed we also found that when measured in transport the 12-15 T Kramer extrapolation overestimates the actual H irr in both types of conductor with an inaccuracy of up to 1.6 T, confirming that high field characterization is a necessary tool to evaluate the actual high field performance of each Nb 3 Sn wire.

36 MATERIALS SCIENCE↗

Building a DFT+U machine learning interatomic potential for uranium dioxide

Despite uranium dioxide (UO 2 ) being a widely used nuclear fuel, fuel performance models rely extensively on empirical correlations of material behavior, leveraging the historical operating experience of UO 2 . Mechanistic models that consider an atomistic understanding of the processes governing fuel performance (such as fission gas release and creep) will enable a better description of fuel behavior under non-prototypical conditions such as in new reactor concepts or for modified UO 2 fuel compositions. To this end, molecular dynamics simulation is a powerful tool for rapidly predicting physical properties of proposed fuel candidates. However, the reliability of these simulations depends largely on the accuracy of the atomic forces. Traditionally, these forces are computed using either a classical force field (FF) or density functional theory (DFT). While DFT is relatively accurate, the computational cost is burdensome, especially for f-electron elements, such as actinides. By contrast, classical FFs are computationally efficient but are less accurate. For these reasons, we report a new accurate machine learning interatomic potential (MLIP) for UO 2 that provides high-fidelity reproduction of DFT forces at a similar low cost to classical FFs. We employ an active learning approach that autonomously augments the DFT training data set to iteratively refine the MLIP. To further improve the quality of our predictions, we utilize transfer learning to retrain our MLIP to higher-accuracy DFT+U data. We validate our MLIPs by comparing predicted physical properties (e.g., thermal expansion and elastic properties) with those from existing classical FFs and DFT/DFT+U calculations, as well as with experimental data when available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

An Advanced Microscopic Energy Consumption Model for Automated Vehicle:Development, Calibration, Verification

The automated vehicle (AV) equipped with the Adaptive Cruise Control (ACC) system is expected to reduce the fuel consumption for the intelligent transportation system. This paper presents the Advanced ACC-Micro (AA-Micro) model, a new energy consumption model based on micro trajectory data, calibrated and verified by empirical data. Utilizing a commercial AV equipped with the ACC system as the test platform, experiments were conducted at the Columbus 151 Speedway, capturing data from multiple ACC and Human-Driven (HV) test runs. The calibrated AA-Micro model integrates features from traditional energy consumption models and demonstrates superior goodness of fit, achieving an impressive 90% accuracy in predicting ACC system energy consumption without overfitting. A comprehensive statistical evaluation of the AA-Micro model's applicability and adaptability in predicting energy consumption and vehicle trajectories indicated strong model consistency and reliability for ACC vehicles, evidenced by minimal variance in RMSE values and uniform RSS distributions. Conversely, significant discrepancies were observed when applying the model to HV data, underscoring the necessity for specialized models to accurately predict energy consumption for HV and ACC systems, potentially due to their distinct energy consumption characteristics.

Ma, Ke↗