Search NASA⌕ Search

SEARCH · Search NASA

Results for “random processes”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Improved renormalization group computation of likelihood functions for cosmological data sets

Evaluation of likelihood functions for cosmological large scale structure data sets (including CMB, galaxy redshift surveys, etc.) naturally involves marginalization, i.e., integration, over an unknown underlying random signal field. Recently, I showed how a renormalization group method can be used to carry out this integration efficiently by first integrating out the smallest scale structure, i.e., localized structure on the scale of differences between nearby data cells, then combining adjacent cells in a coarse graining step, then repeating this process over and over until all scales have been integrated. Here I extend the formulation in several ways in order to reduce the prefactor on the method's linear scaling with data set size. The key improvement is showing how to integrate out the difference between specific adjacent cells before summing them in the coarse graining step, compared to the original formulation in which small-scale fluctuations were integrated more generally. I suggest some other improvements in details of the scheme, including showing how to perform the integration around a maximum likelihood estimate for the underlying random field. In the end, an accurate likelihood computation for a million-cell Gaussian test data set runs in two minutes on my laptop, with room for further optimization and straightforward parallelization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Synthesis and thermomechanical characteristics of zwitterionic poly(arylene ether sulfone) copolymers

Charge-containing polymeric materials have been widely studied for a range of applications. The fundamental relationships between the charge species, charge density, and the microscale morphology of charge-containing polymers are critical for defining the application in which they may be used. In this work, a series of thermoplastic poly(arylene ether sulfone) (PAES) copolymers with controllable sulfobetaine charge contents (0~100 mol%) and high molecular weights (Mw ~ 65 kDa) were prepared. All the zwitterionic PAESs synthesized showed thermal stability up to 250 °C. DSC and tensile tests showed a decreased T g and mechanical strength with the incorporation of increasing charge density, which suggests a plasticization effect by the charged group. Electron microscopy and X-ray scattering data confirmed the absence of microphase separation in the ion-containing random copolymer system, which corroborated with the observation of a single T g for all zwitterionic copolymers studied. Furthermore, the relative hydrophilicity of the samples was evaluated by water uptake measurements, which revealed that the water uptake of the zwitterionic PAESs can reach up to 64% for the 72 mol% zwitterion copolymer while the free-standing film in the wet state still maintains a Young's modulus of 185 MPa. Here, the thermoplastic charge-containing copolymers in this work demonstrated potential for applications such as coatings or water purification membranes, in which balancing hydrophilicity, processibility, and thermomechanical performance are important.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Anti-survivin single-domain antibodies derived from an artificial library including three synthetic random regions by in vitro selection using cDNA display

Highlights: • Single-domain antibodies can be selected with an artificial DNA library synthesized chemically. • cDNA display method is useful for in vitro selection from a large single-domain antibodies library (e.g. 1013). • A Brevibacillus protein expression system is suitable for synthesis of functional single-domain antibodies. Single-domain antibodies (variable domain of the heavy chain of a heavy chain antibody; VHH) are promising reagents for therapeutics and diagnostics because of their stability, cost-effective production and material workability as a small antibody. Currently, general acquisition of a VHH using immunization of camelids is inconvenient from the standpoint of animal protection, cost and the process is time-consuming. Thus, a straightforward and efficient method for screening VHHs against a target molecule is required. In this study, we examined whether in vitro selection of a VHH against a target protein could be performed by a cDNA display method with an artificial VHH library that had the three complementarity-determining regions (CDRs) randomized by chemical synthesis. The results revealed that a particular VHH against survivin, which is a member of the inhibitor of apoptosis family, was selected with affinity in the range of 10{sup −7} to 10{sup −8} M. The in vitro selection of a VHH using cDNA display with an artificial synthesized library without animal immunization was shown to be effective for rapid and inexpensive screening of VHHs against a target protein.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Layered Electrode Architectures Enabled by Holey Graphene Hosts

Battery electrodes are typically prepared using a slurry-based method, where high boiling point organic solvents are typically used with a polymer binder to disperse active electrode materials and conductive carbon additives followed by casting into current collector sheets. In this process, the use of organic solvents are environmentally hazardous, energy intensive, and time-consuming, while polymeric binders add weight and can undergo parasitic reactions during battery cycling. Here we discuss the use of a unique conductive carbon nanomaterial, namely holey graphene, as a multifunctional host that can be processed via a facile, solvent-free, binder-free, cold pressing method to prepare electrodes with unique architectures. This approach can accommodate a broad range of battery chemistries, such as lithium oxygen/carbon dioxide (Li-O2/CO2), lithium sulfur/selenium (Li-S/Se), and various lithium ion chemistries. The successful use of dry, cold compression allows the convenient modification of electrode mass loading with unique architectures containing active materials randomly mixed or layered with single or multiple layers of one or more active materials. In the conventional slurry-based electrode fabrication method, it is difficult, if not impossible, to achieve such layered architectures. In particular, the layered electrode architectures from the dry, cold press approach are easy to fabricate without significantly sacrificing device performance, and are amenable to continuous roll-to-roll dry processing. In addition, these architectures offer unprecedented perspectives on the battery material loading vs. utilization efficiency, as well as enabling unique characterization opportunities to advance mechanistic understanding of various battery chemistries. Potential applications of this unique electrode fabrication approach for solid-state batteries will also be discussed.

holey graphene, batteries, electrode architecture,↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗
DOE OSTI2018

Ischemic flap survival improvement by composition-selective fat grafting with novel adipose tissue derived product - stromal vascular fraction gel

Highlights: • SVF-gel is a minimal manipulated fat product accommodating SVF cells. • SVF-gel improves ischemic skin flap survival with comparable efficacy as cell therapy. • Pro-angiogenic effect's partly from direct SVF-gel incorporation. Flap necrosis due to insufficient blood supply is a common postoperative complication in random pattern flaps. Stem cell therapies have emerged as promising biologics for tissue ischemia. A novel fat derived product, stromal vascular fraction gel (SVF-gel), can be prepared with lipoaspirate through simple mechanical processing, removing only the lipid content. SVF-gel enriches adipose-derived stem cells and potentially beneficial for flap necrosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Real-Time In Situ Observation of CsPbBr 3 Perovskite Nanoplatelets Transforming into Nanosheets

The manipulation of nano-objects through heating is an effective strategy for inducing structural modifications and therefore changing the optoelectronic properties of semiconducting materials. Despite its potential, the underlying mechanism of the structural transformations remains elusive, largely due to the challenges associated with their in situ observations. To address these issues, we synthesize temperature-sensitive CsPbBr 3 perovskite nanoplatelets and investigate their structural evolution at the nanoscale using in situ heating transmission electron microscopy. We observe the morphological changes that start from the self-assembly of the nanoplatelets into ribbons on a substrate. We identify several paths of merging nanoplates within ribbons that ultimately lead to the formation of nanosheets dispersed randomly on the substrate. These observations are supported by molecular dynamics simulations. We correlate the various paths for merging to the random orientation of the initial ribbons along with the ligand mobility (especially from the edges of the nanoplatelets). This leads to the preferential growth of individual nanosheets and the merging of neighboring ones. These processes enable the creation of structures with tunable emission, ranging from blue to green, all from a single material. Our real-time observations of the transformation of perovskite 2D nanocrystals reveal a route to achieve large-area nanosheets by controlling the initial orientation of the self-assembled objects with potential for large-scale applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1971

A frequency division multiplex technique for transmitting commands

Reliable random access multiplexer is controlled and operated over two twisted-pair telephone lines at distances of 5.6 to 8.0 km with control signals of less than 10 volts rms. Technique adapts to any process or environmental control system where data source is necessary.

Brey, H.↗
NASA NTRS1978

Radiative transfer theory for active remote sensing of half-space random media

In active remote sensing of low-loss and scattering-dominant areas, the effect of volume scattering can be modeled as a half-space random medium with lateral and vertical fluctuations. Correlation functions are assumed to be Gaussian laterally and exponential vertically. A radiative transfer theory is developed in this paper to calculate backscattering cross sections. An iteration process is used. When calculated to first order in albedo, the result gives the single-scattering effects and confirms previous work. Calculated to second order, it is found that the backscattered power exhibits depolarization effects. This important consequence is studied numerically by illustrating the backscattering cross sections as a function of incidence angles and frequencies.

Tsang, L.↗
NASA NTRS1978

The REH theory of protein and nucleic acid divergence - A retrospective update

The random evolutionary hits (REH) theory of evolutionary divergence, originally proposed in 1972, is restated with attention to certain aspects of the theory that have caused confusion. The theory assumes that natural selection and stochastic processes interact and that natural selection restricts those codon sites which may fix mutations. The predicted total number of fixed nucleotide replacements agrees with data for cytochrome c, a-hemoglobin, beta-hemoglobin, and myoglobin. The restatement analyzes the magnitude of possible sources of errors and simplifies calculational methodology by supplying polynomial expressions to replace tables and graphs.

Holmquist, R.↗
NASA NTRS1985

Turbulent solutions of equations of fluid motion

Some turbulent solutions of the unaveraged Navier-Stokes equations (equations of fluid motion) are reviewed. Those equations are solved numerically in order to study the nonlinear physics of incompressible turbulent flow. The three components of the mean-square velocity fluctuations are initially equal for the conditions chosen. The resulting solutions show characteristics of turbulence, such as the linear and nonlinear excitation of small-scale fluctuations. For the stronger fluctuations the initially nonrandom flow develops into an apparently random turbulence. The cases considered include turbulence that is statistically homogeneous or inhomogeneous and isotropic or anisotropic. A statistically steady-state turbulence is obtained by using a spatially periodic body force. Various turbulence processes, including the transfer of energy between eddy sizes and between directional components and the production, dissipation, and spatial diffusion of turbulence, are considered. It is concluded that the physical processes occurring in turbulence can be profitably studied numerically.

Deissler, R. G.↗
NASA NTRS1987

Experiments in fault tolerant software reliability

The reliability of voting was evaluated in a fault-tolerant software system for small output spaces. The effectiveness of the back-to-back testing process was investigated. Version 3.0 of the RSDIMU-ATS, a semi-automated test bed for certification testing of RSDIMU software, was prepared and distributed. Software reliability estimation methods based on non-random sampling are being studied. The investigation of existing fault-tolerance models was continued and formulation of new models was initiated.

Mcallister, David F.↗
NASA NTRS1989

Tidal disruption of open clusters in their parent molecular clouds

A simple model of tidal encounters has been applied to the problem of an open cluster in a clumpy molecular cloud. The parameters of the clumps are taken from the Blitz, Stark, and Long (1988) catalog of clumps in the Rosette molecular cloud. Encounters are modeled as impulsive, rectilinear collisions between Plummer spheres, but the tidal approximation is not invoked. Mass and binding energy changes during an encounter are computed by considering the velocity impulses given to individual stars in a random realization of a Plummer sphere. Mean rates of mass and binding energy loss are then computed by integrating over many encounters. Self-similar evolutionary calculations using these rates indicate that the disruption process is most sensitive to the cluster radius and relatively insensitive to cluster mass. The calculations indicate that clusters which are born in a cloud similar to the Rosette with a cluster radius greater than about 2.5 pc will not survive long enough to leave the cloud. The majority of clusters, however, have smaller radii and will survive the passage through their parent cloud.

Long, Kevin↗
NASA NTRS1996

Uncertainty and Intelligence in Computational Stochastic Mechanics

Classical structural reliability assessment techniques are based on precise and crisp (sharp) definitions of failure and non-failure (survival) of a structure in meeting a set of strength, function and serviceability criteria. These definitions are provided in the form of performance functions and limit state equations. Thus, the criteria provide a dichotomous definition of what real physical situations represent, in the form of abrupt change from structural survival to failure. However, based on observing the failure and survival of real structures according to the serviceability and strength criteria, the transition from a survival state to a failure state and from serviceability criteria to strength criteria are continuous and gradual rather than crisp and abrupt. That is, an entire spectrum of damage or failure levels (grades) is observed during the transition to total collapse. In the process, serviceability criteria are gradually violated with monotonically increasing level of violation, and progressively lead into the strength criteria violation. Classical structural reliability methods correctly and adequately include the ambiguity sources of uncertainty (physical randomness, statistical and modeling uncertainty) by varying amounts. However, they are unable to adequately incorporate the presence of a damage spectrum, and do not consider in their mathematical framework any sources of uncertainty of the vagueness type. Vagueness can be attributed to sources of fuzziness, unclearness, indistinctiveness, sharplessness and grayness; whereas ambiguity can be attributed to nonspecificity, one-to-many relations, variety, generality, diversity and divergence. Using the nomenclature of structural reliability, vagueness and ambiguity can be accounted for in the form of realistic delineation of structural damage based on subjective judgment of engineers. For situations that require decisions under uncertainty with cost/benefit objectives, the risk of failure should depend on the underlying level of damage and the uncertainties associated with its definition. A mathematical model for structural reliability assessment that includes both ambiguity and vagueness types of uncertainty was suggested to result in the likelihood of failure over a damage spectrum. The resulting structural reliability estimates properly represent the continuous transition from serviceability to strength limit states over the ultimate time exposure of the structure. In this section, a structural reliability assessment method based on a fuzzy definition of failure is suggested to meet these practical needs. A failure definition can be developed to indicate the relationship between failure level and structural response. In this fuzzy model, a subjective index is introduced to represent all levels of damage (or failure). This index can be interpreted as either a measure of failure level or a measure of a degree of belief in the occurrence of some performance condition (e.g., failure). The index allows expressing the transition state between complete survival and complete failure for some structural response based on subjective evaluation and judgment.

Ayyub, Bilal M.↗
NASA NTRS2013

A Satellite Mortality Study to Support Space Systems Lifetime Prediction

Estimating the operational lifetime of satellites and spacecraft is a complex process. Operational lifetime can differ from mission design lifetime for a variety of reasons. Unexpected mortality can occur due to human errors in design and fabrication, to human errors in launch and operations, to random anomalies of hardware and software or even satellite function degradation or technology change, leading to unrealized economic or mission return. This study focuses on data collection of public information using, for the first time, a large, publically available dataset, and preliminary analysis of satellite lifetimes, both operational lifetime and design lifetime. The objective of this study is the illustration of the relationship of design life to actual lifetime for some representative classes of satellites and spacecraft. First, a Weibull and Exponential lifetime analysis comparison is performed on the ratio of mission operating lifetime to design life, accounting for terminated and ongoing missions. Next a Kaplan-Meier survivor function, standard practice for clinical trials analysis, is estimated from operating lifetime. Bootstrap resampling is used to provide uncertainty estimates of selected survival probabilities. This study highlights the need for more detailed databases and engineering reliability models of satellite lifetime that include satellite systems and subsystems, operations procedures and environmental characteristics to support the design of complex, multi-generation, long-lived space systems in Earth orbit.

Fox, George↗
NASA NTRS1993

Application of the Active Pixel Sensor Concept to Guidance and Navigation

Charge-coupled devices (CCDs) have been used extensively in the past in star trackers and fine guidance systems. A new technology, the active pixel sensor, is a possible successor to CCDs. This technology potentially features the same sensitivity and performance of the CCD with additional improvements. These improvements include random access capability, easy window-of-interest readout, non-destructive readout for signal-to-noise improvement, high radiation tolerance, simplified clocking voltages, and easy integration with other on-chip signal processing circuitry. The state-of-the-art of this emerging technology and its potential application to guidance and navigation systems is discussed.

Eisenman, Allan R.↗