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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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The JPL Physical Oceanography Distributed Active Archive Center (DAAC) is one of nine centers processing data for the Earth Observation System. As the physical oceanography data center, it archives, processes, and distributes data to researchers from various satellites. Data holdings can be searched through the Global Change Master Directory, and an advanced information management system is now in prototype.
Geospatial resource assessments frequently require timely geospatial data processing that involves large multivariate remote sensing data sets. In particular, for disasters, response requires rapid access to large data volumes, substantial storage space and high performance processing capability. The processing and distribution of this data into usable information products requires a processing pipeline that can efficiently manage the required storage, computing utilities, and data handling requirements. In recent years, with the availability of cloud computing technology, cloud processing platforms have made available a powerful new computing infrastructure resource that can meet this need. To assess the utility of this resource, this project investigates cloud computing platforms for bulk, automated geoprocessing capabilities with respect to data handling and application development requirements. This presentation is of work being conducted by Applied Sciences Program Office at NASA-Stennis Space Center. A prototypical set of image manipulation and transformation processes that incorporate sample Unmanned Airborne System data were developed to create value-added products and tested for implementation on the "cloud". This project outlines the steps involved in creating and testing of open source software developed process code on a local prototype platform, and then transitioning this code with associated environment requirements into an analogous, but memory and processor enhanced cloud platform. A data processing cloud was used to store both standard digital camera panchromatic and multi-band image data, which were subsequently subjected to standard image processing functions such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), band stacking, reprojection, and other similar type data processes. Cloud infrastructure service providers were evaluated by taking these locally tested processing functions, and then applying them to a given cloud-enabled infrastructure to assesses and compare environment setup options and enabled technologies. This project reviews findings that were observed when cloud platforms were evaluated for bulk geoprocessing capabilities based on data handling and application development requirements.
The method of potential solutions of Fokker-Planck equations is used to develop a transport equation for the joint probability of N stochastic variables with Lochner’s generalized Dirichlet distribution as its asymptotic solution. Individual samples of a discrete ensemble, obtained from the system of stochastic differential equations, equivalent to the Fokker-Planck equation developed here, satisfy a unit-sum constraint at all times and ensure a bounded sample space, similarly to the process developed in for the Dirichlet distribution. Consequently, the generalized Dirichlet diffusion process may be used to represent realizations of a fluctuating ensemble of N variables subject to a conservation principle. Compared to the Dirichlet distribution and process, the additional parameters of the generalized Dirichlet distribution allow a more general class of physical processes to be modeled with a more general covariance matrix.
Growing demand for electricity in buildings is creating new opportunities and challenges for electric and natural gas planning. To date, electric and gas planning processes have largely been siloed, and only a few jurisdictions have started to examine how these two processes could be linked together. The report explores the current state of electric and gas system planning, and the benefits and challenges of integrating these planning processes. We frame the characteristics of an integrated electric and gas distribution planning process and identify indicators of progress toward the characteristics.
Radiation therapy depends on predictably and reliably delivering dose to tumors and sparing normal tissues. Protons with kinetic energy of a few hundred MeV can selectively deposit dose to deep seated tumors without an exit dose, unlike x-rays. The better dose distribution is attributed to a phenomenon known as the Bragg peak. The Bragg peak is due to relatively high energy deposition within a given distance or high Linear Energy Transfer (LET). In addition, biological response to radiation depends on the dose, dose rate, and localized energy deposition patterns or LET. At present, the LET can only be measured at a given fixed point and the LET spatial distribution can only be inferred from calculations. The goal of this study is to develop and test a method to measure LET over extended areas. Traditionally, radiochromic films are used to measure dose distribution but not for LET distribution. We report the first use of these films for measuring the spatial distribution of the LET deposited by protons. The radiochromic film sensitivity diminishes for large LET. A mathematical model correlating the film sensitivity and LET is presented to justify relating LET and radiochromic film relative sensitivity. Protons were directed parallel to radiochromic film sandwiched between solid water slabs. This study proposes the scaled-normalized difference (SND) between the Treatment Planning system (TPS) and measured dose as the metric describing the LET. The SND is correlated with a Monte Carlo (MC) calculation of the LET spatial distribution for a large range of SNDs. A polynomial fit between the SND and MC LET is generated for protons having a single range of 20 cm with narrow Bragg peak. Coefficients from these fitted polynomial fits were applied to measured proton dose distributions with a variety of ranges. An identical procedure was applied to the protons deposited from Spread Out Bragg Peak and modulated by 5 cm. Gamma analysis is a method for comparing the calculated LET with the LET measured using radiochromic film at the pixel level over extended areas. Failure rates using gamma analysis are calculated for areas in the dose distribution using parameters of 25% of MC LET and 3 mm. The processed dose distributions find 5%–10% failure rates for the narrow 12.5 and 15 cm proton ranges and 10%–15% for proton ranges of 15, 17.5, and 20 cm and modulated by 5 cm. It is found through gamma analysis that the measured proton energy deposition in radiochromic film and TPS can be used to determine LET. This modified film dosimetry provides an experimental areal LET measurement that can verify MC calculations, support LET point measurements, possibly enhance biologically based proton treatment planning, and determine the polymerization process within the radiochromic film.
The main astronomical source of r-process elements has not yet been identified. One plausible site is neutron star mergers (NSMs), but from the perspective of the Galactic chemical evolution, it has been pointed out that NSMs cannot reproduce the observed r-process abundance distribution of metal-poor stars at [Fe/H]<−3. Recently, Tsujimoto and Shigeyama pointed out that NSM ejecta can spread into a much larger volume than ejecta from a supernova. We re-examine the enrichment of r-process elements by NSMs considering this difference in propagation using the chemical evolution model under the hierarchical galaxy formation. The observed r-process enhanced stars around [Fe/H]∼−3 are reproduced if the star formation efficiency is lower for low-mass galaxies under a realistic delay-time distribution for NSMs. We show that a significant fraction of NSM ejecta escape from its host proto-galaxy to pollute intergalactic matter and other proto-galaxies. The propagation of r-process elements over proto-galaxies changes the abundance distribution at [Fe/H]<−3 and obtains distribution compatible with observations of the Milky Way halo stars. In particular, the pre-enrichment of intergalactic medium explains the observed scarcity of extremely metal-poor stars without Ba and abundance distribution of r-process elements at [Fe/H]≲−3.5.
We describe an implementation to solve Poisson's equation for an isolated system on a unigrid mesh using FFTs. The method solves the equation globally on mesh blocks distributed across multiple processes on a distributed-memory parallel computer. Test results to demonstrate the convergence and scaling properties of the implementation are presented. The solver is offered to interested users as the library PSPFFT.
The development of the algorithms for data processing and distribution, the circuitry, and the performance of the Seasat low rate data processing system are reviewed. The system controls data from the radar altimeter, scatterometer, microwave radiometer, and the visible and IR radiometer for independent transmission of each instrument's readings. The downlink operates at 25 kb/sec, and a yearlong program of geophysical evaluation proceeded shortly after launch, allowing on-line engineering evaluation and alteration of the control algorithms in the system. Some data is preformatted for immediate distribution and storage in archival quality. A catalog and abstracts are provided to users allowing a RAM search from remote terminals for historical conditions. Procedures for verifying and altering the algorithms are detailed.
Industrial data analytics methods play a central role in improving energy storage performance and efficiency, impacting the future of electrified transportation and renewable electricity generation. However, significant challenges hinder the large-scale deployment of batteries. Conventional methods rely on centralized collection and processing of fleet-level data, leading to database size issues and privacy concerns due to potential data breaches. To enable scalable deployment of battery management systems, this article proposes a federated battery diagnosis and prognosis model, which distributes the processing of battery standard current-voltage-time-usage data in a privacy-preserving manner. Instead of transferring the raw data, this approach communicates only the locally processed parameters, thus reducing communication load and preserving data confidentiality. The federated model offers a paradigm shift in battery health management through privacy-preserving distributed methods for battery data processing and lifetime prediction, ensuring the reliable and sustainable deployment of lithium-ion batteries in a rapidly evolving world.
Stochastic modelling is critical for studying many biochemical processes in a cell, in particular when some reacting species have low population numbers. For many such cellular processes the spatial distribution of the molecular species plays a key role. The evolution of spatially heterogeneous biochemical systems with some species in low amounts is accurately described by the mesoscopic model of the Reaction-Diffusion Master Equation. The Inhomogeneous Stochastic Simulation Algorithm provides an exact strategy to numerically solve this model, but it is computationally very expensive on realistic applications. We propose a novel adaptive time-stepping scheme for the tau-leaping method for approximating the solution of the Reaction-Diffusion Master Equation. This technique combines effective strategies for variable time-stepping with path preservation to reduce the computational cost, while maintaining the desired accuracy. The numerical tests on various examples arising in applications show the improved efficiency achieved by the new adaptive method.
Measurements of radial velocity variations from the spectroscopic monitoring of stars and their companions are essential for a broad swath of astrophysics; these measurements provide access to the fundamental physical properties that dictate all phases of stellar evolution and facilitate the quantitative study of planetary systems. The conversion of those measurements into both constraints on the orbital architecture and individual component spectra can be a serious challenge, however, especially for extreme flux ratio systems and observations with relatively low sensitivity. Gaussian processes define sampling distributions of flexible, continuous functions that are well-motivated for modeling stellar spectra, enabling proficient searches for companion lines in time-series spectra. We introduce a new technique for spectral disentangling, where the posterior distributions of the orbital parameters and intrinsic, rest-frame stellar spectra are explored simultaneously without needing to invoke cross-correlation templates. To demonstrate its potential, this technique is deployed on red-optical time-series spectra of the mid-M-dwarf binary LP661-13. We report orbital parameters with improved precision compared to traditional radial velocity analysis and successfully reconstruct the primary and secondary spectra. We discuss potential applications for other stellar and exoplanet radial velocity techniques and extensions to time-variable spectra. The code used in this analysis is freely available as an open-source Python package.
We demonstrate that a nonthermal distribution of particles described by a kappa distribution can be accurately approximated by a weighted sum of Maxwell–Boltzmann distributions. We apply this method to modeling collision processes in kappa-distribution plasmas, with a particular focus on atomic processes important for solar physics. The relevant collision process rate coefficients are generated by summing appropriately weighted Maxwellian rate coefficients. This method reproduces the rate coefficients for a kappa distribution to an estimated accuracy of better than 3%. This is equal to or better than the accuracy of rate coefficients generated using “reverse-engineering” methods, which attempt to extract the needed cross sections from the published Maxwellian rate coefficient data and then reconvolve the extracted cross sections with the desired kappa distribution. Our approach of summing Maxwellian rate coefficients is easy to implement using existing spectral analysis software. Moreover, the weights in the sum of the Maxwell–Boltzmann distribution rate coefficients can be found for any value of the parameter κ, thereby enabling one to model plasmas with a time-varying κ. Tabulated Maxwellian fitting parameters are given for specific values of κ from 1.7 to 100. We also provide polynomial fits to these parameters over this entire range. Several applications of our technique are presented, including the plasma equilibrium charge state distribution (CSD), predicting line ratios, modeling the influence of electron impact multiple ionization on the equilibrium CSD of kappa-distribution plasmas, and calculating the time-varying CSD of plasmas during a solar flare.
Solar cells based on n-type multicrystalline silicon (mc-Si) wafers are a promising path to reduce the cost per kWh of photovoltaics; however, the full potential of the material and how to optimally process it are still unknown. Process optimization requires knowledge of the response of the metal-silicide precipitate distribution to processing, which has yet to be directly measured and quantified. To supply this missing piece, we use synchrotron-based micro-X-ray fluorescence (μ-XRF) to quantitatively map >250 metal-rich particles in n-type mc-Si wafers before and after phosphorus diffusion gettering (PDG). We find that 820°C PDG is sufficient to remove precipitates of fast-diffusing impurities and that 920°C PDG can eliminate precipitated Fe to below the detection limit of μ-XRF. Thus, the evolution of precipitated metal impurities during PDG is observed to be similar for n- and p-type mc-Si, an observation consistent with calculations of the driving forces for precipitate dissolution and segregation gettering. Measurements show that minority-carrier lifetime increases with increasing precipitate dissolution from 820°C to 880°C PDG, and that the lifetime after PDG at 920°C is between the lifetimes achieved after 820°C and 880°C PDG.
This paper presents an optimization framework for sequential reconfiguration using an assortment of switching devices and repair process in distribution system restoration. Compared to existing studies, this paper considers types, capabilities and operational limits of different switching devices, making it applicable in practice. We develop a novel multi-phase method to find the optimal sequential operation of various switching devices and repair faulted areas. We consider circuit breakers, reclosers, sectionalizers, load breaker switches, and fuses. The switching operation problem is decomposed into two mixed-integer linear programming (MILP) subproblems. The first subproblem determines the optimal network topology and estimates the number of steps to reach that topology, while the second subproblem generates a sequence of switching operations to coordinate the switches. For repairing the faults, we design an MILP model that dispatches repair crews to clear faults and replace melted fuses. After clearing a fault, we update the topology of the network by generating a new sequence of switching operations, and the process continues until all faults are cleared. To improve the computational efficiency, a network reduction algorithm is developed to group line sections, such that only switchable sections are present in the reduced network. The proposed method is validated on the IEEE 123-bus and 8500-bus systems.
Methods, media and systems for managing a distributed application running in a plurality of digital processing devices are provided. In some embodiments, a method includes running one or more processes associated with the distributed application in virtualized operating system environments on a plurality of digital processing devices, suspending the one or more processes, and saving network state information relating to network connections among the one or more processes. The method further include storing process information relating to the one or more processes, recreating the network connections using the saved network state information, and restarting the one or more processes using the stored process information.
Purpose: To investigate a Greedy Reassignment algorithm in order to mitigate the effects of low weight spots in proton pencil beam scanning (PBS) treatment plans. Methods: To convert a plan from the treatment planning system’s (TPS) to a deliverable plan, post processing methods can be used to adjust the spot maps to meets the minimum MU constraint. Existing methods include: deleting low weight spots (Cut method), or rounding spots with weight above/below half the limit up/down to the limit/zero (Round method). An alternative method called Greedy Reassignment was developed in this work in which the lowest weight spot in the field was removed and its weight reassigned equally among its nearest neighbors. The process was repeated with the next lowest weight spot until all spots in the field were above the MU constraint. The algorithm performance was evaluated using plans collected from 190 patients (496 fields) treated at our facility. The evaluation criteria were the γ-index pass rate comparing the pre-processed and post-processed dose distributions. A planning metric was further developed to predict the impact of post-processing on treatment plans for various treatment planning, machine, and dose tolerance parameters. Results: For fields with a gamma pass rate of 90±1%, the metric has a standard deviation equal to 18% of the centroid value. This showed that the metric and γ-index pass rate are correlated for the Greedy Reassignment algorithm. Using a 3rd order polynomial fit to the data, the Greedy Reassignment method had 1.8 times better metric at 90% pass rate compared to other post-processing methods. Conclusion: We showed that the Greedy Reassignment method yields deliverable plans that are closest to the optimized-without-MU-constraint plan from the TPS. The metric developed in this work could help design the minimum MU threshold with the goal of keeping the γ-index pass rate above an acceptable value.
Representing uncertainty in model inputs often means finding a balance between uncertainty and physical reality. Developing wide distributions may seem conservative in principle, but this approach may lead to unrealistic model results. Characterizing the current state of knowledge of stochastic inputs presents many challenges, especially if the data available are limited or have limited relevance to the site. Relationships among these inputs may also be important to represent but are typically complex or difficult to define. Often special adjustments must be made to account for reduced credibility in particular data. If parameters are strongly related to one another, a correlation structure may be developed for input to the model. Other techniques such as regression models may be used to incorporate relationships between the information available and the desired parameters. The process of developing distributions must consider details of the model in terms of what the distribution is meant to represent. This paper uses the example of a probabilistic fate and transport model for hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in the regional aquifer at Los Alamos National Laboratory (LANL). For many parameters, a single draw is applied to all space and time over which the model is run, for a single iteration. This simplification is often made for many reasons, and can often be beneficial, but also adds additional complexity in the distribution development process. Defining the distributional goals as they relate to the modeling process is an important step, which should take place prior to evaluation of the data. Usually, the distributions developed are meant to characterize the average value of the parameter over the spatial and temporal domain of the model. Distribution development requires consideration of many sources of information on the parameter where available, ideally from multiple references. Examples of different sources include data from different references but also from different conditions, measurement methods, or experimental types. Depending on these conditions and the reliability or relevance of particular references, different sources of data may each contribute valuable information but have varying relevance to the site. In these cases, weighting data unequally is a useful way to incorporate this information. As an example, aqueous dispersivity data are available for a variety of rock types. Only a few values are available for the desired rock type, and this is not enough to develop a distribution. Therefore, dispersivity values from other rock types are included in distribution development but are down-weighted such that the best data have the most influence on the distribution developed. In another case, K{sub d} distributions in the model are meant to represent a known composition of multiple soil types. Data from these materials are weighted accordingly to develop a distribution for the weighted average K{sub d} across soil types. Other cases include varying reliability of different sources, and weighting data according to the confidence in these sources. In some cases, input parameters are correlated with one another. An example is advective porosity, which is positively correlated with total porosity and must be less than total porosity. Paired data with both parameters must exist to discern the relationship between the two parameters and if it is necessary to build a correlation structure into the model. In general, correlated parameters may be represented in the model by a multivariate distribution, or perhaps more desirably, capturing the correlation within the developed distributions which may be treated as independent from one another. In the example of porosity, this can be done by transforming advective porosity into a proportion of total porosity which may be drawn independently from the distribution of total porosity. This paper explores how complex data and correlations can be incorporated to meet the distributional goals of the model, using the RDX regional model as a detailed example. (authors)
The problems of combustion instability in an annular combustion chamber are investigated. A modified Galerkin method was used to produce a set of modal amplitude equations from the general nonlinear partial differential acoustic wave equation. From these modal amplitude equations, the two variable perturbation method was used to develop a set of approximate equations of a given order of magnitude. These equations were modeled to show the effects of velocity sensitive combustion instabilities by evaluating the effects of certain parameters in the given set of equations. By evaluating these effects, parameters which cause instabilities to occur in the combustion chamber can be ascertained. It is assumed that in the annular combustion chamber, the liquid propellants are injected uniformly across the injector face, the combustion processes are distributed throughout the combustion chamber, and that no time delay occurs in the combustion processes.