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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Conditional Tree Reduction in the ADAPT Framework

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. A DDET may be orders of magnitude larger than a traditional event tree for the same initiating event, as more of the uncertainty space is likely to be explored and the tree does not require manual assembly. Because of the size, manual inspection of the entire DDET is often infeasible. This problem may be approached from the front by reducing the number of sampled branching values or by online pruning of the tree, thus reducing the total number of branches. Utilizing post-processing techniques to draw actionable insights via clustering has also been proposed. One post-processing tool to reduce the DDET according to user-defined rules has been recently implemented in the Analysis of Dynamic Accident Progression Trees (ADAPT) DDET driver code. The tool creates a 'slice' of the DDET comprised of the set of branches that meet a set of user-input rules, for immediate visualization or for further calculations conditional on the rules being met. This extension is expected to ease analysis of the results of large and complex DDETs, leading to greater applicability of DDETs in PRA. It should be emphasized that this tool is a visualization aide and does not impact the theoretical basis of the DDET approach. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Analysis of dislocation configurations in SiC crystals through X-ray topography aided by ray tracing simulations

Silicon carbide as a wide bandgap semiconductor is of great research interest for its widespread deployment in a range of electronic and optoelectronic devices, particularly in power electronics. However, defects in silicon carbide crystals are still major concerns that is hampering the development of high-performance devices. X-ray topography, particularly using the synchrotron beam has been instrumental in characterizing and analyzing defect configurations in silicon carbide crystals to optimize crystal growth as well as understand the effect of defects on device performance. Here, in recent years, the use of ray-tracing simulation technique based on the orientation contrast mechanism to simulate contrast of defects observed on actual X-ray topographs has proven to be an effective approach to investigate the nature of crystallographic defects in various semiconductors. This review discusses the principle of ray-tracing simulation and its application and modifications to incorporate the effects of surface relaxation and photoelectric absorption to better simulate different dislocations observed in 4H–SiC as well as 6H–SiC crystals of various orientations. The adaptation to weak beam topography and plane wave topography is also discussed. The application of ray-tracing simulation in dislocation characterization of silicon carbide of different polytypes is systematically reviewed including different types of dislocations observed in both off-axis wafers and axial-sliced samples through synchrotron X-ray topography under various beam conditions, recording geometries and reflections. The result of ray-tracing simulation is further utilized in other studies including the investigation of effective penetration depth of all types of dislocations lying on the basal plane on grazing-incidence X-ray topography.

Engineering↗
DOE OSTI2023

Using low-coverage whole genome sequencing (genome skimming) to delineate three introgressed species of buffalofish ( Ictiobus )

Consumption of buffalofish has been sporadically associated with Haff disease-like illnesses involving sudden onset muscle pain and weakness due to skeletal muscle rhabdomyolysis, but determination of precisely which species are associated with these illnesses has been impeded by a lack of species-specific DNA-based markers. Here, three closely related species of buffalofish native to the Mississippi River Basin (Ictiobus bubalus, Ictiobus cyprinellus and Ictiobus niger) that have previously proven genetically indistinguishable using both mitochondrial and nuclear single-locus sequencing were reliably discriminated using low-coverage whole genome sequencing (‘genome skimming’). Using 44 specimens representing the three species collected from the mid/upper (Missouri) and lower (Louisiana) regions of the species’ native ranges, the SISRS (Site Identification from Short Read Sequences) bioinformatics pipeline was adapted to (1) identify over 620Mbp of putatively homologous nuclear sequence data and (2) isolate over 140,000 single-nucleotide polymorphisms (SNPs) that supported accurate species delimitation, all without the use of a reference genome or annotation data. These sites were used to classify Ictiobus spp. samples with genome-skim data, along with a larger set (n = 67) where ultraconserved elements (UCEs) were sequenced. Analyses of whole mitochondrial data revealed more limited signal. Nearly all samples matched their purported species based on morphologic identification, but two Missouri samples morphologically identified as I. niger grouped with samples of I. bubalus, albeit with significant enrichment of I. niger SNPs. To our knowledge this is the first report of a DNA-based tool to reliably discriminate these three morphologically distinct species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Conformal Hierarchical Simulation-Based Inference with Local Validity

Trustworthy and interpretable uncertainty quantification is a long-standing challenge in artificial intelligence. Simulation-based inference (SBI) comprises a broad swath of approaches for estimating latent parameters with uncertainties. Although flexible neural density estimators in SBI can be remark- ably expressive capturing highly structured, high-dimensional posteriors their credible regions can be badly mis-calibrated and are often only accompanied by heuristic coverage checks. We present the first SBI framework that delivers finite-sample local valid coverage guarantees that hold in the neighborhood of each observation. Our framework can couple any off-the-shelf hierarchical SBI engine with a confor- mal Bayesian post-processing step that operates on the posterior predictive density. A kernel-weighted conformity score adapts the conformal quantile to the local geometry of the data, yielding prediction sets that are simultaneously (i) marginally calibrated, (ii) locally valid, and (iii) hierarchical, handling global and observation-specific parameters in a single pass. Through experiments on synthetic data and benchmarks from neuroscience and physics, we show that our approach attains 1 − α coverage, where prior SBI methods under- or over-cover. Our approach also maintains a competitive, credible set size with minimal computational overhead. Finally, our approach can be used to make predictions on real data and give valid credible regions modulo weight-initialization-based model mis-specification.

Trivedi, Shubhendu [Fermilab]↗
DOE OSTI2021

Tailored computational approaches to interrogate heavy element chemistry and structure in condensed phase

In this chapter we are presenting a brief review of the challenges encountered in the study of 4f and 5f block elements in the condensed phase. Their recovery, use in molten salt reactors and other interesting applications necessitate the use of molecular dynamics and large-scale models that take into account both the electronic structure and relativistic corrections. Sampling the multitude of electronic and atomic configurational states is at the heart of reliable predictions of structure, reactivity, dynamics and transport of heavy metals in complex environments. We present three examples that combine lanthanide elements with large scale models: i) our recent developments of a versatile adaptive learning method that enables global optimization in high dimensional spaces, ii) results of computed pKa values of lanthanide aqua complexes, and iii) structure and computed EXAFS of heavy elements in molten salts. The latter two employ our recently-optimized lanthanide pseudo-potentials and companion basis sets for condensed phase ab initio molecular dynamics.

Nguyen, Manh Thuong↗
DOE OSTI2023

Motion Planning Algorithms for Safety and Quantum Computing Efficiency

Motion planning remains a fundamental problem in robotics. Sampling-based algorithms use randomization to allow efficient solutions to this complex problem. As mobile robots and autonomous vehicles become more prevalent in everyday life, motion planning must be applied to increasingly challenging scenarios. Safety has become a paramount concern in motion planning for ensuring robotic applications enrich human lives. To date, many motion planning techniques to increase safety in the face of uncertain and dynamic environments have been developed. This dissertation first addresses distributional safety of Rapidly-Exploring Random Trees (RRT) through our algorithm W-Safe RRT. To acknowledge distributional uncertainty and poor modeling, W-Safe RRT uses the Wasserstein metric to provide a probabilistic bound on the distributional distance between a robot and obstacles. Human-interpretable environmental agent classification allows online safety margin adaptation. We propose and analyze an integrating region method for online classification that increases actor labeling accuracy based on behavioral feature values when compared to state of the art methods. The method performs class assignments based on local maximum likelihood in a created behavioral feature-space, allowing a notion of classification uncertainty. Model-based methods with safety guarantees can quickly become computationally in tractable, especially with multiple agents, higher dimensions, and plentiful unknowns. Sampling based algorithms have been parallelized for computation with multi-core computers and GPUs. We consider the use of quantum algorithms and computers for sampling-based motion planning for the first time. Quantum computing performs operations on superpositions of states and can solve certain problems much more efficiently than classical computers, but introduces previously unseen challenges. With Quantum-RRT, we recast the motion planning problem into a database-search structure and use Quantum Amplitude Amplification to find reachable states in the database with a quadratic performance increase over classical methods. We address two error sources with this method: quantum measurement and quantum oracle errors. We then extend this method to Parallel Quantum-RRT, which uses a manager-worker architecture with multiple parallel quantum workers to increase database search efficiency. We compare algorithm architectures and characterize probabilities of multiple workers finding solutions. Lastly, we test in simulation the quantum algorithms against classical versions in a wide variety of scenarios, concluding that a similar parallelization improvement is to be found in the quantum case as was found in the parallelization of classical RRT.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Crystal structures and phase transitions in Sr doped Ba{sub 2}InTaO{sub 6} perovskites

The crystal structures of the double perovskites of the type Ba{sub 2−x}Sr{sub x}InTaO{sub 6} have been studied by synchrotron X-ray and neutron powder diffraction methods. The structures are based on ordered array of the tilted InO{sub 6} and TaO{sub 6} octahedra, with the Ba/Sr cations being completely disordered. The structure evolves from cubic to monoclinic Fm3{sup ¯}m→{sup x∼0.6}I4/m→{sup x∼1.2}I2/m→{sup x∼1.6}P2{sub 1}/n. The changes in the symmetry induced by replacing the Ba with Sr have been identified by a symmetry-mode analysis. Variable temperature S-XRD studies of selected examples have shown that heating the samples induces the same sequence of transitions observed in the composition dependent studies. - Graphical abstract: The evolution of the structure of the B-site ordered perovskites has been determined by high resolution powder diffraction. The nature of the transitions are probed by examination of the symmetry-adapted modes. Display Omitted - Highlights: • Synthesis of Ba{sub 2−x}Sr{sub x}InTaO{sub 6} cation ordered perovskites. • Synchrotron X-ray and neutron diffraction structural studies of Ba{sub 2−x}Sr{sub x}InTaO{sub 6}. • Phase transitions in Ba{sub 2−x}Sr{sub x}InTaO{sub 6}. • Symmetry-adapted mode analysis of phase transitions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Facile scalable synthesis of Co{sub 3}O{sub 4}/carbon nanotube hybrids as superior anode materials for lithium-ion batteries

Graphical abstract: Co{sub 3}O{sub 4}/MWCNT hybrids were synthesized via strong ultra-sonication assisted shaking processes. The resultant samples as anode electrode display enhanced cycling performance and rate capability compared with pure Co{sub 3}O{sub 4} particle. - Highlights: • Co{sub 3}O{sub 4}/MWCNT hybrids were synthesized via ultra-sonication assisted shaking process. • The resulting Co{sub 3}O{sub 4} nanoparticles are highly dispersed onto MWCNT network backbone. • Co{sub 3}O{sub 4}/MWCNT hybrid displays highly enhanced lithium storage properties. • The present synthetic approach is facile, controllable, and scalable. - Abstract: In this report, Co{sub 3}O{sub 4}/multiple-wall carbon nanotube (MWCNT) hybrid materials were synthesized via strong ultrasonication-assisted shaking and magnetic stirring processes. The prepared samples were well characterized by utilizing powder X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Raman spectroscopy techniques. Results indicated that the resulting Co{sub 3}O{sub 4} nanoparticles were highly dispersed in the MWCNT network backbone and further form Co{sub 3}O{sub 4}/MWCNT hybrid materials. The obtained Co{sub 3}O{sub 4}/MWCNT hybrids can be employed as anode electrode in Lithium-ion batteries and deliver as high as discharge capacity of 1250 mA h g{sup −1} at a current density of 0.2 C, additionally, 81% of the discharge capacity for sample 2 with 20 wt.% MWCNT loading could be retained after 70 cycles, which could be associated with the specific hybrid structure of the electrode as well as the addition of MWCNT. Most importantly, the present synthetic approach is facile, controllable, and scalable, which allowing it more easily adapted to prepare other hybrid materials with specific architectures.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Community structure of arbuscular mycorrhizal fungi associated with Robinia pseudoacacia in uncontaminated and heavy metal contaminated soils

The significance of arbuscular mycorrhizal fungi (AMF) in soil remediation has been widely recognized because of their ability to promote plant growth and increase phytoremediation efficiency in heavy metal (HM) polluted soils by improving plant nutrient absorption and by influencing the fate of the metals in the plant and soil. However, the symbiotic functions of AMF in remediation of polluted soils depend on plant–fungus–soil combinations and are greatly influenced by environmental conditions. To better understand the adaptation of plants and the related mycorrhizae to extreme environmental conditions, AMF colonization, spore density and community structure were analyzed in roots or rhizosphere soils of Robinia pseudoacacia. Mycorrhization was compared between uncontaminated soil and heavy metal contaminated soil from a lead–zinc mining region of northwest China. Samples were analyzed by restriction fragment length polymorphism (RFLP) screening with AMF-specific primers (NS31 and AM1), and sequencing of rRNA small subunit (SSU). The phylogenetic analysis revealed 28 AMF group types, including six AMF families: Glomeraceae, Claroideoglomeraceae, Diversisporaceae, Acaulosporaceae, Pacisporaceae, and Gigasporaceae. Of all AMF group types, six (21%) were detected based on spore samples alone, four (14%) based on root samples alone, and five (18%) based on samples from root, soil and spore. Glo9 (Rhizophagus intraradices), Glo17 (Funneliformis mosseae) and Acau3 (Acaulospora sp.) were the three most abundant AMF group types in the current study. Soil Pb and Zn concentrations, pH, organic matter content, and phosphorus levels all showed significant correlations with the AMF species compositions in root and soil samples. Overall, the uncontaminated sites had higher species diversity than sites with heavy metal contamination. This study highlights the effects of different soil chemical parameters on AMF colonization, spore density and community structure in contaminated and uncontaminated sites. The tolerant AMF species isolated and identified from this study have potential for application in phytoremediation of heavy metal contaminated areas.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

SU-E-T-653: Quantifying Inter-Fraction Range Uncertainty for Input Into Robust Proton Planning

Purpose: To provide a method for quantifying centre- and tumour site-specific population range uncertainty due to inter-fraction motion for use within robust proton planning and plan analysis. PTV-based and probabilistic/robust plans depend upon accurate knowledge of geometric and range uncertainties. If the uncertainty model is inaccurate, both types of plan, PTV or probabilistic, could produce under-dosing of the target and/or overdosing of OAR. Methods: Daily mega-voltage volumetric imaging data from previously treated radiotherapy patients has been used to investigate inter-fraction changes to water equivalent path-length (WEPL). Daily image-guidance scans were carried out for each patient and corrected for changes in CTV position (using rigid transformations). An effective depth algorithm was used to determine residual range changes, after corrections were applied, by comparing WEPL within the CTV at each fraction for several beam angles. This method has been demonstrated for nine prostate and seven head and neck. Results: Inter-fraction range changes for the head and neck patient sample were Σ=3.39mm, σ=4.72mm and overall mean =−1.82mm; for prostate Σ=5.64mm, σ=5.91mm and overall mean =0.98 mm. The choice of beam angle for head and neck did not affect the inter-fraction range error significantly; however this was not the same for prostate. Greater range changes were seen using a lateral beam compared to an anterior beam for prostate due to relative motion of the prostate and femoral heads. Conclusion: A method has been developed to quantify population range changes due to inter-fraction motion. The results show a non-zero mean for these samples, as well as significant Σ and σ, highlighting the importance of quantifying uncertainties and use of image guidance. Such information can be used in robust optimisation algorithms and for analysing treatment plan robustness. It may also help in establishing beam start conditions at planning and for establishing adaptive planning protocols.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Poplar Interactome: Project Final Report

The feedstock plant Poplar has many advantages over traditional crop plants. Not only Poplar needs low energy input and off season storage as compared to feedstocks such as corn, in the winter season Poplar biomass is stored on the stem/trunk, and Poplar plantations serve as large carbon sink. A key constraint to the expansion of cellulosic bioenergy sources such as in Poplar however, is the negative consequence of converting land use from food crops to energy crops. Therefore in order for Poplar to become a viable energy crop it needs to be grown mostly on marginal land unsuitable agricultural crops. For this we need a better understanding of abiotic stress and adaptation response in poplar. In the process we expected to find new and existing poplar genes and their function that respond to sustain abiotic stress. We carried out an extensive gene expression study on the control untreated and stress (drought, salinity, cold and heat) treated poplar plants. The samples were collected from the stem, leaf and root tissues. The RNA of protein coding genes and regulatory smallRNA genes were sequenced generating more than a billion reads. This is the first such known study in Poplar plants. These were used for quantification and genomic analysis to identify stress responsive genes in poplar. Based on the quantification and genomic analysis, a select set of genes were studied for gene-gene interactions to find their association to stress response. The data was also used to find novel stress responsive genes in poplar that were previously not identified in the Poplar reference genome. The data is made available to the public through the national and international genomic data archives.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Neurogenesis Deep Learning: Extending deep networks to accommodate new classes

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing – data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Improved Treatment of Host-galaxy Correlations in Cosmological Analyses with Type Ia Supernovae

Improving the use of Type Ia supernovae (SNe Ia) as standard candles requires a better approach to incorporate the relationship between SNe Ia and the properties of their host galaxies. Using a spectroscopically confirmed sample of ~1600 SNe Ia, we develop the first empirical model of underlying populations for SNe Ia light-curve properties that includes their dependence on host-galaxy stellar mass; we find a significant correlation between stretch population and stellar mass (99.9% confidence) and a weaker correlation between color and stellar mass (90% confidence). These populations are important inputs to simulations that are used to model selection effects and correct distance biases within the BEAMS with Bias Correction (BBC) framework. Here we improve BBC to also account for SNe Ia-host correlations, and we validate this technique on simulated data samples. Here, we recover the input relationship between SNe Ia luminosity and host-galaxy stellar mass (the mass step, γ) with a bias of 0.004 ±0.001 mag, which is a factor of 5 improvement over previous methods that have a γ bias of ~0.02 ± 0.001 mag. We adapt BBC for a novel dust-based model of intrinsic brightness variations, which results in a greatly reduced mass step for data (γ = 0.017 ± 0.008) and for simulations (γ = 0.006 ± 0.007). Analyzing simulated SNe Ia, the biases on the dark energy equation of state, w, vary from Δw = 0.006(5) to 0.010(5) with our new BBC method; these biases are significantly smaller than the 0.02(5) w bias using previous BBC methods that ignore SNe Ia-host correlations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Characterizing particle-based thermal storage performance using optical methods for use in next generation concentrating solar power plants

Concentrating Solar Power (CSP) generation is an attractive option for low-emission power generation; however, the high costs of thermal storage associated with concentrating solar create a large barrier for their use and adaptation into modern life. Lowering their operation costs, while maintaining high thermal storage and transfer performance is essential. Solid particle-based heat exchange systems can reduce CSP cost but are often less efficient. Efforts to increase their performance have led to use of binary size particle mixes. Presented is an optical-based thermal analysis technique used to measure near-wall thermal conductivity of particle beds essential in determining their heat exchanger efficiency. Modulated Photothermal Radiometry is used to make dynamic temperature measurements, allowing for the extraction of the most relevant thermal properties like thermal conductivity, specific heat, and effusivity. The system uses a modulated laser source causing a damped periodic heat flux, resulting in a frequency and thermal property dependent surface temperature, of which is measured using radiometry. Lock-In techniques are used to extrapolate the amplitude of the signal. Plotting the amplitude against the root angular frequency allows for effusivity measurement by ratio to a known sample. Using specific heat measurements from literature and density measurements, the thermal conductivity of the particle mixes can be calculated. The simplicity of MPTR to probe through the depth of the bed is ideal for use in CSP for dynamic thermal performance monitoring.

Corona, Javier↗
DOE OSTI2022

Lewis Acid Site Assisted Bifunctional Activity of Tin Doped Gallium Oxide and Its Application in Rechargeable Zn‐Air Batteries

The enhanced safety, superior energy, and power density of rechargeable metal‐air batteries make them ideal energy storage systems for application in energy grids and electric vehicles. However, the absence of a cost‐effective and stable bifunctional catalyst that can replace expensive platinum (Pt)‐based catalyst to promote oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) at the air cathode hinders their broader adaptation. Here, in this work, it is demonstrated that Tin (Sn) doped β‐gallium oxide (β‐Ga 2 O 3 ) in the bulk form can efficiently catalyze ORR and OER and, hence, be applied as the cathode in Zn‐air batteries. The Sn‐doped β‐Ga 2 O 3 sample with 15% Sn (Sn x =0.15 ‐Ga 2 O 3 ) displayed exceptional catalytic activity for a bulk, non‐noble metal‐based catalyst. When used as a cathode, the excellent electrocatalytic bifunctional activity of Sn x =0.15 ‐Ga 2 O 3 leads to a prototype Zn‐air battery with a high‐power density of 138 mW cm −2 and improved cycling stability compared to devices with benchmark Pt‐based cathode. The combined experimental and theoretical exploration revealed that the Lewis acid sites in β‐Ga 2 O 3 aid in regulating the electron density distribution on the Sn‐doped sites, optimize the adsorption energies of reaction intermediates, and facilitate the formation of critical reaction intermediate (O*), leading to enhanced electrocatalytic activity.

Lewis acid sites↗
DOE OSTI2025

Development of a portable high-T c spherical neutron polarimetry device at the Oak Ridge National Laboratory

Spherical neutron polarimetry is a powerful polarized neutron scattering technique used to determine complex magnetic structures which are only partly accessible by other methods. This technique measures the full neutron polarization change upon scattering from a sample by fully decoupling the incoming and outgoing neutron polarization with a zero-field chamber placed at the sample position. Recent advancements and testing are presented for a new spherical neutron polarimetry device utilizing high-T c superconducting YBCO films, PHiTPAD, at the High Flux Isotope Reactor at Oak Ridge National Laboratory. Furthermore, we introduce a conceptual design that utilizes wavelength-independent adiabatic transitions to adapt spherical neutron polarimetry for use with pulsed neutron sources, thereby expanding its potential applications in neutron scattering research.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar

We develop space-time adaptive processing (STAP) methods by leveraging the advantages of sparse signal processing techniques in order to detect a slowly-moving target. We observe that the inherent sparse characteristics of a STAP problem can be formulated as the low-rankness of clutter covariance matrix when compared to the total adaptive degrees-of-freedom, and also as the sparse interference spectrum on the spatio-temporal domain. By exploiting these sparse properties, we propose two approaches for estimating the interference covariance matrix. In the first approach, we consider a constrained matrix rank minimization problem (RMP) to decompose the sample covariance matrix into a low-rank positive semidefinite and a diagonal matrix. The solution of RMP is obtained by applying the trace minimization technique and the singular value decomposition with matrix shrinkage operator. Our second approach deals with the atomic norm minimization problem to recover the clutter response-vector that has a sparse support on the spatio-temporal plane. We use convex relaxation based standard sparse-recovery techniques to find the solutions. With extensive numerical examples, we demonstrate the performances of proposed STAP approaches with respect to both the ideal and practical scenarios, involving Doppler-ambiguous clutter ridges, spatial and temporal decorrelation effects. As a result, the low-rank matrix decomposition based solution requires secondary measurements as many as twice the clutter rank to attain a near-ideal STAP performance; whereas the spatio-temporal sparsity based approach needs a considerably small number of secondary data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗