Plasma Signatures of Small Orbital Debris: LANL Theory and Modeling / Experimental effort.
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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 International Atomic Energy Agency (IAEA) has obligations to apply safeguards to nuclear material and facilities within States subject to IAEA safeguards agreements. As a result of these obligations, safeguards will need to be applied to Generation IV reactors, including liquid fueled molten salt reactors (MSRs), if they are deployed in States with safeguards agreements. Recently, the IAEA has noted that a growing number of Member States are showing interest in MSR technology. This has been accompanied by an increase in short term development and deployment activities relating to MSRs. To ensure MSRs can be safeguarded effectively and efficiently upon possible future deployment, safeguards approaches and techniques need to be investigated now.
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This presentation is for the paper INL/CON-22-65800
We investigate composite neutrino models where heavy neutrinos emerge as bound states from a near-conformal strongly coupled sector. Standard Model neutrinos mix with these composite singlets via an inverse seesaw mechanism, where the anomalous scaling dimensions of the composite-sector operators naturally suppress light neutrino masses to sub-eV scales. Matching the conformal dynamics onto low-energy theory yields enhanced electromagnetic transition dipole operators with couplings $d_{μN} \sim 10^{-6}$-$10^{-8}\,\mathrm{GeV}^{-1}$, parametrically larger than the loop-level predictions of minimal Dirac or Majorana models. We carry out a dedicated event-level simulation of the production-and-decay chain $νX \to \mathcal{U} X \to νγX$ and compute the resulting event rates at MiniBooNE and MINERvA within the model, accounting for the composite production cross section and decay kinematics in detail. We further present predictions for the photon energy, angular, and multiplicity distributions. For the benchmark scenarios accessible at these experiments the radiative signal is predominantly single-photon; the composite structure additionally permits fragmentation of the up-scattered state into multiple heavy neutrinos, each decaying as $N\toνγ$, with multi-photon final states emerging for lighter compositeness scales or higher beam energies as a qualitatively new probe of the composite dynamics.
A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.
Flight 10 of NASA's Subsonic Assessment (SASS) Ozone and Nitrogen Oxide Experiment (SONEX) extended southwest of Lajes, Azores. A variety of chemical signatures were encountered. These signatures are examined in detail, relating them to meteorological data from a high resolution numerical model having horizontal grid spacing of 30 and 90 km and 26 vertical levels. The meteorological output at hourly intervals is used to create backward trajectories from the locations of the chemical signatures. Four major categories of chemical signatures are discussed-stratospheric, lightning, continental pollution, and a transition layer. The strong stratospheric signal is encountered just south of the Azores in a region of depressed tropopause height. Three chemical signatures at different altitudes in the upper troposphere are attributed to lightning. Backward trajectories arriving at locations of these signatures are related to locations of cloud-to-ground lightning. Results show that the trajectories pass through regions of lightning 1-2 days earlier over the eastern Gulf of Mexico and off the southeast coast of the United States. The lowest leg of the flight exhibits a chemical signature consistent with continental pollution. Trajectories arriving at this signature are found to pass over the highly populated Northeast Corridor of the United States. Surface based pollution apparently is lofted to the altitudes of the trajectories by convective clouds along the East Coast that did not contain lightning. Finally, a chemical transition layer is described. Its chemical signature is intermediate to those of lightning and continental pollution. Trajectories arriving in this layer pass between the trajectories of the lightning and pollution signatures. Thus, they probably are impacted by both sources.
The use of computational fluid dynamics (CFD) for the analysis of sonic booms generated by aircraft has been shown to increase the accuracy and reliability of predictions. CFD takes into account important three-dimensional and nonlinear effects that are generally neglected by modified linear theory (MLT) methods. Up to the present time, CFD methods have been primarily used for analysis or prediction. Some investigators have used CFD to impact the design of low boom configurations using trial and error methods. One investigator developed a hybrid design method using a combination of Modified Linear Theory (e.g. F-functions) and CFD to provide equivalent area due to lift driven by a numerical optimizer to redesign or modify an existing configuration to achieve a shaped sonic boom signature. A three-dimensional design methodology has not yet been developed that completely uses nonlinear methods or CFD. Constrained numerical optimization techniques have existed for some time. Many of these methods use gradients to search for the minimum of a specified objective function subject to a variety of design variable bounds, linear and nonlinear constraints. Gradient based design optimization methods require the determination of the objective function gradients with respect to each of the design variables. These optimization methods are efficient and work well if the gradients can be obtained analytically. If analytical gradients are not available, the objective gradients or derivatives with respect to the design variables must be obtained numerically. To obtain numerical gradients, say, for 10 design variables, might require anywhere from 10 to 20 objective function evaluations. Typically, 5-10 global iterations of the optimizer are required to minimize the objective function. In terms of using CFD as a design optimization tool, the numerical evaluation of gradients can require anywhere from 100 to 200 CFD computations per design for only 10 design variables. If one CFD computation requires an hour of computational time on a Cray computer, one can see that the use of constrained numerical optimization quickly becomes impractical.Hence, in order to practically couple a numerical design optimization technique with a CFD method, the CFD method must be extremely efficient with running times on the order of only minutes. The CFD Euler code developed under NASA sponsorship and referred to as MIM3D-SB for the most part fulfills these efficiency requirements. Analysis of wing- body configurations can be computed in a matter of a few minutes. The present study will concentrate on the feasibility of the use of this CFD code in conjunction with a numerical design optimization technique for the sonic boom reduction of candidate HSCT configurations. A preliminary supersonic aircraft design system has been established that utilizes the numerical design optimization code NPSOL developed at Stanford University coupled with the supersonic NUM3D-SB CFD code. Many questions still need to be answered in regard to using CFD and numerical optimizers as design tools. There are difficulties related to both the CFD codes and the numerical optimizers. Numerical optimizers can converge to a local minima rather than a global minima. This behavior is largely a function of the initial guess in the design space. The optimizer also is searching for a minimum of the function in terms of its derivative without any regard to the actual function value. Numerically (i.e. CFD) determined gradients can also generate spurious numerical local minima. In addition, for the sonic boom problem, grid fineness will also determine the accuracy of the final design solution. Design optimization methods work well on problems defined by continuous objective functions. The sonic boom signature design problem is not necessarily defined by a continuous objective function. The signature can have a variety of shapes; i.e. from N-wave to multiple shocks. The far-field or ground signature may not transition continuously from one shape to another and hence, may exhibit discontinuous behavior. This is also a source of difficulty in using design optimization methods.In the following sections, several low boom and one reference aircraft configuration will be analyzed to predict their sonic boom signature characteristics. Modifications to some of these designs will also be presented to demonstrate the feasibility of using CFD as a design tool and to demonstrate the feasibility of designing shaped sonic boom signatures. Design modifications to some configurations will be presented to demonstrate the feasibility of achieving shaped signatures with reduced levels and not necessarily to represent realistic or aerodynamically efficient design modifications. Fuselage volume or camber are used as design variables in order to have a minimal effect on the primary wing aerodynamics. The paper will also seek to demonstrate whether a hybrid or ramped signature is feasible to achieve. For the low-boom configurations, the CFD predicted signatures will be compared qualitatively to their MLT design signatures.