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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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Lessons Learned from Bacterial Transport Experiments at the South Oyster Site
This paper provides a high-level review of bacterial transport experiments conducted by a multi-investigator, multi-institution, multi-disciplinary team of researchers under the auspices of the U. S. Department of Energy. The experiments considered were conducted during the time period 1999-2001 at a field site near the town of Oyster, Virginia known as the South Oyster Site, and included four major experimental campaigns aimed at understanding and quantifying bacterial transport in the subsurface environment. Several key elements of the research are discussed here: 1) Quantification of bacterial transport in physically and biogeochemically heterogeneous aquifers, 2) evaluation of the efficacy of conventional colloid filtration theory, 3) scale effects in bacterial transport, 4) development of new methods for microbial enumeration and screening for low adhesion strains, 5) application of novel hydrogeophysical techniques for aquifer characterization, and 6) experiences regarding management of a large field research effort.
Machine-learning-aided cognitive reconfiguration for flexible-bandwidth HPC and data center networks [Invited]
This paper proposes a machine-learning (ML)-aided cognitive approach for effective bandwidth reconfiguration in optically interconnected datacenter/high-performance computing (HPC) systems. The proposed approach relies on a Hyper-X-like architecture augmented with flexible-bandwidth photonic interconnections at large scales using a hierarchical intra/inter-POD photonic switching layout. We first formulate the problem of the connectivity graph and routing scheme optimization as a mixed-integer linear programming model. A two-phase heuristic algorithm and a joint optimization approach are devised to solve the problem with low time complexity. Then, we propose an ML-based end-to-end performance estimator design to assist the network control plane with intelligent decision making for bandwidth reconfiguration. Numerical simulations using traffic distribution profiles extracted from HPC applications traces as well as random traffic matrices verify the accuracy performance of the ML design estimator ( < <#comment/> 9 % <#comment/> error) and demonstrate up to 5 × <#comment/> throughput gain from the proposed approach compared with the baseline Hyper-X network using fixed all-to-all intra/inter-portable data center interconnects.
Improvements of NSLS-II X-ray Diagnostic Beamlines
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Learning to Branch with Interpretable Machine Learning Models
This presentation describes an algorithm for applying machine learning to branching to speed up the solution of integer optimization problems. These problems are challenging and solved multiple times a day by power systems operators. We show that our approach speeds up a widely used open-source optimization solver.
Chapter 2-1: Learning from the Biological Viewpoint: Adaptive Control and Learning Systems - Part 2
Adaptive patterns in central nervous tissue learning process
Eta Carinae: What We have learned recently with HST/STIS, VLT/UVES and VLTI: What We Might Learn with VLT/VLTI across the 2008/9 Minimum
Eta Carinae, the LBV that we have learned to love and hate, has revealed many clues of its character over the past seven years by studies with HST and VLT. Based upon X-Ray, optical and IR observations, Eta Carinae is convincingly a massive binary system that uniquely has major nebular ejecta that are connected with historical outbursts. We have successhlly followed the stellar and nebular changes over the 5.5 year cycle, especially with STIS and RXTE, and across the spectroscopic minimum in 2003.5 with STIS, CHANDRA, RXTE, FUSE, and VLT/UVES. The HST/STIS high spatial resolution, combined with appropriate spectral resolving powers from 1150 to 10300 A, has revealed much about the Central Source and especially the spatially resolved extended stellar atmosphere and the ejecta, known as the Homunculus. Indeed the neutral, dusty outer Homunculus, ejected in the 1840s, envelops the newly discovered ionized Little Homunculus, ejected in the 1890s. In line of sight, multiple hot clumps, both ionized and neutral, are seen in absorption and provide much information on the physical conditions of these massive ejecta. Against the nebular-scattered starlight, wind and nebular absorptions provide views at different angles from line of sight. The VLT/UVES studies from 2002 through 2004 provided very important time-sampled spectra of both the star as seen directly and as seen by the SE lobe viewing the polar region of the star. The VLTI 2.2 micron measures of the central source are consistent with a prolate spheroid with its axis extending along the axis of the Homunculus. This is consistent with the noticeably larger wind mass and higher terminal velocity along the axis of the Homunculus compared to that measured in line of sight at about 45 degrees from the polar axes. We understand the system to be a massive primary with an O or WR companion in a very highly elliptical orbit. The spectroscopic minimum occurs during periastron, when the greatly extended primary atmosphere and wind bottles up the ionizing uv radiation of the companion star. This transient drop in uv photons leads to recombination of much of the wind structure and of the nearby ejecta. Doubly-ionized elements (iron, neon, argon, vanadium) recombine to singly ionized forms and extended structures on the scales of below 0.03 arcseconds to an arcsecond change, appear, or disappear. With the loss of the STIS on HST, ground-based observations, especially with high spatial-resolution facilities, including the VLT and VLTI will be key to further studies across the minimum centered on 2009.0. Now is the time to plan for these studies.
Learning Effectiveness of the NASA Digital Learning Network
Student participation in actual investigations which develop inquiry and intellectual skills has long been regarded as an essential component of science instructions (Schwab, 1962; White, 1999). Such investigations give students an opportunity to appreciate the spirit of science and promote an understanding of the nature of science. However, classroom research conducted over the past 20 years describes science teaching as primarily teacher centered. Typical instruction consists of whole class, noninteractive activities in which individual seatwork has constituted the bulk of classroom interactions (Tobin and Gallagher, 1997). Students typically learn science from textbooks and lectures. Their main motivation is to do reasonably well on tests and examinations (Layman, 1999). During the past five years, infrastructure constraints have reduced to the point that many schools systems can now afford low cost, high quality video conferencing equipment (International Society for Technology in Education, 2003). This study investigates the use of interactive video conferencing vs. face to face interaction with hands-on, inquiry based activities. Some basic questions to be addressed are: How does the delivery method impact the students understanding of the goals of the experiment? Are students explanation of the strategies of experimentation different based on the method of instruction that was provided. Do students engaged in a workshop with the instructor in the room vs. an instructor over video conferencing have different perception of the understanding of the subject materials?
A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models
Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.
MSFC Thermal Testing Lessons Learned Course. Section 3: Lessons Learned from Protoflight StarBurst Instrument Thermal Vacuum Test
MSFC thermal engineers will be presenting lessons learned while preparing and conducting thermal tests. This course will cover practical real world examples of thermal chamber and vacuum chamber testing from engineering development units to system level testing on flight hardware. Special topics include large scale testing preparation, requirement tailoring from parent documents, and cryogenic development testing.
Learning to Branch with Interpretable Machine Learning Models
The data consists of a slide deck that was presented at the INFORMS 2023 conference. The presentation summarizes our approach to learning how to branch and compares our approach to the popular solver SCIP and a state-of-the-art ML-based branching rule.
Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group-Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO-F, GFPO-FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy ($H_{T}$) trigger sensitive to pileup variation, and an anomaly-detection (AD) trigger based on reconstruction loss for rare or non-standard signatures. On Monte Carlo streams, our agent increases the fraction of in-tolerance time intervals by 48% ($H_T$) and 28% (AD), with a cumulative gain of up to 2% in signal efficiency on those in-tolerance intervals. Transferring from simulation to \emph{real} collision data (CMS Run 283408), the same agent, without fine-tuning, achieves a 56% ($H_T$) and 28% (AD) in-tolerance improvement over baselines, with further signal-efficiency gain on both triggers. To our knowledge, this is the \emph{first} demonstration of RL-based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPO_LHC (see repo for details).
SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images
The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state-of-the-art fake image detection methods overfit to their training data and catastrophically fail when evaluated on curated hard test sets with strong distribution shifts. In this work, we argue that it is more principled to learn a tight decision boundary around the real image distribution and treat the fake category as a sink class. To this end, we propose SimLBR, a simple and efficient framework for fake image detection with Latent Blending Regularization (LBR). Our method significantly improves cross-generator generalization, achieving up to +24.85% accuracy and +69.62% recall on the challenging Chameleon benchmark. SimLBR is also highly efficient, training orders of magnitude faster than existing approaches. Furthermore, we emphasize the need for reliability-oriented evaluation in fake image detection, introducing risk-adjusted metrics and worst-case estimates to better assess model robustness. All the code and models are availabe at: https://github.com/mvrl/SimLBR
Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches
Not Available
Demonstration of Intelligent HVAC Load Management With Deep Reinforcement Learning: Real-World Experience of Machine Learning in Demand Control
We report that buildings account for 40% of total primary energy consumption and 30% of all CO 2 emissions worldwide. A large portion of building energy consumption is due to heating, ventilation, and air-conditioning (HVAC) systems. In the summer, for example, more than 50% of a building’s electricity consumption is used for cooling. With proper energy management, buildings can provide load shifting, peak shaving, frequency regulation, and many other demand response services.
Operation Optimization using Reinforcement Learning with Integrated Artificial Reasoning Framework
In large and complex systems, operational decision-making requires a systematic analysis with a vast amount of data from both process parameters and component status monitoring. In this paper, we present an integrated artificial reasoning approach for system state transition models that can help operational decision-making with explainable and traceable reasoning. The integrated artificial reasoning framework is a physics-based approach of defining the system structure in a Bayesian network, so we leveraged it in a Markov decision process (MDP) for finding optimal operational solutions. In our proposed framework, the MDP is implemented on a dynamic Bayesian network (DBN), which represents causalities in a system. The multilevel flow modeling was utilized in order to extract these causalities in a more efficient and objective manner. Since multilevel flow modeling is based on the fundamental energy and mass conservation laws, the target system is decomposed into several mass, energy, and information structures, which serve as the basis for a DBN. The MDP consists of the processes of finding a solution for the Bellman equation, which can be derived from the conditional probability equations of the constructed DBN. System operators can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from the component degradation process or random failures. We analyzed a simplified example system to illustrate finding an optimal operational policy with this approach.
A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis
Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to enable and understanding of their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transfected with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produce. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of novel DB positions and enzymatic specificity.
Is Remote Learning as Effective as In-Person Learning for Contouring Education? A Prospective Comparison of Face-to-Face versus Online Delivery of the Anatomy and Radiology Contouring Bootcamp
The Anatomy and Radiology Contouring (ARC) Bootcamp was a face-to-face (F2F) intervention providing integrated education for radiation oncology (RO) residents and medical physicists. To increase access, we launched an online offering in 2019. We evaluated the effect of the online course on participants' knowledge acquisition, contouring skills, and self-confidence by comparing it with the F2F course.