Vacuum jacketed umbilical lines technology advancement study. Task 4 - Rotary joint Final technical report
Vacuum jacketed components on launch umbilical tower service arms at Launch Complex 39 - low heat cryogenic rotary joint
Mursinna, R. C.↗
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Vacuum jacketed umbilical lines technology advancement study. Task 5 - Vacuum probes Final technical report
Vacuum jacketed components on launch umbilical tower service arms at Launch Complex 39 - vacuum probes
Rewerts, G. R.↗
Vacuum jacketed umbilical lines technology advancement study. Task 7 - Bayonet joint Final technical report
Vacuum jacketed components on launch umbilical tower service arms at Launch Complex 39 - bayonet joints in cryogenic propellant and vent lines
Mursinna, R. C.↗
A chimera grid scheme
A mesh system composed of multiple overset body-conforming grids is described for adapting finite-difference procedures to complex aircraft configurations. In this so-called 'chimera mesh,' a major grid is generated about a main component of the configuration and overset minor grids are used to resolve all other features. Methods for connecting overset multiple grids and modifications of flow-simulation algorithms are discussed. Computational tests in two dimensions indicate that the use of multiple overset grids can simplify the task of grid generation without an adverse effect on flow-field algorithms and computer code complexity.
Steger, J. L.↗
Real-time control of connected vehicles in signalized corridors using pseudospectral convex optimization
Recent advances in Connected and Automated Vehicle (CAV) technologies have opened up new opportunities to enable safe, efficient, and sustainable transportation systems. However, developing reliable and rapid speed control algorithms in highly dynamic environments with complex inter-vehicle interactions and nonlinear vehicle dynamics is still a daunting task. In this paper, we develop a novel speed control method for CAVs to produce optimal speed profiles that minimize the fuel consumption and avoid idling at signalized intersections. To this end, an optimal control problem is formulated using the information of the upcoming traffic signal to adapt vehicles' speeds to avoid frequent stop-and-go driving patterns. Here, by applying the pseudospectral discretization method and the sequential convex programming method, the computational efficiency is greatly improved, enabling potential real-time on-vehicle applications. In addition, the algorithm is implemented under a model predictive control framework to ensure online control with instant response for collision avoidance and robust vehicle coordination. The proposed algorithm is verified through numerical simulations of three different traffic scenarios. The convergence and accuracy of the proposed approach are demonstrated by comparing with a popular nonlinear solver. Furthermore, the benefit of the proposed method in both traffic mobility and fuel efficiency is validated using the speed profile determined from a traffic following model in a simulation software as the baseline.
42 ENGINEERING↗
Scalable multiscale modeling of platelets with 100 million particles
Here, we developed the core components of the AI-aided multiple time stepping algorithm for multiscale modeling of cell dynamics. This algorithm was implemented and analyzed on two supercomputer architectures with an application of simulating the aggregation of 250 platelets, or 102 million particles. To scale on these computers with complex memory and network architectures with GPUs, we devised a biomechanics-informed task mapping scheme to optimize load imbalance, communications, and memory utilization. Our simulations, scaling well up to 192 nodes on a Summit-like supercomputer with a peak speed of 11 petaflops, achieved a rate of 423 μs/day which is 500 times faster than the conventional algorithm using static time step and this has enabled studies of record size blood clots at record spatial–temporal resolutions. Additionally, we discovered the sensitive dependence of the scalability and execution time on the methods of decomposition, CPU–GPU coupling, and task mapping.
97 MATHEMATICS AND COMPUTING↗
Geometry-independent microstructure optimization for electron beam powder bed fusion additive manufacturing
The ability to manipulate the formation of microstructure is one of the potential advantages of additive manufacturing. Yet, the additive manufacturing process is riddled with complex interactions between processing parameters, geometry, and spot melting sequence which makes the task of controlling microstructure challenging. This paper proposes a method to solve the microstructure texture optimization problem by leveraging established optimization techniques and increasingly fast and accurate analytical heat transfer models. Respectively, we apply two different optimization algorithms, genetic algorithm and concurrent greedy line search, in order to induce arbitrarily shaped microstructure regions and we use a heat transfer model to evaluate the fitness of solutions in an iterative approach that is far more efficient than pure trial and error. The objective of the optimization algorithm is to control local thermal conditions – hence microstructure formation – during the melting process by manipulating the order and the dwell time of all the points of the infill pattern sequence. We then analyze the optimized sequence to determine important path characteristics that affect the microstructure formation. Through examples, we show that relative order and frequency, as measured by weighted average and variance of spot order, are important factors that influence the final characteristics.
36 MATERIALS SCIENCE↗
Machine-Learning Analysis of Moisture Carryover in Boiling Water Reactors
Moisture carryover (MCO) is modeled in the General Electric Type-4 boiling water reactor (BWR) using machine-learning methods and data from operating plants. Understanding MCO and the conditions that give rise to an elevated value is important since excessive MCO can damage critical turbine components, can result in elevated dose levels to on-site personnel, and can interfere with late-cycle power management. The analysis of MCO takes into account simplifying reactor symmetries and important geometric dependencies. The plant data are taken from several reactors and were collected over multiple years and multiple fuel cycles. A brief description of the origin of MCO in U.S. BWR plants is given. A machine-learning model is constructed from the data using applicable algorithms and data-reduction techniques. Matching model complexity with available data is one of the more challenging machine-learning tasks. Too many features and too little data will lead to overfitting. The data for each fuel cycle included over 6876 original features, 9 for each fuel bundle. Two approaches are used to reduce the data set into a manageable number of features. The first was an engineering analysis that resulted in the selection of steam quality Q and steam liquid phase velocity V L as the main features driving MCO. Using a Q and a V L for each fuel bundle gives 1528 Q and a V L feature describing the reactor behavior. An analysis of different functional forms of these two variables led to the actual inputs to the neural network model. The second approach involved the use of statistical techniques such as Pearson’s correlation and k-means analysis. The identified groupings of bundles behaved similarly. Treating each grouping as a single feature further reduced the input variable set to a manageable number. Here, a model selection criterion is proposed, and results are presented along with a discussion of related issues.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Advocating Feedback Control for Human-Earth System Applications
This paper proposes a feedback control perspective for Human-Earth Systems (HESs) which essentially are complex systems that capture the interactions between humans and nature. Recent attention in HES research has been directed towards devising strategies for climate change mitigation and adaptation, aimed at achieving environmental and societal objectives. However, existing approaches heavily rely on HES models, which inherently suffer from inaccuracies due to the complexity of the system. Moreover, overly detailed models often prove impractical for optimization tasks. We propose a framework inheriting from feedback control strategies the robustness against model errors, because inaccuracies are mitigated using measurements retrieved from the field. The framework comprises two nested control loops. The outer loop computes the optimal inputs to the HES, which are then implemented by actuators controlled in the inner loop. Potential fields of applications are also identified and a numerical example is provided.
biological system modeling↗
Supo Thermal Model Development IV
This report describes the continuation of the Computational Fluid Dynamics (CFD) model of the Supo cooling system described in the reports, Supo Thermal Model Development, II, and III. In the previous reports, the solution natural convection of Supo was simulated using a 2D, axisymmetric model that altered the actual geometry. A 3D geometry of Supo will more accurately estimate the natural convection heat transfer coefficient (HTC) of the system since Supo contains several non-symmetric internal components that can only be modeled in 3D. This, in turn, will improve the natural convection HTC results for comparison purposes with past operational data and previous 2D CFD results. Unfortunately, the complexities of performing multiphase 3D CFD simulations has proven too large of a task for our current capabilities. Instead, this report will focus on the 3D model development for the 2.39 kW/L (31.3 kW) case and limitations and challenges associated with the 3D simulations.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Masked Symbol Modeling for Demodulation of Oversampled Baseband Communication Signals in Impulsive Noise-Dominated Channels
Recent breakthroughs in natural language processing show that attention mech- anism in Transformer networks, trained via masked-token prediction, enables models to capture the semantic context of the tokens and internalize the grammar of language. While the application of Transformers to communication systems is a burgeoning field, the notion of context within physical waveforms remains under-explored. This paper addresses that gap by re-examining inter-symbol con- tribution (ISC) caused by pulse-shaping overlap. Rather than treating ISC as a nuisance, we view it as a deterministic source of contextual information embedded in oversampled complex baseband signals. We propose Masked Symbol Model- ing (MSM), a framework for the physical (PHY) layer inspired by Bidirectional Encoder Representations from Transformers methodology. In MSM, a subset of symbol-aligned samples is randomly masked, and a Transformer predicts the missing symbol identifiers using the surrounding “in-between” samples. Through this objective, the model learns the latent syntax of complex baseband waveforms. We illustrate MSM’s potential by applying it to the task of demodulating sig- nals corrupted by impulsive noise, where the model infers corrupted segments by leveraging the learned context. Our results suggest a path toward receivers that interpret, rather than merely detect communication signals, opening new avenues for context-aware PHY layer design.
Bedir, Oguz↗
Defining quantum-ready primitives for hybrid HPC-QC supercomputing: a case study in Hamiltonian simulation
As computational demands in scientific applications continue to rise, hybrid high-performance computing (HPC) systems integrating classical and quantum computers (HPC-QC) are emerging as a promising approach to tackling complex computational challenges. One critical area of application is Hamiltonian simulation, a fundamental task in quantum physics and other large-scale scientific domains. This paper investigates strategies for quantum-classical integration to enhance Hamiltonian simulation within hybrid supercomputing environments. By analyzing computational primitives in HPC allocations dedicated to these tasks, we identify key components in Hamiltonian simulation workflows that stand to benefit from quantum acceleration. To this end, we systematically break down the Hamiltonian simulation process into discrete computational phases, highlighting specific primitives that could be effectively offloaded to quantum processors for improved efficiency. Our empirical findings provide insights into system integration, potential offloading techniques, and the challenges of achieving seamless quantum-classical interoperability. We assess the feasibility of quantum-ready primitives within HPC workflows and discuss key barriers such as synchronization, data transfer latency, and algorithmic adaptability. These results contribute to the ongoing development of optimized hybrid solutions, advancing the role of quantum-enhanced computing in scientific research.
97 MATHEMATICS AND COMPUTING↗
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach
Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper’s advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration of hyperparameters associated with data preprocessing, FNO architecture-related hyperparameters, and various model training strategies. We aim to obtain an optimal set of hyperparameters leading to the most performant model. Moreover, on top of the commonly used mean squared error for model training, we propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms. The numerical experiments show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days. Utilizing DeepHyper, we demonstrate an approach to enhance the use of FNO in ocean dynamics forecasting, offering a scalable solution with improved precision.
97 MATHEMATICS AND COMPUTING↗
Chemical releases in the ionosphere
The study of the interaction between the atmosphere, ionosphere and magnetosphere is identified as a major task worthy of pursuit. The present review demonstrates the major contributions to this complex problem already made by active experiments involving the injection of chemicals and energetic electron beams into the atmosphere, ionosphere and magnetosphere. Through the use of chemical releases, it has been possible to investigate a number of quantities including high-altitude winds and electric fields, the detailed configurations of the geomagnetic field within the ionosphere and the magnetosphere, as well as the propagation of energetic particle beams and their interaction with natural neutral and ionized constituents of the high atmosphere. So far, virtually all of this effort has been accomplished using rockets. In the future, it is obvious that satellite platforms will play a greater role, both in making injections and in observing their effects.
Davis, T. N.↗
Shock capturing finite difference algorithms for supersonic flow past fighter and missile type configurations
The construction of a reliable, shock capturing finite difference method to solve the Euler equations for inviscid, supersonic flow past fighter and missile type configurations is highly desirable. The numerical method must have a firm theoretical foundation and must be robust and efficient. It should be able to treat subsonic pockets in a predominantly supersonic flow. The method must also be easily applicable to the complex topologies of the aerodynamic configuration under consideration. The ongoing approach to this task is described and for steady supersonic flows is presented. This scheme is the basic numerical method. Results of work obtained during previous years are presented.
Osher, S.↗
Human factors issues in performing life science experiments in a 0-G environment
An overview of the environmental conditions within the Spacelab and the planned Space Station Freedom is presented. How this environment causes specific Human Factors problems and the nature of design solutions are described. The impact of these problems and solutions on the performance of life science activities onboard Spacelab (SL) and Space Station Freedom (SSF) is discussed. The first area highlighted is contamination. The permanence of SSF in contrast to the two-week mission of SL has significant impacts on crew and specimen protection requirements and, thus, resource utilization. These requirements, in turn impose restrictions on working volumes, scheduling, training, and scope of experimental procedures. A second area is microgravity. This means that all specimens, materials, and apparatus must be restrained and carefully controlled. Because so much of the scientific activity must occur within restricted enclosures (gloveboxes), the provisions for restraint and control are made more complex. The third topic is crewmember biomechanics and the problems of movement and task performance in microgravity. In addition to the need to stabilize the body for the performance of tasks, performance of very sensitive tasks such as dissection is difficult. The issue of space sickness and adaption is considered in this context.
Gonzalez, Wayne↗
An ICAI architecture for troubleshooting in complex, dynamic systems
Ahab, an intelligent computer-aided instruction (ICAI) program, illustrates an architecture for simulator-based ICAI programs to teach troubleshooting in complex, dynamic environments. The architecture posits three elements of a computerized instructor: the task model, the student model, and the instructional module. The task model is a prescriptive model of expert performance that uses symptomatic and topographic search strategies to provide students with directed problem-solving aids. The student model is a descriptive model of student performance in the context of the task model. This student model compares the student and task models, critiques student performance, and provides interactive performance feedback. The instructional module coordinates information presented by the instructional media, the task model, and the student model so that each student receives individualized instruction. Concept and metaconcept knowledge that supports these elements is contained in frames and production rules, respectively. The results of an experimental evaluation are discussed. They support the hypothesis that training with an adaptive online system built using the Ahab architecture produces better performance than training using simulator practice alone, at least with unfamiliar problems. It is not sufficient to develop an expert strategy and present it to students using offline materials. The training is most effective if it adapts to individual student needs.
Fath, Janet L.↗
Evaluation and selection of assembly plans
Two criteria are introduced for the evaluation and selection of assembly plans. The first criterion is to maximize the number of different sequences encompassed by the assembly plan. The second criterion is to maximize the amount of parallelism (i.e., simultaneity) that is possible in the execution of the assembly tasks. While the metrics corresponding to the criteria used in previous work can be expressed as a sum of terms, each being a function of a task or a state, the metrics corresponding to the criteria introduced are more complex functions of the whole assembly plan. An algorithm that performs a heuristic search for the best assembly plan over the AND/OR graph representation of assembly plans introduced in previous work is presented. Admissible heuristics for each of the two criteria are presented.
Homem De Mello, L. S.↗