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At least 1,153 records · Page 64

Using statistical modeling to predict and understand fusion experiments

Over 300 cryogenic layered direct-drive inertial confinement fusion implosions have been successfully executed on the OMEGA Laser System in the last decade. However, extracting sufficient understanding from these experiments to develop new designs or to identify or mitigate degradation sources remains challenging. Recently, a statistical modeling approach was developed to successfully design and predict improved implosion experiments on OMEGA. Here, we show that one-dimensional simulations can be used to predict the outcomes of systematically perturbed three-dimensional simulations and that this statistical modeling approach can be used to identify or rule out physical mechanisms for some of the degradation sources observed on the OMEGA Laser System for direct-drive cryogenic inertial confinement fusion. In this instance, we investigate the fusion yield dependencies on the ion temperature asymmetries and laser beam size observed in experiments by comparing with trends in three-dimensional synthetic simulation databases. Using the statistical model on these systematically perturbed simulations, we find that the statistically inferred dependency on the measured ion temperature asymmetries is well explained by imposed ℓ =1 modes. However, we find that the dependency on the laser beam size is only dominated by the illumination non-uniformity for some extreme cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

The NASA Commercial Crew Program (CCP) Mission Assurance Process

In 2010, NASA established the Commercial Crew Program in order to provide human access to the International Space Station and low earth orbit via the commercial (non-governmental) sector. A particular challenge to NASA has been how to determine the commercial providers transportation system complies with Programmatic safety requirements. The process used in this determination is the Safety Technical Review Board which reviews and approves provider submitted Hazard Reports. One significant product of the review is a set of hazard control verifications. In past NASA programs, 100 percent of these safety critical verifications were typically confirmed by NASA. The traditional Safety and Mission Assurance (SMA) model does not support the nature of the Commercial Crew Program. To that end, NASA SMA is implementing a Risk Based Assurance (RBA) process to determine which hazard control verifications require NASA authentication. Additionally, a Shared Assurance Model is also being developed to efficiently use the available resources to execute the verifications. This paper will describe the evolution of the CCP Mission Assurance process from the beginning of the Program to its current incarnation. Topics to be covered include a short history of the CCP; the development of the Programmatic mission assurance requirements; the current safety review process; a description of the RBA process and its products and ending with a description of the Shared Assurance Model.

Commercial Crew Program↗

Virtual machine-based simulation platform for mobile ad-hoc network-based cyber infrastructure

In modeling and simulating complex systems such as mobile ad-hoc networks (MANETs) in de-fense communications, it is a major challenge to reconcile multiple important considerations: the rapidity of unavoidable changes to the software (network layers and applications), the difficulty of modeling the critical, implementation-dependent behavioral effects, the need to sustain larger scale scenarios, and the desire for faster simulations. Here we present our approach in success-fully reconciling them using a virtual time-synchronized virtual machine(VM)-based parallel ex-ecution framework that accurately lifts both the devices as well as the network communications to a virtual time plane while retaining full fidelity. At the core of our framework is a scheduling engine that operates at the level of a hypervisor scheduler, offering a unique ability to execute multi-core guest nodes over multi-core host nodes in an accurate, virtual time-synchronized manner. In contrast to other related approaches that suffer from either speed or accuracy issues, our framework provides MANET node-wise scalability, high fidelity of software behaviors, and time-ordering accuracy. The design and development of this framework is presented, and an ac-tual implementation based on the widely used Xen hypervisor system is described. Benchmarks with synthetic and actual applications are used to identify the benefits of our approach. The time inaccuracy of traditional emulation methods is demonstrated, in comparison with the accurate execution of our framework verified by theoretically correct results expected from analytical models of the same scenarios. In the largest high fidelity tests, we are able to perform virtual time-synchronized simulation of 64-node VM-based full-stack, actual software behaviors of MANETs containing a mix of static and mobile (unmanned airborne vehicle) nodes, hosted on a 32-core host, with full fidelity of unmodified ad-hoc routing protocols, unmodified application executables, and user-controllable physical layer effects including inter-device wireless signal strength, reachability, and connectivity.

97 MATHEMATICS AND COMPUTING↗

NASA's Spaceflight Visual Impairment and Intracranial Hypertension Research Plan: An accelerated Research Collaboration

To date six long duration astronauts have experienced in flight visual changes and post flight signs of optic disc edema, globe flattening, choroidal folds, hyperoptic shifts and or raised intracranial pressure. In some cases the changes were transient while in others they are persistent with varying degrees of visual impairment. Given that all astronauts exposed to microgravity experience a cephalad fluid shift, and that both symptomatic and asymptomatic patients have exhibited optic nerve sheath edema on MRI, there is a high probability that all astronauts develop in-flight idiopathic intracranial hypertension to some degree. Those who are susceptible, have an increased likelihood of developing treatment resistant papilledema resulting in visual impairment and possible long-term vision loss. Such an acquired disability would have a profound mission impact and would be detrimental to the long term health of the astronaut. The visual impairment and increased intracranial pressure phenomenon appears to have multiple contributing factors. Consequently, the working "physiological fault bush" with elevated intracranial pressure at its center, is divided into ocular effects, and CNS and other effects. Some of these variables have been documented and or measured through operational data gathering, while others are unknown, undocumented and or hypothetical. Both the complexity of the problem and the urgency to find a solution require that a unique, non-traditional research model be employed such as the Accelerated Research Collaboration(TM) (ARC) model that has been pioneered by the Myelin Repair Foundation. In the ARC model a single entity facilitates and manages all aspects of the basic, translational, and clinical research, providing expert oversight for both scientific and managerial efforts. The result is a comprehensive research plan executed by a multidisciplinary team and the elimination of stove-piped research. The ARC model emphasizes efficient and effective communication between management and investigators; and real-time sharing of scientific discoveries in an effort to solve complex problems.

Otto, Christian↗

Interactive design and analysis of future large spacecraft concepts

An interactive computer aided design program used to perform systems level design and analysis of large spacecraft concepts is presented. Emphasis is on rapid design, analysis of integrated spacecraft, and automatic spacecraft modeling for lattice structures. Capabilities and performance of multidiscipline applications modules, the executive and data management software, and graphics display features are reviewed. A single user at an interactive terminal create, design, analyze, and conduct parametric studies of Earth orbiting spacecraft with relative ease. Data generated in the design, analysis, and performance evaluation of an Earth-orbiting large diameter antenna satellite are used to illustrate current capabilities. Computer run time statistics for the individual modules quantify the speed at which modeling, analysis, and design evaluation of integrated spacecraft concepts is accomplished in a user interactive computing environment.

Garrett, L. B.↗

Validating Connected, Automated, and Electric Vehicle Models and Simulation - Research Performance Progress Report

The objective of this project is to test connected and automated vehicles with both electrified and internal combustion engine powertrains to support updates and validation of modeling and simulation tools. This includes the development of the components and network architecture to execute and collect empirical data for multiple scenarios and traffic interactions. Specific program objectives include: • Translate Lab algorithms into vehicle and infrastructure controls • Conduct physical testing at realistic scale • Evaluate system performance • Improve models using empirical data • Improve control algorithms from lessons learned • Identify system and algorithm assumptions which need refinement It is important to note that the objective of this project was not to demonstrate the efficacy of the selected algorithms to improve energy efficiency but rather to validate and improve modeling and simulation tools using empirical data. While it is a desirable outcome to concurrently demonstrate improved energy efficiency through use of these algorithms, and in most cases that was the outcome, the success of this project was not predicated on the performance of the algorithm towards improving energy efficiency across all scenarios and test matrices.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Asymmetric Semiconductor Nanorod/Oxide Nanoparticle Hybrid Materials: Model Nanomaterials for Light-Activated Formation of Fuels from Sunlight. Formal Progress Report -- Award DE-FG02-05ER15753

Executive Summary on Project Accomplishments: We focused our efforts for this project on the synthesis and characterization of semiconductor nanomaterials composed of semiconductor nanorods (NRs - e.g., CdSe, CdSe@CdS, CdS) with metal (Au, Pt, Co) or metal oxide (CoxOy) nanoparticle (NP) “tips.” These systems are attractive model systems where control of spatial, energetic and compositional features of both NRs and NP tips potentially enhances the efficiency of photogeneration and directional transport of charges, and photoelectrochemical conversion of sunlight to fuels. Synthetic methods to control material dimensions (20-200 nm in length), topology (one vs. two NP tips) and NR/NP tip compositions have been developed in the current project period (Pyun). We also achieved, for the first time in heterostructured nanorod materials, estimates of both valence band energies (E VB ) and conduction band energies (E CB ), using unique combinations of in vacuuo ultraviolet photoelectron spectroscopy (UPS, Armstrong), and waveguide spectroelectrochemistry (Saavedra), respectively. The spectroelectrochemical measurements in particular provide a unique path to estimation of E CB , and the distribution in E CB brought about by modification of NR composition. The combination of both approaches promises to be universally applicable to the characterization of energetics in nanomaterials of interest both for photovoltaic and sunlight-to-fuel photoelectrochemical assemblies.

14 SOLAR ENERGY↗

Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training

In this paper, we extend an existing runtime system (the TensorFlow runtime) to enable automatic concurrency control and scheduling of operations. We explore performance modeling to predict the performance of operations with various thread- level parallelism. Our performance model is highly accurate and lightweight. Leveraging the performance model, our runtime system employs a set of scheduling strategies that co-run opera- tions to improve hardware utilization and system throughput. Our runtime system demonstrates a significant performance benefit. Comparing with using the recommended configurations for concurrency control and operation scheduling in TensorFlow, our approach achieves 36% performance (execution time) im- provement on average (up to 49%) for four neural network models, and achieves high performance close to the optimal one manually obtained by the user.

Liu, Jiawen↗

Water Impact Test and Simulation of a Composite Energy Absorbing Fuselage Section

In March 2002, a 25-ft/s vertical drop test of a composite fuselage section was conducted onto water. The purpose of the test was to obtain experimental data characterizing the structural response of the fuselage section during water impact for comparison with two previous drop tests that were performed onto a rigid surface and soft soil. For the drop test, the fuselage section was configured with ten 100-lb. lead masses, five per side, that were attached to seat rails mounted to the floor. The fuselage section was raised to a height of 10-ft. and dropped vertically into a 15-ft. diameter pool filled to a depth of 3.5-ft. with water. Approximately 70 channels of data were collected during the drop test at a 10-kHz sampling rate. The test data were used to validate crash simulations of the water impact that were developed using the nonlinear, explicit transient dynamic codes, MSC.Dytran and LS-DYNA. The fuselage structure was modeled using shell and solid elements with a Lagrangian mesh, and the water was modeled with both Eulerian and Lagrangian techniques. The fluid-structure interactions were executed using the fast general coupling in MSC.Dytran and the Arbitrary Lagrange-Euler (ALE) coupling in LS-DYNA. Additionally, the smooth particle hydrodynamics (SPH) meshless Lagrangian technique was used in LS-DYNA to represent the fluid. The simulation results were correlated with the test data to validate the modeling approach. Additional simulation studies were performed to determine how changes in mesh density, mesh uniformity, fluid viscosity, and failure strain influence the test-analysis correlation.

Fasanella, Edwin L.↗

A modeling analysis program for the JPL table mountain Io sodium cloud

Progress and achievements in the first year are discussed in three main areas: (1) review and assessment of the massive JPL Table Mountain Io sodium cloud data set, (2) formulation and execution of a plan to perform further processing of this data set, and (3) initiation of modeling activities. The complete 1976-79 and 1981 data sets are reviewed. Particular emphasis is placed on the superior 1981 Region B/C images which provide a rich base of information for studying the structure and escape of gases from Io as well as possible east-west and magnetic longitudinal asymmetries in the plasma torus. A data processing plan is developed and is undertaken by the Multimission Image Processing Laboratory of JPL for the purpose of providing a more refined and complete data set for our modeling studies in the second year. Modeling priorities are formulated and initial progress in achieving these goals is reported.

Smyth, W. H.↗

Artificial Neural Network Modeling for Airline Disruption Management

Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems (UAS), operations and infrastructure, to the system. To this end, we use historical data on airline scheduling and operations recovery to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, we provide a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.

Kolawole Ogunsina↗

Task planning with uncertainty for robotic systems

In a practical robotic system, it is important to represent and plan sequences of operations and to be able to choose an efficient sequence from them for a specific task. During the generation and execution of task plans, different kinds of uncertainty may occur and erroneous states need to be handled to ensure the efficiency and reliability of the system. An approach to task representation, planning, and error recovery for robotic systems is demonstrated. Our approach to task planning is based on an AND/OR net representation, which is then mapped to a Petri net representation of all feasible geometric states and associated feasibility criteria for net transitions. Task decomposition of robotic assembly plans based on this representation is performed on the Petri net for robotic assembly tasks, and the inheritance of properties of liveness, safeness, and reversibility at all levels of decomposition are explored. This approach provides a framework for robust execution of tasks through the properties of traceability and viability. Uncertainty in robotic systems are modeled by local fuzzy variables, fuzzy marking variables, and global fuzzy variables which are incorporated in fuzzy Petri nets. Analysis of properties and reasoning about uncertainty are investigated using fuzzy reasoning structures built into the net. Two applications of fuzzy Petri nets, robot task sequence planning and sensor-based error recovery, are explored. In the first application, the search space for feasible and complete task sequences with correct precedence relationships is reduced via the use of global fuzzy variables in reasoning about subgoals. In the second application, sensory verification operations are modeled by mutually exclusive transitions to reason about local and global fuzzy variables on-line and automatically select a retry or an alternative error recovery sequence when errors occur. Task sequencing and task execution with error recovery capability for one and multiple soft components in robotic systems are investigated.

Cao, Tiehua↗

Development of Physics-Based Transition Models for Unstructured-Mesh CFD Codes Using Deep Learning Models

Predicting transition locations over a vehicle surface is of fundamental importance for many engineering applications. With the transition information, the Reynolds-averaged Navier-Stokes (RANS) computations can turn on the turbulence model at the right locations so that drag, lift and other aerodynamic quantities can be accurately predicted. In contrast to the popularity of RANS-based transition modeling in which transition onset is governed by the turbulence equations, physics-based transition models that account for instability waves within the boundary layer, thus more compliant to flow physics, only gained more attention in recent years. This paper describes the development of a new physics-based transition model based on either the linear stability theory (LST) or parabolized stability equations (PSE). The model is designed to communicate with a structured or unstructured-mesh RANS solver back and forth in order to more accurately compute transition fronts over a three-dimensional body. In the developed model, the Python suite of interface codes in conjunction with the LASTRAC software can be executed autonomously to produce transition onset locations for a given laminar or RANS-computed transitional state. In addition, as a proof of concept, the tool set consists of a deep learning neural network model that has been designed and trained to predict instability wave evolutions inside the boundary layer for various instability wave mechanisms across a selected speed range. A machine-learned intelligent profile interpolation model has also been devised to enable reliable instability-wave spectra predictions with just a few points in the mean flow profiles.

Transition Modeling↗

An agentic artificially intelligent X-ray scientist

Executing experimental tasks in both normal research laboratories and large-scale scientific facilities often requires extensive human supervision and remains a key challenge on the path to fully autonomous, artificial intelligence (AI)-driven science. Here we demonstrate a large language model-driven agent that autonomously performs X-ray sample alignment on a synchrotron beamline by planning actions, executing instrumental commands, interpreting observations and iterating towards experimental goals. Based on existing large language models with structured tool-use via the model context protocol, our AI X-ray scientist was guided and tested using an in-house-built virtual experimental setup that mirrors a six-circle diffractometer at an operational synchrotron beamline. The agentic workflow developed in the virtual environment was directly deployed on a real beamline, where it correctly identified reference reflections and determined the orientation matrix, an essential first step in any type of single-crystal scattering experiment. Our AI X-ray scientist responded effectively to unexpected experimental conditions, demonstrating adaptive problem-solving and readiness for addressing practical experimental situations. Our study provides a step towards autonomous operation across diverse experimental environments at large-scale scattering facilities.

Chen, Zhantao (ORCID:0000000319543868)↗

Addressing the Hard Factors for Command File Errors by Probabilistic Reasoning

Command File Errors (CFE) are managed using standard risk management approaches at the Jet Propulsion Laboratory. Over the last few years, more emphasis has been made on the collection, organization, and analysis of these errors for the purpose of reducing the CFE rates. More recently, probabilistic modeling techniques have been used for more in depth analysis of the perceived error rates of the DAWN mission and for managing the soft factors in the upcoming phases of the mission. We broadly classify the factors that can lead to CFE's as soft factors, which relate to the cognition of the operators and hard factors which relate to the Mission System which is composed of the hardware, software and procedures used for the generation, verification & validation and execution of commands. The focus of this paper is to use probabilistic models that represent multiple missions at JPL to determine the root cause and sensitivities of the various components of the mission system and develop recommendations and techniques for addressing them. The customization of these multi-mission models to a sample interplanetary spacecraft is done for this purpose.

Meshkat, Leila↗

Simulations of hybrid system varying solar radiation and microturbine response time

Hybrid power systems, such as combinations of renewable power sources with intermittent power production and non-renewable power sources, theoretically increase the reliability and thus integration of renewable sources in the electrical system. However, a recent increase in the number of hybrid installations has sparked interest in the effects of their connection to the grid, especially in remote areas. This paper analyses a photovoltaic-gas microturbine hybrid system dimensioned to be installed in La Paz (Mexico).The research presented in this paper studies and quantifies the effects on the total electric power produced, varying both the solar radiation and the gas microturbine response time. The gas microturbine and the photovoltaic panels are modelled using Matlab/Simulink software, obtaining a platform where different tests to simulate real conditions have been executed. They consist of diverse ramps of irradiance that replicate solar radiation variations, and different microturbine response times reproduced by the time constants of a first order transfer function that models the microturbine dynamic response. The results obtained show that when radiation varies quickly it does not produce significant differences in the power guarantee or the microturbine gas consumption, to any microturbine response time. However, these two parameters are highly variable with smooth radiance variations. The maximum total power variation decreases greatly as the radiation variation gets lower. In addition, by decreasing the microturbine response time, it is possible to appreciably increase the power guarantee although the maximum power variation and gas consumption increase. Only in cases of low radiation variation is there no appreciable difference in the maximum power variation obtained by the different turbine response times.

14 SOLAR ENERGY↗

Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.

97 MATHEMATICS AND COMPUTING↗

Hybrid reduced order modeling for assembly calculations

While the accuracy of assembly calculations has greatly improved due to the increase in computer power enabling more refined description of the phase space and use of more sophisticated numerical algorithms, the computational cost continues to increase which limits the full utilization of their effectiveness for routine engineering analysis. Reduced order modeling is a mathematical vehicle that scales down the dimensionality of large-scale numerical problems to enable their repeated executions on small computing environment, often available to end users. This is done by capturing the most dominant underlying relationships between the model's inputs and outputs. Previous works demonstrated the use of the reduced order modeling for a single physics code, such as a radiation transport calculation. This paper extends those works to coupled code systems as currently employed in assembly calculations. Finally, numerical tests are conducted using realistic SCALE assembly models with resonance self-shielding, neutron transport, and nuclides transmutation/depletion models representing the components of the coupled code system.

97 MATHEMATICS AND COMPUTING↗