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MEXEC: An Onboard Integrated Planning and Execution Approach for Spacecraft Commanding
The traditional form of spacecraft commanding is with sequences that specify when commands should execute based on a schedule generated on the ground. Some sequences have control logic and event driven responses to increase flexibility, but it is limited. An approach to increase autonomy is to use goal-based planning and commanding. Using this paradigm, intention and behavior is modeled on board the spacecraft. In this paper we describe MEXEC (Multi-mission EXECutive), a multi-mission, task-based, onboard planning and execution software designed specifically to be used as flight software. As a path to infusion for future flight projects, we describe two experiments performed on the ASTERIA CubeSat and testbed that demonstrate that MEXEC can be integrated and used for spacecraft operations and increase robustness and science return compared to the standard sequences that were being used.
Superconducting Notch Filter for RFI Mitigation in Ground-Based Radio Telescope
This paper presents a high-rejection, thin-film HTS, microstrip bandstop filter to prevent a local and high-power RFI source from interfering with NASA GGAO’s VGOS cryogenic receiver. This filter has an excellent 2.7%, 50-dB-fractionalbandwidth and a center stop-band frequency of 9.41 GHz. It does not contain any narrow or interdigital features as found in some designs, which reduces the fringing electric fields and improves its power handling capability. The YBCO films were grown on 435-µm-thick R-plane sapphire substrate and the anisotropic behavior was modeled and simulated with a high degree of accuracy. The device was tested while cooled to 77 K and the measurements agree well with simulation.
Global Sensitivity Analyses for Test Planning with Black-Box Models for Mars Sample Return
This work describes sensitivity analyses performed on complex black-box models used to support experimental test planning under limited resources in the context of the Mars Sample Return program, which aims at bringing to Earth rock and atmospheric samples from Mars. We develop a systematic workflow that allows the analysts to simultaneously obtain quantitative insights on key drivers of uncertainty, on the direction of impact, and the presence of interactions. We apply novel optimal transport-based global sensitivity measures to tackle the multivariate nature of the output. On the modeling side, we apply multi-fidelity techniques that leverage low-fidelity models to speed up the calculations and make up for the limited amount of high-fidelity samples, while keeping these in the loop for accuracy guarantees. The sensitivity analysis reveals insights useful for the analysts to understand the model's behavior and identify the factors to focus on during testing in order to maximize the value of information extracted from them to ensure mission success when limited resources are available.
In-situ Nanoscale Ablation
An understanding of the ablation of carbon-based materials is crucial to modeling the behavior of atmospheric entry spacecrafts equipped with thermal protection systems (TPS).Carbon is the backbone of TPS systems such as PICA (phenolic-impregnated carbon ablator).Therefore in the present work we study ablation of highly oriented pyrolytic graphite (HOPG)in oxygen at temperatures up to 1000°C done within a gas reaction cell housed in a scanning transmission electron microscope (STEM). Observation of the HOPG oxidation on specimens sectioned parallel and normal to the carbon basal planes in the presence of oxygen are reported.Pitting caused by residual oxygen and/or platinum particles from the sectioning process was observed before oxygen gas flow was established at 750°C in the specimen sectioned with the carbon basal planes parallel to the beam direction. Introduction of oxygen flow caused rapid oxidation moving in a uniform front that completely consumed the HOPG in approximately 1.5minutes. The specimen sectioned with the carbon basal planes normal to the beam direction did not show the same pitting phenomenon but exhibited rapid oxidation at 1000°C that proceeded in a uniform front and completed in approximately 1 minute. All specimens tested had a husk resembling the original specimen shape left over after oxidation. It is concluded that this husk is most likely ash impurity from the HOPG or impurities from the sectioning process or E-chip. A residual gas analyszer (RGA) was successfully used to monitor gas flows during the experiments but was yet unsuccessful in monitoring oxidation gas products produced during the experiment.Future studies will optimize the RGA setup in order to maximize the potential for detecting these species. These in-situ studies successfully show how this highly ordered carbon ablates on a nano- and micro-scopic length scale and can be used to provide fundamental understanding of carbon ablation that can be used to design the next-generation of TPS systems.
Physics-Informed Machine Learning-Aided System Space Discretization
Decision-making is the process of identifying and choosing alternatives based on an agreed-upon set of metrics and preferences established by the decision-maker. There are options to be considered during the decision-making process and each option offers a different trajectory and associated success profile in moving from a given system state to the desired system state. The decision-making process typically involves uncertainties associated with the current component and system states. In this sense, probabilistic risk assessment (PRA) can be an analytical method and tool for accomplishing the probabilistic aspect of the decision-making process. Dynamic PRA is an evolution of conventional PRA methodology in which driving forces on modeled plant elements and the element behaviors are explicitly modeled over time. In the recent past, risk assessment methodologies have evolved to address risk issues in a continuously evolving environment and a novel probabilistic dynamics framework in continuous time and state-space discretization forms has been proposed. While state-space discretization has shown its strength in both consequence and causal reasoning modes, several challenges, including the computational requirement and physically meaningful system state identification, exist. Conventional system space discretization has usually been done by either the equal width discretization method or a data-driven method. Those methods naturally possess challenges coming from the physical understanding of discretized system space (i.e., system state) and the trajectory moving from a given system state to another system state. The purpose of this paper is to present a physics-based and data-driven system state discretization method such that one can justify what the discretized system space implies and understand the state trajectory from the viewpoint of operational actions.
A Study of Wake Characteristics of Marine Turbine Arrays: Preprint
As the marine energy industry continues to develop and installations grow from single turbines to small arrays and larger farms, understanding and predicting wake behavior using modeling tools is necessary for array design and optimization. The wake characteristics of upstream turbines, including velocity deficit, wake swirling, tip vortices, wake shape and direction, and flow recovery, influence the inflow conditions experienced by downstream turbines in an array and, subsequently, affect the individual turbine loading and power generated by the array. These wake characteristics are influenced by many factors, including site geography, inflow shear profile, turbulence, waves, the strength of the current resource, gravitational forces, storm surges, and seasonal effects like temperature and wind conditions. Efficient array modeling tools that capture these wake effects and resulting turbine-to-turbine interactions need to be developed to optimize farm layouts and design individual turbines for operation within arrays. Unlike the wind industry, the marine energy research community is largely limited to the use of either numerical tools or tank/flume testing to understand marine turbine wake interactions. This paper will investigate recent advances in numerical and experimental marine turbine wake modeling, with a focus on recent publications and on both axial-flow and cross-flow marine turbines. A summary of recent research on these topics is provided. Additionally, a demonstration of turbine wake interactions using computational fluid dynamics is presented.
Nondestructive Evaluation of Concrete: Elastic Property Imaging Through Full-Waveform Inversion
Concrete is a vital material in construction—especially in the nuclear industry, where it is used in critical structures such as containment vessels. Over time, concrete can degrade due to harsh operational and environmental conditions, necessitating that its elastic properties be accurately evaluated to ensure structural integrity and safety. Traditional nondestructive evaluation methods such as ultrasound-based techniques often rely on simplifying assumptions that may not hold true for concrete. This paper presents an advanced ultrasound-based method that uses elastic full-waveform inversion (EFWI) to create detailed images of concrete’s mechanical properties. By accurately modeling wave behaviors such as scattering and reflection, we aim to overcome the limitations of conventional ultrasonic-based methods. In this work, the imaging problem involved reconstructing the various elastic properties of a heterogenous concrete block with three steel rebars embedded in it. The ultrasonic measurements were synthetically generated from multiple sources and receivers, and the reconstruction process was performed using a gradient-based optimization algorithm. Our approach leveraged EFWI to reconstruct high-resolution images of the pressure wave speed, shear wave speed, and density. Multiple misfit functions—including L2-norm, cross-correlation (CC), and L1-norm—combined with total variation (TV) regularization and parameter constraints using a Sigmoid function—were explored for the reconstruction. The results demonstrated that using the L1-norm misfit function in conjunction with TV regularization and Sigmoid constraints significantly improved the reconstruction quality in comparison to traditional methods. This approach provided clearer images with fewer artifacts and better captured background heterogeneity. Our findings highlight that, when properly designed, EFWI carries great potential for providing comprehensive, more accurate, and more reliable assessments of concrete conditions, as is crucial for the maintenance and safety of nuclear power plant structures.
Mu2e Production Target Thermal Test Project
The Mu2e experiment requires a production target that is capable of operating under extreme thermal conditions caused by an 8 GeV proton beam. This project’s objective supports the development of the Mu2e Pro- duction Target by testing the Stickman model’s thermal behavior. Angel Flores Luviano has assisted Jonathan Williams in progressing this project by contributing to the development of a Radiative Cooling Test Fixture (RCTF) that will be used to evaluate the thermal behavior of the new production target model. Engineering calculations were performed to an- alyze thermal performance and pressure drop within the cold well and wa- ter cooling circuit. These calculations also determined the optimal sizing for key components of the water system. An engineering note was made to document these calculations. CAD models of the water circuit piping, thermocouple mounting bars, and radiator chimney were developed, and prototype test rig components were fabricated using 3D printing. Future work will focus on continued RCTF development which includes control system integration, heater hardware design, and interfaces that can be scaled up to increase thermal capacity.
Automated Generation of Fault Management Artifacts from a Simple System Model
Our understanding of off-nominal behavior - failure modes and fault propagation - in complex systems is often based purely on engineering intuition; specific cases are assessed in an ad hoc fashion as a (fallible) fault management engineer sees fit. This work is an attempt to provide a more rigorous approach to this understanding and assessment by automating the creation of a fault management artifact, the Failure Modes and Effects Analysis (FMEA) through querying a representation of the system in a SysML model. This work builds off the previous development of an off-nominal behavior model for the upcoming Soil Moisture Active-Passive (SMAP) mission at the Jet Propulsion Laboratory. We further developed the previous system model to more fully incorporate the ideas of State Analysis, and it was restructured in an organizational hierarchy that models the system as layers of control systems while also incorporating the concept of "design authority". We present software that was developed to traverse the elements and relationships in this model to automatically construct an FMEA spreadsheet. We further discuss extending this model to automatically generate other typical fault management artifacts, such as Fault Trees, to efficiently portray system behavior, and depend less on the intuition of fault management engineers to ensure complete examination of off-nominal behavior.
Non-Fermi Liquid Behavior in the Three-Dimensional Hubbard Model
We present a numerical study on the non-Fermi liquid behavior of a three-dimensional strongly correlated system. The Hubbard model in a simple cubic lattice is simulated by the dynamical cluster approximation; in particular, the quasi-particle weight is calculated at finite dopings for a range of temperatures. By fitting the quasi-particle weight to the marginal Fermi liquid form at finite doping near the putative quantum critical point, we find evidence of a separatrix between Fermi liquid and non-Fermi liquid regions. Our results suggest that a marginal Fermi liquid and possibly a quantum critical point exist in the non-symmetry broken solution of the three-dimensional interacting electron systems. We also calculate the spectral function, close to the half-filling, and we obtain evidence of pseudogap.
An Introduction to Markov Modeling: Concepts and Uses
Kharkov modeling is a modeling technique that is widely useful for dependability analysis of complex fault tolerant systems. It is very flexible in the type of systems and system behavior it can model. It is not, however, the most appropriate modeling technique for every modeling situation. The first task in obtaining a reliability or availability estimate for a system is selecting which modeling technique is most appropriate to the situation at hand. A person performing a dependability analysis must confront the question: is Kharkov modeling most appropriate to the system under consideration, or should another technique be used instead? The need to answer this gives rise to other more basic questions regarding Kharkov modeling: what are the capabilities and limitations of Kharkov modeling as a modeling technique? How does it relate to other modeling techniques? What kind of system behavior can it model? What kinds of software tools are available for performing dependability analyses with Kharkov modeling techniques? These questions and others will be addressed in this tutorial.
Emergent Behaviors and the Limits of Large Language Model Generalization
Over the past couple years, large language models (LLMs) have rapidly grown in size and performance across wide numbers of tasks, leading some to herald them as machine learning models with truly “general” capabilities. We address some of the ways that the language used to describe these models can be both deceptive and a poor framing for making progress towards understanding their capabilities, and also review arguments on to what extent these models can represent and interact with the meaning of language. Emergent behaviors in these highly complex models are unpredictable and research into understanding them is still in early phases, which limits the strength of claims of generality about them. Moreover, while these models demonstrate surprising emergent reasoning capabilities on many tasks, there are still many limitations in generalization that overall high benchmark performance can hide. We review the literature showcasing limitations of LLMs as well as techniques to mitigate or overcome these challenges, while also highlighting how fundamental problems in model evaluation may prevent true claims of generality. We conclude with a practical section on using LLMs on real world problems while appropriately evaluating and validating these tasks to prevent or mitigate the downstream impacts of incorrect results.
Creating Systems Engineering Products with Executable Models in a Model-Based Engineering Environment
Applying systems engineering across the life-cycle results in a number of products built from interdependent sources of information using different kinds of system level analysis. This paper focuses on leveraging the Executable System Engineering Method (ESEM) which automates requirements verification (e.g. power and mass budget margins and duration analysis of operational modes) using executable SysML models. The particular value proposition is to integrate requirements, and executable behavior and performance models for certain types of system level analysis. The models are created with modeling patterns that involve structural, behavioral and parametric diagrams, and are managed by an open source Model Based Engineering Environment (named OpenMBEE). This paper demonstrates how the ESEM is applied in conjunction with OpenMBEE to create key engineering products (e.g. operational concept document) for the Alignment and Phasing System (APS) within the Thirty Meter Telescope (TMT) project, which is under development by the TMT International Observatory (TIO).
Safety evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections
Connected and Automated Vehicles (CAVs) can potentially improve the performance of the transportation system by reducing human errors. This paper investigates the safety impact of CAVs in a mixed traffic with conventional vehicles at intersections. Analyzing real-world AV crashes in California revealed that rear-end crashes at intersections are the dominant crash type. Therefore, to enhance our understanding of the future interactions between human-driven vehicles with CAVs at intersections, a simulation framework was developed to model the mixed traffic environment of Automated Vehicles (AV), cooperative AVs, and conventional human-driven vehicles. In order to model AVs driving behavior, Adaptive Cruise Control (ACC) and cooperative ACC (CACC) models are utilized. Particularly, this study explores system improvements due to automation and connectivity across varying CAV market penetration scenarios. ACC and CACC car following models are used to mimic the behavior of AVs and cooperative AVs. Real-world connected vehicle data are utilized to modify and tune the acceleration/deceleration regimes of the Wiedemann model. Next, the driving volatility concept capturing variability in vehicle speeds was utilized to calibrate the simulation to represent the safety performance of a real-world environment. Two surrogate safety measures are used to evaluate the safety performance of a representative intersection under different market penetration rate of CAVs: the number of longitudinal conflicts and driving volatility. At low levels of ACC market penetration, the safety improvements were found to be marginal, but safety improved substantially with more than 40% ACC penetration. Additional safety improvements can be achieved more quickly through the addition of cooperation and connectivity through CACC. Furthermore, ACC/CACC vehicles were found to improve mobility performance in terms of average speed and travel time at intersections.
A comparison of two plasma models
The time dependent behavior of a plasma which surrounds a highly biased conducting sphere is considered. This plasma is treated as either a cold two component fluid or as a warm plasma whose time development can be found by solving the Vlasov equation. Both models demonstrate oscillatory behavior, but the electric fields predicted by the models are quantitatively quite different in regions close to the surface of the sphere and very similar otherwise. A broadening of the electron distribution function with time is observed indicating local heating of the plasma near the surface of the sphere.
Computer-aided operations engineering with integrated models of systems and operations
CONFIG 3 is a prototype software tool that supports integrated conceptual design evaluation from early in the product life cycle, by supporting isolated or integrated modeling, simulation, and analysis of the function, structure, behavior, failures and operation of system designs. Integration and reuse of models is supported in an object-oriented environment providing capabilities for graph analysis and discrete event simulation. Integration is supported among diverse modeling approaches (component view, configuration or flow path view, and procedure view) and diverse simulation and analysis approaches. Support is provided for integrated engineering in diverse design domains, including mechanical and electro-mechanical systems, distributed computer systems, and chemical processing and transport systems. CONFIG supports abstracted qualitative and symbolic modeling, for early conceptual design. System models are component structure models with operating modes, with embedded time-related behavior models. CONFIG supports failure modeling and modeling of state or configuration changes that result in dynamic changes in dependencies among components. Operations and procedure models are activity structure models that interact with system models. CONFIG is designed to support evaluation of system operability, diagnosability and fault tolerance, and analysis of the development of system effects of problems over time, including faults, failures, and procedural or environmental difficulties.