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At least 577 records · Page 32

Optimal Trajectories for the Helicopter in One-Engine-Inoperative Terminal-Area Operations

This paper presents a summary of a series of recent analytical studies conducted to investigate one-engine-inoperative (OEI) optimal control strategies and the associated optimal trajectories for a twin engine helicopter in Category-A terminal-area operations. These studies also examine the associated heliport size requirements and the maximum gross weight capability of the helicopter. Using an eight states, two controls, augmented point-mass model representative of the study helicopter, continued takeoff (CTO), rejected takeoff (RTO), balked landing (BL), and continued landing (CL) are investigated for both vertical-takeoff-and-landing (VTOL) and short-takeoff-and-landing (STOL) terminal-area operations. The formulation of the non-linear optimal control problems with considerations for realistic constraints, solution methods for the two-point boundary-value problem, a new real-time generation method for the optimal OEI trajectories, and the main results of this series of trajector optimization studies are presented. In particular, a new balanced-weight concept for determining the takeoff decision point for VTOL Category-A operations is proposed, extending the balanced-field length concept used for STOL operations.

Chen, Robert T. N.↗

Outer-Planet Mission Analysis Using Solar-Electric Ion Propulsion

Outer-planet mission analysis was performed using three next generation solar-electric ion thruster models. Optimal trajectories are presented that maximize the delivered mass to the designated outer planet. Trajectories to Saturn and Neptune with a single Venus gravity assist are investigated. For each thruster model, the delivered mass versus flight time curve was generated to obtain thruster model performance. The effects of power to the thrusters and resonance ratio of Venutian orbital periods to spacecraft period were also studied. Multiple locally optimal trajectories to Saturn and Neptune have been discovered in different regions of the parameter search space. The characteristics of each trajectory are noted.

Woo, Byoungsam↗

Processing Motion Signals in Complex Environments

Motion information is critical for human locomotion and scene segmentation. Currently we have excellent neurophysiological models that are able to predict human detection and discrimination of local signals. Local motion signals are insufficient by themselves to guide human locomotion and to provide information about depth, object boundaries and surface structure. My research is aimed at understanding the mechanisms underlying the combination of motion signals across space and time. A target moving on an extended trajectory amidst noise dots in Brownian motion is much more detectable than the sum of signals generated by independent motion energy units responding to the trajectory segments. This result suggests that facilitation occurs between motion units tuned to similar directions, lying along the trajectory path. We investigated whether the interaction between local motion units along the motion direction is mediated by contrast. One possibility is that contrast-driven signals from motion units early in the trajectory sequence are added to signals in subsequent units. If this were the case, then units later in the sequence would have a larger signal than those earlier in the sequence. To test this possibility, we compared contrast discrimination thresholds for the first and third patches of a triplet of sequentially presented Gabor patches, aligned along the motion direction. According to this simple additive model, contrast increment thresholds for the third patch should be higher than thresholds for the first patch.The lack of a measurable effect on contrast thresholds for these various manipulations suggests that the pooling of signals along a trajectory is not mediated by contrast-driven signals. Instead, these results are consistent with models that propose that the facilitation of trajectory signals is achieved by a second-level network that chooses the strongest local motion signals and combines them if they occur in a spatio-temporal sequence consistent with a trajectory. These results parallel the lack of increased apparent contrast along a static contour made up of similarly oriented elements.

Verghese, Preeti↗

A study of the accuracy of neutrally buoyant bubbles used as flow tracers in air

Research has been performed to determine the accuracy of neutrally buoyant and near neutrally buoyant bubbles used as flow tracers in air. Theoretical, computational, and experimental results are presented to evaluate the dynamics of bubble trajectories and factors affecting their ability to trace flow-field streamlines. The equation of motion for a single bubble was obtained and evaluated using a computational scheme to determine the factors which affect a bubble's trajectory. A two-dimensional experiment was also conducted to experimentally determine bubble trajectories in the stagnation region of NACA 0012 airfoil at 0 deg angle of attack using a commercially available helium bubble generation system. Physical properties of the experimental bubble trajectories were estimated using the computational scheme. These properties included the density ratio and diameter of the individual bubbles. the helium bubble system was then used to visualize and document the flow field about a 30 deg swept semispan wing with simulated glaze ice. Results were compared to Navier-Stokes calculations and surface oil flow visualization. The theoretical and computational analysis have shown that neutrally buoyant bubbles will trace even the most complex flow patterns. Experimental analysis revealed that the use of bubbles to trace flow patterns should be limited to qualitative measurements unless care is taken to ensure neutral buoyancy. This is due to the difficulty in the production of neutrally buoyant bubbles.

Kerho, Michael F.↗

Optimizing infrasound observations for sample return capsule re-entry: Insights from OSIRIS-REx and Hayabusa2

The atmospheric entry of meteoroids presents a rare and unpredictable phenomenon, posing challenges for systematic observation and detailed characterization. Such events are nonetheless critical for advancing understanding of acoustic wave propagation, atmospheric structure, and entry dynamics. In contrast, sample return capsules (SRCs) from space missions follow well constrained re-entry trajectories, enabling planned observations of shock wave generation and propagation under controlled conditions. This study compares two SRC atmospheric entries, Hayabusa2 in 2020 and OSIRIS-REx in 2023, to assess how different infrasound array configurations influence shock wave detection and trajectory validation. Hayabusa2’s re-entry was monitored using a distributed network of 28 portable infrasound sensors across seven arrays in Woomera, Australia, permitting three-dimensional reconstruction of the trajectory and analysis of wave characteristics. For OSIRIS-REx, a compact four-sensor array deployed near Eureka Airport provided trajectory confirmation through arrival-time differences and back-azimuth estimates. Spectral and waveform analyses revealed differences in signal properties associated with variations in entry angle and velocity. The results illustrate both the strengths and the limitations of the deployed array configurations. The dense and distributed arrays during Hayabusa2’s re-entry enabled detailed trajectory reconstruction, whereas the compact array at Eureka primarily provided confirmation of signal coherence and back-azimuth consistency. These case studies highlight that even relatively small arrays, if located close to the predicted ground track, can still capture useful information on arrival direction and timing. Such insights provide practical guidance for planning future observational campaigns of SRC returns and other controlled atmospheric entries, and may also inform approaches to opportunistic observations of meteoroid events and other atmospheric acoustic phenomena.

celestial mechanics: orbit determination↗

Automated Classification of Vehicle Movements at Signalized Intersections Using Vehicle Trajectories

Accurate vehicle movement classification through signalized intersections is of paramount importance to the analysis of intersection performance and the optimization of traffic control strategies. Conventional techniques for tracking vehicle turning movements depend on infrastructure-based strategies like human counts, loop detectors, and video analytics, all of which are costly, prone to errors, and spatially constrained. High-frequency trajectory data can be utilized to determine vehicle movement patterns in a scalable and infrastructure-independent method due to the adoption of connected vehicles (CVs). In recent years, several studies have utilized connected vehicle data to generate performance measures. Most of the trajectory-based performance measures approaches, however, require map matching-i.e., extracting geospatial references from maps to identify the movements that individual vehicles make at a signalized intersection. These approaches are often time-consuming and hinder scalability since geographic features need to be provided for an analysis to be conducted. Map matching methods are prone to errors as different map versions change these geographic features. This research presents a novel automatic classification pipeline that uses CV trajectory data to classify vehicle movements at signalized crossings, specifically pass-through left-turn and right-turn maneuvers. The process starts by filtering trips that cross a spatial bounding box that has been defined at the target intersection. Approach and departure headings for each trajectory crossing the boundary are computed and are clustered together to identify dominant movements. The proposed algorithm is used to classify the movement of vehicles at 10 intersections in the state of California, and the results indicate that the algorithm can classify movements at these intersections with varying traffic volumes and road network configurations, all in a map-less framework with no need for conflation of vehicle trajectories to a digital base map.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A multiscale model of immune surveillance in micrometastases gives insights on cancer patient digital twins

Abstract Metastasis is the leading cause of death in patients with cancer, driving considerable scientific and clinical interest in immunosurveillance of micrometastases. We investigated this process by creating a multiscale mathematical model to study the interactions between the immune system and the progression of micrometastases in general epithelial tissue. We analyzed the parameter space of the model using high-throughput computing resources to generate over 100,000 virtual patient trajectories. We demonstrated that the model could recapitulate a wide variety of virtual patient trajectories, including uncontrolled growth, partial response, and complete immune response to tumor growth. We classified the virtual patients and identified key patient parameters with the greatest effect on the simulated immunosurveillance. We highlight the lessons derived from this analysis and their impact on the nascent field of cancer patient digital twins (CPDTs). While CPDTs could enable clinicians to systematically dissect the complexity of cancer in each individual patient and inform treatment choices, our work shows that key challenges remain before we can reach this vision. In particular, we show that there remain considerable uncertainties in immune responses, unreliable patient stratification, and unpredictable personalized treatment. Nonetheless, we also show that in spite of these challenges, patient-specific models suggest strategies to increase control of clinically undetectable micrometastases even without complete parameter certainty.

Mathematical & Computational Biology↗

Probing reaction channels via reinforcement learning

Abstract Chemical reactions are dynamical processes involving the correlated reorganization of atomic configurations, driving the conversion of an initial reactant into a result product. By virtue of the metastability of both the reactants and products, chemical reactions are rare events, proceeding fleetingly. Reaction pathways can be modelled probabilistically by using the notion of reactive density in the phase space of the molecular system. Such density is related to a function known as the committor function, which describes the likelihood of a configuration evolving to one of the nearby metastable regions. In theory, the committor function can be obtained by solving the backward Kolmogorov equation (BKE), which is a partial differential equation (PDE) defined in the full dimensional phase space. However, using traditional methods to solve this problem is not practical for high dimensional systems. In this work, we propose a reinforcement learning based method to identify important configurations that connect reactant and product states along chemical reaction paths. By shooting multiple trajectories from these configurations, we can generate an ensemble of states that concentrate on the transition path ensemble. This configuration ensemble can be effectively employed in a neural network-based PDE solver to obtain an approximation solution of a restricted BKE, even when the dimension of the problem is very high. The resulting solution provides an approximation for the committor function that encodes mechanistic information for the reaction, paving a new way for understanding of complex chemical reactions and evaluation of reaction rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Open-Loop Control Co-Design of Semisubmersible Floating Offshore Wind Turbines Using Linear Parameter-Varying Models

Here, this paper discusses a framework to design elements of the plant and control systems for floating offshore wind turbines in an integrated manner using linear parameter-varying models. Multiple linearized models derived from aero-elastic simulation software in different operating regions characterized by the incoming wind speed are combined to construct an approximate low-fidelity model of the system. The combined model is then used to generate open-loop, optimal control trajectories as part of a nested control co-design strategy that explores the system’s power production and stability using the platform pitch tilt as a proxy in the context of crucial plant and control design decisions. The radial distance between the central and outer columns and the diameter of the outer columns of the semisubmersible platform are the plant design variables. The platform stability and power production are studied for different plant design decisions. The effect of plant decisions on subsequent power production and stability response of the floating wind turbine is quantified in terms of the levelized cost of energy. The results show that the inner-loop constraints and the plant design decisions affect the turbine’s power and, subsequently, the cost of the system.

17 WIND ENERGY↗

Keeping LAMMPS cutting edge

Since its inception 30 years ago, LAMMPS has grown to be a world-class molecular dynamics code and a cornerstone of computational materials science research. This project aimed to keep LAMMPS at the forefront of molecular dynamics simulations by adapting LAMMPS to the latest developments in machine learning technology and hardware. Initially, the project set out to provide a unified implementation of active learning for efficient training data generation in LAMMPS, but the research trajectory pivoted to address more immediate and impactful opportunities. On the hardware side, recent record-breaking molecular dynamics simulations were developed on the Cerebras wafer-scale AI chip, and this project has developed an interface between LAMMPS and the hardware-specific molecular dynamics code to accelerate and simplify development and user adoption. On the software side, PyTorch’s Ahead-of-Time (AOT) compilation features promised increased performance for state-of-the-art equivariant neural network potentials, and this project laid the groundwork for their adoption in LAMMPS, resulting in a nearly 20x acceleration in extreme cases. Combined with a comprehensive benchmark study of LAMMPS across all current exascale systems, this project has reinforced LAMMPS’s role as a versatile, high-performance tool for current and future materials science applications.

36 MATERIALS SCIENCE↗

Autonomous planetary rover at Carnegie Mellon

This report describes progress in research on an autonomous robot for planetary exploration. In 1989, the year covered by this report, a six-legged walking robot, the Ambler, was configured, designed, and constructed. This configuration was used to overcome shortcomings exhibited by existing wheeled and walking robot mechanisms. The fundamental advantage of the Ambler is that the actuators for body support are independent of those for propulsion; a subset of the planar joints propel the body, and the vertical actuators support and level the body over terrain. Models of the Ambler's dynamics were developed and the leveling control was studied. An integrated system capable of walking with a single leg over rugged terrain was implemented and tested. A prototype of an Ambler leg is suspended below a carriage that slides along rails. To walk, the system uses a laser scanner to find a clear, flat foothold, positions the leg above the foothold, contacts the terrain with the foot, and applies force enough to advance the carriage along the rails. Walking both forward and backward, the system has traversed hundreds of meters of rugged terrain including obstacles too tall to step over, trenches too deep to step in, closely spaced rocks, and sand hills. In addition, preliminary experiments were conducted with concurrent planning and execution, and a leg recovery planner that generates time and power efficient 3D trajectories using 2D search was developed. A Hero robot was used to demonstrate mobile manipulation. Indoor tasks include collecting cups from the lab floor, retrieving printer output, and recharging when its battery gets low. The robot monitors its environment, and handles exceptional conditions in a robust fashion, using vision to track the appearance and disappearance of cups, onboard sonars to detect imminent collisions, and monitors to detect the battery level.

Whittaker, William↗

Inverse kinematics problem in robotics using neural networks

In this paper, Multilayer Feedforward Networks are applied to the robot inverse kinematic problem. The networks are trained with endeffector position and joint angles. After training, performance is measured by having the network generate joint angles for arbitrary endeffector trajectories. A 3-degree-of-freedom (DOF) spatial manipulator is used for the study. It is found that neural networks provide a simple and effective way to both model the manipulator inverse kinematics and circumvent the problems associated with algorithmic solution methods.

Choi, Benjamin B.↗

Efficient visual grasping alignment for cylinders

Monocular information from a gripper-mounted camera is used to servo the robot gripper to grasp a cylinder. The fundamental concept for rapid pose estimation is to reduce the amount of information that needs to be processed during each vision update interval. The grasping procedure is divided into four phases: learn, recognition, alignment, and approach. In the learn phase, a cylinder is placed in the gripper and the pose estimate is stored and later used as the servo target. This is performed once as a calibration step. The recognition phase verifies the presence of a cylinder in the camera field of view. An initial pose estimate is computed and uncluttered scan regions are selected. The radius of the cylinder is estimated by moving the robot a fixed distance toward the cylinder and observing the change in the image. The alignment phase processes only the scan regions obtained previously. Rapid pose estimates are used to align the robot with the cylinder at a fixed distance from it. The relative motion of the cylinder is used to generate an extrapolated pose-based trajectory for the robot controller. The approach phase guides the robot gripper to a grasping position. The cylinder can be grasped with a minimal reaction force and torque when only rough global pose information is initially available.

Nicewarner, Keith E.↗

Vision-guided gripping of a cylinder

The motivation for vision-guided servoing is taken from tasks in automated or telerobotic space assembly and construction. Vision-guided servoing requires the ability to perform rapid pose estimates and provide predictive feature tracking. Monocular information from a gripper-mounted camera is used to servo the gripper to grasp a cylinder. The procedure is divided into recognition and servo phases. The recognition stage verifies the presence of a cylinder in the camera field of view. Then an initial pose estimate is computed and uncluttered scan regions are selected. The servo phase processes only the selected scan regions of the image. Given the knowledge, from the recognition phase, that there is a cylinder in the image and knowing the radius of the cylinder, 4 of the 6 pose parameters can be estimated with minimal computation. The relative motion of the cylinder is obtained by using the current pose and prior pose estimates. The motion information is then used to generate a predictive feature-based trajectory for the path of the gripper.

Nicewarner, Keith E.↗

Efficient visual grasping alignment for cylinders

Monocular information from a gripper-mounted camera is used to servo the robot gripper to grasp a cylinder. The fundamental concept for rapid pose estimation is to reduce the amount of information that needs to be processed during each vision update interval. The grasping procedure is divided into four phases: learn, recognition, alignment, and approach. In the learn phase, a cylinder is placed in the gripper and the pose estimate is stored and later used as the servo target. This is performed once as a calibration step. The recognition phase verifies the presence of a cylinder in the camera field of view. An initial pose estimate is computed and uncluttered scan regions are selected. The radius of the cylinder is estimated by moving the robot a fixed distance toward the cylinder and observing the change in the image. The alignment phase processes only the scan regions obtained previously. Rapid pose estimates are used to align the robot with the cylinder at a fixed distance from it. The relative motion of the cylinder is used to generate an extrapolated pose-based trajectory for the robot controller. The approach phase guides the robot gripper to a grasping position. The cylinder can be grasped with a minimal reaction force and torque when only rough global pose information is initially available.

Nicewarner, Keith E.↗

Studies of classical radiation emission from plasma wave undulators

We examine the characteristics of the classical radiation emitted by a relativistic electron beam that propagates perpendicularly through a large amplitude relativistic plasma wave. Such a study is useful for evaluating the feasibility of using relativistic plasma waves as extremely short wavelength undulators for generating short wavelength radiation. The electron trajectories in a plasma wave undulator are obtained using perturbation techniques and are then compared to numerical simulation results. The frequency spectrum and angular distribution of the spontaneous radiation emitted by a single electron and the stimulated radiation gain are obtained analytically, and are then compared to 3-D numerical simulations. The characteristics of the plasma wave undulator are compared to the ac FEL undulator and the conventional FEL.

Williams, Ronald L.↗

Guidance of Nonlinear Nonminimum-Phase Dynamic Systems

The first two years research work has advanced the inversion-based guidance theory for: (1) systems with non-hyperbolic internal dynamics; (2) systems with parameter jumps; (3) systems where a redesign of the output trajectory is desired; and (4) the generation of recovery guidance maneuvers.

Devasia, Santosh↗