Accreting protoplanets: Spectral signatures and magnitude of gas and dust extinction at H α

Context. Accreting planetary-mass objects have been detected at H α, but targeted searches have mainly resulted in non-detections. Accretion tracers in the planetary-mass regime could originate from the shock itself, making them particularly susceptible to extinction by the accreting material. High-resolution (R > 50 000) spectrographs operating at H α should soon enable one to study how the incoming material shapes the line profile. Aims. We calculate how much the gas and dust accreting onto a planet reduce the H α flux from the shock at the planetary surface and how they affect the line shape. We also study the absorption-modified relationship between the H α luminosity and accretion rate. Methods. We computed the high-resolution radiative transfer of the H α line using a one-dimensional velocity–density–temperature structure for the inflowing matter in three representative accretion geometries: spherical symmetry, polar inflow, and magnetospheric accretion. For each, we explored the wide relevant ranges of the accretion rate and planet mass. We used detailed gas opacities and carefully estimated possible dust opacities. Results. At accretion rates of Ṁ ≲ 3 × 10−6 MJ yr−1, gas extinction is negligible for spherical or polar inflow and at most AH α ≲ 0.5 mag for magnetospheric accretion. Up to Ṁ ≈ 3 × 10−4 MJ yr−1, the gas contributes AH α ≲ 4 mag. This contribution decreases with mass. We estimate realistic dust opacities at H α to be κ ~ 0.01–10 cm2 g−1, which is 10–104 times lower than in the interstellar medium. Extinction flattens the LH α –Ṁ relationship, which becomes non-monotonic with a maximum luminosity LH α ~ 10−4 L⊙ towards Ṁ ≈ 10−4 MJ yr−1 for a planet mass ~10 MJ. In magnetospheric accretion, the gas can introduce features in the line profile, while the velocity gradient smears them out in other geometries. Conclusions. For a wide part of parameter space, extinction by the accreting matter should be negligible, simplifying the interpretation of observations, especially for planets in gaps. At high Ṁ, strong absorption reduces the H α flux, and some measurements can be interpreted as two Ṁ values. Highly resolved line profiles (R ~ 105) can provide (complex) constraints on the thermal and dynamical structure of the accretion flow.

Fluid-Structure Interaction Analysis of Aircraft Hydraulic Pipe with Complex Constraints Based on Discrete Time Transfer Matrix Method

Fluid-structure interaction (FSI) is prevalent in aircraft hydraulic pipes due to high-pressure fluid pulsation, complex pipe path routing and boundary constraints, which pose a serious threat to the safety and reliability of the aircraft hydraulic system. This paper focuses on the FSI response of aircraft hydraulic pipes with complex constraints. A comprehensive fourteen-equation model for describing the FSI of pipe conveying fluid with wide pressure and Reynolds number range is proposed. The excitation models and complex boundary constraints of liquid-filled pipes are established. Moreover, based on the transfer matrix method (TMM), combined with the time discreteness and analytical integral method, a discrete time transfer matrix method (DTTMM) for solving the FSI fourteen-equation model in time domain is presented. Then, the numerical solution and experiment of an ARJ21-700 aircraft hydraulic pipe with complex constraints is carried out with four working conditions. The obtained results verify the correctness of the proposed model and solution method, and reveal the universal laws of the FSI response about aircraft hydraulic pipes, which can also provide theoretical and experimental references for modeling, solutions and verification in the FSI analysis of pipe conveying fluid.

Human Performance of Novice Schedulers for Complex Spaceflight Operations Timelines

Objective Investigate the effects of scheduling task complexity on human performance for novice schedulers creating spaceflight timelines. Background Future astronauts will be expected to self-schedule, yet will not be experts in creating timelines that meet the complex constraints inherent to spaceflight operations. Method Conducted a within-subjects experiment to evaluate scheduling task performance in terms of scheduling efficiency, effectiveness, workload, and situation awareness while manipulating scheduling task complexity according to the number of constraints and type of constraints. Results Each participant ( n = 15) completed a set of scheduling problems. Results showed main effects of the number of constraints and type of constraint on efficiency, effectiveness, and workload. Significant interactions were observed in situation awareness and workload for certain types of constraints. Results also suggest that a lower number of constraints may be manageable by novice schedulers when compared to scheduling activities without constraints. Conclusion Results suggest that novice schedulers' performance decreases with a high number of constraints, and future scheduling aids may need to target a specific type of constraint. Application Knowledge on the effect of scheduling task complexity will help design scheduling systems that will enable self-scheduling for future astronauts. It will also inform other domains that conduct complex scheduling, such as nursing and manufacturing.

The Evolution of the Popocatépetl Volcanic Complex: Constraints on Periodic Edifice Construction and Destruction by Sector Collapse

Popocatépetl is one of the most active volcanoes in North America. Its current predominantly mild activity is contrasted by a history of large effusive and explosive eruptions and sector collapse events, which was first summarised by Espinasa-Pereña and Martin-Del Pozzo (2006). Since then, a wealth of new radiometric, geophysical and volcanological data has been published, requiring a re-evaluation of the evolution of the Popocatépetl Volcanic Complex (PVC). Herein, we combine existing literature with new field observations, aerial imagery and digital elevation model interpretations to produce an updated and improved reconstruction of the growth and evolution of the PVC through all of its history. This will be fundamental for the assessment and mitigation of risks associated with potential future high-magnitude activity of the PVC. The PVC consists of four successive volcanic edifices separated by three sector collapse events producing avalanche deposits: Tlamacas (>538 - >330 ka, described here for the first time), Nexpayantla (∼330 - >96 ka), Ventorrillo (∼96 ka - 23.5 ka) and Popocatépetl (<23.5ka) edifices. The newly described Tlamacas collapse propagated towards ENE forming part of the Mayorazgo avalanche deposit.Supplementary material:https://doi.org/10.6084/m9.figshare.c.5709190

Hybrid Multiagent Collaboration for Time-Critical Tasks: A Mathematical Model and Heuristic Approach

Principal–assistant agent teams are often employed to solve tasks in multiagent collaboration systems. Assistant agents attached to the principal agents are more flexible for task execution and can assist them to complete tasks with complex constraints. However, how to employ principal–assistant agent teams to execute time-critical tasks considering the dependency between agents and the constraints among tasks is still a challenge so far. In this paper, we investigate the principal–assistant collaboration problem with deadlines, which is to allocate tasks to suitable principal–assistant teams and construct routes satisfying the temporal constraints. Two cases are considered in this paper, including single principal–assistant teams and multiple principal–assistant teams. The former is formally formulated in an arc-based integer linear programming model. We develop a hybrid combination algorithm for adapting larger scales, the idea of which is to find an optimal combination of partial routes generated by heuristic methods. The latter is defined in a path-based integer linear programming model, and a branch-and-price-based (BP-based) algorithm is proposed that introduces the number of assistant-accessible tasks surrounding a task to guide the route construction. Experimental results validate that the hybrid combination algorithm and the BP-based algorithm are superior to the benchmarks in terms of the number of served tasks and the running time.

A Reinforcement Learning Framework for Pooled Oligonucleotide Design

The goal of oligonucleotide (oligo) design is to select oligos that optimize a set of design criteria. Oligo design problems are combinatorial in nature and require computationally intensive models to evaluate design criteria. Even relatively small problems can be intractable for brute-force approaches that test every possible combination of oligos, so heuristic approaches must be used to find near-optimal solutions. We present a general reinforcement learning framework, called OligoRL, to solve oligo design problems with complex constraints. OligoRL allows "black-box" design criteria and can be adapted to solve many oligo design problems. We highlight the flexibility of OligoRL by building tools to solve three distinct design problems: 1.) finding pools of random DNA barcodes that lack restriction enzyme recognition sequences (CutFreeRL); 2.) compressing large, non-degenerate oligo pools into smaller degenerate ones (OligoCompressor); and 3.) finding Not-So-Random hexamer primer pools that avoid rRNA and other unwanted transcripts during RNA-seq library preparation (NSR-RL). OligoRL demonstrates how reinforcement learning offers a general solution for complex oligo design problems. OligoRL and its associated software tools are available as a Julia package at http://jensenlab.net/tools.

Constraint Handling in Bayesian Optimization -- A Comparative Study of Support Vector Machine, Augmented Lagrangian and Expected Feasible Improvement

Many engineering problems involve complex constraints which can be computationally costly. To reduce the overall numerical cost, such constrained optimization problems are solved via surrogate models constructed on a Design of Experiment (DoE). Meanwhile, complex constraints may lead to infeasible initial DoE, which can be problematic for subsequent sequential optimization. In this study, we address constrained optimization problem in a Bayesian optimization framework. A comparative study is conducted to evaluate the performance of three approaches namely Expected Feasible Improvement (EFI) and slack Augmented Lagrangian method (AL) and Expected Improvement with Probabilistic Support Vector Machine in constraint handling with feasible or infeasible initial DoE. AL is capable to start sequential optimization with infeasible initial DoE, while EFI requires extra a priori enrichment to find at least one feasible sample. Empirical experiments are performed on both analytical functions and a low pressure turbine disc design problem. Through these benchmark problems, EFI and AL are shown to have overall similar performance in problems with inequality constraints. However, the performance of EIPSVM is affected strongly by the corresponding hyperparameter values. In addition, we show evidences that with an appropriate handling of infeasible initial DoE, EFI does not necessarily underperform compared with AL solving optimization problems with mixed inequality and equality constraints.