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Approximation optimal trajectories - selection of estimation variables in least squares program, and computer program application
Tropical convection over rainforests modulates atmospheric circulation and the energy and hydrological cycles across multiple scales. However, the scarcity of observations still limits our understanding of these processes. Although numerical models are utilized to investigate atmospheric physical processes, their performance depends on the choice of parameterizations and grid resolution. Here, in this work, we introduce and apply a multi‐variable, multi‐physics framework to evaluate and rank the Advanced Research Weather Research and Forecasting (WRF–ARW) configurations at 1‐km resolution over the central Amazon during the wet season. A 48‐member ensemble combines three land‐surface models (LSMs), four planetary boundary layer (PBL), and four microphysics (MP) schemes. Model performance for seven convection‐related near‐surface and boundary‐layer variables is assessed using Taylor diagrams and the Taylor skill score (TSS), analysis of variance (ANOVA)‐based sensitivity metrics, and non‐parametric rank tests. We then construct a combined, weighted TSS to identify configurations that are comparatively robust across variables. Results showed that most configurations reproduce near‐surface temperature, sensible and latent heat fluxes, and boundary‐layer height reasonably well, whereas humidity and rainfall remain challenging. LSM choice has the strongest impact on the surface fluxes and a secondary influence on near‐surface temperature and humidity, PBL schemes dominate boundary‐layer height, and MP schemes exert the largest control on rainfall. No single configuration is optimal for all variables, but the combination of Noah (LSM), Yonsei University (PBL), and Morrison (MP) emerges as the most robust configuration for this case, with the WRF single‐moment six‐class scheme (WSM6) providing a competitive, computationally cheaper MP alternative. The framework is general and can be applied to other regions, seasons, and convective regimes.
The authors consider a two-layer quasigeostrophic model with linear surface drag and forcing that relaxes to a zonal baroclinically unstable equilibrium state consisting of a meridionally confined temperature gradient. It is observed that the most energetic wave in the time-mean climate has near zero frequency and is not driven by upscale nonlinear energy transfers. This wave has a zonal scale near the long-wave cutoff of the equilibrium state, and its energy balance is mainly between baroclinic generation and dissipation. This maintenance mechanism is different from that suggested by beta-plane, two-dimensional, and quasigeostrophic turbulence arguments and may be relevant to the dynamics of zonally asymmetric low-frequency variability in the atmosphere, particularly in the Southern Hemisphere.
An integrated probabilistic approach has been developed to assess composites for high temperature applications. This approach was used to determine thermal and mechanical properties and their probabilistic distributions of a 5-harness 0/90 Sylramic fiber/CVI-SiC/Mi-SiC woven Ceramic Matrix Composite (CMC) at high temperatures. The purpose of developing this approach was to generate quantitative probabilistic information on this CMC to help complete the evaluation for its potential application for HSCT combustor liner. This approach quantified the influences of uncertainties inherent in constituent properties called primitive variables on selected key response variables of the CMC at 2200 F. The quantitative information is presented in the form of Cumulative Density Functions (CDFs). Probability Density Functions (PDFS) and primitive variable sensitivities on response. Results indicate that the scatters in response variables were reduced by 30-50% when the uncertainties in the primitive variables, which showed the most influence, were reduced by 50%.
In developing a flexible body spacecraft simulation for the Launch Abort System of the Orion vehicle, when a rapid mass depletion takes place, the dynamics problem with time varying eigenmodes had to be addressed. Three different techniques were implemented, with different trade-offs made between performance and fidelity. A number of technical issues had to be solved in the process. This paper covers the background of the variable mass flexibility problem, the three approaches to simulating it, and the technical issues that were solved in formulating and implementing them.
Previous work on the perception and control of simulated vehicle speed has examined the contributions of optical flow rate (angular visual speed) and texture, or edge rate (frequency of passing terrain objects or markings) on the perception and control of forward speed. However, these studies have not examined the ability to selectively use edge rate or flow rate. The two studies reported here show that subjects found it very difficult to arbitrarily direct attention to one or the other of these variables; but that the ability to selectively use these variables is linked to the visual contextual information about the relative validity (linkage with speed) of the two variables. The selectivity also resulted in different velocity adaptation levels for events in which flow rate and edge rate specified forward speed. Finally, the role of visual context in directing attention was further buttressed by the finding that the incorrect perception of changes in ground texture density tended to be coupled with incorrect perceptions of changes in forward speed.
The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM aids the HERON software module in the evaluation of the optimal dispatch for the different IES components. Set-point trajectories are required to meet limits on both production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To evaluate the feasibility of HERON generated set-points and to do so in an acceptable time, FARM employs reduced order models to represent the dynamic behavior of the systems to be dispatched. These surrogate models take the form of a linear dynamic system with sets of Linear Parameter Varying (LPV) matrices that are mapped to the system operating space. These matrices are derived from the trajectories of system state variables and system output variables during transients. The accuracy of LPV matrices depends on the selection of state variables. In previous reports, state variables were selected by adopting a complicated workflow requiring multiple software licenses and an advanced level of user expertise. In this report, a new workflow that automates the state variable selection process is presented. It significantly reduces the frequency of user interventions and does not require multiple software licenses. Each module in the new workflow is described in detail, and the input / output examples in each step of the workflow are provided. It was demonstrated that this workflow can greatly reduce the complexity of the state variable selection process, and that the updated FARM-Gamma and FARM-Delta validators can benefit from this workflow when solving the power dispatch problem of a representative IES test case. Finally, some code improvements that can further enhance the efficiency are suggested.
In vitro selection is a powerful tool that can be used to understand basic principles of molecular evolution. We used in vitro selection to understand how changes in length and the accumulation of point mutations enable the evolution of functional RNAs. Using RNA populations of various lengths, we performed a series of in vitro experiments to select for ribozymes with RNA ligase activity. We identified a core ribozyme structure that was robust to changes in RNA length, high levels of mutagenesis, and increased selection pressure. Elaboration on this core structure resulted in improved activity which we show is consistent with a larger trend among functional RNAs in which increasing motif size can lead to an exponential improvement in fitness. We conclude that elaboration on conserved core structures is a preferred mechanism in RNA evolution. This conclusion, drawn from selections of RNAs from random sequences, is consistent with proposed evolutionary histories of specific biological RNAs. More generally, our results indicate that modern RNA structures can be used to infer ancestral structures. Our observations also suggest a mechanism by which structural outcomes of early RNA evolution would be largely reproducible even though RNA fitness landscapes consist of disconnected clusters of functional sequences.
The relationship between the use of U.S. government technical reports by U.S. aerospace engineers and scientists and selected institutional and sociometric variables was investigated. The methodology used for this study was survey research. Data were collected by means of a self-administered mail questionnaire. The approximately 34,000 members of the American Institute of Aeronautics and Astronauts (AIAA) served as the study population. The response rate for the survey was 70 percent. A dependent relationship was found to exist between the use of U.S. government technical reports and three of the institutional variables (academic preparation, years of professional aerospace work experience, and technical discipline). The use of U.S. government technical reports was found to be independent of all of the sociometric variables. The institutional variables best explain the use of U.S. government technical reports by U.S. aerospace engineers and scientists.
Just prior to the thesis research, an all-sky survey in hard x rays with the HEAO-1 satellite and further observations in the optical resulted in a catalog of about 700 x-ray sources with known optical counterparts. This sample includes 43 cataclysmic variables, which are binaries consisting of a detached white-dwarf and a Roche lobe filling companion star. This thesis consists of studies of the x-ray selected sample of catalcysmic variables.
A study of the motor starting of a Brayton cycle power system was conducted to provide estimates of system sensitivity to several controllable parameters. These sensitivity estimates were used as a basis for selection of an optimum motor-start scheme to be implemented on the 2- to 10-kilowatt Brayton power system designed and presently under test. The studies were conducted with an analog simulation of the Brayton power system and covered a range of frequencies from 400 Hz (33 percent design) to 1200 Hz (design), voltage-to-frequency ratios of 0.050 (50 percent design) to 0.100 (design), turbine-inlet temperatures of 800 K (1440 R, 70 percent design) to 1140 K (2060 deg R, design), and prestart pressure levels of 14.5 psia to 29.0 psia. These studies have shown the effect of selected system variables on motor starting. The final selection of motor-start variables can therefore be made on the basis of motor-start inverter complexity, battery size and weight, desired steady-state pressure level after startup, and other operational limitations. In general, the study showed the time required for motor starting to be inversely proportional to motor frequency, voltage, turbine-inlet temperature, and pressure level. An increase in any of these parameters decreases startup time.
In many scenarios it is desirable for planetary landers to select or modify their landing sites autonomously during descent. We present a landing site selection algorithm which is optimized to work in conjunction with a Simultaneous Localization and Mapping system. Our algorithm selects landing sites based on site slope, roughness, and operator-defined interest. In addition, we generate guidance commands and approximate fuel consumption for the highest ranked sites. We validate our algorithm with LiDAR and inertial data gathered by a vertical take-off and landing vehicle.
In view of the objections of many subjects to overnight fasting prior to their blood being drawn for analyses, the effect of eating breakfast on the results of subsequent blood analyses of selected blood constituents and on cardiovascular variables was investigated in 47 men and 34 women who were subjected to blood analyses on two occasions, one week apart: once fasting and once after breakfast. Results suggest that subjects need not fast overnight before blood is being drawn for determinations of the HDL-C levels, hemoglobin, hematocrit, total cholesterol, or phosphorus. However, based on other studies, it is suggested breakfast should not have a high content of fat.
By using the Lee-Springer filament winding model temperatures, degrees of cure, viscosities, stresses, strains, fiber tensions, fiber motions, and void diameters were calculated in graphite-epoxy composite cylinders during the winding and subsequent curing. The results demonstrate the type of information which can be generated by the model. It is shown, in reference to these results, how the model, and the corresponding WINDTHICK code, can be used to select the appropriate process variables.
Herein this article presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types. Our proposed new hybrid models (named hybridM) merge the Monte Carlo Tree Search structure (MCTS) for categorical variables with Gaussian Processes (GP) for continuous ones. hybridM leverages the upper confidence bound tree search (UCTS) for MCTS strategy, showcasing the tree architecture’s integration into Bayesian optimization. Our innovations, including dynamic online kernel selection in the surrogate modeling phase and a unique UCTS search strategy, position our hybrid models as an advancement in mixed-variable surrogate models. Numerical experiments underscore the superiority of hybrid models, highlighting their potential in Bayesian optimization.
In order to study the Milky Way, RR Lyrae (RRL) variable stars identified by Gaia, ASAS-SN, and ZTF sky survey projects have been analyzed as tracers in this work. Photometric and spectroscopic information of 3417 RRLs including proper motions, radial velocity, and metallcity are obtained from observational data of Gaia, LAMOST, GALAH, APOGEE, and RAVE. Precise distances of RRLs with typical uncertainties less than 3% are derived by using a recent comprehensive period–luminosity–metallicity relation. Our results from kinematical and chemical analysis provide important clues for the assembly history of the Milky Way, especially for the Gaia–Sausage ancient merger. The kinematical and chemical trends found in this work are consistent with those of recent simulations that indicated that the Gaia–Sausage merger had a dual origin in the Galactic thick disk and halo. As recent similar works have found, the halo RRL sample in this work contains a subset of radially biased orbits besides a more isotropic component. This higher orbital anisotropy component amounts to β ≃ 0.8, and it contributes between 42% and 83% of the halo RRLs at 4 < R( kpc) < 20.
We show that Bayesian model selection is equivalent to optimization of a suitably defined free energy. The entropy term in the free energy is defined over the space of possible model selections and model parameters for a given model. Bayesian model selection thus follows a minimum description length principle.
The data comes from a two-story light commercial building which can be used to physically simulate light commercial buildings common in the nation's existing building stock. Based on a data collection plan, these measurements were performed for five different building operations. In the data set for each period, 98 data variables were selected and collected, in which 7 variables are weather data and the rest are all building and system operation data.