Density Model Research Articles

Subtidal seagrass detector: development of a deep learning seagrass detection and classification model for seagrass presence and density in diverse habitats from underwater photoquadrats

This paper presents the development and evaluation of a Subtidal Seagrass Detector (the Detector). Deep learning models were used to detect most forms of seagrass occurring in a diversity of habitats across the northeast Australian seascape from underwater images and classify them based on how much the cover of seagrass was present. Images were collected by scientists and trained contributors undertaking routine monitoring using drop-cameras mounted over a 50 x 50 cm quadrat. The Detector is composed of three separate models able to perform the specific tasks of: detecting the presence of seagrass (Model #1); classify the seagrass present into three broad cover classes (low, medium, high) (Model #2); and classify the substrate or image complexity (simple of complex) (Model #3). We were able to successfully train the three models to achieve high level accuracies with 97%, 80.7% and 97.9%, respectively. With the ability to further refine and train these models with newly acquired images from different locations and from different sources (e.g. Automated Underwater Vehicles), we are confident that our ability to detect seagrass will improve over time. With this Detector we will be able rapidly assess a large number of images collected by a diversity of contributors, and the data will provide invaluable insights about the extent and condition of subtidal seagrass, particularly in data-poor areas.

Joint Gramian inversion of gravity, magnetic and gravity gradient tensor data for the modelling of Mesoproterozoic iron oxide deposits, southeast Missouri, USA

SUMMARY Within the iron metallogenic province of southeast Missouri, USA, there are several mines that contain not only economic iron resources, magnetite and/or hematite, but also contain rare earth elements, copper and gold. An area including three major deposits, Pea Ridge, Bourbon and Kratz Spring, was selected for detailed modelling for the upper crustal magnetic susceptibility and density structures. For the study area, ground gravity and high-resolution airborne magnetic and gravity gradiometry data sets are available. An efficient and novel joint inversion algorithm for the simultaneous inversion of these multiple data sets is presented. The Gramian coupling constraint is used to correlate the reconstructed density and magnetic susceptibility models. The implementation relies on the structures of the sensitivity matrices and an efficient minimization algorithm to achieve significant reductions in the memory requirements and computational costs. Consequently, it is feasible to use a laptop computer for the inversion of multiple data sets, each containing thousands of data points, for the recovery of models on the study area, each including approximately one million model parameters. This is the first time that these multiple data sets have been simultaneously inverted for this area. The L1-norm stabilizer is used to provide compact and focused images of the ore deposits. For contrast, independent inversions of each data set are also discussed. In general, our results provide new insights about the concealed ore deposits in the Mesoproterozoic basement rocks of southeast Missouri. Both short- and long-wavelength anomalies exist in the recovered models; these provide a high-resolution image of the subsurface. The geometry and physical properties of the known deposits are determined very well. Additionally, some unknown concealed deposits are revealed; these could be economically valuable and should be considered in future geophysical and geological investigations.

Model-based preference quantification

Any prescriptive theory of decision-making (DM) has to cope with the common DM agents’ inability to fully specify their preferences dependent on several attributes. The paper provides the needed preference completion and quantification for fully probabilistic design (FPD) of DM strategies. FPD (covering the usual Bayesian DM) probabilistically models the agent’s environment and quantifies its preferences via an ideal probabilistic model of the closed DM loop. The probability density (pd) models (closed-loop) behaviour, a collection of involved random variables. Its ideal twin is high on desired behaviours, small on undesired and zero on forbidden ones. The FPD-optimal strategy minimises the Kullback-Leibler divergence (KLD) of the closed-loop modelling pd to the ideal twin. The exposed preference quantification chooses the optimal ideal pd from the set of pds compatible with partially-specified agent’s preferences. The optimal ideal pd minimises the KLD minima reached by the optimal strategies for respective imminent ideal pds. This preference-focused twin of the minimum KLD principle was applied to special sets of ideal pds. The paper extends them towards exploration and balancing contradictory wishes on states and actions.

Deciphering the driving forces in crystal packing by analysis of electrostatic energies and contact enrichment ratios.

Hirshfeld surface analysis is a widely used tool for identifying the types of intermolecular contacts that contribute most significantly to crystal packing stabilization. One useful metric for analyzing these contacts is the contact enrichment descriptor, which indicates the types of contacts that are over- or under-represented. In this statistical study, enrichment ratios were combined with electrostatic energy (Eelec) data for a variety of compound families. To compute the electrostatic interaction energy between atoms, charge density models from the ELMAM2 database of multipolar atoms were used. As expected, strong hydrogen bonds such as O/N-H...N and O/N-H...O typically display large enrichment values and have the most negative (i.e. favorable) electrostatic energies. Conversely, contacts that are repulsive from an electrostatic perspective are usually the most under-represented. Analyzing the enrichment ratio and electrostatic energy indicators was shown to help identify which favorable contacts are the most competitive with each other. For weaker interactions, such as hydrophobic contacts, the behavior is less clear cut and can depend on other factors such as the chemical content of the molecule. The anticorrelation between contact enrichment and Eelec is generally lost for weaker contacts. However, we observed that C...C contacts are often enriched in crystal structures containing heterocycles, despite the low electrostatic attraction. For molecules with only weak hydrogen bond donors/acceptors and hydrophobic groups, the correlation between contact enrichment and Eelec is still evident for the strongest of these interactions. However, there are some exceptions where the most favorable contacts from an electrostatic perspective are not the most over-represented. This can occur in cases where the shape of the molecule is complex or elongated, favoring dispersion forces and shape complementarity in the packing.

Deep dives and high tissue density increase mean dive costs in California sea lions (Zalophus californianus).

Diving is central to the foraging strategies of many marine mammals and seabirds. Still, the effect of dive depth on foraging cost remains elusive because energy expenditure is difficult to measure at fine temporal scales in wild animals. We used depth and acceleration data from eight lactating California sea lions (Zalophus californianus) to model body density and investigate the effect of dive depth and tissue density on rates of energy expenditure. We calculated body density in 5 s intervals from the rate of gliding descent. We modeled body density across depth in each dive, revealing high tissue densities and diving lung volumes (DLVs). DLV increased with dive depth in four individuals. We used the buoyancy calculated from dive-specific body-density models and drag calculated from swim speed to estimate metabolic power and cost of transport in 5 s intervals during descents and ascents. Deeper dives required greater mean power for round-trip vertical transit, especially in individuals with higher tissue density. These trends likely follow from increased mean swim speed and buoyant hinderance that increasingly outweighs buoyant aid in deeper dives. This suggests that deep diving is either a 'high-cost, high-reward' strategy or an energetically expensive option to access prey when prey in shallow waters are limited, and that poor body condition may increase the energetic costs of deep diving. These results add to our mechanistic understanding of how foraging strategy and body condition affect energy expenditure in wild breath-hold divers.

Modeling of a Dual Air-Gap Liquid-Cooled Eddy Current Retarder Considering Transient Permeability

This paper proposes a model for the electromagnetic performance of the dual air-gap liquid-cooled eddy current retarder (DAL-ECR) considering the transient permeability. First, the structure and working principle of the DAL-ECR are introduced. Next, the analysis model of the static air-gap flux density considering the flux leakage and end effect is established based on the piecewise function method and the magnetic equivalent circuit (MEC). Then, based on the skin effect of the electromagnetic field in the retarder, an iterative method for solving the transient permeability of the stator is proposed. According to Faraday’s and Ampere’s laws, the analysis model of the static air-gap flux density, and the transient permeability, the analysis model of the transient air-gap flux density is established. The braking torque of the DAL-ECR is then calculated while taking the actual path of the eddy current and the skin effect on the permeability of the stator into consideration. Finally, the calculation accuracy of the model was verified by the finite element method (FEM) and the bench test.

Forest Clearing Dynamics and Its Relation to Remotely Sensed Carbon Density and Plant Species Diversity in the Puuc Biocultural State Reserve, Mexico

The Puuc Biocultural State Reserve (PBSR) is a unique model for tropical dry forest conservation in Mexico. Preserving forest biodiversity and carbon within the PBSR depends on maintaining low-impact productive activities coordinated by multiple communal and private landowners. In this study, we used state-of-the-art remote sensing data to investigate past spatial patterns in forest clearing dynamics and their relation to forest carbon density and forest plant species richness and diversity in the context of the forest conservation goals of the PBSR. We used a Landsat-based continuous change detection product for the 2000–2021 period and compared it to carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data. The estimated error-adjusted area of detected annual forest clearings from the year 2000 until the year 2021 was 230,511 ha in total (±19,979 ha). The analysis of annual forest clearing frequency and area suggests that although forest clearing was significantly more intensive outside of the PBSR than within the PBSR during the entire 2000–2021 period, there is no evidence suggesting that the frequency and magnitude of forest clearing changed over the years after the creation of the PBSR in 2011. However, an emergent hotspot analysis shows that high spatiotemporal clustering of forest clearing events (hotspots) during the 2012–2021 period was less common than prior to 2011, and these more recent hotspots have been confined to areas outside the PBSR. After comparing forest clearing events to carbon density and tree species richness models, the results show that landowners outside the PBSR often clear forests with lower carbon density and species diversity than those inside the PBSR. This suggests that, compared to landowners outside the PBSR, landowners within the PBSR might be practicing longer fallow periods allowing forests to attain higher carbon density and tree species richness and hence better soil nutrient recovery after land abandonment. In conclusion, our results show that the PBSR effectively acted as a stabilizing forest management scheme during the 2012–2021 period, minimizing the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests within the PBSR. We recommend continuing efforts to provide alternative optimal field data collection strategies and modeling techniques to spatially predict key tropical forest attributes. Combining these models with continuous change detection datasets will allow for underlying ecological processes to be revealed and the generation of information better adapted to forest governance scales.

Discovery of high energy and stable prismane derivatives by the high-throughput computation and machine learning combined strategy

Motivated by the excellent detonation performance of octanitrocubane, prismane is another potential backbone with high strain energy in energetic molecule design. In this work, we aim to screen out candidates of highly energetic molecules from the space of prismane derivatives. The high-throughput computation (HTC) is performed based on 200 molecules derived from the molecule space of 1503 prismane derivatives with four substituents. Based on the calculated results, the machine learning (ML) models of density, detonation velocity, detonation pressure, heat of formation and detonation heat are established, and thereby the performances of the remaining 1303 samples are predicted. It is found that the –NHNO2 group increases density, while both –NO2 and –C(NO2)3 groups promote detonation performances. Based on the detonation velocity and bond dissociation energy as criteria representing energy and molecular stability, four molecules were screened out with good detonation performance and acceptable thermal stability. This work demonstrates the efficiency of HTC and ML combined strategy for screening high-quality energetic molecules.

Predicting crack growth of paving materials under indirect tensile fatigue loads

Cracking of paving materials affects the reliability and safety of pavement structures, and indirect tension (IDT) test is commonly used to investigate the cracking behavior of the paving materials. However, the existing studies usually adopted a reduced modulus or strength to indirectly quantify cracking damage under the IDT fatigue loads. A direct analysis method for the crack growth under the IDT fatigue loads is still missing. The objective of this study is to propose a crack growth prediction model under the IDT fatigue loads based on continuum damage mechanics (CDM), which can directly quantify and predict the crack growth of the paving materials. Two types of paving materials, cement-treated aggregates and asphalt mixtures, were investigated. A damage density model based on CDM was proposed to quantify the crack growth under the IDT fatigue loads. The model coefficients are obtained by the IDT horizontal tensile stress, material surface energy, resilient modulus, and the size of the IDT specimen. The surface crack length of the IDT specimen is obtained using the solved damage density with introducing a crack depth ratio. Results show that the cement-treated aggregates show relatively more brittle cracking where the cracks are penetrative and going through the whole thickness of the IDT specimen. In contrast, the asphalt mixtures exhibit more ductile cracking where multiple cracks present and are nonpenetrative. The cracks of the asphalt mixture penetrate only 48% and 46% of the specimen thickness when the IDT fatigue loads are 540 kPa (1 Hz) and 700 kPa (10 Hz), respectively. The damage density calculated by the CDM-based model can accurately predict the crack lengths for both the cement treated aggregates and the asphalt mixtures, which are found consistent with the measured crack lengths from the IDT tests. This study provides a mechanics model for determining the crack growth under the IDT fatigue loads that can serve as a tool for evaluating the fatigue crack resistance of the paving materials like the asphalt mixtures and cement treated aggregates.

Calculation of double differential neutron cross-sections of 56Fe and 90Zr isotopes

This study is concerned with the calculations of double differential neutron cross-sections of the structural fusion materials of 56Fe and 90Zr isotopes that are bombarded with protons. Calculations were performed using the level density models of the TALYS 1.95 code and PHITS 3.22 Monte Carlo code. Constant Temperature Fermi Gas, Back Shifted Fermi Gas, and Generalized Super Fluid Models were employed for level density models. Calculations were performed at 22.2 MeV proton energies. Calculations were compared with the experimental data taken from Experimental Nuclear Reaction Data (EXFOR). In conclusion, the results showed that the level density model results of TALYS 1.95 codes for the double differential neutron cross-sections of 56Fe and 90Zr isotopes are consistent with experimental data. On the other hand, PHITS 3.22 results gave lower cross-section values than experimental data at 120 and 150°.

Structure of the Lithosphere and Conditions of Formation of Oceanic Rises in the Sub-Antarctic Sector of the South Atlantic Using Density and Physical Modeling

The kinematic reorganization of plate boundaries, accompanied by the cessation of old spreading centers and the formation of new ones, and manifestations of plume magmatic activity in the southeastern part of the Antarctic sector of the South Atlantic led to the formation of a complex structural plan of the region. As a result of these processes, a system of ridges, rises, and plateaus with varying morphological expressions and different geophysical characteristics were formed. Results of density modeling of the crust and lithosphere structure along profiles extending from the Falkland Plateau to the Mozambique Ridge and crossing a series of rises and ridges separated by deepwater basins showed that rises have different crustal structures, indicating different origins. The conditions for the formation of different types of submarine rises were studied based on physical modeling. A new experimental model of lithosphere and submarine rises formation in the region was constructed, in which the fracture of the large Agulhas magmatic province into the Agulhas Plateau and the Northeast Georgia rise played an important role, as did the accretion of oceanic crust on the Agulhas spreading ridge and subsequent jump of the spreading axis, leading to the cessation of spreading on this ridge and the formation of the southern segment of the Mid-Atlantic Ridge and its associated Meteor and Islas Orcadas rises. Jumps of spreading axes, accompanied by the periodic activity of hotspots, played an important role in the formation of submarine rises of different genetic types, which determined the different structures of their crust.

Process based modelling of power density for wire laser additive manufacturing using a coaxial head

Process based modelling of power density for wire laser additive manufacturing using a coaxial head

Plating and Stripping of Lithium Metal Stabilized by a Block Copolymer Electrolyte: Local Current Density Measurement and Modeling

Plating and stripping of lithium protrusions in lithium metal symmetric cells containing a solid block copolymer electrolyte was studied as a function of time in 3D using time-resolved X-ray tomography. These measurements enabled determination of the spatial variation in current densities at the plating and stripping electrodes. The initial interelectrode distance was 27 μm. Correlation functions were calculated to reveal the relationships between current densities at the two electrodes and local electrolyte thickness. Current densities at opposing electrode locations during protrusion growth is uncorrelated until the local interelectrode distance decreases to less than 6 μm, just before the cell shorts. Mass balance was used to determine the area from which lithium ions that form a protrusion were stripped. Computational modeling of the plating and stripping process reveals the interplay between electrochemical and mechanical driving forces and their effect on nonuniform current distribution. Model predictions were compared with experiments without resorting to any adjustable parameters. The computed correlation functions were in qualitative agreement with experiments. Finally, the model was used to calculate contour plots of electrochemical potential within the electrolyte, shedding light on how geometry, salt concentration, interelectrode distance, and mechanical stress influence local rates of electrochemical reaction.

On Detecting Interstellar Scintillation in Narrowband Radio SETI

To date, the search for radio technosignatures has focused on sky location as a primary discriminant between technosignature candidates and anthropogenic radio frequency interference (RFI). In this work, we investigate the possibility of searching for technosignatures by identifying the presence and nature of intensity scintillations arising from the turbulent, ionized plasma of the interstellar medium. Past works have detailed how interstellar scattering can both enhance and diminish the detectability of narrowband radio signals. We use the NE2001 Galactic free electron density model to estimate scintillation timescales to which narrowband signal searches would be sensitive, and discuss ways in which we might practically detect strong intensity scintillations in detected signals. We further analyze the RFI environment of the Robert C. Byrd Green Bank Telescope with the proposed methodology and comment on the feasibility of using scintillation as a filter for technosignature candidates.

WHIM-hunting through cross-correlations between optical and SZ effect data in the Virgo cluster filaments

Context. The physical state of most of the baryonic matter in the local universe is unknown, commonly referred to as the “missing baryon problem”. It has been theorized that at least half of these missing baryons are in a warm-hot, low-density phase, outside of the virialized dark-matter halos. Aims. We attempted to find the signature of this warm-hot intergalactic medium (WHIM) phase in the filaments of the nearby Virgo cluster by using optical and Sunyaev-Zeldovich effect data. Methods. Specifically, we used a filament-galaxy catalog created from the HyperLeda database and an all-sky Compton-y map extracted from the Planck satellite data for two-dimensional cross-correlation analysis by applying a spherical harmonics transform. The significance test is based on the null-test simulations, which exploits advanced cut-sky analysis tools for a proper map reconstruction. To place upper limits on the WHIM density in the Virgo filaments, realistic baryonic density modeling within the cosmic filaments was done based on state-of-the-art hydro-simulations, within the signal-boosting routine. Results. The cross-correlation signal is found to be too dim compared to the noise level in the Plancky-map. At a 3 σ confidence level, the upper limit on volume-average WHIM density turns out to be ⟨ ne ⟩< 4 × 10−4 cm−3, which is indeed consistent with the WHIM parameter space, as predicted from simulations.

An Object‐Oriented Bayesian Gravity Inversion Scheme for Inferring Density Anomalies in Planetary Interiors

AbstractGravity inversions have contributed greatly to our knowledge of the interior of planetary bodies and the processes that shaped them. However, previous global gravity inversion methods neglect the inference of mantle density anomalies when using techniques to decrease the non‐uniqueness of the inversion. In this work, we present a novel global gravity inversion algorithm, named THeBOOGIe, suited to inferring global‐scale density anomalies within the crust and mantle of planetary bodies. The algorithm embraces the nonuniqueness inherent in gravity inversions by not prescribing at the outset a density interface or depth range of interest. Instead, the method combines a Bayesian approach with a flexible incorporation of prior geological or geophysical information to infer density anomalies at any depth. A validation test using synthetic lunar‐like gravity data shows that THeBOOGIe can constrain the lateral location of crustal density anomalies but tends to overestimate their thicknesses. Importantly, THeBOOGIe can detect deep mantle density anomalies and quantify the level of confidence in the inferred density models. Our results show that THeBOOGIe can provide complementary information to one‐dimensional seismic models of the interior of the terrestrial planets and the Moon by constraining density anomalies that are not spherically symmetric. Additionally, THeBOOGIe is specially suited to constraining the interior of partially differentiated bodies where these large‐scale density anomalies are more likely to exist. Finally, thanks to the flexible use of priors, THeBOOGIe is an essential tool to understand the interior of planetary bodies lacking additional constraints.

Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems

In this work we investigate the performance of various lower-fidelity models of seawater intrusion in coastal aquifer management problems. The variable density model is considered as the high-fidelity model and a pumping optimization framework is applied on a hypothetical coastal aquifer system in order to calculate the optimal pumping rates which are used as a benchmark for the lower-fidelity approaches. The examined lower-fidelity models could be classified in two categories: (1) physics-based models, which include several widely used variations of the sharp-interface approximation and (2) machine learning assisted models, which aim to improve the efficiency of the SI approach. The Random Forest method was utilized to create a spatially adaptive correction factor for the original sharp-interface model, which improves its accuracy without compromising its efficiency as a lower-fidelity model. Both the original sharp-interface and Machine Learning assisted model are then tested in a single-fidelity optimization method. The optimal pumping rated which were calculated using the Machine Learning based SI model sufficiently approximate the solution from the variable density model. The Machine Learning assisted approximation seems to be a promising surrogate for the high-fidelity, variable density model and could be utilized in multi-fidelity groundwater management frameworks.

Data-constrained Solar Modeling with GX Simulator

To facilitate the study of solar flares and active regions, we have created a modeling framework, the freely distributed GX Simulator IDL package, that combines 3D magnetic and plasma structures with thermal and nonthermal models of the chromosphere, transition region, and corona. Its object-based modular architecture, which runs on Windows, Mac, and Unix/Linux platforms, offers the ability to either import 3D density and temperature distribution models, or to assign numerically defined coronal or chromospheric temperatures and densities, or their distributions, to each individual voxel. GX Simulator can apply parametric heating models involving average properties of the magnetic field lines crossing a given voxel, as well as compute and investigate the spatial and spectral properties of radio, (sub)millimeter, EUV, and X-ray emissions calculated from the model, and quantitatively compare them with observations. The package includes a fully automatic model production pipeline that, based on minimal users input, downloads the required SDO/HMI vector magnetic field data, performs potential or nonlinear force-free field extrapolations, populates the magnetic field skeleton with parameterized heated plasma coronal models that assume either steady-state or impulsive plasma heating, and generates non-LTE density and temperature distribution models of the chromosphere that are constrained by photospheric measurements. The standardized models produced by this pipeline may be further customized through specialized IDL scripts, or a set of interactive tools provided by the graphical user interface. Here, we describe the GX Simulator framework and its applications.

BEYONDPLANCK

We present a Bayesian method for estimating instrumental noise parameters and propagating noise uncertainties within the global BEYONDPLANCKGibbs sampling framework, which we applied toPlanckLow Frequency Instrument (LFI) time-ordered data. Following previous works in the literature, we initially adopted a 1/fmodel for the noise power spectral density (PSD), but we found the need for an additional lognormal component in the noise model in the 30 and 44 GHz bands. We implemented an optimal Wiener-filter (or constrained realization) gap-filling procedure to account for masked data. We then used this procedure to both estimate the gapless correlated noise in the time-domain,ncorr, and to sample the noise PSD parameters,ξn = {σ0, fknee, α, Ap}. In contrast to previousPlanckanalyses, we assumed piecewise stationary noise only within each pointing period (PID), and not throughout the full mission, but we adopted the LFI Data Processing Center results as priors onαandfknee. We generally found best-fit correlated noise parameters that are mostly consistent with previous results, with a few notable exceptions. However, a detailed inspection of the time-dependent results has revealed many important findings. First and foremost, we find strong evidence for statistically significant temporal variations in all noise PSD parameters, many of which are directly correlated with satellite housekeeping data. Second, while the simple 1/fmodel appears to be an excellent fit for the LFI 70 GHz channel, there is evidence for additional correlated noise that is not described by a 1/fmodel in the 30 and 44 GHz channels, including within the primary science frequency range of 0.1–1 Hz. In general, most 30 and 44 GHz channels exhibit deviations from 1/fat the 2–3σlevel in each one-hour pointing period, motivating the addition of the lognormal noise component for these bands. For certain periods of time, we also find evidence of strong common mode noise fluctuations across the entire focal plane. Overall, we conclude that a simple 1/fprofile is not adequate for obtaining a full characterization of thePlanckLFI noise, even when fitted hour-by-hour, and a more general model is required. These findings have important implications for large-scale CMB polarization reconstruction with thePlanckLFI data and the current work is a first attempt at understanding and mitigating these issues.

Wing geometry and kinematic parameters optimization of bat-like robot fixed-altitude flight for minimum energy

In this paper, the geometry parameters (WGP) and kinematic parameters (WKP) of bat wings are optimized by using the revised quasi-steady aerodynamic model and genetic particle swarm optimization algorithm (GPSO) according to the structural characteristics and kinematic law of bat, to meet the requirement of minimum flapping energy and improve the endurance of bat-like robot. For the first time, the dynamic model of bat flapping motion is improved by considering the effects of active deformation, passive flexible deformation, gravity moment, and inertia moment of the wing structure. Based on the power density model, the optimization objective function is established, and the lift-weight ratio (LtoW), Reynolds number (Re), aspect ratio (AR), Strouhal number (St), and boundary constraints of each parameter are added to complete the optimization of WGP and WKP. The optimized aerodynamic forces and power densities are compared with the estimates for actual bats flying at a fixed-altitude, and the results showed that the optimized parameters resulted in more stable aerodynamic forces and reduced energy consumption by half. Finally, the sensitivity analysis of the influence of each parameter on the LtoW and power density is carried out to better understand the effect of each parameter on keeping fixed-altitude flight and reducing energy consumption. This study can provide the theoretical basis for reasonable parameter design of bat-like robot.