Typical Mission Research Articles

Flow physics and boundary layer optimization over a NACA airfoil by camber morphing at subsonic speeds

In this paper, the variable camber morphing strategy is adopted in a NACA airfoil through computational investigation to enhance the lift to drag ratio ([Formula: see text] specifically for military UAV applications. The typical mission profile is also focused on enhancing the aerodynamic performance of the UAV during various flight segments by variable camber morphing. The airfoil camber is changed dynamically at different instances based on the mission profile requirements thereby altering the [Formula: see text] characteristics. The concept of bio-inspired aerodynamics has received a greater attention in recent years because of the proven nature oriented real-time application. Hence, the concept of bio-inspired variable camber morphing is proposed herein that minimizes the use of unconventional control surfaces to attain the required performance at different segments of flight. MQ9 Reaper UAV model is chosen to implement the proposed variable camber morphing strategy at the mission flight segments. NACA 4412 cambered airfoil has been considered as the baseline model airfoil for the present study because of its higher zero lift angle characteristics. The lower camber of the airfoil section is changed from 1% to 3% at different angles of attack (AoA) for the time instances such as 1[Formula: see text]s, 2[Formula: see text]s and 3[Formula: see text]s, respectively. The lift coefficient ([Formula: see text] of the airfoil is also significantly increased through camber morphing at different flight segments during each time step with negligible flow separation as observed through streamline patterns. Hence, the friction drag coefficient is also retained under optimum level as concluded through the boundary layer profiles.

An Architectural Model of a Software Module for Piloting UAV Constellations

Technology behind unmanned aircraft vehicles (UAVs) has been rapidly advancing in the recent years. This propelled the adoption rate and widened the range of UAV uses, such as in sport activities, cartography, surveillance, transportation, and military. While using single, individually controlled UAVs is already very common, using UAV constellations is becoming increasingly popular due to advantages in flexibility, scalability and sensing power. However, the use of UAV constellations remains limited to very specific applications due to number of challenges involved. One of the challenges is the lack of generic software system that would allow controlling UAV constellations in different types of flight missions. In order to address this problem, we propose an architectural model of a software module for UAV constellation piloting.

Hardware qualification for scientific ballooning missions

The European Stratospheric Balloon Observatory (ESBO) initiative aims at simplifying the access to stratospheric balloon missions. We plan to provide platforms and support with instrument design in order to support scientists. During the design process, the inevitable question of qualification for the harsh flight conditions arises. Unfortunately, there is no existing standard for qualification of stratospheric ballooning hardware. Thus, we developed a qualification procedure for use within ESBO and similar projects.In this paper, we present our analysis of the environmental conditions in the stratosphere. While conditions at typical balloon float altitudes are similar to the space environment, there are also some relevant differences. For example, the thermal environment is dominated by radiation and thermal conduction, but the remaining atmosphere still supports a certain amount of convection. The remaining atmospheric pressure in the stratosphere also leads to reduced arcing distances. Vibrational loads are far less than for space missions, but quasi-static or shock loads may occur. The criticality of radiation increases with mission duration.Based on the environmental conditions, we present the qualification procedures for ESBO, which are based on the European Cooperation for Space Standardization (ECSS) standards for space systems. Overtesting against too high requirements leads to overengineering, driving mission cost and mitigating the advantages of balloons over space missions. Therefore, we modified the ECSS standards to fit typical scientific ballooning missions over several days at altitudes up to 40 km. Furthermore, we analyzed design rules for space systems with regard to their relevance for scientific ballooning, including material and component selection. We present the experience from the hardware qualification process for the ESBO prototype STUDIO (Stratospheric UV Demonstrator of an Imaging Observatory). Even though boundary conditions are different for each individual mission, we aimed for a broader approach: We investigated more general requirements for scientific ballooning missions to support future flights.

Sınır Güvenliği ve Müdahale Görevi Yapan İHA’ların ÇKKV Yöntemleri ile Değerlendirilmesi

İnsansız Hava Aracı (İHA), kendisini kullanan bir insan taşımayan, kaldırma kuvvetini oluşturabilmek amacıyla aerodinamik kuvvetleri kullanabilen, kendi kendine uçabilen veya uzaktan yönetilebilen, tekrar kullanılabilen veya kullanılamayan ve ölümcül veya ölümcül olmayan faydalı yükler taşıma kabiliyetine sahip motorlu hava aracıdır. Teknolojinin yükselen bir hızla gelişmesiyle beraber İHA’ların kabiliyetleri de aynı oranda artmış ve birçok askeri görevde kullanılmaya başlanmıştır. Fakat, askeri başarı elde edebilmek yalnızca bu modern gelişmiş hava araçlarına sahip olmak yeterli değildir. Görevlerin ifa oranlarını arttırmak ve alan hakimiyetini yakalayarak savaşı kazanmak için görev tipine uygun sistemlerin kullanım planlamasının yapılması şarttır. Bu tür planlamalarla görevlerdeki başarı oranı artacak ve kullanan taraf üstünlük elde edecektir. Bu çalışmada gelişmiş orduların envanterinde bulunan farklı türlerdeki 9 İHA’nın sınır güvenliği ve müdahale görevlerini yerine getirmesi için çok kriterli karar verme (ÇKKV) yöntemleri ile değerlendirilmesi yapılmıştır. Bu 9 İHA büyüklük, tür, kabiliyet vb. yönlerden birbirinden ayrılmaktadır. Bu çalışmanın amacı, belirlenen görev için uygun İHA’nın sıralamasının tespit edilmesidir. Çalışmada kullanılan kriterler İHA’lar konusunda uzman kişilerle değerlendirilip 8 ana başlıkta gruplandırılmıştır. Kriterlerin ağırlıklandırılması için ikili kıyaslamalar yapılarak, kriter ağırlıkları AHP yöntemiyle ortaya konmuştur. İHA’ların sıralamaları ve seçimini yapabilmek için literatüre son yıllarda kazandırılmış olan ÇKKV yöntemlerinden ARAS, EDAS ve WASPAS metotları ile literatüre daha eski yıllarda kazandırılmış sıkça kullanılan MAUT, TOPSIS ve VIKOR yöntemleri kullanılmıştır. Böylece hem eski yöntemlerin hem de yeni yöntemlerin kullanımı ile farklı bir bakış açısına sahip çözüm geliştirilmeye çalışılmıştır. Uygulanan yöntemlerden bulunan önceliklendirme sonuçları analiz edilmiş ve sonuçlar normalize edilip ortalamaları bulunarak her İHA’ya ait nihai bütünleşik önceliklendirme puanı (NBÖP) elde edilmiştir. Böylece önceliklendirme değerlerinin güvenilirlik, doğruluk ve tutarlılık düzeylerinin arttırılması amaçlanmıştır. Uygulanan yöntemler sonucu bulunan sonuçlar yorumlanmıştır.

Combined LOFAR and DEMON Spectrums for Simultaneous Underwater Acoustic Object Counting and F0 Estimation

In a typical underwater acoustic target detection mission, we have to estimate the target number (N), perform source separation when N>1, and consequently predict the motion parameters such as fundamental frequency (F0) from separated noises for each target. Although deep learning methods have been adopted in each task, their successes strongly depend on the feed-in features. In this paper, we evaluate several time-frequency features and propose a universal feature extraction strategy for object counting and F0 estimation simultaneously, with a convolutional recurrent neural network (CRNN) as the backbone. On one hand, LOFAR and DEMON are feasible for low-speed and high-speed analysis, respectively, and are combined (LOFAR + DEMON) to cope with full-condition estimation. On the other hand, a comb filter (COMB) is designed and applied to the combined spectrum for harmonicity enhancement, which will be further streamed into the CRNN for prediction. Experiments show that (1) in the F0 estimation task, feeding the filtered combined feature (LOFAR + DEMON + COMB) into the CRNN achieves an accuracy of 98% in the lake trial dataset, which is superior to LOFAR + COMB (83%) or DEMON + COMB (94%) alone, demonstrating that feature combination is plausible. (2) In a counting task, the prediction accuracy of the combined feature (LOFAR + DEMON, COMB included or excluded) is comparable to the state-of-the-art on simulation dataset and dominates the rest on the lake trial dataset, indicating that LOFAR + DEMON can be used as a common feature for both tasks. (3) The inclusion of COMB accelerates the convergence speed of the F0 estimation task, however, it penalizes the counting task by a depression of 13% on average, partly due to the merging effects brought in by the broadband filtering of COMB.

A Study on Millimeter Wave SAR Imaging for Non-Destructive Testing of Rebar in Reinforced Concrete.

In this study, we investigate a millimeter wave (mmWave) synthetic aperture radar (SAR) imaging scheme utilizing a low-cost frequency modulated continuous wave (FMCW) radar to take part in non-destructive testing which could be a useful tool for both civilian and military demands. The FMCW radar working in the frequency range from 76 GHz to 81 GHz is equipped with a 2-D moving platform aiming to reconstruct the 2-D image of the shape of the target object. Due to the lab environment containing several devices and furniture, various noise and interference signals from the floor are not avoidable. Therefore, the digital signal processing algorithms are joined to remove the undesired signals as well as improve the target recognition. This study adopts the range migration algorithms (RMAs) on the processed reflected signal data to form the image of the target because of its verified ability in this type of mission. On the other hand, the integration of compressed sensing (CS) algorithms into the SAR imaging system is also researched which helps to improve the performance of the system by reducing the measurement duration while still maintaining the image quality. Three minimization algorithms are used involving the imaging system as the CS solvers reconstruct the radar data before being processed by RMA to form the image. The proposed imaging scheme demonstrates its good ability with high azimuth resolution in the mission of detecting tiny cracks in the rebar of reinforced concrete. In addition, the participation of CS algorithms improves the performance of the scheme as the cracks on the rebar can be located on the images, which are reconstructed from only 30% of the dataset. The comparison of CS solvers shows that ADMM outperforms the other candidates in the reconstruction task.

Heterogeneous mission planning for a single unmanned aerial vehicle (UAV) with attention-based deep reinforcement learning.

Large-scale and complex mission environments require unmanned aerial vehicles (UAVs) to deal with various types of missions while considering their operational and dynamic constraints. This article proposes a deep learning-based heterogeneous mission planning algorithm for a single UAV. We first formulate a heterogeneous mission planning problem as a vehicle routing problem (VRP). Then, we solve this by using an attention-based deep reinforcement learning approach. Attention-based neural networks are utilized as they have powerful computational efficiency in processing the sequence data for the VRP. For the input to the attention-based neural networks, the unified feature representation on heterogeneous missions is introduced, which encodes different types of missions into the same-sized vectors. In addition, a masking strategy is introduced to be able to consider the resource constraint (e.g., flight time) of the UAV. Simulation results show that the proposed approach has significantly faster computation time than that of other baseline algorithms while maintaining a relatively good performance.

Estimates of the Decarbonization Potential of Alternative Fuels for Shipping as a Function of Vessel Type, Cargo, and Voyage

Fuel transition can decarbonize shipping and help meet IMO 2050 goals. In this paper, HFO with CCS, LNG with CCS, bio-methanol, biodiesel, hydrogen, ammonia, and electricity were studied using empirical ship design models from a fleet-level perspective and at the Tank-To-Wake level, to assist operators, technology developers, and policy makers. The cargo attainment rate CAR (i.e., cargo that must be displaced due to the low-C propulsion system), the ES (i.e., TTW energy needed per ton*n.m.), the CS (economic cost per ton*n.m.), and the carbon intensity index CII (gCO2 per ton*n.m.) were calculated so that the potential of the various alternatives can be compared quantitatively as a function of different criteria. The sensitivity of CAR towards ship type, fuel type, cargo type, and voyage distance were investigated. All ship types had similar CAR estimates, which implies that considerations concerning fuel transition apply equally to all ships (cargo, containership, tankers). Cargo type was the most sensitive factor that made a ship either weight or volume critical, indirectly impacting on the CAR of different fuels; for example, a hydrogen ship is weight-critical and has 2.3% higher CAR than the reference HFO ship at 20,000 nm. Voyage distance and fuel type could result in up to 48.51% and 11.75% of CAR reduction. In addition to CAR, the ES, CS, and CII for a typical mission were calculated and it was found that HFO and LNG with CCS gave about 20% higher ES and CS than HFO, and biodiesel had twice the cost, while ammonia, methanol, and hydrogen had 3–4 times the CS of HFO and electricity about 20 times, suggesting that decarbonisation of the world’s fleet will come at a large cost. As an example of including all factors in an effort to create a normalized scoring system, an equal weight was allocated to each index (CAR, ES, CS, and CII). Biodiesel achieved the highest score (80%) and was identified as the alternative with the highest potential for a deep-seagoing containership, followed by ammonia, hydrogen, bio-methanol, and CCS. Electricity has the lowest normalized score of 33%. A total of 100% CAR is achievable by all alternative fuels, but with compromises in voyage distance or with refuelling. For example, a battery containership carrying an equal amount of cargo as an HFO-fuelled containership can only complete 13% of the voyage distance or needs refuelling seven times to complete 10,000 n.m. The results can guide decarbonization strategies at the fleet level and can help optimise emissions as a function of specific missions.

Towards Rechargeable Aluminum Metal Batteries for Low-Temperature Space & Electromobility Applications

Rechargeable aluminum metal batteries are promising next-generation electrochemical energy storage devices due to the high energy density, inherent safety, low cost, earth abundance, and recyclability of aluminum. Aluminum batteries using graphite cathodes and chloroaluminate-containing electrolytes have recently been the focus of intense research in the energy storage and electroplating community, particularly due to their fast-charging capabilities. Engineered graphite cathodes have been shown to exhibit supercapacitor-like rate performance since chloroaluminate anions intercalate into graphite with significant pseudocapacitive characteristics1,2. Batteries with high considerable pseudocapacitive charge storage3 are especially attractive for energy storage systems that need to function at low temperatures and ideally exhibit high specific power. For example, robotic spacecraft that embark on planetary science missions often have typical mission temperature targets of -40 to -60 °C. However, state-of-the-art lithium-ion batteries with predominant faradaic diffusion-limited charge storage suffer from lithium plating, dendritic growth, and severe capacity loss at low temperatures. While organic electrolyte mixtures have been developed that enable moderate low-temperature performance, new battery concepts, including those using ionic liquid electrolytes, are needed to enable new paradigms for low-temperature space missions and electromobility.Herein we present, for the first time, results towards rechargeable aluminum-graphite batteries designed specifically for low-temperature applications that demonstrate high capacity retention and favorable electrochemical kinetics at temperatures down to -40 °C. We first disentangle quantitatively the faradaic diffusion-limited, pseudocapacitive, and capacitive contributions to the overall charge storage for different graphite materials. Moreover, we shed light on the relationships between graphite structure, ion mass transport, and the overall rates of electrochemical aluminum deposition/dissolution and chloroaluminate anion intercalation. We then develop ionic liquid electrolytes with mixed anion-cation compositions to impart disorder and disrupt crystallization at low temperatures. The resulting aluminum-graphite batteries were characterized by variable-temperature and rate cyclic voltammetry (CV), galvanostatic cycling, electrochemical impedance spectroscopy (EIS), and solid-state nuclear magnetic resonance (NMR) spectroscopy to understand their reaction mechanisms and factors limiting their rate performance. The ionic liquid electrolyte mixtures were also characterized by a combination of differential scanning calorimetry (DSC) and electrochemical methods to understand their freezing points and electrochemical stability. The results provide fundamental insights into the design of aluminum-graphite batteries for low-temperature space and electromobility applications.

System analysis and requirements derivation of a hydrogen-electric aircraft powertrain

In contrast to sustainable aviation fuels for use in conventional combustion engines, hydrogen-electric powertrains constitute a fundamentally novel approach that requires extensive effort from various engineering disciplines. A transient system analysis has been applied to a 500 kW shaft-power-class powertrain. The model was fed with high-level system requirements to gain a fundamental understanding of the interaction between sub-systems and components. Transient effects, such as delays in pressure build up, heat transfer and valve operation, substantially impact the safe and continuous operation of the propulsion system throughout a typical mission profile, which is based on the Daher TBM850. The lumped-parameters network solver provides results quickly, which are used to derive requirements for subsystems and components, which support their in-depth future development. E.g. heat exchanger transfer rates and pressure drop of the motor's novel hydrogen cooling system are established. Furthermore, improvements to the system architecture, such as a compartmentalization of the tank, are identified.

Modeling and optimization of the energy system for series hybrid electric fixed-wing VTOL

In this paper, the fixed-wing vertical take-off and landing (VTOL) aircraft is studied. Based on the typical mission profile, the flight process of fixed-wing VTOL aircraft is divided into rotor mode and fixed-wing mode, and the dynamic models of the two modes are established respectively. The power transmission model of the series hybrid electric system is established, the concept of hybridization for power (Hp ) is introduced, and an optimization method of power allocation based on Hp is proposed. The optimization method is verified by simulation, and the results show that, compared with the pure electric energy system, the performance of the optimized hybrid energy system is improved obviously.

Corrosion of metal parts on satellites by iodine exposure in space

Iodine becomes increasingly popular as alternative propellant for electric propulsion (EP) systems offering several advantages over established xenon. However, iodine is also a reactive and corrosive element. Thus, a careful material selection for the EP system itself, but also for components employed on the satellite is required in the light of typical space mission durations of several years. Here, we carefully define an approach for mimicking long-term interaction of material specimens with iodine in a space environment. The space conditions cover typical iodine atmospheres (10− 1 to 10− 4 Pa), which occur in the vicinity of a satellite employing an iodine-fed EP system, and exposure times, which correspond to 10 years of mission duration. The approach is used to expose a wide range of metal specimens commonly used on spacecraft to iodine. Chemical modifications of the surfaces of the treated samples are analyzed by x-ray photoelectron spectroscopy (XPS). The elemental metals Fe, Ti, Al, and Nb chemically react with iodine, whereas the elemental metals Ni, Cr, Ta, W, and Mo are basically inert. The stainless-steel and aluminum metal alloys show the same behavior as the corresponding dominant elemental specimens, i.e., Fe and Al, respectively. Somewhat surprisingly, Cr as constituent in stainless steel reacts with iodine, in contrast to elemental Cr. Nevertheless, our studies reveal that long-term exposure to low-pressure iodine atmospheres is not critical for the macroscopic structural integrity of all tested specimens even over space mission durations of several years. The reaction with iodine is macroscopically a surface effect, which mainly affects the optical appearance.

Dynamic thermal modeling of the refrigerated liquified CO2 tanker in carbon capture, utilization, and storage chain: A truck transport case study

Proper estimation of the energy consumption of tanker-based methods for the storage and transport of liquified CO2 gas is a major concern in the techno-economic analysis and selection between the existing technologies within the carbon capture, utilization, and storage (CCUS) chain. In this study, the dynamic thermal modeling of a liquified CO2 insulated-body tanker truck equipped with a refrigeration unit is presented to simulate the transient thermal performance of the tanker truck during a typical long-distance delivery mission. The main purpose of this research is to provide a more precise estimation of the heat leak quantity to the tanker, pressure build-up, and the refrigeration cooling demand and energy consumption. A two-way coupled set of unsteady-state differential equations are established on the participating elements included in the non-equilibrium thermal system of the tanker and refrigeration unit. The dynamic variations of ambient condition parameters, including the ambient temperature, solar radiation levels, degree of cloudiness, and wind velocity are incorporated into the model based on the annual average meteorological data in three modes of ambient conditions: the warmest week, a moderate week, and the coldest week of the year. The computational fluid dynamic (CFD) modeling of the liquid and gas phase CO2 inside the vessel is also implemented in order to determine the required heat transfer coefficients used in the model. According to the modeling results, the average vessel inward heat leak changes from 11.08 W/m2 in the warmest week of the year to 8.18 W/m2 and 5.11 W/m2 for a moderate and the coldest week, respectively, with 26.17 % and 53.88 % decrease. On the other hand, the refrigeration coefficient of performance (COP) is improved from 1.10 in the warmest week to 1.23 and 1.36 in a moderate and the coldest week, respectively, by 11.82 % and 23.63 % increase. As a measure of energy consumption of the refrigerated CO2 tanker truck at different ambient conditions, the average compressor power consumption changes from 56 W/m3CO2 in the warmest week of the year to 41 W/m3CO2 and 28 W/m3CO2 in a moderate and the coldest week, respectively, with 26.78 % and 50.00 % decrease. The novel modeling approach proposed in this study could also be used to study various design aspects of the refrigerated CO2 transport and storage tankers.

Fuzzy Logic-Based Energy Management Strategy of Hybrid Electric Propulsion System for Fixed-Wing VTOL Aircraft

An energy management strategy for a series hybrid electric propulsion system designed for a fixed-wing vertical take-off and landing (VTOL) aircraft is presented in this paper. The proposed method combines an ideal operating line (IOL) and fuzzy logic. Fuzzy logic is used to dynamically and optimally allocate the output power of the generator and the battery pack according to the power requirement of the aircraft and the SOC of the battery pack. The IOL controller is used to optimize the internal combustion engine (ICE) operating point to improve the fuel economy of the system. The detailed aircraft model and energy system model are established. The flight process of a 100 kg scale VTOL aircraft under a typical mission profile is simulated. The simulation results show that running the ICE based on IOL can greatly improve its efficiency The introduction of fuzzy logic to optimize the power allocation of the generator and battery pack improves the overall efficiency of the system. The feasibility and effectiveness of the energy management strategy proposed in this paper are verified, and the design ideas and analysis methods are provided for the energy management of a hybrid electric aircraft.

A novel compilation method of comprehensive mission spectrum of aero-engine maneuvering load based on use-related mission segment

Comprehensive Mission Spectrum (CMS) of an aero-engine can reflect the usage characteristics of the engine. It can provide load input for engine life prediction and accelerated mission test. In this paper, a novel compilation method of CMS of aero-engine maneuvering load based on mission segment is proposed. Firstly, the use-related Typical Mission Segment (TMS) of maneuvering load is divided and identified according to spectral characteristics. Secondly, the mathematical model of different kinds of TMS are established based on stochastic process theory. Finally, the CMS of maneuvering load is compiled based on TMS. The proposed method can accurately quantify the compilation of CMS. The compiled CMS shows good agreement with the original load spectrum. According to damage consistency inspection, the compiled CMS is consistent with the damage caused by the original load spectrum in terms of low cycle fatigue.

Predictive Monitoring System for Autonomous Mobile Robots Battery Management Using the Industrial Internet of Things Technology.

The core of the research focuses on analyzing the discharge characteristic of a lithium NMC battery in an autonomous mobile robot, which can be used as a model to predict its future states depending on the amount of missions queued. In the presented practical example, an autonomous mobile robot is used for in-house transportation, where its missions are queued or delegated to other robots in the system depending on the robots’ predicted state of charge. The system with the implemented models has been tested in three scenarios, simulating real-life use cases, and has been examined in the context of the number of missions executed in total. The main finding of the research is that the battery discharge characteristic stays consistent regardless of the mission type or length, making it usable as a model for the predictive monitoring system, which allows for detection of obstruction of the default shortest paths for the programmed missions. The model is used to aid the maintenance department with information on any anomalies detected in the robot’s path or the behavior of the battery, making the transportation process safer and more efficient by alerting the employees to take action or delegate the excessive tasks to other robots.

Machine learning predictions of lithium-ion battery state-of-health for eVTOL applications

Electric vertical take-off and landing (eVTOL) aircrafts is one solution for the urban and peri-urban mobility. To monitor the aircraft systems and functions and to mitigate safety concerns, prognostic and health management (PHM) uses state-of-the-art machine learning approaches. Lithium-ion battery, as the main power source of eVTOL, needs to be monitored. To anticipate the battery ageing, the most important parameter to predict is its state-of-health (SoH). In this article, we present a machine learning methodology to predict and forecast the batteries SoH (regression). This study was based on a public dataset consisting of 22 cells that were tested over several hundreds of charge/discharge cycles, with power demands simulating typical eVTOL missions. After feature engineering and preprocessing using a rolling window, 5 machine learning models were adjusted to the train dataset using cross-validation and grid search (linear regression, support vector machines, k-nearest neighbors (kNN), random forest and gradient boosted trees). kNN was found to give the best validation and test scores for the lowest training time (1 μs/point). Finally, after finer hyperparameters tuning (number of observations in the rolling window and k neighbors), kNN was found accurate to forecast SoH up to 200 cycles (≈500 h) ahead (test-R2 ≈ 0.98).

Navigating the policy compliance roadmap for small satellites

This paper explores U.S. space policies and how they apply to satellite missions that may not fit the typical satellite mission mold. It presents a policy compliance “roadmap” for satellites from diverse agencies and identifies areas where further work is underway to address the challenges posed by the evolution of the space industry. Also, it lays out a coherent way forward for all small satellites navigating the approval quagmire and for mission managers of multi-payload rideshares who wish to smooth the path to launch approval.

Simulating Dropsondes to Assess Moist Static Energy Variability in Tropical Cyclones

AbstractInteractions between clouds, water vapor, radiation, and circulation influence tropical cyclone (TC) development. Many of these interactions can be quantified by understanding tendencies of the spatial variance of moist static energy (MSE). Dropsondes from aircraft reconnaissance sample profiles needed to compute MSE at fine vertical resolution, and may be useful in analyzing these feedbacks on TCs in situ. However, dropsondes are spatially sparse, and sample limited column depths depending on the type of reconnaissance mission. We use idealized convection‐permitting simulations to examine how MSE variability, and the feedbacks that influence it, are resolved using selected patterns of grid points meant to resemble dropsonde launch points in reconnaissance flight patterns. We first examine the column depth necessary to capture the MSE variability of the full atmosphere. We then study how these simulated flight patterns depict MSE variance and its relevant diabatic feedbacks in TCs of varying structure and intensity.

A Neural Network Warm-Started Indirect Trajectory Optimization Method

The mission of spacecraft usually faces the problem of an unknown deep space environment, limited long-distance communication and complex environmental dynamics, which brings new challenges to the intelligence level and real-time performance of spacecraft onboard trajectory optimization algorithms. In this paper, the optimal control theory is combined with the neural network. Then, the state–control sample pairs and the state–costate sample pairs obtained from the high-fidelity algorithm are used to train the neural network and further drive the spacecraft to achieve optimal control. The proposed method is used on two typical spacecraft missions to verify the feasibility. First, the system dynamics of the hypersonic reentry problem and fuel-optimal moon landing problem are described and then formulated as highly nonlinear optimal control problems. Furthermore, the analytical solutions of the optimal control variables and the two-point boundary value problem are derived based on Pontryagin’s principle. Subsequently, optimal trajectories are solved offline using the pseudospectral method and shooting methods to form large-scale training datasets. Additionally, the well-trained deep neural network is used to warm-start the indirect shooting method by providing accurate initial costates, and thus the real-time performance of the algorithm can be greatly improved. By mapping the nonlinear functional relationship between the state and the optimal control, the control predictor is further obtained, which provides a backup optimal control variables generation strategy in the case of shooting failure, and ensures the stability and safety of the onboard algorithm. Numerical simulations demonstrate the real-time performance and feasibility of the proposed method.