Uncertainties In Initial Conditions Research Articles

NONLINEAR DYNAMICS METHODS APPLICATIONS TO PRODUCT LIFECYCLE MANAGEMENT

The research being carried out is aimed at applying nonlinear dynamics methods in product life cycle management to improve communication between processes and achieve environmental sustainability. The purpose of the article is to create a nonlinear mathematical model for analysing product lifecycle management. Chaotic nonlinear dynamical processes of product lifecycle are deterministic but have the property that even small uncertainties in initial conditions lead to exponentially growing errors in the management that limit long-term forecasts. The emergence of chaotic behaviour in processes of product lifecycle was explored, focusing on fluctuations. The study employed methods such as systems analysis, correlation analysis, nonlinear dynamics, and the Surrogate model of chaotic Lorenz waterwheel. It was identified that sharp technological innovations as the primary drivers of short-term fluctuations impacting fixed efficiency and effectiveness of product life cycle process development. The resulting nonlinear dynamic model allowed for flexible operation, transitioning between equilibrium, periodic, and chaotic states based on coefficient values for asset growth rates and time constants reflecting product life cycle process dynamics. The research results are valuable for companies seeking to reduce their environmental impact and implement circular economy principles.

A novel nonlinearity-aware adaptive observer for estimating surface concentration and state of charge of lithium-ion batteries

Physically based electrochemical models can provide internal state information of batteries, exhibiting significant potential for application in battery management systems across various lithium-ion battery products. However, the complex structure and limited observability of electrochemical models make their integration into battery management systems challenging. In this paper, a novel nonlinearity-aware adaptive (i.e., NAA) observer based on a simplified electrochemical model is proposed for estimating the electrode surface concentration and state of charge. The proposed NAA observer can identify the nonlinearity of the electrochemical model and switches to the most suitable observer structure automatically. Experimental tests on a cylindrical battery were conducted to observe the performance of the observer. The results demonstrate rapid convergence and high accuracy of all estimated states under an initial state error of 20%. Furthermore, to thoroughly validate the performance of the proposed observer, a comparison was made with an interconnected sliding mode observer based on electrochemical models, considering initial state uncertainty, sensor noise, and computation time.

Uncertainty-resilient constrained rendezvous trajectory optimization via stochastic feedback control and unscented transformation

This paper proposes an innovative approach for constrained rendezvous trajectory optimization (CRTO) using stochastic optimal feedback control and unscented transform (UT) uncertainty quantification. The method overcomes limitations of traditional deterministic CRTO solutions that suffer from uncontrolled terminal state error growth and severely deteriorated path constraint satisfaction when initial state uncertainty, dynamics uncertainty, and navigation uncertainty are considered. The approach involves constructing a stochastic optimal feedback control (SOFC) problem with chance constraints and introducing linear feedback control to regulate both the mean and variance of terminal state errors. UT is employed to approximately quantify the state errors and their propagation, transforming the SOFC problem into an unconstrained deterministic optimization (UDO) problem. Differential dynamic programming (DDP) is then used to solve the UDO problem. The obtained stochastic optimal solution provides a robust rendezvous trajectory and a corresponding explicit closed-loop guidance law, improving terminal accuracy and path constraint satisfaction in the presence of various considered uncertainties. The influence of different term weights in the objective function on terminal accuracy and path constraint satisfaction is also studied. The effectiveness of the proposed approach is verified through Monte Carlo simulation, demonstrating the robustness of the closed-loop control strategy. The results highlight the potential of the method in enhancing terminal accuracy and path constraint satisfaction in uncertain rendezvous scenarios.

The evolution of a non-autonomous chaotic system under non-periodic forcing: A climate change example.

In this article, we approach the problem of measuring and interpreting the mid-term climate of a non-autonomous chaotic dynamical system in the context of climate modeling. To do so, we use a low-dimensional, conceptual model for the Earth system with different timescales of variability and subjected to non-periodic external forcing. We introduce the concepts of an evolution set and its distribution, which are dependent on the starting state of the system, and explore their links to different types of initial condition uncertainty and the rate of external forcing. We define the convergence time as the time that it takes for the evolution distribution of one of the dependent variables to lose memory of its initial condition. We suspect a connection between convergence times and the classical concept of mixing times, but the precise nature of this connection needs to be explored. These results have implications for the design of influential climate and Earth system model ensembles and raise a number of issues of mathematical interest.

A Monte Carlo approach to understanding the impacts of initial-condition uncertainty, model uncertainty, and simulation variability on the predictability of chaotic systems: Perspectives from the one-dimensional logistic map.

The predictability of the logistic map is investigated for the joint impact of initial-condition (IC) and model uncertainty (bias + random variability) as well as simulation variability. To this end, Monte Carlo simulations are carried out where IC bias is varied in a wide range of 10-15-10-3, and, similarly, model bias is introduced in comparable range. It is found that while the predictability limit of the logistic map can be continuously extended by reducing IC bias, the introduction of the model bias imposes an upper limit to the predictability limit beyond which further reductions in IC bias do not lead to an extension in the predictability limit, effectively restricting the feasible joint space spanned by the IC-model biases. It is further observed that imposing a lower limit to the allowed variability among ensemble solutions (so as to prevent the ensemble variability from collapse) results in a similar constraint in the joint IC-model-bias space; but this correspondence breaks down when the imposed variability limit is too high (∼x>0.7 for the logistic map). Finally, although increasing the IC random variability in an ensemble is found to consistently extend the allowed predictability limit of the logistic map, the same is not observed for model parameter random variability. In contrast, while low levels of model parameter variability have no impact on the allowed predictability limit, there appears to be a threshold at which an abrupt transition occurs toward a distinctly lower predictability limit.

Uncertainty propagation in the simulation of a nuclear power plant operational transient

With the continuous deployment of various intermittent energy sources on the grid there is an increasing opportunity for nuclear reactors to perform power maneuvers which can help alleviate issues related to supply oscillations during some periods of high renewable output. While utility specific codes can model such transients for safety assessments, there is a need to increase confidence in the application of these codes and to quantify the capabilities and accuracies of models in predicting these transients. In this work a power maneuver from 100 % full power to 59 % full power for a large CANDU reactor was studied using the TRACE-PARCS toolset. The work was computationally expensive as it examined both the effects of i) nuclear data library uncertainty which requires re-generation of the few-group cross sections and regeneration of an equilibrium core using 4 years of synthetic fuelling, and ii) initial core burn-up distribution involving the simulation of online fuelling operations over a period of 1400 days.The figures of merit considered in this work include changes in the average liquid zone controller fill level, individual channel powers, and the timing of adjuster bank withdrawals and reinsertions. Amongst the 100 s of simulated transients, the average time between adjuster bank withdrawals to override the Xenon transient and absolute standard deviations in these timings were found to be consistent between different nuclear datasets. The observed standard deviations were small, suggesting that the sensitivity to both nuclear data and initial core configuration is relatively small for both the adjuster withdrawal phase and the reinsertion phase. The contributions to total uncertainty due to both the core initial conditions and the nuclear data uncertainty were determined. During the early phase of the transient (i.e., adjuster withdrawals) nuclear data uncertainty and initial condition uncertainties were found to contribute nearly equally to total uncertainty, with slightly larger contributions due to nuclear data uncertainty. For adjuster bank reinsertions later in the transient it was observed that the contribution to total uncertainty by initial condition uncertainty was on average twice that of nuclear data uncertainties.The transients were compared to an actual power maneuver in a ∼900 MW CANDU reactor. In all cases, the model’s behaviour, while qualitatively correct, systematically over-predicted the time durations between adjuster movements and this difference exceeded the envelope of simulations that account for nuclear data and initial core configuration. Sensitivity studies performed to investigate the discrepancies suggest that additional refinements to the RRS emulator used in this work are required to match the station RRS more closely and that the transient evolution is highly sensitive to the initial excess reactivity in the core, which was observed to be lower in the station than in the TRACE simulations. Based on the simulation results the combined impact of the emulator responses, initial average zone levels, and incrementals modelling have a larger uncertainty than those related to nuclear data and initial core burnup distribution.

The Two- to Four-Day Predictability of Midlatitude Cyclones: Don’t Sweat the Small Stuff

Abstract We use convection-permitting idealized simulations of moist midlatitude cyclones to compare the growth of synoptic-scale perturbations derived from an adjoint model with the growth of equal-energy-norm, monochromatic-wavelength perturbations at the smallest resolved scale. For initial magnitudes comparable to those of initial-condition uncertainties in present-day data assimilation systems, the adjoint perturbations produce a “forecast bust,” significantly changing the intensity and location of the cyclone and its accompanying precipitation. In contrast, the small-scale-wave perturbations project strongly onto the moist convection, but the upscale growth from the random displacement of individual convective cells does not significantly alter the cyclone’s development nor its accompanying precipitation through 2–4-day lead times. Instead, the differences in convection generated at early times become negligible because the development of subsequent convection is driven by the mostly unchanged synoptic-scale flow. Reducing the perturbation magnitudes by factors of 10 and 100 demonstrates that nonlinear dynamics play an important role in the displacement of the cyclone by the full-magnitude adjoint perturbations, and that the upscale growth of small-magnitude, small-scale perturbations is too slow to significantly change the cyclone. These results suggest that a sensitive dependence on the synoptic-scale initial conditions, analogous to that of the Lorenz (1963) system, may be more relevant to 2–4-day midlatitude-cyclone forecast busts than the upscale error growth in the Lorenz (1969) model.

Double black hole mergers in nuclear star clusters: eccentricities, spins, masses, and the growth of massive seeds

ABSTRACT We investigate the formation of intermediate mass black holes (IMBHs) through hierarchical mergers of stellar origin black holes (BHs), as well as BH mergers formed dynamically in nuclear star clusters. Using a semi-analytical approach that incorporates probabilistic mass-function-dependent double BH (DBH) pairing, binary-single encounters, and a mass-ratio-dependent prescription for energy dissipation in hardening binaries, we find that IMBHs with masses of $\mathcal {O}(10^2)$–$\mathcal {O}(10^4)\, \rm M_\odot$ can be formed solely through hierarchical mergers in time-scales of a few 100 Myrs to a few Gyrs. Clusters with escape velocities ≳400 km s−1 inevitably form high-mass IMBHs. The spin distribution of IMBHs with masses ≳ 103 M⊙ is strongly clustered at χ ∼ 0.15; while for lower masses, it peaks at χ ∼ 0.7. Eccentric mergers are more frequent for equal-mass binaries containing first- and/or second-generation BHs. Metal-rich, young, dense clusters can produce up to 20 per cent of their DBH mergers with eccentricity ≥0.1 at $10\, \rm Hz$, and ∼2–9 per cent of all in-cluster mergers can form at >10 Hz. Nuclear star clusters are therefore promising environments for the formation of highly eccentric DBH mergers, detectable with current gravitational-wave detectors. Clusters of extreme mass (∼108 M⊙) and density (∼108 M⊙ pc−3) can have about half of all of their DBH mergers with primary masses ≥100 M⊙. The fraction of in-cluster mergers increases rapidly with increasing cluster escape velocity, being nearly unity for vesc ≳ 200 km s−1. Cosmological merger rate of DBHs from nuclear clusters varies ⪅0.01–1 Gpc−3 yr−1, where the large error bars come from uncertainties in the cluster initial conditions, number density distribution, and redshift evolution of nucleated galaxies.

Forecast uncertainty and ensemble spread in surface currents from a regional ocean model

An operational ocean Ensemble Prediction System (EPS) for the coastal seas off Northern Norway is evaluated by comparing with high-frequency radar current speed estimates. The EPS is composed of 24 members for which the ocean current is not perturbed nor constrained but forced with an atmosphere ensemble. The ocean ensemble spread stems from (i) accumulated differences in wind-forcing history and (ii) constraints of sea surface temperature by data assimilation. The intention of the ensemble is to reflect the actual uncertainty in initial conditions, which are largely unknown in terms of mesoscale circulation. We find a low but pronounced predictive skill in surface currents along with a good statistic skill. Additionally, current speeds show deterioration of the validation metrics over the forecast range. Further, high-resolution wind forcing seems to provide better forecast skill in currents compared to lower resolution forcing. In general, the ensemble exhibits the ability to predict forecast uncertainty.

Evacuation of Vessels in Dockyards: A Model Validation Study

Evacuation modelling is continuously open to new scenarios and applications. This study examines the possibility to simulate and predict the evacuation of workers aboard vessels in dockyards. First, we provide new data for a better understanding and quantification of workers’ evacuation performance. Second, we use an existing model (STEPS) and apply a validation protocol from the observed evacuation of 150 workers during an unannounced evacuation drill of a Ro-Pax ferry during the repair period in a dry dock. Despite the uncertainty of initial conditions to configure the scenario, the model accurately represents the observed evacuation (e.g. functional analysis ERD = 0.08; EPC = 1; SC = 0.89) suggesting that current agent-based evacuation models, with appropriate inputs and small adaptations, could be applied to these new challenging scenarios. Overall, this study provides evidence for this new application opportunity to model developers, model users and the research community and promotes the use of evacuation modelling for assessing fire safety in shipyards.

Extension of iterative learning control design for batch processes with time-delay in the input subject to random cycle-varying uncertainties

For uncertain time-delay batch processes with repetitive nature, this work deals with an unresolved problem of robust iterative learning control (ILC) design, in which we simultaneously remove the conventional assumptions of cycle-invariant uncertainties in reference trajectories, initial states, external disturbances, process dynamics, and time-delay. Moreover, we consider the variation of all existing uncertainties from a random point of view. Based on the idea of the internal model control (IMC) and indirect ILC, a novel IMC-based ILC structure is proposed. Then, we derive the practical convergence conditions, which have two design functions. By introducing a simple method for adjusting these functions, not only can we ensure that the expected tracking error converges monotonically to zero-neighborhood (in the L2-norm sense) but we can also achieve an acceptable convergence rate. Moreover, it is shown that the satisfaction of derived convergence conditions can always be guaranteed for any process. Simulation tests are provided to demonstrate the effectiveness of the proposed design.

Unsteady flow enhancement on an airfoil using sliding window weak-constraint four-dimensional variational data assimilation

This study establishes a continuous sliding window weak-constraint four-dimensional variational approach for reproducing a complete instantaneous flow from sparse spatiotemporal velocity observations. The initial condition, boundary condition, and model-form uncertainties are corrected simultaneously by a spatiotemporally varying additive forcing, coupled with the large eddy simulation (LES) framework, which reinforces subgrid-scale viscosity stresses and simplifies gradient computation. The additive force undergoes a Stokes–Helmholtz decomposition to ensure divergence-free projection and natural pressure determination. The model is theoretically derived to minimize discrepancies between the sparse velocity observations and the numerical predictions of the primary-adjoint system, enabling optimal contribution of the additive force. Synthetic data from a fine-grid LES of the vortical flow over an NACA0012 airfoil are used as observations. The algorithm is evaluated on a benchmark case, where observations are subsampled at 1/400 000 spatiotemporal resolution required for an LES. The sliding window strategy expands the dependence domain of the observations and mitigates the impact of primary-adjoint chaos, achieving over 90% pointwise correlation for filtered parameters and 80% spectral correlation for all of the resolved wavenumbers. Despite the lack of near-wall observations, streaks are accurately recovered due to the convective sensitivity of the observations from the outer flow. While the pressure fluctuation in the inflow region is not as well excited as in LES, recovery is augmented downstream. In both the inner and outer wall layers, the pressure distributions are obtained reasonably well by capturing the signatures of the vortical structure and their downstream convection. The robustness of the algorithm to observation noise is demonstrated. Finally, the impact of temporal resolution on estimation is evaluated, establishing a resolution threshold for successful reconstruction.

Series-form solutions of generalized fractional-fisher models with uncertainties using hybrid approach in Caputo sense

The field of fuzzy calculus has emerged as a powerful mathematical tool which can effectively deal with uncertainties and impressions that are common in real-world situations. In particular, it has proven useful in modeling and analysis of complex biological systems with uncertain parameters. The current study focuses on analysis of (n+1)-dimensional fractional Fisher equations (FFEs) in fuzzy environment. The objective is to provide semi-analytical solutions for fuzzy (n+1)- dimensional FFEs by considering Caputo-gH fractional derivative. The uncertainty in initial conditions is injected through triangular fuzzy numbers and obtained fuzzy (n+1)-dimensional FFEs are solved using hybrid of homotopy perturbation with Laplace transform in fuzzy-Caputo sense, which provides a powerful mathematical framework for examining complex behavior. The derived series solutions are validated against existing results from the literature and found to be improved. The obtained results are analyzed by means of determining the fuzzy solutions and residual errors at varying fractional orders, membership function, spatial coordinate x, and time t. These analytical findings are visualized in graphical form for ease of comprehension. The conducted study yields significant insights about the behavior of fractional model having uncertain conditions, and highlights the efficiency of proposed methodology. The results of this study have important implications for understanding the dynamics of biological systems with uncertainty, and hence can be useful in wide variety of applications in different fields such as ecology, epidemiology, and economics.

Game Theoretic Approach for Robust Trajectory Tracking by a Multirotor

In this article, the problem of precisely tracking a nominal trajectory by an under-actuated multirotor platform is formulated as a game against bounded external disturbances, measurement noise, and initial condition uncertainty. The nominal trajectory is composed of desired position and yaw angle, which is known to be a differentially flat output for the multirotor state dynamics. Using this property, a linearized error dynamics is obtained in the vicinity of the nominal trajectory. Using the linearized error dynamics, a linear-quadratic differential game approach is used to propose an integrated estimation and control method for trajectory tracking. A salient feature of this approach is that the drag coefficients are estimated online from the information of the tracking error. Moreover, there are only six parameters to be tuned for both estimation and control loops. The intuitive rationale behind the tuning of these parameters is also discussed. Simulations as well as experimental validations are presented to demonstrate the performance and applicability of the controllers.

Data‐based analysis of iterative learning control for MIMO nonaffine nonlinear systems with multiple nonrepetitive uncertainties

AbstractTwo challenging problems are addressed in this work for the convergence analysis of iterative learning control (ILC), that is, the rigorous assumption on repetitive conditions and the severe dependence on linear or nonlinear parametric models. Consequently, a data‐based analysis method of ILC is presented for a general multi‐input multi‐output nonaffine nonlinear system in the existence of multiple nonrepetitive uncertainties in initial states, external disturbances, reference trajectories, and plant models. An extended iterative dynamic relationship is constructed to extract the linear relationship of I/O dynamics for a nonaffine nonlinear system between two different iterations. The data‐based double dynamics analysis method is developed for analyzing that the tracking error is convergent and the I/O sequence is bounded. The tracking error is shown to converge to a finite bound related to nonrepetitive uncertainties and achieve a perfect convergence if nonrepetitive uncertainties are also iteratively convergent to zero. Notably, the presented analysis method merely uses the I/O data without relying on the plant model. Simulations validate the effectiveness of our theoretical results.

Accurate Measurements and Simulations of the Evaporation and Trajectories of Individual Solution Droplets

A refined numerical model for the evaporation and transport of droplets of binary solutions is introduced. Benchmarking is performed against other models found in the literature and experimental measurements of both electrodynamically trapped and freefalling droplets. The model presented represents the microphysical behavior of solutions droplets in the continuum and transition regimes, accounting for the unique hygroscopic behavior of different solutions, including the Fuchs-Sutugin and Cunningham slip correction factors, and accounting for the Kelvin effect. Simulations of pure water evaporation are experimentally validated for temperatures between 290 K and 298 K and between relative humidity values of approximately 0% and 85%. Measurements and simulations of the spatial trajectories and evaporative behavior of aqueous sodium chloride droplets are compared for relative humidity values between 0 and 40%. Simulations are shown to represent experimental data within experimental uncertainty in initial conditions. Calculations of a time-dependent Péclet number, including the temperature dependence of solute diffusion, are related to morphologies of sodium chloride particles dried at different rates. For sodium chloride solutions, dried particles are composed of collections of reproducibly shaped crystals, with higher evaporation rates resulting in higher numbers of crystals, which are smaller.

Stochastic trajectory optimization for 6-DOF spacecraft autonomous rendezvous and docking with nonlinear chance constraints

This paper addresses the problem of stochastic trajectory planning for 6-DOF spacecraft close proximity in the presence of external disturbances and initial state uncertainties. Nonlinear chance constraints on collision avoidance, docking corridor, sensor field-of-view, and control magnitude are considered in the corresponding optimal control problem. Nominal trajectory along with affine feedback controller that restricts the dispersion caused by disturbances are optimized simultaneously. The nonlinear stochastic dynamics are transformed into equality constraints on the first two statistical moments of the random states by linearization and discretization, and further, the non-convex covariance update equation is relaxed to a semidefinite cone constraint. The nonlinear path chance constraints are first approximated by affine chance formations, and then are convexified by introducing auxiliary variables instead of direct linearization of the covariance matrix. Combined with the tail bound of sub-exponential random variables, the constraints on control input are replaced with conservative deterministic inequalities. The sub-optimal solution to the original problem is obtained by iteratively solving a series of semidefinite cone programs. The effectiveness of the proposed framework is verified using numerical simulations.

Performance of a Convective-Scale Ensemble Prediction System on 2017 Warm-Season Afternoon Thunderstorms over Taiwan

Abstract A 16-member convective-scale ensemble prediction system (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective systems, the CEPS provides short-range forecasts using initial conditions from a rapid-updated ensemble data assimilation system. This study aims to identify the behavior of the CEPS forecasts, especially the impact of different ensemble configurations and forecast lead times. Warm-season afternoon thunderstorms (ATs) from 30 June to 4 July 2017 are selected. Since ATs usually occur between 1300 and 2000 LST, this study compares deterministic and probabilistic quantitative precipitation forecasts (QPFs) launched at 0500, 0800, and 1100 LST. This study demonstrates that initial and boundary perturbations (IBP) are crucial to ensure good spread–skill consistency over the 18-h forecasts. On top of IBP, additional model perturbations have insignificant impacts on upper-air and precipitation forecasts. The deterministic QPFs launched at 1100 LST outperform those launched at 0500 and 0800 LST, likely because the most-recent data assimilation analyses enhance the practical predictability. However, it cannot improve the probabilistic QPFs launched at 1100 LST due to inadequate ensemble spreads resulting from limited error growth time. This study points out the importance of sufficient initial condition uncertainty on short-range probabilistic forecasts to exploit the benefits of rapid-update data assimilation analyses. Significance Statement This study aims to understand the behavior of convective-scale short-range probabilistic forecasts in Taiwan and the surrounding area. Taiwan is influenced by diverse weather systems, including typhoons, mei-yu fronts, and local thunderstorms. During the past decade, there has been promising improvement in predicting mesoscale weather systems (e.g., typhoons and mei-yu fronts). However, it is still challenging to provide timely and accurate forecasts for rapid-evolving high-impact convection. This study provides a reference for the designation of convective-scale ensemble prediction systems; in particular, those with a goal to provide short-range probabilistic forecasts. While the findings cannot be extrapolated to all ensemble prediction systems, this study demonstrates that initial and boundary perturbations are the most important factors, while the model perturbation has an insignificant effect. This study suggests that in-depth studies are required to improve the convective-scale initial condition accuracy and uncertainty to provide reliable probabilistic forecasts within short lead times.

The Influence of Anthropogenic and Environmental Disturbances on Parameter Estimation of a Dengue Transmission Model.

Some deterministic models deal with environmental conditions and use parameter estimations to obtain experimental parameters, but they do not consider anthropogenic or environmental disturbances, e.g., chemical control or climatic conditions. Even more, they usually use theoretical or measured in-lab parameters without worrying about uncertainties in initial conditions, parameters, or changes in control inputs. Thus, in this study, we estimate parameters (including chemical control parameters) and confidence contours under uncertainty conditions using data from the municipality of Bello (Colombia) during 2010-2014, which includes two epidemic outbreaks. Our study shows that introducing non-periodic pulse inputs into the mathematical model allows us to: (i) perform parameter estimation by fitting real data of consecutive dengue outbreaks, (ii) highlight the importance of chemical control as a method of vector control, and (iii) reproduce the endemic behavior of dengue. We described a methodology for parameter and sub-contour box estimation under uncertainties and performed reliable simulations showing the behavior of dengue spread in different scenarios.

Inferring surface energy fluxes using drone data assimilation in large eddy simulations

Abstract. Spatially representative estimates of surface energy exchange from field measurements are required for improving and validating Earth system models and satellite remote sensing algorithms. The scarcity of flux measurements can limit understanding of ecohydrological responses to climate warming, especially in remote regions with limited infrastructure. Direct field measurements often apply the eddy covariance method on stationary towers, but recently, drone-based measurements of temperature, humidity, and wind speed have been suggested as a viable alternative to quantify the turbulent fluxes of sensible (H) and latent heat (LE). A data assimilation framework to infer uncertainty-aware surface flux estimates from sparse and noisy drone-based observations is developed and tested using a turbulence-resolving large eddy simulation (LES) as a forward model to connect surface fluxes to drone observations. The proposed framework explicitly represents the sequential collection of drone data, accounts for sensor noise, includes uncertainty in boundary and initial conditions, and jointly estimates the posterior distribution of a multivariate parameter space. Assuming typical flight times and observational errors of light-weight, multi-rotor drone systems, we first evaluate the information gain and performance of different ensemble-based data assimilation schemes in experiments with synthetically generated observations. It is shown that an iterative ensemble smoother outperforms both the non-iterative ensemble smoother and the particle batch smoother in the given problem, yielding well-calibrated posterior uncertainty with continuous ranked probability scores of 12 W m−2 for both H and LE, with standard deviations of 37 W m−2 (H) and 46 W m−2 (LE) for a 12 min vertical step profile by a single drone. Increasing flight times, using observations from multiple drones, and further narrowing the prior distributions of the initial conditions are viable for reducing the posterior spread. Sampling strategies prioritizing space–time exploration without temporal averaging, instead of hovering at fixed locations while averaging, enhance the non-linearities in the forward model and can lead to biased flux results with ensemble-based assimilation schemes. In a set of 18 real-world field experiments at two wetland sites in Norway, drone data assimilation estimates agree with independent eddy covariance estimates, with root mean square error values of 37 W m−2 (H), 52 W m−2 (LE), and 58 W m−2 (H+LE) and correlation coefficients of 0.90 (H), 0.40 (LE), and 0.83 (H+LE). While this comparison uses the simplifying assumptions of flux homogeneity, stationarity, and flat terrain, it is emphasized that the drone data assimilation framework is not confined to these assumptions and can thus readily be extended to more complex cases and other scalar fluxes, such as for trace gases in future studies.