Set Of Model Parameters Research Articles

How unique are pulsar wind nebulae models? Implementation of a multi-parameter, automatic fitting for time-dependent spectra

Due to the computational cost of calculating a great number of variations of the parameters, detailed radiative models of pulsar wind nebulae (PWNe) do not usually contain fitting algorithms. As a consequence, most of the models in the literature are, in fact, qualitative fits based on visual inspection. This is particularly true when complex, time-dependent models are considered. Motivated by improvements in the computational efficiency of the current PWN models that were obtained in the last years, we here explore the inclusion of automatic fitting algorithms into a fully time-dependent model. Incorporating an efficient fitting tool based on the Nelder-Mead algorithm, we blindly find fitting solutions for the Crab nebula and 3C 58 with a time-dependent radiation model to compute the spectral and dynamical evolution of young and middle-aged PWNe. This inclusion allows us, in addition of more faithfully determining the quality of the fit, to tackle whether there exist degeneracy in the selected PWNe models. We find both for Crab and 3C58, that the fits are well determined, and that no other significantly different set of model parameters is able to cope with experimental data equally well. The code is also able to consider the system's age as a free parameter, recursively determining all other needed magnitudes depending on age accordingly. We use this feature to consider whether a detailed multi-frequency spectra can constrain the nebula age, finding that in fact this is the case for the two PWNe studied.

Estimating the Role of Climate Internal Variability and Sources of Uncertainties in Hydrological Climate-Impact Projections

Hydrological climate-impact projections in the future are limited by large uncertainties from various sources. Therefore, this study aimed to explore and estimate the sources of uncertainties involved in climate change-impacted assessment, in a representative watershed of Northeastern China. Moreover, recent studies have indicated that the climate internal variability (CIV) plays an important role in various hydrological climate-impact projections. Six downscaled global climate models (GCMs) under two emission scenarios, and a calibrated Soil and Water Assessment Tool (SWAT) model were used to obtain hydrological projections in future periods. The CIV and signal-to-noise ratio (SNR) are investigated to analyze the role of internal variability in hydrological projections. The results shows that the internal variability shows a considerable influence on hydrological projections, which need to be particularly partitioned and quantified. Moreover, it is worth noting the CIV can propagate from precipitation and ET to runoff projections through the hydrological simulation process. In order to partition the CIV and the sources of uncertainties, the uncertainty decomposed frameworks based on analysis of variance (ANOVA) are established. The results demonstrate that the CIV and GCMs are the dominant contributors of runoff in the rainy season. In contrast, the CIV and SWAT model parameter sets provided obvious uncertainty to the runoff in January to May, and October to December. The findings of this study advised that the uncertainty is complex in the hydrological simulation process; hence, it is meaningful and necessary to estimate the uncertainty in the climate simulation process. The uncertainty analysis results can effectively provide efforts for reducing uncertainty, and then give some positive suggestions to stakeholders for adaption countermeasures under climate change.

A joint hydrogen and syngas chemical kinetic model optimized by particle swarm optimization

In this work, we propose a novel data-driven framework for detailed kinetic mechanisms optimization applying the heuristic algorithm, namely canonic Particle Swarm Optimization (PSO). The PSO is more effective and robust in coping with uncertainties and incomplete information than deterministic and probabilistic optimization algorithms and is more suitable for machine learning applications. In the proposed framework, to avoid trapping in a local minimum, 1000 local optimums have been obtained and statistically handled to select the final feasible model parameter set with reduced uncertainty intervals and parameter correlations. The developed framework was successfully used for the optimization of the joint H2 and syngas oxidation chemical kinetic model. The data set collected for the model optimization includes 41 reactions and 16 species, and 3000 experimental data targets supplied with uncertainty boundaries measured in shock tubes, jet stirred reactors, plug flow reactors, and premixed laminar flames under wide ranges of temperature, pressure, equivalence ratio, and H2/CO ratios. The initially estimated uncertainties of the reaction rate constants for 15 key reactions were significantly constrained. The reaction rate constants for the H2 oxidation sub-model were re-optimized and their uncertainties were further reduced.

Pinpointing the origin of the increased driving voltage during prolonged operation in a phosphorescent OLED based on an exciplex host

We report on the origin of the reduced power efficiency in a red phosphorescent OLED with an exciplex host after prolonged operation. The power efficiency is reduced solely by an increased driving voltage while the radiant flux remains constant. An electrical model describing the driving voltage increase is, thus, sufficient to explain the reduced power efficiency. The electrical model of the fresh OLED and at different stages of degradation was devised from four different measurement methods. Using multiple measurement methods to determine the model parameters results in a rather unique set of model parameters, despite the large number of model parameters (38) as revealed by a correlation analysis. The increase in driving voltage could be reproduced by modifying only 7 out of the 38 model parameters. A sensitivity analysis identified the parameters with the largest effect (66%) on the driving voltage increase to be the trap density and the mobility of the employed hole transporting layer. This work highlights the benefit of using multiple measurement methods to derive reliable model parameters and the use of a sensitivity analysis to pinpoint the origin of the investigated property. • Increased driving voltage after prolonged operation. • Radiant flux remained constant, so an electrical model is sufficient. • Determination of reliable model parameters by using multiple measurement methods. • Sensitivity analysis reveals origin of the increased driving voltage. • Hole traps in TAPC are the main cause of the increased driving voltage.

Learning differentiable dynamics models for shape control of deformable linear objects

Robots manipulating deformable linear objects (DLOs) – such as surgical sutures in medical robotics, or cables and hoses in industrial assembly – can benefit substantially from accurate and fast differentiable predictive models. However, the off-the-shelf analytic physics models fall short of differentiability. Recently, neural-network-based data-driven models have shown promising results in learning DLO dynamics. These models have additional advantages compared to analytic physics models, as they are differentiable and can be used in gradient-based trajectory planning. Still, the data-driven approaches demand a large amount of training data, which can be challenging for real-world applications. In this paper, we propose a framework for learning a differentiable data-driven model for DLO dynamics with a minimal set of real-world data. To learn DLO twisting and bending dynamics in a 3D environment, we first introduce a new suitable DLO representation. Next, we use a recurrent network module to propagate effects between different segments along a DLO, thereby addressing a critical limitation of current state-of-the-art methods. Then, we train a data-driven model on synthetic data generated in simulation, instead of foregoing the time-consuming and laborious data collection process for real-world applications. To achieve a good correspondence between real and simulated models, we choose a set of simulation model parameters through parameter identification with only a few trajectories of a real DLO required. We evaluate several optimization methods for parameter identification and demonstrate that the differential evolution algorithm is efficient and effective for parameter identification. In DLO shape control tasks with a model-based controller, the data-driven model trained on synthetic data generated by the resulting models performs on par with the ones trained with a comparable amount of real-world data which, however, would be intractable to collect.

Development of a unified constitutive model coupled with a continuum damage model for design and evaluation of high-temperature components

A unified constitutive model (UCM) coupled with a continuum damage model (CDM) is developed to design and evaluate high-temperature components in the energy, aerospace, and petrochemical industries. While different constitutive models can predict certain aspects of thermomechanical creep-fatigue responses, a generally applicable model for both short and long-term responses, including stress relaxation and tertiary creep/damage, and strain softening has not been available. Hence, this study unifies strain-focused viscoplastic and creep-rupture-focused damage models to predict fatigue, creep, and creep-fatigue interactions using a single set of model parameters. Two CDMs, Kachanov and isotropic damage, are evaluated by coupling these with a modified Chaboche UCM. The strengths and limitations of the original Kachanov and isotropic damage models in predicting a broad set of low-cycle fatigue and creep responses for a commercially important material, modified Grade 91 steel, are determined. Based on the evaluations, a modified isotropic damage model is proposed. The proposed UCM-CDM is experimentally validated against a large set of modified Grade 91 steel responses, including cyclic softening, rate-dependence, short-term stress relaxation, long-term creep, thermomechanical fatigue, and creep-fatigue interaction at temperatures 400 to 625 °C. The UCM is further validated by simulating a set of modified Grade 91 steel notch specimen creep responses. The modified UCM is demonstrated to simulate the influence of stress triaxiality and prior fatigue on creep rupture life. Finally, the proposed UCM is evaluated by analyzing a thick cylinder under thermal transient loading to demonstrate the modified UCM’s applicability for the design and evaluation of high-temperature components.

Integrated phase equilibria experimental study and thermodynamic modeling of the Cr–Si–O, Fe–Cr–O and Fe–Cr–Si–O systems

Complete characterization of the Fe–Cr–Si–O system at high temperatures (700–2600 °C) is performed using integrated experimental and thermodynamic modeling research methodology. Experimental technique includes high-temperature equilibration, quenching and electron probe X-ray microanalysis (EPMA). Main challenges in the experiments are the choice of holding materials (substrate), fixing the oxygen partial pressure in a wide range, using the secondary standards for EPMA correction, overcoming slow reactions to achieve solid state equilibria. Thermodynamic modeling involves critical assessment of literature data, development of the mathematical models for the Gibbs energies of all phases in the system, and optimization of model parameters. The main modeling challenge is to obtain a self-consistent database, which requires a re-assessment of the Cr–Si–O and Fe–Cr–O sub-systems, and an accurate simultaneous description of all experimental data from literature and from the present study. There are several outcomes of the present study: a critical review of earlier work; a set of diagrams representing phase equilibria and distribution of elements among phases within the system; a set of thermodynamic model parameters for liquid and solid phases in the system. The results of the work will be applied to solve chemical engineering problems in pyrometallurgical production of copper, nickel and steel, recycling of waste electronics and designing refractory materials.

Dynamic Parameter Calibration Framework for Opinion Dynamics Models.

In the past decade, various opinion dynamics models have been built to depict the evolutionary mechanism of opinions and use them to predict trends in public opinion. However, model-based predictions alone cannot eliminate the deviation caused by unforeseeable external factors, nor can they reduce the impact of the accumulated random error over time. To solve this problem, we propose a dynamic framework that combines a genetic algorithm and a particle filter algorithm to dynamically calibrate the parameters of the opinion dynamics model. First, we design a fitness function in accordance with public opinion and search for a set of model parameters that best match the initial observation. Second, with successive observations, we tracked the state of the opinion dynamic system by the average distribution of particles. We tested the framework by using several typical opinion dynamics models. The results demonstrate that the proposed method can dynamically calibrate the parameters of the opinion dynamics model to predict public opinion more accurately.

Sub-optimal control of the greenhouse environment: Crop models with and without an assimilates buffer

A simplified crop model with an assimilates buffer (labelled 2S) is developed and compared to a more traditional model without a buffer (1S). The study explores the effects that the different model formulations have on the optimal control of the greenhouse environment. Values of the parameters specific to the 2S model are estimated from information in the literature. The size of the buffer is estimated in two different ways. Based on my previous modelling experience, a constant buffer co-state (virtual value of assimilates) is chosen to guide the control decisions. Its value is also estimated in two different ways: Calculated optimal values of the assimilates co-state, for various model parameter sets, turn out to be similar to values based on the proportion between assimilates and fresh fruit. Results for Dutch conditions and typical control equipment, show that the 2S optimal control strives to achieve balanced (sink = source) growth in summer, by increasing temperature and restricting CO 2 enrichment relative to the 1S control, where growth is always balanced (by definition). Utilizing 2S also improves the positive correlation between mean daily temperature and mean daily global radiation, a correlation which could be used to guide the control in practice. • Two crop growth models are developed: with and without an assimilates buffer. • Parameters quantifying buffer size and assimilates partitioning are estimated. • A constant buffer co-state is used to optimize greenhouse environmental control. • Optimal control strives to achieve balanced growth. • Control of model with buffer boosts sink strength at the expense of source strength.

Calibration of a constitutive model for volcanic sands under simple shear conditions

The simple shear response of air-fall volcanic (pyroclastic) soils under both saturated and unsaturated conditions is interpreted through an elastoplastic constitutive model with hydraulic-hardening and porosity-dependent critical state. Extensive experimental data collected under various testing protocols (i.e., simple shear, direct shear, and triaxial tests) enable the theoretical identification of the variability of the fundamental physical properties (i.e., frictional resistance, dilatancy, and water retention behavior) and the identification of a band of admissible values for prescribed confidence levels. In this paper, a constitutive model specifically developed to account for the simple shear loading is adopted and the calibration of the model parameters is performed accounting for such data scatter. The calibration procedure led to a single set of constitutive parameters through 3 main steps. The identification of range of variation for each parameter is carried out using the data derived from laboratory tests. Then, sensitivity analyses are performed on the main physical and constitutive parameters. Mathematical indicators quantifying the difference between the measurements and the predictions are proposed to investigate the role of the material properties and evaluate the model performance. Furthermore, an optimization algorithm is adopted to identify the optimal set of model parameters which best fits all the considered tests (i.e., simple shear tests under saturated and unsaturated conditions and wetting tests). The results imply that the variability of the hydro-mechanical properties must be considered to satisfactorily simulate the constitutive behaviors of the volcanic soils under a variety of simple shear testing regimes.

A New Algorithm to Solve the Extended-Oxley Analytical Model of Orthogonal Metal Cutting in Python

This paper presents a new implementation method of the Extended-Oxley analytical model, previously proposed by Lalwani in 2009, for orthogonal cutting of metals with a Johnson–Cook thermo-elastoplastic flow law. The present work aims to improve the implementation of this analytical model in order to propose a unified solution that overcomes the main shortcomings of the original model: the non-uniqueness of the solution, the low accuracy of the obtained solution, and the relatively long computational time for a purely analytical approach. In the proposed implementation, the determination of the optimal set of model parameters is based on an optimization method using the Python LMFIT library with which we have developed a dual Levenberg–Marquardt optimization algorithm. In this paper, the performance and efficiency of the developed model are presented by comparing our results for a 1045 steel with the simulation results obtained in the original paper proposed by Lalwani. The comparison shows a considerable gain in terms of computational speed (more than 2000 times faster than the original model), uniqueness of the obtained solution, and accuracy of the obtained numerical solution (almost zero force imbalance).

A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT) is a biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, comprising transitions from an epithelial state to partial or hybrid EMT state(s), to a mesenchymal state. Recent experimental studies have shown that, within a population of epithelial cells, heterogeneous phenotypical profiles arise in response to different time- and TGFβ dose-dependent stimuli. This offers a challenge for computational models, as most model parameters are generally obtained to represent typical cell responses, not necessarily specific responses nor to capture population variability. In this study, we applied a data-assimilation approach that combines limited noisy observations with predictions from a computational model, paired with parameter estimation. Synthetic experiments mimic the biological heterogeneity in cell states that is observed in epithelial cell populations by generating a large population of model parameter sets. Analysis of the parameters for virtual epithelial cells with biologically significant characteristics (e.g., EMT prone or resistant) illustrates that these sub-populations have identifiable critical model parameters. We perform a series of in silico experiments in which a forecasting system reconstructs the EMT dynamics of each virtual cell within a heterogeneous population exposed to time-dependent exogenous TGFβ dose and either an EMT-suppressing or EMT-promoting perturbation. We find that estimating population-specific critical parameters significantly improved the prediction accuracy of cell responses. Thus, with appropriate protocol design, we demonstrate that a data-assimilation approach successfully reconstructs and predicts the dynamics of a heterogeneous virtual epithelial cell population in the presence of physiological model error and parameter uncertainty.

Canine Smooth Muscle Contraction Model.

Smooth muscle is found extensively in the human body, including in blood vessels, airways, the gastrointestinal tract, and the urinary bladder. Although the contractile proteins of smooth muscle are very similar to those of striated muscle, smooth muscle's contractile mechanism has not been studied as extensively as those for cardiac and skeletal muscle. Previous studies developed a lumped model of muscle contraction and applied it to cardiac muscle and to skeletal muscle. In this study, this model is used to quantitatively describe the contractile properties of canine smooth muscle, using data from the literature. Results show that a single equation relating muscle force to muscle length and time, and a single set of model parameters, is able to describe smooth muscle's passive and active isometric forces, isometric twitch contractions, isotonic contractions, and an inverse force-velocity relation. The latter arises from the model without assumption of a particular force-velocity curve embodied as a contractile element. This new constitutive relation may be used to describe smooth muscle within larger physiological models, for instance to describe blood vessel constriction or urinary bladder function.

Modeling the Effect of Stress Ratio, Loading Frequency and Fiber Orientation on the Fatigue Response of Composite Materials.

Fatigue life models are widely used to predict the fatigue behavior at arbitrary cycle counts of composite structures subjected to cyclic or highly dynamic loads. However, their predictive capacity and determination of model parameters are strongly dependent on loading conditions and large experimental efforts. This research aims to develop a new model which uses a single model parameter to predict the variation trend and distribution pattern of fatigue experimental data points subjected to different stress ratios, loading frequencies and fiber orientations. Validation of the model with several sets of experimental data shows that the proposed model is capable of adequately considering the effects of stress ratio, loading frequency and fiber orientation on the fatigue behavior of composite materials and correctly predicting the variation trend of the experimental data points using only one set of model parameters regardless of stress ratios, loading frequencies and fiber orientations.

Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems

In recent years, various applications have emerged requiring linear topologies of wireless sensor networks (WSN). Such topologies are used in pipeline (water/oil/gas) monitoring systems. The linear structure has a significant impact on network performance in terms of delay, throughput, and power consumption. Regarding communication efficiency, routing protocols play a critical role, considering the special requirements of linear topology and energy resources. Therefore, the challenge is to design effective routing protocols that can address the diverse requirements of the monitoring system. In this paper, we present various wireless communication technologies and existing leak detection systems. We review different routing protocols focusing on multi-hop hierarchical protocols, highlighting the limitations and design issues related to packet routing in linear pipeline leak detection networks. Additionally, we present a LoRa multi-hop model for monitoring aboveground oil pipelines. A set of model parameters are identified such as the distance between sensors. In addition, the paper determines some calculations to estimate traffic congestion and energy consumption. Several alternative model designs are investigated. The model is evaluated using different multi-hop communication scenarios, and we compare the data rate and energy to provide an energy-efficient and low-cost leak detection system.

Determination of Parameters Characteristic of Dynamic Weakening Mechanisms During Seismic Faulting in Cohesive Rocks

AbstractWhile sliding at seismic slip‐rates of ∼1 m/s, natural faults undergo an abrupt decrease of shear stress called dynamic weakening. Asperity‐scale (<<mm) processes related to flash heating and weakening and, meso‐scale (mm‐cm) processes involving shear across the bulk slip‐zone, related to frictional melting or viscous flow of minerals, have been invoked to explain pronounced velocity‐dependent weakening. Here we present a compilation of ∼100 experiments performed with two rotary shear apparatuses. Cohesive rock cylinders of basalt, gabbro, granitoid rocks and calcitic marble were sheared at various values of effective normal stress (σneff = 5–40 MPa), target slip‐rate (Vt = 0.1–6.5 m/s) and fluid pressure (Pf = 0–15 MPa). To account for the uncertainties of constitutive parameters, we introduce a norm‐based optimization procedure on a set of model parameters by comparing the shear stress evolution inferred from the proposed weakening models with the shear stress measured during the experiments. We analyze the fit to experimental data of each weakening model and we discuss a composite model in which two weakening mechanisms (namely flash heating and bulk melting, flash heating and dislocation/diffusion creep) are used to test the hypothesis that they match the shear stress evolution in different slip ranges. We found that for slip smaller than a slip‐switch distance δ0, the weakening is better described by mechanisms occurring at the asperity scale whereas for larger slip values the bulk model performs better. The inferred δ0 values decrease with normal stress suggesting that during earthquakes bulk mechanisms can govern shear stress evolution after a few centimeters of slip.

Heat capacity and thermodynamic properties of PbS: Optimization based on calorimetric and electrochemical data

Heat capacity of PbS (galena) has been measured in the temperature range from 12 to 338 K using adiabatic calorimetry. Results of the measurements are consistent with previous adiabatic calorimetric data measurements but differ notably from those obtained by relaxation calorimetry. Using experimental data on adiabatic heat capacity measurements in the range of 12–338 K (including our data), results of EMF measurements, and literature data on H0(T) – H0(298.15 K) as inputs, we have optimized the thermodynamic properties of lead sulfide (PbS, galena) based on the quasi-harmonic model vibrational spectrum proposed by Kieffer and advanced to consider intrinsic anharmonicity. In this approach, the volume and temperature dependence of vibrational frequencies are considered, and the enthalpy of formation of PbS, ΔfH0(298.15 K), is treated as an optimized model parameter. The thermodynamic properties of PbS are described with a single set of model parameters over the entire range of its stability (0–1374 K) at a pressure of 1 bar. The model can be optimized based on either the reduced (Cp0 and ΔfG0(T)) or full (Cp0, H0(T) – H0(298.15 K), ΔfG0(T)) datasets. The both variants of the optimization predict the similar thermodynamic properties of PbS, in particular, values of the enthalpies of formation differ less than 0.1 kJ mol−1 and are consistent with ΔfH0(298.15 K) obtained experimentally by oxide melt solution calorimetry. The low-temperature adiabatic calorimetry data and high-temperature results of EMF experiments (or other reliable experimental data on ΔfG0(T)) represent a minimal sufficient set of input data providing optimization of thermodynamic properties at ambient pressure by the technique established in present study.

Dynamic relationship network and international management of enterprise supply chain by particle swarm optimization algorithm under deep learning

AbstractThe traditional enterprise decision evaluation model based on neural network has the problems of mismatch with the optimal solution and slow convergence speed. In order to enable companies to make decisions that are in line with changes in the market, the particle swarm optimization (PSO) algorithm is used to optimize deep learning neural networks. Firstly, the model parameter setting is improved, and the inertia weight strategy of normal distribution attenuation is combined. On this basis, a normal distribution decay inertial weight particle swarm optimization (NDPSO) is proposed. The inertia weight of the optimized algorithm maintains a large value in the initial stage, which makes the PSO algorithm maintain a large step size in the optimization process and a small value in the later stage. Through experimental analysis, the trend parameter of the best normal distribution of the algorithm is obtained as 0.4433 and then using the detection function, the NDPSO algorithm is tested by two types of test functions. The NDPSO algorithm is compared with the optimization results of other algorithms which are optimized on the Sphere function. The minimum value of 554.29, the average value of 2032.11, and the standard deviation of 918.47, all of them are at the leading level. Taking into account other experimental results, it is proved that the normal distribution decay inertia weight can balance the global search and local development capabilities from the perspective of parameter improvement. It can speed up the convergence with ensuring the convergence accuracy. The improved PSO algorithm has certain optimization capabilities for neural network models. The use of optimized neural network models can enable companies to make decisions in line with changes in the market and optimize the dynamic relationship network of the company's supply chain, which is of great significance to the implementation of the company's international management.

Initial excitation based discrete-time multi-model adaptive online identification

This paper proposes a novel multi-model adaptive identification (MMAI) algorithm for discrete-time linear time invariant (LTI) uncertain systems with tunable performance. The uncertain plant parameter vector is assumed to belong to a known convex hull of a finite number of vertices; these vertices are considered as initial choice of vertices of an adaptive model parameter set. The estimated parameter, corresponding to the uncertain plant parameter, is computed at every instant as a convex combination of the model set vertices. To update the vertices of the adaptive model parameter set, a switched adaptive update law is proposed, along with a novel discrete-time initial excitation (IE) condition, which is imposed on the regressor signal. The proposed discrete-time IE condition is online verifiable and is milder than persistence of excitation (PE) condition, required for parameter convergence in classical adaptive estimation routines. The switched adaptive law guarantees exponential convergence of the vertices of the model set as well as the estimated parameter, to the true plant parameter, provided the regressor signal satisfies the IE condition. The properties of the designed MMAI strategy are validated through suitable simulation examples.

Consideration of specific key points improves outcome of decompression treatment in patients with endocrine orbitopathy: pre-/post-OP comparison and biomechanical simulation

Endocrine orbitopathy is typically treated by resecting orbital walls. This procedure reduces intraorbital pressure by releasing intraorbital tissue, effectively alleviating the symptoms. However, selection of an appropriate surgical plan for treatment of endocrine orbitopathy requires careful consideration because predicting the effects of one-, two-, or three-wall resections on the release of orbital tissues is difficult. Here, based on our experience, we describe two specific orbital sites (’key points’) that may significantly improve decompression results. Methodological framework of this work is mainly based on comparative analysis pre- and post-surgery tomographic images as well as image- and physics-based simulation of soft tissue outcome using the finite element modelling of mechanical soft tissue behaviour. Thereby, the optimal set of unknown modelling parameters was obtained iteratively from the minimum difference between model predictions and post-surgery ground truth data. This report presents a pre-/post-surgery study indicating a crucial role of these particular key points in improving the post-surgery outcome of decompression treatment of endocrine orbitopathy which was also supported by 3D biomechanical simulation of alternative two-wall resection plans. In particular, our experimental results show a nearly linear relationship between the resection area and amount of tissue released in the extraorbital space. However, a disproportionately higher volume of orbital outflow could be achieved under consideration of the two special key points. Our study demonstrates the importance of considering natural biomechanical obstacles to improved outcomes in two-wall resection treatment of endocrine orbitopathy. Further investigations of alternative surgery scenarios and post-surgery data are required to generalize the insights of this feasibility study.