Thermal Constraints Research Articles

Form-finding of thermal-adaptive pin-bar assemblies based on eigenvalue modification

Lattice structures with tunable expansion properties have been investigated in multidisciplinary fields to control the temperature effects of structures or materials. The expected thermal adaptivity can be achieved by optimizing the structural geometry. A novel method for the form-finding of thermal-adaptive pin-bar assemblies is developed in this paper by considering the control of structural temperature effects as the minimization of the potential energy of the system. Based on the stationarity condition of the potential energy with respect to the nodal coordinates, the compatibility relationship between the thermal elongations of members and the target nodal displacements is proven to be the sufficient and necessary condition for structural thermal adaptivity. The solvability of the compatibility equation is determined by the rank equality between the compatibility matrix and its augmented form, which can be measured by the number of nonzero eigenvalues of its Gramian matrix. The analytical relationship between the eigenvalues of the Gramian matrix and the nodal coordinates is established using the matrix perturbation theory. A numerical strategy based on Newton’s method is proposed in which the eigenvalues are gradually modified by adjusting the nodal coordinates until the rank equality is satisfied. To address the existence of multiple solutions with structural thermal adaptivity, structural symmetry and periodicity constraints are introduced to narrow the solution space. The thermal-adaptive configurations of three illustrative pin-bar assemblies are analyzed using the proposed form-finding method, and the expected thermal deformations are verified for the obtained configurations using the finite element software ABAQUS. Comparing the results obtained by the proposed method with those obtained by nonlinear programming and the genetic algorithm validates the advantages of the proposed method in terms of computational time, optimality of the obtained configuration and applicability to complex structural geometries.

Thermal habitat fragmentation in stratified lakes induces resource waves that brook charr track across seasons

The spatial configuration of thermal habitats constrains the thermoregulatory performance of ectotherms. Thermal landscapes also vary through time, which is particularly relevant in seasonal environments such as temperate lakes. Indeed, elevated temperatures in the epilimnion of dimictic lakes during summer could substantially reduce the use of this habitat by cold‐stenothermic fish during the stratified period. The main objective of this study was to evaluate whether thermal habitat fragmentation in stratified lakes modulates accessibility to resources that brook charr, Salvelinus fontinalis, which is a mobile consumer, can track across seasons. More specifically, we hypothesize that reduced access to the littoral habitat during summer enhances foraging opportunities in this habitat during winter. We used an automatic acoustic telemetry system offering full coverage of the lake to continuously record brook charr locations across seasons, and we estimated zoobenthos abundances in the littoral habitat using image processing and semi‐automatic classification. While brook charr concentrate in the metalimnion of the pelagic habitat in summer, most individuals in winter shift to a shallow bay that is unexploited in summer due to thermal constraints. In this habitat, zoobenthos abundance is more than twice as high at the end of the summer compared to littoral habitats close to the thermal refuge in the pelagic habitat. Surprisingly, brook charr showed strong within‐lake site fidelity between two consecutive summers, which suggests that spatial memory could be a key driver of seasonal habitat use in this lacustrine population. Overall, our results suggest that thermal barriers create fragmentation between littoral and pelagic habitats that in turn produces resource opportunities that brook charr can track across seasons.

Research on the adjustable range of air conditioner setting temperature considering user comfort

Abstract Traditional air conditioner (AC) temperature control typically sets temperature goals or establishes a uniform temperature range without considering industrial characteristics and user comfort preferences. As a result, users cannot achieve differentiated and precise control. This paper proposes a data-driven approach to predict the range of AC set temperatures under the constraint of user comfort. Firstly, a random forest feature importance screening method is employed to extract key factors influencing AC set temperatures. Secondly, a convolutional neural network (CNN) model is constructed to estimate AC set temperatures for five typical user categories. Finally, based on the Predicted Mean Vote (PMV) model, user thermal comfort constraints are established to calculate the flexible adjustment range of AC for users. Through simulation analysis using real AC data from a certain region in Eastern China, the proposed standard for estimating user air conditioner set temperatures in this paper ensures a root mean square error not exceeding 0.5 and an average absolute percentage error not surpassing 1.8%. The obtained air conditioner adjustment ranges reflect the characteristics of users in different industries, meeting engineering requirements.

Optimal scheduling of energy routers based on distributed CAES systems

Abstract With the rise of renewable energy, distributed energy, and other different forms of energy, the architecture and function of the energy router are relatively perfect, for the topology of the energy router itself, different forms of energy in the energy router flow, conversion to realize the large-scale use of distributed energy equipment has also gradually become a hot spot of research. However, there are fewer studies on the energy router with a distributed CAES system to participate in the optimal scheduling of energy for integrated energy systems. Therefore, this paper is based on the operational domain model of an energy router with a distributed CAES system and analyzes the physical characteristics and coupling relationship of non-energy storage components, takes air pressure, temperature, and oil volume as state variables, takes charging, power generation, and heating power as external interfaces, and utilizes internal interface variables such as air quality and flow rate to realize energy storage, flow, and other internal interface variables to realize the communication between energy storage and non-energy storage elements, and finally determine the operation domain of the system. Secondly, the feasible domain of the energy router based on the distributed CAES system is portrayed, and the optimization of the system parameters is guided with the help of the relative distance by establishing the electric and thermal power constraints and the storage state constraints. Finally, the effectiveness of the feasible domain analysis method in the energy router of a distributed CAES system is verified by taking a northern region as an example.

An improved golden jackal optimization algorithm for combined economic emission dispatch problems

In this research paper, a new improved golden jackal optimization (IGJO) algorithm is applied to address the combined economic emission dispatch (CEED) problem, along with various thermal generator constraints such as valve point loading (VPL) effect, generator limits (GL) in power system. The hunting behavior of the golden jackals is mimicked in the golden jackal optimization (GJO) algorithm. The main aim of the CEED problem is to find the best optimal generation scheduling while minimizing both fuel cost and emission besides meeting the different power system constraints. The original GJO algorithm faces challenges when dealing with high-dimensional optimization problems, as it tends to get trapped in local optima. To address this issue the opposition-based learning (OBL) method was adopted in this GJO algorithm to obtain the global optimal solution and ensure enhanced performance in finding the solution for the CEED problems. To assess the competitiveness of the IGJO algorithm, it is used for various CEED test problems available in the literature, and results are contrasted with other recent heuristic optimization algorithms. Simulation results show that the proposed IGJO performs more effectively than the other compared algorithms in terms of solution quality, and robustness.

Performance analysis of dual material control gate cavity on source electrically doped TFET biosensor for biomedical applications

The fabrication complexity, low sensitivity, detection speed, and costs associated with nanoscale devices have been significant concerns in the development of label-free biosensors. To address these issues, we report a novel dual material control gate cavity on source electrically doped tunnel field-effect transistor (DM-CG-CS-ED-TFET) for label-free biosensors. In this regard, the n+ drain and p+ source regions within the proposed device are induced by applying polarity gate (PG) bias voltages of PG-1 = +1.2 V and PG-2 = −1.2 V, respectively, across the respective polarity gate electrodes. This approach not only overcomes doping control issues but also avoids thermal budget constraints and minimizes fabrication complexity when compared to conventional TFET. For biomolecule sensing in the device, a nanogap cavity is created within the gate dielectric by selectively etching a portion of the polarity gate dielectric layer towards the source side. The performance of the proposed biosensor device is evaluated based on the variations in carrier concentration profile, energy band diagram, electric field, transfer (IDS - VGS) characteristics, drain current (IDS) sensitivity, ON-state current (ION) sensitivity, switching ratio (ION/IOFF) and subthreshold swing (SS) sensitivity. The sensitivity of the device is also investigated based on nano-cavity dimensions, practical challenges such as various filling factors, and the step profile generated from the steric hindrance. Moreover, the effect of temperature on sensitivity has also been investigated. For this, various biomolecules including Streptavidin (k = 2.1), APTES (k = 3.57), ferrocytochrome c (k = 4.7), keratin (k = 8) and Gelatin (k = 12), have been investigated for their performance using Silvaco ATLAS device simulator. The simulation results demonstrate that the proposed biosensor is a viable option for biosensing applications in biomedical engineering.

Future malaria environmental suitability in Africa is sensitive to hydrology.

Changes in climate shift the geographic locations that are suitable for malaria transmission because of the thermal constraints on vector Anopheles mosquitos and Plasmodium spp. malaria parasites and the lack of availability of surface water for vector breeding. Previous Africa-wide assessments have tended to solely represent surface water using precipitation, ignoring many important hydrological processes. Here, we applied a validated and weighted ensemble of global hydrological and climate models to estimate present and future areas of hydroclimatic suitability for malaria transmission. With explicit surface water representation, we predict a net decrease in areas suitable for malaria transmission from 2025 onward, greater sensitivity to future greenhouse gas emissions, and different, more complex, malaria transmission patterns. Areas of malaria transmission that are projected to change are smaller than those estimated by precipitation-based estimates but are associated with greater changes in transmission season lengths.

Seasonal shifts in the habitat selection patterns of male American Marten (Martes americana) at a fine spatial scale.

Old-growth forests harbor a large amount of complex structural features that result in a wide array of wildlife habitats. However, intensive forest management is gradually converting old-growth forest into younger, even-aged stands, reducing structural complexity and threatening the persistence of old-growth-dependent species. Maintaining elements of complex stand structure is critical to the conservation of old-growth forest specialists that use different habitat components at different periods of their annual cycle, and it requires a comprehensive understanding of seasonal variation in the habitat needs of these species. However, difficulties in observing free-ranging animals have sometimes limited our ability to assess such variations in habitat requirements, especially for small, elusive species. To address this, we used GPS telemetry collars to describe fine-scale habitat selection patterns of 6 male American Martens (Martes americana) during 2 contrasting periods of the year (snow-free, from mid-April to mid-November; snow-covered, from mid-November to mid-April), an objective formerly hard to achieve using conventional VHF telemetry. We used resource selection functions conducted at the fourth order of selection to compare habitat characteristics found at the sites used by martens (GPS locations, n = 100) to those found on an equal number of available sites (random points, n = 100) within each individual seasonal home range. We conducted vegetation surveys on these 200 sites to describe habitat and built candidate models representing different concurrent hypotheses. Our results showed that proxies of prey availability, predator avoidance, and thermal constraints were the primary factors influencing marten habitat selection during both periods, although their respective importance differed between periods. Martens selected sites with a high density of large-diameter snags (≥30·ha-1), high conifer canopy closure (≥53%), and a dense lateral cover (≥81%) during the snow-free period, but selected sites with a high volume of coarse woody debris (≥64 m3·ha-1) and high conifer canopy closure (≥48%) during the snow-covered period. Our results highlight the importance of contrasting seasonal changes in habitat selection patterns of small carnivores and may help maintain structural attributes in the landscape that are suitable for male American Martens.

Physics-informed deep learning and linear programming for efficient optimization of combined cycle power plants

Efficient operation of combined cycle power plants (CCPP) involves solving mixed-integer nonlinear optimization problems, constrained by thermodynamic differential equations, thermal constraints, and temporal dependencies between variables. The optimization problem aims to maximize profit for day-ahead operation by minimizing short-term and long-term costs, and accounting for volatility in ambient conditions and energy price. In operational terms, the challenge lies in developing an efficient and robust optimization algorithm capable of determining optimal values for thousands of temporal variables. It must also have the capacity to evaluate a multitude of scenarios encompassing various weather and market conditions. To address this challenge, this paper proposes a hybrid optimizer that utilizes deep learning models trained on historical operational data of a real CCPP, along with a surrogate physics-driven piecewise-linear model of CCPP, to determine the optimal values of thousands of control setpoints, with strong temporal co-dependencies, within a two-minute runtime required by industry constraints. A multi-resolution architecture is proposed to efficiently implement this optimizer. The algorithm sequentially solves the problem in coarse and fine resolutions to accelerate convergence. Final results are checked for feasibility using pre-trained deep learning models, ensuring accuracy and validity. The study shows that the proposed model provides comparable results to the exact solution while reducing computational costs, up to 95%. This research has practical implications for power plant system operators, as well as traders and dispatchers, enabling them to make optimal decisions for day-ahead contracting purposes and CCPP operation in a reasonable time, ultimately leading to more efficient and cost-effective power plant management. Furthermore, the methodology introduced in this work could potentially be extended to other industrial applications that require large-scale, complex optimizations, where the precise governing principles of a process can be approximated by piecewise-linear functions.

Optimization of bulk microdefect performance in epitaxial silicon wafer

Bulk microdefects (BMDs) in epitaxial silicon wafers are pivotal for advanced node integrated circuits, offering enhanced mechanical strength and metal gettering capabilities. This study introduces two heat treatment procedures to optimize BMD density and radial uniformity in lightly doped wafers, addressing the challenges associated with large-diameter crystal growth and the thermal budget constraints of advanced node processes. We demonstrate that Rapid Thermal Annealing (RTA) at temperatures exceeding 1175 °C significantly improves BMD uniformity. A post-RTA stabilization step, when performed prior to epitaxial growth, enhances BMD density by retaining nuclei generated by RTA. Conversely, when the RTA with stabilization step follows epitaxial growth, a higher BMD density and larger BMD size are achieved. Light Scattering Tomography (LST) analysis confirms the optimal conditions for these treatments. The findings contribute to the semiconductor industry by optimizing wafer properties for high-performance ICs.

Risk of migration: not all gregarious locust nymphs reach appropriate refuges.

Sedentary animals choose appropriate refuges against predators, while migratory ones may not necessarily do so. In ectotherms, refuge selection is critical during low temperatures, because they cannot actively evade predators. To understand how migratory ectotherms alter their defensive behaviors depending on refuge quality in cold temperatures, we evaluated migratory gregarious desert locust nymphs (Schistocerca gregaria) in the Sahara Desert, where daily thermal constraints occur. We recorded how roosting plant type (bush/shrub) and its height influenced two alternative defense behaviors (dropping/stationary) during cold mornings, in response to an approaching simulated ground predator. Most locusts in bushes dropped within the bush and hid irrespective of their height, whereas those roosting > 2 m height in shrubs remained stationary. These defenses are effective and match with refuge plant types because dynamic locomotion is not required. When nymphs roosted on shrubs < 1.5-m height, which was an unsafe position, nearly half showed both defensive behaviors, indicating that escaping decisions become ambiguous when the refuges are inappropriate. These results suggest that locusts display flexible defensive behaviors when finding appropriate refuges and selecting refuge before daily thermal limitations occur could be critical for migratory ectotherms, which is a risk associated with migration.

Hydrothermal–pumped-storage scheduling considering renewable energy uncertainty by improved cheetah optimization

The growth of renewable wind and solar energy in modern power systems affects the short-term scheduling of hydrothermal power generation. This article presents the optimum day-ahead scheduling of wind–solar–hydrothermal systems with pumped-storage plants (PSPs). To assess the contribution of PSPs in decreasing the generation costs of a renewable integrated power system, a scheduling model is formulated that considers different security constraints. An improved cheetah optimizer (ICO) is implemented on a renewable integrated test power system to solve the short-term optimum wind–solar–pumped-storage plant–hydrothermal scheduling (OWSPHTS) problem, taking into account all thermal, hydraulic and network constraints. Optimum solutions are obtained with different objectives considering renewable uncertainties. The proposed ICO solution method is compared with the similar algorithms in terms of optimal fuel costs, emissions, convergence success rate and computation time. The comparative solutions show that the introduced optimization technique is efficient for solving the real-world OWSPHTS problem.

Joint In-Orbit Computation and Communication for Minimizing Download Time From LEO Satellites

Downloading a large amount of data from a low Earth orbit satellite to a ground station can be challenging due to the limited contact window, dynamic channel quality, solar energy supply, and thermal management without an atmosphere. Considering such dynamics, this paper proposes a joint design of in-orbit computation and communication for download time minimization. We combine the non-convex thermal constraints and energy constraints into unified energy budget constraints with upper bound approximation, and computational efficiency is achieved by decomposing the resulting large-scale problem into a non-convex communication sub-problem, a convex computation sub-problem solvable with interior point method and a master problem that optimizes the energy budget allocation between computation and communication. The communication sub-problem is solved with a generalized-benders-decomposition-based algorithm that decouples downlink scheduling and power allocation based on a closed-form solution of optimal dual variables in the power allocation primal problem. And the master problem is solved with ternary search by proving the minimal download time is quasi-convex with respect to the energy budget allocation between computation and communication. Simulation results demonstrate that the proposed solution effectively reduces the download time, especially under strict energy constraints and severe channel variations.

Green Data Center Cooling Control via Physics-guided Safe Reinforcement Learning

Deep reinforcement learning (DRL) has shown good performance in tackling Markov decision process (MDP) problems. As DRL optimizes a long-term reward, it is a promising approach to improving the energy efficiency of data-center cooling. However, enforcement of thermal safety constraints during DRL’s state exploration is a main challenge. The widely adopted reward-shaping approach adds negative reward when the exploratory action results in unsafety. Thus, it needs to experience sufficient unsafe states before it learns how to prevent unsafety. In this article, we propose a safety-aware DRL framework for data-center cooling control. It applies offline imitation learning and online post-hoc rectification to holistically prevent thermal unsafety during online DRL. In particular, the post-hoc rectification searches for the minimum modification to the DRL-recommended action such that the rectified action will not result in unsafety. The rectification is designed based on a thermal state transition model that is fitted using historical safe operation traces and able to extrapolate the transitions to unsafe states explored by DRL. Extensive evaluation for chilled water and direct expansion-cooled data centers in two climate conditions show that our approach saves 18% to 26.6% of total data-center power compared with conventional control and reduces safety violations by 94.5% to 99% compared with reward shaping. We also extend the proposed framework to address data centers with non-uniform temperature distributions for detailed safety considerations. The evaluation shows that our approach saves 14% power usage compared with the PID control while addressing safety compliance during the training.

Feasibility of high temperature superconducting cables for energy harvesting in large space-based solar power satellite applications: Electromagnetic, thermal and cost considerations

The aim of this paper is to present feasibility of application of High Temperature Superconducting (HTS) cables for Space-Based Solar Power (SBSP) application. SBSP is a promising technology that can deliver an infinite amount of clean and eco-friendly energy to the Earth. To deliver the harvested solar energy to the power systems on Earth, efficient energy transmission is critically important. To address the challenges of conventional transmission means, an ideal solution would be using HTS cables. In this research, the design procedure of a Direct Current (DC) HTS cable considering electromagnetic, thermal, and cost constraints is presented. The study considered a 2 MW bipolar DC HTS cable in five operational temperatures: 20 K, 30 K, 50 K, 65 K, and 77 K. The results were showed that the cost and weight of the designed HTS cables were increased by operational temperature increase. However, the cooling cost of HTS cable in higher temperatures, was less than lower temperatures. Also, the total efficiency of the HTS cable and cooling system were increased, when operational temperatures were changed from 20 K to 77 K, from 99.70% to 99.97%, respectively.

Experimental constraints on formation of silica-rich igneous rims around chondrules in CR chondrites

Silica-rich igneous rims (SIRs) occur commonly as an outer rim layer on porphyritic chondrules in CR (Renazzo-like) chondrites, as well as more rarely in other chondrite groups. Formation conditions for SIRs can provide insight into chemical and physical conditions in the chondrule-forming region of the protoplanetary disk, as well as helping to understand temporal changes to successive thermal events within the CR and other chondrule formation regions. Both accretion and condensation models have been proposed as formation mechanisms of SIRs, but a lack of robust thermal constraints prevents a detailed interpretation. We have carried out an experimental study aimed to define the conditions of SIR formation by reproducing the texture, mineralogy, mineral chemistry, and silica polymorphs present in natural SIRs. Experiments conducted on an SIR bulk composition, at peak temperatures of 1310–1507 °C with linear cooling rates between 30 and 90 °C/hr successfully reproduce natural SIRs that contain cristobalite, low-Ca pyroxene, Ca-rich pyroxene, and glass. Microcrystalline mesostasis was formed in an experiment with lower cooling rates at low temperatures (6 °C/hr from <1200 °C). All silica in the experiments was cristobalite, including in experiments with maximum temperatures as low as 1310 °C. Since the cristobalite stability field in the silica phase diagram is >1470 °C, it is clear that silica polymorphs are not robust temperature indicators and that the presence of cristobalite in natural SIRs does not necessarily indicate high peak temperatures. We suggest that accretion of SIR precursors onto solidified chondrules, followed by melting, is the most likely scenario for their formation. Our constraints on SIR formation are similar to those that are usually discussed for chondrules. Therefore, in the CR chondrule-forming region, a repeatable heating mechanism is required that does not change significantly during the time in which condensing material evolved to highly silica-rich compositions. The need for recurring heating events is a general constraint for modeling chondrule formation.

Influence of variable fluid properties on mixed convective Darcy–Forchheimer flow relation over a surface with Soret and Dufour spectacle

Abstract The thermo-diffusion applications of nanofluid subject to variable thermal sources have been presented. The significance of Darcy–Forchheimer effects is attributed. The flow comprises the mixed convection and viscous dissipation effects. Furthermore, the variable influence of viscosity, thermal conductivity, and mass diffusivity is treated to analyze the flow. The analysis of problem is referred to convective mass and thermal constraints. The analytical simulations are proceeded with homotopy analysis method. The convergence region is highlighted. Novel physical contribution of parameters is visualized and treated graphically. It is noted that larger Brinkman number leads to improvement in heat transfer. The concentration pattern boosted due to Soret number. The wall shear force enhances with Hartmann number and variable thermal conductivity coefficient.

A tripe diffusion bioconvective model for thixotropic nanofluid with applications of induced magnetic field

Owing to enhanced thermal characteristics of nanomaterials, multidisciplinary applications of such particles have been utilized in the industrial and engineering processes, chemical systems, solar energy, extrusion processes, nuclear systems etc. The aim of current work is to suggests the thermal performances of thixotropic nanofluid with interaction of magnetic force. The suspension of microorganisms in thixotropic nanofluid is assumed. The investigation is further supported with the triple diffusion flow. The motivations for considering the triple diffusion phenomenon are associated to attaining more thermal applications. The flow pattern is subject to novel stagnation point flow. The convective thermal constraints are incorporated. The modeled problem is numerically evaluated by using shooting technique. Different consequences of physical parameters involving the problem are graphically attributed. The insight analysis is presented for proposed problem with different engineering applications. It is claimed that induced magnetic field enhanced due to magnetic parameter while declining results are observed for thixotropic parameter. The heat transfer enhances due to variation of Dufour number. Furthermore, low profile of nanoparticles concentration has been observed for thixotropic parameter and nano-Lewis number.

Influence of Wide-Bandgap Semiconductors in Interleaved Converters Sizing for a Fuel-Cell Power Architecture

This study presents a decision-support methodology to design and optimize modular Boost converters in the context of fuel-cell electric vehicles. It involves the utilization of interleaved techniques to reduce fuel-cell current ripple, enhance system efficiency, tackle issues related to weight and size concerns, and offer better flexibility and modularity within the converter. The methodology incorporates emerging technologies by wide-bandgap semiconductors, providing better efficiency and higher temperature tolerance. It employs a multiphysical approach, considering electrical, thermal, and efficiency constraints to achieve an optimal power architecture for FCHEVs. Results demonstrate the advantages of wide-bandgap semiconductor utilization in terms of volume reduction and efficiency enhancements for different power levels. Results from one of the considered power levels highlight the feasibility of certain architectures through the utilization of WBG devices. These architectures reveal improvements in both efficiency and volume reduction as a result of incorporating WBG devices. Additionally, the analysis presents a comparison of manufacturing cost between standard and wide-bandgap semiconductors to demonstrate the market penetration potential.

Age, Kinematic and Thermal Constraints of Syn‐Orogenic Low‐Temperature Deformation Events: Insights From Thermochronology and Structural Data of the Nekézseny Thrust (Alpine‐Carpathian‐Dinaric Area)

AbstractUnraveling the age and kinematics of low temperature deformation events is crucial in understanding the late‐stage evolution of orogens. However, accurate age constraints can often be challenging to obtain due to unideal outcrop conditions, large sedimentary hiatuses or the lack of well‐defined thermal events. In this study, we show on the example of the Nekézseny Thrust, a poorly exposed late orogenic thrust in the southern Western Carpathians, that a combined approach of structural analysis and multi‐method thermochronology can provide the necessary temporal, kinematic and thermal constraints for a detailed reconstruction of the deformation history. While structural mapping revealed that the Late Cretaceous Uppony Gosau Basin in the footwall of the Nekézseny Thrust underwent a significant post‐Campanian and pre‐Miocene shortening, K/Ar dating of fault gouge samples from the main fault zone constrained the primary thrusting event to the Maastrichtian. Based on the acquired apatite fission‐track and (U‐Th)/He ages, subsequent heating of the Upper Cretaceous sediments due to tectonic burial was limited to 75–100°C, followed by deformation‐related and gradual cooling between the Eocene and Early Miocene. Considering the reconstructed deformation history, as well as the large‐scale tectonic affinity of the displaced units in its footwall and hanging wall, the Nekézseny Thrust is a far‐traveled (ca. 600 km) segment of the Late Cretaceous Alps‐Dinarides contact zone, whose development was linked to the switch from lower plate to upper plate position with respect to the Sava Zone and Alpine Tethys sutures, respectively.