The researchers’ work, published as a preprint under review in the journal Wind Energy Science, focuses on what aerodynamicists call attached-flow unsteady aerodynamics. Attached flow means the air moves smoothly over the blade surface without separating into the chaotic, energy-dissipating conditions known as stall. On the outboard region of a rotating blade, the section that produces most of the power and carries most of the aerodynamic load, the flow is usually attached even though the angle of attack and the relative velocity are constantly changing. Getting the unsteady forces right in this regime is therefore not a niche concern; it underpins essentially every engineering model of dynamic stall, since stall models are built on top of the attached-flow baseline.

The theoretical machinery for unsteady airfoil behavior dates back nearly a century to classical unsteady thin-airfoil theory, a framework that describes how lift and moment on a thin wing section respond to changes in motion and in the incoming flow. The problem, according to the Danish team, is not the theory itself but its implementation. Although the underlying mathematics is classical, existing descriptions in the literature do not, in the authors’ assessment, provide a complete and internally consistent route for putting the attached-flow contributions into the aerodynamic solvers that industry and researchers actually use. Different codes have adopted different subsets of the required effects, and the consequences of those omissions have been difficult to quantify.

Li and colleagues set out to close that gap by assembling all of the attached-flow ingredients into a single, consistent framework, expressed in a form that is ready for implementation. Their formulation covers several distinct elements that a complete engineering model must combine: the lookup of two-dimensional airfoil polar data, which relates lift, drag, and moment coefficients to the angle of attack; the memory effect of the shed wake, in which vorticity shed into the wake at earlier times continues to influence the loads at the current moment; the non-circulatory, or apparent-mass, loads that arise from the acceleration of air near the airfoil; and consistent definitions of the magnitude and direction of the resulting force. The framework is also designed to connect naturally with models of separated-flow dynamics, so that dynamic stall can be layered on top of the attached-flow foundation.

A key methodological choice in the new work is the way the shed wake memory is formulated. Rather than using the angle of attack as the aerodynamic state variable that carries the history of the flow, the researchers use the downwash velocity, the vertical velocity imparted to the flow at the airfoil. This change matters because the downwash is the quantity that more directly reflects the physical state of the circulatory system, and formulating the memory in terms of it clarifies the implications of the associated modeling choices. Starting from the dimensional loads of classical unsteady thin-airfoil theory, the authors derive both the circulatory contributions, which come from bound circulation and the shed vortex wake, and the non-circulatory contributions in coefficient form, making them directly usable in engineering codes.

The formulation is written specifically for generalized lifting-line methods, a family of approaches that includes the blade-element momentum method used in most industrial design tools, the lifting-line method, and the actuator-line method often coupled with computational fluid dynamics in research simulations. Because the two-dimensional theory itself is classical and well established, the authors direct their verification effort at the rotor level, where the implementation choices actually matter. They test the framework on two rotor cases designed to probe different aspects of the physics.

The first test case is a coned straight blade, which serves as a cross-method benchmark. By comparing simulations that include all of the required contributions with simulations that deliberately omit three of them, the researchers quantify the errors in rotor-integrated thrust and power that result from each omission. The results are striking for anyone who assumed the neglected terms were negligible. Among the three, the contribution associated with the projection of the lift direction has the largest influence on predicted power. Omitting the mid-chord heaving acceleration contribution produces a different kind of error: it eliminates the cancellation that should occur in the non-circulatory normal force, and that loss of cancellation shows up as an error in the predicted thrust. In other words, terms that might look like small refinements on paper can measurably change the numbers that engineers use to size components and estimate energy production.

The second test case involves a vertical-axis wind turbine operating from zero onset flow, a configuration chosen because it provides an elegant analytical check. In the ideal thin-airfoil limit, the theory demands that all of the circulatory and non-circulatory contributions cancel exactly, yielding zero total rotor torque. The researchers show that their implementation reproduces this cancellation, which verifies that the various pieces of the framework have been assembled consistently. A model that left out any of the required terms would fail this test, producing a spurious torque from a rotor that should, in this idealized limit, produce none. The authors note that the complete model avoids unphysical predictions of this kind, whereas incomplete implementations do not.

Why should anyone outside the wind energy community care about coefficient bookkeeping in engineering models? Because the stakes are enormous. Wind turbine blades are among the largest flexible structures ever built by humans, and their design margins depend on load predictions generated by aeroelastic codes that run millions of simulated seconds of operation. If a simulation systematically mispredicts power or thrust because an unsteady aerodynamic term was dropped decades ago and never revisited, the consequences ripple through certification, blade design, and the economics of offshore wind. The Danish team’s results show that omitting some effects can noticeably change predicted power and loads, which means the industry’s standard simulations may carry errors that are neither random nor negligible.

The study also carries a broader lesson about how engineering science accumulates. Classical theories do not come with implementation manuals, and each generation of engineers translates them into code under practical constraints, sometimes losing pieces of the physics along the way. By deriving every attached-flow contribution in coefficient form, clarifying the role of the downwash-based memory formulation, and demonstrating the errors that follow from common omissions, Li, Gaunaa, and Pirrung have provided what they describe as an implementation-ready method that combines the required aerodynamic effects in one consistent framework. Tested across rotor cases spanning conventional horizontal-axis and vertical-axis machines, the framework supports more reliable aerodynamic and aeroelastic simulations of wind turbines. As rotors grow larger and more flexible, and as the industry pushes turbines into more turbulent offshore environments, the unsteady forces that this work puts on a firmer footing will only become more important to capturing how these giants of the energy transition actually behave in the sky.

Subject of Research: Unsteady attached-flow airfoil aerodynamics for wind turbine simulation models

Article Title: Unsteady airfoil aerodynamics in attached flow: From unsteady thin-airfoil theory to wind turbine implementation

Article References: Li, A., Gaunaa, M., & Pirrung, G. R. (2026). Unsteady airfoil aerodynamics in attached flow: From unsteady thin-airfoil theory to wind turbine implementation. https://doi.org/10.5194/wes-2026-168

Image Credits: AI Generated

DOI: 10.5194/wes-2026-168

Keywords: wind energy, unsteady aerodynamics, thin-airfoil theory, blade-element momentum, shed wake memory, dynamic stall, aeroelasticity, lifting-line methods, vertical-axis wind turbine, rotor loads, Technical University of Denmark, Wind Energy Science

The World Health Organization ranks antimicrobial resistance among the leading global health challenges, and the stakes of getting this science right could hardly be higher. When resistance is misjudged, antibiotics fail in the clinic, infections spread, and the pipeline of effective drugs narrows further. The new opinion piece, authored by Professor Frank Møller Aarestrup of the DTU National Food Institute and colleagues including Professor Thomas Bjarnsholt of the University of Copenhagen and Copenhagen University Hospital, draws on recent experimental findings suggesting that factors such as temperature, oxygen availability, and pH can determine whether a bacterium carrying a resistance gene actually behaves in a resistant manner.

Two underlying studies anchor the argument. One examined how the contrasting pH optima of the beta-lactamase enzymes CTX-M and CMY influence the fitness and resistance ecology of Escherichia coli. The other showed that environmental factors, specifically pH and redox potential, modulate beta-lactamase phenotypes and thereby alter beta-lactam resistance. Together, these findings indicate that the expression of resistance is not an intrinsic, unvarying trait encoded neatly in a gene, but a phenotype that depends on the microenvironment in which the bacterium finds itself at a given moment.

According to Aarestrup, this realization demands that researchers ask a far more granular set of questions. Is a bacterium carrying a resistance gene resistant at a pH of 5 or 8, when standard laboratory testing is performed at a fixed pH of 7.2? What oxygen levels and temperatures permit resistance to manifest? Does a patient’s fever change the answer? Is the bacterium residing in the oxygen-poor environment of the gut or in the oxygenated bloodstream? Current diagnostic protocols, he notes, take none of these biological conditions into account, even though they are precisely the conditions a bacterium encounters inside the human body.

Bjarnsholt frames the conceptual shift in equally stark terms. Resistance genes, he emphasizes, do not function in a vacuum. They exist in different bacterial species and in varying microenvironments, and the scientific community must therefore stop asking only which resistance genes a bacterium possesses and start asking when those genes actually render the bacterium resistant. The distinction is more than semantic. It implies that a bacterium classified as resistant in a standardized assay could behave as fully susceptible in the tissue of a patient, and vice versa, potentially explaining the frustrating clinical scenarios in which a course of antibiotics fails despite a laboratory report predicting success, or succeeds when the laboratory predicted failure.

The researchers acknowledge that embracing this context-dependent view of resistance will make laboratory measurement considerably harder. For decades, enormous effort has gone into standardizing susceptibility testing worldwide, so that a result produced in Copenhagen can be compared with one produced in Nairobi or São Paulo. Measuring resistance across a matrix of pH values, oxygen concentrations, temperatures, and host conditions multiplies the experimental burden many times over. Yet the researchers contend that the payoff justifies the effort, because a richer dataset would capture the true behavior of resistance genes under the conditions that matter in real infections.

Perhaps the most provocative implication concerns treatment strategy. If resistance manifests only when certain conditions are met, then it may become possible to select antibiotics that trigger the development of resistance only under circumstances that never occur in the human body, for example at a temperature far above physiological norms. Such a strategy would, in principle, allow clinicians to treat infections while deliberately choosing drugs whose resistance pathways remain switched off in the patient. Aarestrup suggests that this line of thinking could open entirely new approaches to combating resistance in humans, animals, and the wider environment, moving beyond the current reactive cycle of drug use followed by resistance emergence.

The environmental dimension of the proposal is equally significant. Resistance genes circulate through soil, water, wastewater, and animal populations, and monitoring efforts have long struggled to interpret what the presence of a gene in these reservoirs actually means for human health. If gene presence no longer equates to functional resistance, environmental surveillance could be refined to identify the conditions under which environmental resistance genes become clinically relevant, offering a better chance of intervening before dangerous resistance traits migrate into human pathogens. More targeted treatment of animals could similarly reduce the selective pressure that farms currently exert on bacterial communities.

Realizing this vision will require an enormous collective undertaking. The researchers argue that large volumes of new data on resistance across a range of biological conditions must be collected, effectively restarting resistance gene research from square one. Aarestrup envisions grouping resistance genes into categories and then deploying computational models and artificial intelligence to identify the relationships between genes and varying conditions, a task well suited to machine learning given the combinatorial complexity of gene-by-environment interactions. Such an effort would revisit and reassess much of the knowledge accumulated over decades of research built on the old binary model.

The opinion piece, titled From resistance genes to resistance states and enzymatic context-dependence in antimicrobial resistance, was published on 16 September 2026 and declares no competing interests. Whether the field adopts the proposed framework will depend on whether laboratories, regulators, and diagnostic manufacturers are willing to abandon a century of standardized practice in favor of a messier but potentially far more accurate picture of microbial behavior. If the Danish team is right, the future of antibiotic stewardship may lie not in discovering new drugs alone, but in understanding precisely when, where, and under what conditions the drugs already in use lose their power, and in engineering treatment strategies that keep resistance from ever switching on.

Subject of Research: Context-dependent antibiotic resistance and the environmental conditions that determine whether resistance genes make bacteria resistant

Article Title: Researchers in Denmark aim to find new solutions in the fight against antibiotic resistance

Article References: Researchers in Denmark aim to find new solutions in the fight against antibiotic resistance. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: antibiotic resistance, antimicrobial resistance, Trends in Microbiology, Technical University of Denmark, beta-lactamase, pH, microenvironment, diagnostics, Escherichia coli, resistance genes, public health, WHO