Differential Operator Approximation Based Tightly Coupled Multiphysics Solver Using Cascaded Fourier Network

Slender-body aircraft operating at high angles of attack often experience nonlinear, asymmetric multi-vortex flow structures that generate random, unsteady lateral forces, undermining stability, and maneuverability. Dielectric barrier discharge plasma actuators can eliminate these lateral forces. However, conventional open-loop plasma control method cannot adapt to dynamic flow fields in real time, limiting the overall effectiveness of active flow control. This study introduces a plasma control framework grounded in physical principles and develops plasma actuator design methods to regulate vortex interactions, stabilize flow dynamics, and optimize control efficiency. An intelligent closed-loop flow control strategy based on proximal policy optimization, a deep reinforcement learning algorithm, is utilized to enable real-time plasma parameter adjustments for suppressing lateral force at high angle of attack. The spatiotemporal interaction of plasma-induced and asymmetric vortices was investigated through synchronized pressure measurements and particle image velocimetry. The proximal policy optimization-based parameter optimization model was trained online in an educational open-return wind tunnel and subsequently deployed in a low-speed closed-return wind tunnel. Based on vortex stability analysis and comprehensive results, the closed-loop control algorithm significantly mitigates lateral forces, achieving a 68.5% reduction compared to steady plasma actuation, while improving energy efficiency by 70% over conventional methods.

<p style='text-indent:20px;'>In this paper, the chaotic oscillations of the initial-boundary value problem of linear hyperbolic partial differential equation (PDE) with variable coefficients are investigated, where both ends of boundary conditions are nonlinear implicit boundary conditions (IBCs). It separately considers that IBCs can be expressed by general nonlinear boundary conditions (NBCs) and cannot be expressed by explicit boundary conditions (EBCs). Finally, numerical examples verify the effectiveness of theoretical prediction.</p>

Numerical validation of the volume penalization method in three-dimensional pseudo-spectral simulations

We present a new hardware architecture for calculating the optical flow of real time video streams. Our system produces dense motion fields in real time at high resolutions. We implemented a new version of the Ridge Regression Optical flow algorithm. This architecture design focuses on maximizing parallel operations of large amounts of pixel data and pipelining the data flow to allow for real time throughput. A specialized memory controller unit was designed to access pixel data from seven different frames. This memory control alleviates any memory bottleneck. The new architecture can process 1080p HD video streams at over 60 frames per second. This design requires no processor nor data bus which allows it to be more easily manufactured as an ASIC.

Turbulent flow is ubiquitous in nature and also has wide industrial applications. The direct observation of turbulent flow can help us to gain the fundamental insights of its physics. However, the experimental measurement of complete velocity fields in real time with high spatial and temporal resolutions is difficult due to the limitations of sensors and sensing techniques, such as sensor accessibility and sampling rate. Recently, a physics-based compressive sensing (PBCS) approach was proposed to reduce the number of sensors and amount of data collection to monitor temperature distributions with a very high compression ratio. In this Paper, the PBCS mechanism for monitoring turbulent flow is formulated. By recovering the boundary conditions in the Reynolds-averaged Navier–Stokes equations from some limited velocity measurements, the complete velocity field is reconstructed from the model. The application of PBCS to the steady-state turbulent flow is demonstrated and validated with experimental data of direct measurements.

A new integral equation-based nonoverlapping domain decomposition method (NDDM) is proposed for analyzing electromagnetic (EM) scattering from electrically large PEC objects whose MLFMA models are too large for the user’s computer to accommodate. The integral equation is first built on the entire PEC surface, and the entire surface is partitioned into some nonoverlapping subsurfaces. The RWG functions are used to expand the surface current on the entire surface. Then, each full-RWG function across some boundary curve is split into two half-RWG functions whose unknown coefficients are constrained by the boundary current continuity (using the explicit boundary condition). The convergence of the outer-iterative scheme of the proposed NDDM is investigated theoretically and demonstrated to be very good, and hence a very large problem can be effectively solved in an ordinary computer. Some numerical examples are provided to demonstrate the correctness and robustness of the proposed method, and to compare the proposed method with the existing overlapping DDMs having similar attributes.

This paper extends the generating function approach to the state constrained linear quadratic optimal control problem by utilizing penalties. The original constrained problem is approximated by an unconstrained problem with a novel penalty function by which the latter problem can be solved via generating function method based on Taylor series approximation. In view of this, the optimal solution can be given as the state feedback control with explicit boundary conditions and pre-computed coefficients. This structure is useful in the repetitive on-line computation for optimal solutions satisfying a number of different boundary conditions. Examples demonstrate the effectiveness of the developed method.

The Aerospace – NASA Videomagnetograph began operation one month ago, two years after components were ordered and construction began. The design grew out of a desire to obtain magnetic fields in real time using an optical filter. The aim was to study and analyze magnetic configurations and changes, quantitatively if possible, with high spatial and temporal resolution and as much sensitivity as possible. This instrument is restricted to the line-of-sight component of the magnetic field and is primarily intended for high resolution studies of selected regions of the sun. The rationale behind our approach is shown in the next section and the design details in the following.

Higgs and Goldstone modes are collective excitations of the amplitude and phase of an order parameter that is related to the breaking of a continuous symmetry. We directly studied these modes in a supersolid quantum gas created by coupling a Bose-Einstein condensate to two optical cavities, whose field amplitudes form the real and imaginary parts of a U(1)-symmetric order parameter. Monitoring the cavity fields in real time allowed us to observe the dynamics of the associated Higgs and Goldstone modes and revealed their amplitude and phase nature. We used a spectroscopic method to measure their frequencies, and we gave a tunable mass to the Goldstone mode by exploring the crossover between continuous and discrete symmetry. Our experiments link spectroscopic measurements to the theoretical concept of Higgs and Goldstone modes.

Once room acoustical reflections data are extracted from a physical space or a model, and are encapsulated in a 3D impulse response, they can be used to render immersive sound fields in real time. A range of possibilities then opens for creative use of acoustics in music. A skilled virtual acoustics designer-engineer may rebalance digital signals representing the room response to situate player and listener on the stage or at the back of the auditorium, may modify and arrange temporal segments to re-imagine the aural dimensions of the space, and apply gain and directional placement to shape the impression of immersive presence, adapting acoustics to musicians’ creative needs. In the process of building an idealized acoustical environment for the music, techniques of sound reinforcement and of rendering room acoustics are combined to balance presence with ambience and to deliver a sensation of acoustical power with lift-off. The means exist to move beyond acoustical realism into fictionalized acoustics.

Measuring image encoding fields in real time and applying the information in postprocessing offer improved image quality for MRI, particularly for applications that are intrinsically sensitive to gradient imperfections. For this task, a stand-alone magnetometer system based on multiple (2)H transmit-receive NMR probes has been developed. The conceptual advantages of changing to (2)H NMR probes for (1)H magnetic field monitoring are elucidated here, and the practical design of the probes is described. In comparison to previous (1)H NMR probe-based designs, (2)H probes are perfectly decoupled from standard (1)H imaging. Utilization of RF shielding or other nonoptimal decoupling schemes is therefore not needed. Probes based on (2)H nuclei are also more easily miniaturized for high-resolution imaging. This is particularly important for diffusion tensor and phase-contrast imaging, which rely on strong motion-sensitizing gradients. The presented (2)H NMR probes have been shown to fulfill the requirements for accurate (1)H imaging down to image resolutions of 0.2 mm. Using susceptibility matching techniques, the probe's B(0) inhomogeneity-induced signal dephasing is reduced and monitoring periods beyond 200 msec are achieved. The benefit of real time magnetic field monitoring is highlighted for phase-contrast and non-Cartesian multishot imaging.

Rice is needed daily. This food contains carbs, which the body requires for energy and growth. Economically beneficial rice. Infected rice plants waste field crop and reduce yield if not monitored. Field monitoring is needed to detect rice plant illnesses. Recent agricultural surveillance uses drones with cameras and GPS. It's quick, independent data collection over a vast area. This study proposes a drone-based (IoT) building that detects and classifies rice diseases using real-time data and image processing to boost rice productivity. The method uses a GPS sensor to map ill rice plants in rice fields in real time. This study suggests using the COA to determine the optimal network hyperparameter values for autonomous rice image categorisation. The Swin-Transformer links non-overlapping windows from the previous layer by changing window partitions, thereby capturing multi-scale properties. The improved approach detects six categories, including healthy, leaf blast, and bacterial leaf blight. Experimental analysis used Accuracy, Precision, Recall, Specificity, and F1_score. For training 80% and testing 20% data, the Chimpanzee-based model obtained 96% accuracy, 99% specificity, 96.09% F1_score, 96.26% precision, and 96.14% recall, a 3% to 5% improvement over earlier techniques. The suggested enhancement enhanced literature findings compared to other methods using the same or similar datasets.

We propose a tactile sensor based on computer vision that measures a dense traction field, or a distribution of 3D force vectors over a 2D surface, which humans also effectively sense through a dense array of mechanoreceptors in the skin. The proposed "GelForce" tactile sensor has an elegant and organic design and can compute large and structurally rich traction fields in real time. We present how this sensor can serve as a powerful and intuitive computer interface for both existing and emerging desktop applications.

Farmers face challenges in adapting to environmental changes, yet with decreasing implementation costs and a growing accessibility to technical expertise, progress is evident. This research introduces KisanVishwa, an Android app designed for smart farming. KisanVishwa optimizes precision agriculture systems, enabling farmers to monitor their fields in real time and address agricultural constraints. Tailored to meet the specific needs of today's farmers, the app provides user-friendly tools and the latest information on farm equipment, fertilizers, chemicals, technological advancements, and local distributors and retailers. By integrating KisanVishwa into their operations, farmers can overcome challenges associated with the adoption and maintenance of precision agriculture. KisanVishwa adopts a user-centric design for compatibility across diverse Android devices, integrating monitoring tools and collaborating with agricultural research institutions. Empowering farmers, the app addresses contemporary challenges, fostering informed decisions and efficiency. With its user-friendly interface, KisanVishwa promotes the adoption of precision agriculture, essential resources, and sustainable practices in the agricultural sector. Convincing traditional farmers to adopt advanced technologies may pose challenges. Functionality may depend on reliable internet, posing challenges in areas with limited connectivity.

We have developed a technique to measure low-power electromagnetic fields from mm-wave devices non-intrusively by combining a metasurface, designed to absorb power and focus the radiated power in a thermally isolated region, with an infrared (IR) camera. The metasurface consists of thermally isolated elements of low mass and highly emissive material for maximal IR conversion of the incident wave. The IR camera captures the converted energy and indirectly images the incident electromagnetic field on the metasurface. The setup combines multi-scale, multi-physical processes to conduct measurements of the incident electromagnetic fields in real time. In this work, the technique is presented and discussed. Measurements are carried out to demonstrate the technique and image the electromagnetic field of a radiating device. The results compare well with simulations, and the technique can measure the low power density levels of consumer devices and provide a general visualization of electromagnetic fields in a live setting.