Air Force Institute of Technology

This work utilizes the finite element approach together with an innovative shear strain theory to investigate the static bending behavior, free vibration features, and static buckling phenomena of flexo-magnetic nanoplates. The inquiry specifically examines the fluctuation in both the thickness of the plate and the elasticity of the foundation. The influence of initial geometrical imperfections, including several categories such as local and global faults, is also taken into account. The influences of several factors, including the law governing thickness fluctuation, types of imperfections, boundary conditions, and elastic foundation, on the mechanical response of the plate are considered. Outcomes of the work include new and original discoveries that have not been discussed in previous research, adding to both theoretical comprehension and practical implementation.

The Legendre pair problem is a particular case of a rank-1 semidefinite description problem that seeks to find a pair of vectors $(\textbf{u},\textbf{v})$ each of length $\ell$ such that the vector $(\textbf{u}^{\top },\textbf{v}^{\top })^{\top }$ satisfies the rank-1 semidefinite description. The group $(\mathbb {Z}_\ell \times \mathbb {Z}_\ell )\rtimes \mathbb {Z}^{\times }_\ell$ acts on the solutions satisfying the rank-1 semidefinite description by $((i,j),k)(\textbf{u},\textbf{v})=((i,k)\textbf{u},(j,k)\textbf{v})$ for each $((i,j),k) \in (\mathbb {Z}_\ell \times \mathbb {Z}_\ell )\rtimes \mathbb {Z}^{\times }_\ell$. By applying the methods based on representation theory in Bulutoglu [Discrete Optim 45 (2022)], and results in Ingleton [J Lond Math Soc 1(4): 445–460, 1956] and Lam and Leung [J Algebra 224:91–109, 2000], for a given solution $(\textbf{u}^{\top },\textbf{v}^{\top })^{\top }$ satisfying the rank-1 semidefinite description, we show that the dimension of the convex hull of the orbit of $\textbf{u}$ under the action of $\mathbb {Z}_{\ell }$ or $\mathbb {Z}_\ell \rtimes \mathbb {Z}^{\times }_\ell$ is $\ell -1$ provided that $\ell =p^n$ or $\ell =pq^i$ for i=1,2, any positive integer n, and any two odd primes p, q. Our results lead to the conjecture that this dimension is $\ell -1$ in both cases. We also show that the dimension of the convex hull of all feasible points of the Legendre pair problem of length $\ell$ is $2\ell -2$ provided that it has at least one feasible point.

The discovery of more hydrocarbon wells remains a major recipe to boost the economy of Nigeria, a major oil producer in Africa. The seismic method is prominent for identifying traps in the oil explorations, but cannot indicate if the trap host hydrocarbon. This gap is usually filled by the radiometric method, its characteristics around the Kolmani well 1 have hitherto not been reported. This research focused on identifying promising locations for hydrocarbon accumulation around the Kolmani Well 1 while using the well as a control. To achieve the objectives of this research, the nature of the radioelements was observed over the Kolmani well1 and the outcome of normalizing potential and uranium was also identified. The average values of potassium (K), thorium (Th), and uranium (U) are 0.277%, 8.917 ppm, and 1.431 ppm respectively. The concentration of the three radioelements, K, Th, and U, decreases over the oil well. This was ascribed to enhanced leaching of natural radioelements caused by hydrocarbon-generated groundwater acids. The result of the normalization of K with Th yields a low concentration of K over the Kolmani well1 and the normalization of U with Th yields a high concentration of U over the Kolmani well1. These were in tandem with the report on the application of the method in previous locations. Hence, the results of the normalization of K and U were used to identify four locations with potential for hydrocarbon exploration. The viability of these locations was confirmed with the positive DRAD (Delineation of Radiometric anomalies) value (ranging from -5.1 to +5.2), a pointer to hydrocarbon accumulation in an area, recorded in these locations and the location of the Kolmani well 1.

The 21st century has witnessed an expansion of space operations throughout the Earth–Moon system and the wider Solar System with the introduction of new space-faring countries, decreasing costs of space access, and advances in space system technologies. Many countries are endeavoring to move outside geosynchronous orbit to pursue missions in cislunar space and lunar orbit, as well as on the lunar surface, with invigorated U.S., Chinese, and Russian lunar mission initiatives being principle examples. Coalescing international efforts to return to the Moon may result in not only the pursuance of short-duration crewed lunar missions, but also the attainment of the first extraterrestrial human settlement(s) and the establishment of a permanent lunar base. Site selection for short-duration soft landings and the construction of basing/infrastructure will become a critical function to enhance mission assurance and operational safety. In this paper, an atlas of human activity on the lunar surface is presented featuring a concise mapping of locations associated with historical lunar impactor and soft-landing sites and proposed future basing and infrastructure reports. The atlas seeks to enable a holistic depiction of lunar surface activity to gain insight into where future lunar bases may be located and establish historical patterns of proposed basing.

Simplified models such as the Circular Restricted 3-Body Problem (CR3BP) are widely used to obtain an understanding of periodic and quasi-periodic orbits without the need for a higher-fidelity model. However, such models only account for the gravitational perturbations caused by one additional body on the trajectory of a spacecraft, when real-world systems may be affected by a greater number of significantly massive celestial bodies. Third-body perturbation models used to analytically incorporate additional gravitational effects on a spacecraft do not include the effect of the perturbing body on the other massive bodies in the system. In the Circular Restricted N-Body Problem (CRNBP), an unlimited number of bodies and their effects on both the spacecraft and each other can be effectively modeled. To exhibit these outcomes, trajectories in the Jupiter-Europa CR3BP are compared to trajectories with identical initial conditions in the CRNBP to include the gravitational effects of Io, Ganymede, and Callisto on the satellite in addition to accounting for the gravitational effect of these moons on each other. This demonstration is then expanded to include not only the Galilean moons but also the inner Jovian moons based on their low eccentricities and inclinations. The effect of the initial position of each body, represented by the phasing angle relative to the position of Europa, demonstrates the importance and applicability of the CRNBP as significant perturbations are demonstrated. Several families of orbits are tested and compared to the traditional CR3BP for model validation. The results of propagated orbits in the CRNBP highlight the importance of understanding gravitational perturbations beyond a three-body system, yielding insight which has the potential to aid in the selection of more stable trajectories.

The development of thermal therapy always requires more reasonable temperature distribution predictions. Controlling the amount of heating is a common practice within general thermal therapy operations. This paper used a modified three-phase lag (TPL) bioheat transfer equation to describe the behavior of heat conduction in tissue with thermoelastic effect. To explore the effect of thermal load, the tissue was subjected to a constant surface temperature and a pulsed surface heat flux, respectively. The modified TPL bioheat transfer equation involves mixed derivative terms and higher-order time derivatives of temperature. In analyzing such problems, there are mathematical difficulties. Therefore, the hybrid numerical scheme based on the Laplace transform and an improved discrete method was proposed to solve the present problem. The influence of thermoelastic parameters on the behavior of heat transfer in tissue has been investigated. The results depict the effect of thermal load on thermal response within heat transfer medium is obvious. The thermoelastic effect excites the thermal response oscillation, which is intensified for the reduction of material constant characteristic [Formula: see text] and phase lag [Formula: see text], in the heat conduction medium.

This contribution builds on existing studies by investigating the dynamics of Hall current in Jeffery fluid under radiative heat, convective boundary conditions, Joule heating, and Darcy dissipation. Hall current, an important phenomenon in engineering applications involving strong magnetic fields, highlights the impact of electromagnetic force in examining blood flow rate, determining charge drift velocity, density, and movement, and is used in power generators and high‐voltage transformers. This analysis incorporates dissipative and thermal radiative heat and employs the effects of Hall current and Joule heating, resulting from porous medium resistance, to derive the partial differential equations governing the dynamic systems. These equations are then reduced to ordinary differential equations (ODEs) through similarity variables. The Galerkin weighted residual method (GWRM) is employed to examine the dynamics of Hall current and quadratic thermal buoyancy, shedding light on the thermal properties and hydrodynamics of Jeffrey fluid convection within a porous medium. The analysis reveals that in the presence of an applied magnetic field, the contribution of Hall current to flow and heat dynamics induces a magnetic force that enhances fluid motion and negatively impacts heat energy patterns. The imposition of dissipative heat physically increases the fluid temperature, owing to an increase in buoyancy current. The occurrence of thermal radiation, Hall current, viscous dissipation, and Joule heating can efficiently optimize the rate of heat transfer and shear stress. Moreover, the tabular results indicate that Jeffrey fluid, exhibiting higher relaxation time, will experience a lower friction coefficient and heat transfer rate.

We introduce what we believe to be novel spectral light detection and ranging (LiDAR) architectures that enable ultra-compact systems by a transition from spectral signal processing in space (gratings) to processing in time. The architectures leverage temporal dispersion and the unique spectro-temporal waveforms produced from the cascaded Raman scattering generated in the (H2) filled hollow core fiber. The characterized Raman source yields as many as six Raman orders from 1.06-1.70 μm; their unique spectro-temporal waveforms are measured. System performance simulations based on measured Raman waveforms show that high accuracy measurement of range and reflectivity are possible with proper selection of signal-to-noise ratio and detector bandwidth. Materials classification analysis based on the system performance analysis shows that near-optimal classification is feasible with time domain processing.

In this paper, we use wave-optics simulations to explore the limitations of beam-control compensation. We evaluate performance in terms of the normalized power in a diffraction-limited bucket for the cases of no beam-control compensation, perfect phase compensation, and perfect full-field compensation. From these results, we are able to arrive at the following conclusions: (1) without any form of beam-control compensation, performance begins to degrade when D/r0 > 1; (2) with perfect phase compensation, performance begins to degrade when D/r0 > 1 and (λ/r0)/θ0 > 1; and (3) with perfect full-field compensation, performance begins to degrade when D/r0 > 1 and (λ/D)/θ0 > 1. Here, D is the aperture diameter, r0 is the Fried parameter, λ is the wavelength, and θ0 is the isoplanatic angle. We show (1)–(3) to be true for varying aperture diameters, uniformly distributed turbulence, and varying turbulence profiles. These findings will inform the development of future laser systems that need to sense and correct for the effects of atmospheric turbulence.

Understanding the seasonal variations in the landfill leachate plumes (LLPs) properties and complex connections between concentrations of leachate variability, and its environment is essential for environmental and public health management. This study explores the combined electrical resistivity (ER) data and physiochemical water analysis (PWA) coupled with the excavations to monitor the landfill physiochemical properties (LPPs) due to seasonal variations and their implications on environmental vital organs and public health. The variations in ER and LLP distributions across the overburdened top layer due to seasonal changes were examined. The low ER contrasts were encountered within the ranges of 1.5 Ωm – 19.0 Ωm which was mapped as LLP accumulated zones within the landfill, while high ER values varied between 15 Ωm – 260 Ωm off-the landfill extending beyond 15 m. The results of the PWA indicate high concentrations of heavy metals (HMs) such as iron (Fe), lead (Pb), zinc (Zn), and cadmium (Cd) decreasing with wet seasons and increasing with dry seasons. The overall high concentration of HMs in the LLPs was indeed varied between 9.81 ± 2.15–19.07 ± 3.68, while the electrical conductivity (EC) significantly increased from 17.99 ± 1.92 mg/L to 24.87 ± 3.31 mg/L towards the wet season. The increment and decrement encountered in the LPPs are due to seasonal variation and dilution. The order of decrement in the HMs in the LLPs treads as follows EC > Fe > Zn > Pb > Cd in values, respectively. The near-surface EC aligned well with the ER results and boundaries of the waste disposal site, which was verified by the soil excavations. In addition, the ER method was extended beyond the landfill for adequate monitoring, identifying the subsurface layers, conductive shallow zones mapped as the zones of LLP accumulation, resistive deep and shallow zones mapped as the consolidated lateritic topsoil and crystalline basement rocks in some cases, and a dipping conductive lineament zones identified as fracture zones just before the crystalline basement. In conclusion, the ER technique reveals the vertical and horizontal extents of the LLP escapade, the PWA expressed the concentrations of HMs in the LLPs, heightening the implications on the environmental and human health. Finally, the combined techniques deployed for monitoring the physiochemical properties of LLPs due to seasonal variation and the impacts on the integrity of groundwater quality systems and public health inform sustainable waste management practices, which contributes significantly to the protection of groundwater resources and the development of effective strategies to safeguard groundwater systems and public health for present and future generations. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-77727-6.

A computational study on the effect of magnetohydrodynamic mixed convection of nanofluid flow in a square split lid driven cavity with a block placed near the bottom wall is undertaken. Two different nanoparticles gold and alumina are considered for the study. The observations for the study are obtained by solving the non-dimensionalized governing equations by Finite Element Method with variational approach as accessible with the FreeFEM++ software. The results for different Prandtl numbers ( Pr), Richardson number ( Ri), volume fractions of nanoparticles [Formula: see text], Reynolds number (Re), and MHD parameters (M) are displayed through graphs and figures. It has been observed that the pressure distribution significantly increases with the increment in Reynolds number but both the nanoparticles behave differently. The magnetic field enhancement ( M = 0.1, 0.2, 0.5 and 0.9) decreases the velocity within the cavity. The convective heat transfer is faster in the case of Reynolds number ( Re) = 100 than in the case of Reynolds number ( Re) = 14 or 21. And also increasing the Richardson Number from 0.1 to 1.0, the average Nusselt number shows increment of ∼9.5% and with Ri = 1.0 to 10.0, an increment of ∼3% whereas decrement with higher Reynolds Number ( Re = 21, 100) for Gold and Allumina nanoparticles respectively. The present simulations have various applications for the study of natural phenomenon like climate control, meteorological and geophysical activities and industrial applications like cooling of electronics equipment, heat exchanger.

Army senior military leaders are invested in acquiring modernized aerial platforms and equipment to augment the US Army’s ability to overcome Anti-Access Area Denial (A2AD) threats imposed by modern Integrated Air Defense Systems (IADS). A prominent element of this modernization effort is the employment of autonomous drones to defeat IADS threats while minimizing risk to Army Soldiers. This research utilizes a framework for classifying the levels of autonomous capability along three dimensions: the ability to act alone, the ability to cooperate, and the ability to adapt. A virtual combat model, created using the Advanced Framework for Simulation, Integration, and Modeling (AFSIM), simulates the engagement between an enemy IADS and a friendly formation comprised of autonomous drones, attack helicopters, and a Long Range Precision Fires (LRPF) capability. A designed experiment evaluates drone performance with varying levels of autonomy. The experimental results reveal that low levels of autonomy yield a 20.74% increase in survivability and a 5.52% increase in lethality.

Reliability analysis using satellite failure data for satellites launched in the years 1991–2020 is presented. The analysis is conducted using nonparametric as well as parametric methods. In order to derive a nonparametric reliability estimate from the raw failure data, the Kaplan–Meier estimator is utilized. In order to determine a correct distribution to parameterize the nonparametric estimate, a novel distribution identification framework is applied. The Weibull distribution is consistently identified as best and is then utilized to parameterize the behaviors seen in the nonparametric results. A novel robust regression parameterization method is also applied to improve the traditional regression methodology. Data are split into three decade-long groups with respect to launch dates to investigate time-based trends. Results from this analysis show the most recent decade (2011–2020) to be the worst performing in terms of reliability. This decade in particular is found to be plagued by early or failed reentry cases.

The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm is a data-reducing approach for extracting maximum information from a low-order sparse approximation of a dense dataset for use in continuous prediction of one or more system parameters. The SSPOP algorithm can work directly with discrete data, such as the calculated velocity at nodes in a computational fluid dynamics (CFD) model, and is simpler and faster to implement than conventional gradient-based optimization methods. This research is the first experimental validation of an SSPOP-selected design point (DP), or set of sensor locations, for a flight-by-feel (FBF) flow-sensing system on a wing. We evaluate the absolute and relative computational and experimental performance of five three-sensor DPs on a NACA 4415, 45-degree-swept delta wing for predicting the angle of attack (AoA) from airflow velocity and pressure measurements. The experimental results from artificial hair-cell airflow velocity sensors (AHS) qualitatively validate the computer models but are subject to large errors. The pressure sensor experimental results quantitatively validated the models, with the SSPOP DP error of 0.703 degrees AoA nearly matching the optimum DP error of 0.692 degrees, confirming that SSPOP finds a near-optimal sensor placement solution for flow sensors.

Flight-by-feel (FBF) is an approach to flight control that uses dispersed sensors on the wings of aircraft to detect flight state. While biological FBF systems, such as the wings of insects, often contain hundreds of strain and flow sensors, artificial systems are highly constrained by size, weight, and power (SWaP) considerations, especially for small aircraft. An optimization approach is needed to determine how many sensors are required and where they should be placed on the wing. Airflow fields can be highly nonlinear, and many local minima exist for sensor placement, meaning conventional optimization techniques are unreliable for this application. The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm extracts information from a dense array of flow data using singular value decomposition and linear discriminant analysis, thereby identifying the most information-rich sparse subset of sensor locations. In this research, the SSPOP algorithm is evaluated for the placement of artificial hair sensors on a 3D delta wing model with a 45° sweep angle and a blunt leading edge. The sensor placement solution, or design point (DP), is shown to rank within the top one percent of all possible solutions by root mean square error in angle of attack prediction. This research is the first to evaluate SSPOP on a 3D model and the first to include variable length hairs for variable velocity sensitivity. A comparison of SSPOP against conventional greedy search and gradient-based optimization shows that SSPOP DP ranks nearest to optimal in over 90 percent of models and is far more robust to model variation. The successful application of SSPOP in complex 3D flows paves the way for experimental sensor placement optimization for artificial hair-cell airflow sensors and is a major step toward biomimetic flight-by-feel.

We address a mixed-integer linear programming model which selects a cost-minimizing set of available technologies with which to design a renewable energy system and prescribe their associated dispatch decisions. Realistically sized instances of such models pose computational challenges. To this end, we develop a Lagrangian heuristic based on a decomposition methodology which partitions the model into blocks and optimizes these more manageable, smaller subproblems. It also provides a lower bound to assess solution quality. We apply this methodology to the National Renewable Energy Laboratory’s Renewable Energy Integration and Optimization (REoptTM$\hbox {REopt}^{\textrm{TM}}$) model to generate near-optimal solutions to realistic instances containing, on average, approximately 300,000 variables and at least as many constraints, with a mean 30% optimality gap improvement using a five-minute solution time limit, compared to directly solving the original monolith.

Hexavalent chromium has dominated the corrosion inhibitor’s market as a benchmark alternative due to its unparalleled excellent corrosion inhibition properties. However, it was phased out because of its carcinogenic effects. Subsequently, many alternative inhibitors have been introduced into the inhibitor’s market but failed to meet the performance of this benchmark inhibitor. Recently, intelli-ion (AX1) was reported as a new alternative to hexavalent chromium based on Scanning Kelvin Probe (SKP) carried out on hot-dip galvanized steel (HDG) substrates for chromate and intelli-ion inhibitors. The intelli-ion system showed impressive performance at generation 1, with increased protection offered by the generation 2 product, showing no visible failure after 4 days test procedure. To further validate this, the cut edge corrosion performance of intelli-ion (AX1) and benzotriazole (BTA) was studied on galvanized steel specimen in 5wt.% NaCl solution using Scanning Vibrating Electrode Technique (SVET). From the SVET current density maps of AX1 (specimen A and B) vs. BTA (specimen C) after 24 h in 5 wt.% NaCl solution. The AX1 inhibitor had a better overall cut edge corrosion inhibition performance than the BTA.

Precise simulation of realistic physical phenomena and maintaining high efficiency will be the ultimate goal of computational mechanics. As an effort of such endeavor, an isogeometric analysis (IGA) was proposed by integrating finite element analysis (FEA) and computer-aided design. IGA efficiently predicts a physical behavior with higher fidelity than the original FEA. However, an application to various geometries will be cumbersome owing to the absence of an appropriate pre-processor. To alleviate such limitation, various reinforcements have been attempted, including a finite element (FE)-based IGA. Derived from those initiatives, alternatives will be suggested herein by using a Bernstein–Bézier FE. To obtain approximate C1$C^1$ continuity for a general tetrahedral FE, an approximated Worsey–Piper element split will be presented. Also, Bézier mesh generation will be adopted for an enhanced geometric representation. The present attempt will be validated by comparing the results for various curved geometry. Furthermore, the present method will be applied to a much more complicated configuration to demonstrate the geometric applicability.