11115 research outputs found
Sort by
Natural convective flow of CuO–water nanofluid with variable thermophysical properties in a square-shaped cavity embedded in a porous medium
No full textExcerpt: The study investigates the effects of magnetohydrodynamic (MHD) forces, nonlinear thermal radiation, and variable thermophysical properties on natural convection within a square enclosure containing an inner corrugated circular cylinder. Understanding these effects is crucial for optimizing thermal management in engineering applications such as electronic cooling and energy storage
Measurement system comparison approaches for military queuing model validation
No full textOperationally validating military and defense queuing models requires the rigorous assessment of agreement between functional responses of the model and the system or process of interest. This article provides and contextualizes two distance-based validation methods for operationally validating complex transient-phase military and defense queuing models. The limits of agreement approach and the probability of agreement approach, both developed within the measurement system comparison literature, are contextualized through an illustrative M/M/1 queuing model application derived from the military air traffic control domain. The limits of agreement approach characterizes agreement through an evaluation of the differences between observations and predictions on the same entity, while the probability of agreement approach uses a mixed-effects structural model to characterize the relationship between observations and predictions. The procedures and results of both methods are juxtaposed against common Boolean-based statistical methods and are used to establish global predictive capabilities useful for calibrating military and defense queuing models over multiple settings of controllable input
The Spectral Response of Time-resolved PIV in a Turbulent Boundary Layer
Full text linkThis study presents the application of time-resolved particle image velocimetry (TR-PIV) to measure the mean and fluctuating velocity components in a turbulent boundary layer (TBL) over an axisymmetric body of revolution. A narrow wall-normal strip of the flow was captured using a synchronised high-speed laser and camera at a recording frequency of up to 80 kHz. The resulting streamwise and wall-normal velocity TR-PIV data were validated against hot-wire anemometry measurements and direct numerical simulations (DNS) of a flat plate under matched flow conditions. The mean flow results showed good agreement between all methods, while the expected attenuation due to the spatial averaging was found in the TR-PIV turbulence statistics closer to the wall. A key outcome of this study is the establishment of an effective laser sheet thickness for the TR-PIV using DNS as a reference. This study fills a gap in understanding the spectral response and limitations of TR-PIV in such complex flows, particularly how spatial resolution and noise influence the accuracy of turbulence measurements. The TR-PIV streamwise velocity energy spectra were compared with DNS data that were spatially filtered to match the resolution of the TR-PIV and hot-wire. A transfer function was derived to determine cut-off wavelengths as a function of wall-normal distance. The cut-off wavelengths enabled the quantification of the resolvable turbulence scales within the TBL, revealing that the spatial resolution is a limiting factor for TR-PIV. The methodology was applied to locations with zero and favourable pressure gradients, providing insights into how pressure gradients influence spectral content and the limitations of TR-PIV in capturing the full range of turbulence scales. The outlined methodology is applicable more broadly and can be used to enhance the accuracy of experimental techniques in future boundary layer investigations
Characterizing the Effects of RF Eventization on Barker Coded Radar Signal Discrimination
No full textThis work supports development of an envisioned “RF Event Radio” capability by extending previous communication signal demonstrations into the radar arena. Promising methods in recent communications-based RF eventization works are adopted here and adapted for radar signal demonstration. As a matter of convenience, 13-bit Barker coded radar signals from collection archives are used here for demonstration. Multiple Discriminant Analysis (MDA) and Random Forest (RndF) classifiers are used to discriminate four different radar signal channels. Emphasis is on RndF discrimination performance using non-eventized and eventized fingerprint features generated from pulse two-dimensional Gabor transform (2D-GTX) responses. Resultant classification performance losses (%CΔ) due to eventization span −5.26%\u3c %CΔ\u3c +0.02% using low, medium and high frequency resolution GTX responses. As with previous communication signal eventization, it is expected that radar signal RF eventization will benefit from using more robust convolutional (CNN) and spiking (SNN) neural network classifiers. The use of these classifiers is expected to reduce radar %CΔ eventization losses that will ultimately be traded-off as potential 1000X improvements are realized in neuromorphic processing system
Current State-of-the-art in Multi-scale Modeling in Nano-cancer Drug delivery: Role of AI and Machine Learning
No full textNanomedicine has transformed cancer therapy by enabling targeted drug delivery through nanoparticle-based systems. However, challenges such as inefficient tumor accumulation, poor tissue penetration, and limited cellular uptake hinder therapeutic efficacy. This review explores computational modeling approaches to optimize nanodrug delivery, focusing on multi-scale and stochastic frameworks. Mathematical models have been developed to simulate nanoparticle transport across systemic, tissue, and cellular levels, addressing key processes such as transvascular extravasation, interstitial distribution, and drug release. Additionally, studies integrating artificial intelligence (AI) and machine learning (ML) into in silico models have demonstrated improved predictive accuracy, optimized patient-specific treatments, and refined nanoparticle design. Computational tools for simulating nanoparticle transport, model validation strategies, and the challenges of merging AI with traditional modeling paradigms are discussed. Furthermore, environmental and manufacturing sustainability concerns in nanomedicine production are addressed. By bridging gaps in current research, this work provides a comprehensive overview of computational methodologies, emphasizing their potential to advance precision oncology and accelerate the clinical translation of AI-driven nano-cancer drug delivery systems
Implementation of Actuation Subsystems in a 6DOF High-Speed Vehicle Simulation
No full textQuantifying the actuation power requirements and thermal outputs for a runway takeoff electrified high-speed vehicle is essential to consider when designing the geometry, trajectory, power generation and thermal management systems. The earlier this can be done during the design process of such vehicles, the more optimized the system will be, which is desirable due to the difficulty in achieving such high-speed vehicles. The actuation power and thermal profiles are not often simulated at the conceptual design level due to the considerable work required to develop a controlled 6DOF simulation before the actuators can be analyzed. From the previous chapter, a workflow to obtain such a simulation at the conceptual design level for a notional high speed vehicle was created, presenting the ability for actuation integration. Electromechanical Actuators (EMAs) were implemented into this model, and controlled to meet MIL-STD-1797A flying requirements. A power and thermal analysis was preformed for the subsystem, and the resulting quantities characterizing the power requirements and the thermal profiles were implemented into the SIMULNK model. Upon simulation, the power and thermal profiles for the actuators of each control surface were available for analysis
Machine Learning-Based Material Classification on Spectral Data for a New Multispectral LiDAR Design
No full textThis study presents a machine learning framework for material classification using multispectral LiDAR reflectivity data. The classification results support the prototyping of a Raman-source multispectral LiDAR operating within high atmospheric transmission windows, enabling real-time range and spectral measurements to enhance material differentiation and object classification
Feasibility Evaluation of Secure Offline Large Language Models with Retrieval-Augmented Generation for CPU-Only Inference
No full textRecent advances in large language models and retrieval-augmented generation, a method that enhances language models by integrating retrieved external documents, have created opportunities to deploy AI in secure, offline environments. This study explores the feasibility of using locally hosted, open-weight large language models with integrated retrieval-augmented generation capabilities on CPU-only hardware for tasks such as question answering and summarization. The evaluation reflects typical constraints in environments like government offices, where internet access and GPU acceleration may be restricted. Four models were tested using LocalGPT, a privacy-focused retrieval-augmented generation framework, on two consumer-grade systems: a laptop and a workstation. A technical project management textbook served as the source material. Performance was assessed using BERTScore and METEOR metrics, along with latency and response timing. All models demonstrated strong performance in direct question answering, providing accurate responses despite limited computational resources. However, summarization tasks showed greater variability, with models sometimes producing vague or incomplete outputs. The analysis also showed that quantization and hardware differences affected response time more than output quality; this is a tradeoff that should be considered in potential use cases. This study does not aim to rank models but instead highlights practical considerations in deploying large language models locally. The findings suggest that secure, CPU-only deployments are viable for structured tasks like factual retrieval, although limitations remain for more generative applications such as summarization. This feasibility-focused evaluation provides guidance for organizations seeking to use local large language models under privacy and resource constraints and lays the groundwork for future research in secure, offline AI systems
An Event-Based Time-Incremented SNN Architecture Supporting Energy-Efficient Device Classification
No full textRecent advances in Radio Frequency (RF)-based device classification have shown promise in enabling secure and efficient wireless communications. However, the energy efficiency and low-latency processing capabilities of neuromorphic computing have yet to be fully leveraged in this domain. This paper is a first step toward enabling an end-to-end neuromorphic system for RF device classification, specifically supporting development of a neuromorphic classifier that enforces temporal causality without requiring non-neuromorphic classifier pre-training. This Spiking Neural Network (SNN) classifier streamlines the development of an end-to-end neuromorphic device classification system, further expanding the energy efficiency gains of neuromorphic processing to the realm of RF fingerprinting. Using experimentally collected WirelessHART transmissions, the TI-SNN achieves classification accuracy above 90% while reducing fingerprint density by nearly seven-fold and spike activity by over an order of magnitude compared to a baseline Rate-Encoded SNN (RE-SNN). These reductions translate to significant potential energy savings while maintaining competitive accuracy relative to Random Forest and CNN baselines. The results position the TI-SNN as a step toward a fully neuromorphic “RF Event Radio” capable of low-latency, energy-efficient device discrimination at the edge