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AIAA science and Technology Forum and Exposition, AIAA sciTech Forum 2025

作者： Liang, Tony Vasile, Joseph D. SURVICE Engineering Company U.S. Army Combat Capabilities Development Command - Army Research Laboratory Aberdeen Proving Ground MD21005 United States

ISBN: (纸本)9781624107238

stationary cross flow computational investigations of blunted conical and ogival cylindrical forebodies at 4 degrees angle of attack are presented. Base flow simulations were performed at frees tream Reynolds numbers of Re/m = 15.2 × 106 at Mach 6 to match the conditions of Boeing/AFOsR Mach 6 Quiet Tunnel (BAM6QT) at Purdue university. The geometric variations between the ogive and conical forebodies appears to have an influence on the vortexlines where lines on conical forebodies were more skewed towards the leeward side than on ogival for ebodies. Three nose tip bluntness were studied for each forebody configuration, i.e., rb= 0.05, 0.025 and 0.015, respectively. Geometric variations is shown to have an effect on vortexline trajectories and the growth of stationary crossflow disturbances. A correlation between the strength of the derivatives of base flow mass flux at the inflection point and to N-factor grow this suggested. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Angle of attack

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development of Multi-Fidelity surrogate Models for Rotor Performance Prediction

Development of Multi-Fidelity Surrogate Models for Rotor Per...

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AIAA science and Technology Forum and Exposition, AIAA sciTech Forum 2025

作者： Anusonti-Inthra, Phuriwat U.S. Army Combat Capabilities Development Command Army Research Laboratory Aberdeen Proving Ground MD21005 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

surrogate models have been used to provide relatively fast predictions of rotor performance for design optimization, modeling and simulation for many types of rotorcrafts. The surrogate models rely on vast amount of data from physics-based and/or reduced-order models at various flight conditions to train the models that can be used to provide fast/accurate predictions of rotor performance in su.s.quent simulations. Most of these models uses a single fidelity dataset, while some employ data fusion techniqu.s.on multi-fidelity data to improve the efficiency/accuracy of the surrogate models. In this study, multi-fidelity approaches based on linear Gau.s.an process regression (GPR) and gradient enhanced kriging (cokriging) are used to predict the s76 rotor performance at various flight conditions. The input variables for the surrogate models are rotor advance ratio and collective pitch. The multi-fidelity models are trained using data from nine high-fidelity CFD simulations and hundreds of low-fidelity comprehensive analysis simulations. From the results, the predicted mean value of the rotor thrust using the cokriging approach is matched to the CFD training data better than that from the linear GPR model. However, the predicted variance of the rotor thrust from GPR model tends to be smaller than that from the cokriging model. At high advance ratio/high collective, both surrogate models (linear GPR and cokriging) can not accurately predict the rotor thrust due to the lack of high-fidelity training data in those areas. The rotor torque surrogate model using cokriging produces excellent torque prediction with very small variance. The reduction in low-fidelity training data does not affect the accuracy of the cokriging surrogate model, while a modest reduction (from 100% to 70%) of the high-fidelity training data has a significant effect on the predicted torque variance (six-fold increase in L2 norm of the variance). The training time for the linear GPR is more than 1000 t

关键词： Data fusion

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simulating Extreme Maneuvers and Flight Conditions using Coupled Rigid Body- and Computational Fluid-Dynamics

Simulating Extreme Maneuvers and Flight Conditions using Cou...

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AIAA science and Technology Forum and Exposition, AIAA sciTech Forum 2025

作者： Burchett, Bradley T. sahu, Jubaraj Gruenwald, Benjamin Army Research Directorate FCDD-RLA-WD U.S. Army Combat Capabilities Development Command - Army Research Laboratory Aberdeen Proving Ground MD21005 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

A coupled physics simulation was enhanced to permit the simulation of extreme attitudes. This provides the capability to simulate highly dynamic flight conditions in a virtual environment. Rather than assuming a flyable trajectory will lead to certain flight conditions, coupled physics simulations permit the user to test for flyability including energy limitations in gliding flight, and boundaries on actuator authority. Much like a piloted flight test, this enhanced capability enables the analyst to "push the flight envelope" and explore the fringes of an aerodynamic model, especially regions with high uncertainty due to boundary layer separation, and complex flows from vortex and shock interactions. In our framework, one can test control algorithms to failure with realistic aerodynamic predictions including approach to stall and deep stall conditions. The existing simulation provides an open flight control system architecture. We demonstrate the full simulation capabilities by incorporating a gain scheduled three loop autopilot with linear and machine learning adaptive elements, and ‘flying’ maneuvers with extreme attitudes, large changes in airspeed, and control deflections approaching saturation on this virtual platform. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Flight envelopes

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Automatic Linear Model Generation using Gau.s.Process Regression surrogate Models for Aerodynamic sensitivities

Automatic Linear Model Generation using Gauss Process Regres...

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AIAA science and Technology Forum and Exposition, AIAA sciTech Forum 2025

作者： Burchett, Bradley T. Bryson, Joshua T. FCDD-RLA-WD U.S. Army Combat Capabilities Development Command - Army Research Laboratory Aberdeen Proving Ground MD21005 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

state-of-the art aerodynamic data fusion uses kriging and cokriging methods with Gau.s.process surrogate models. These models are globally differentiable and provide built-in uncertainty predictions. We propose using these surrogates to rapidly find aerodynamic sensitivities in parallel with aerodynamic prediction grids, and hence global sets of linear models that can be used for feedback control design. This paper includes a case study modeling the longitudinal dynamics of a projectile. We apply adaptive sampling to refine a Gau.s.process model of the aerodynamics and monitor accuracy of the force and moment predictions and their sensitivities. Once model refinement reaches a point of diminishing returns, we find a suite of linear models and use those models to design two types of controllers. We conclude by testing the controllers in a coupled physics simulation to confirm the Gau.s.process model can generate accurate linear models suitable for controller design, and to demonstrate controller performance and overall success of the abbreviated workflow. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Feedback control

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Phenomenological modeling of plasma-assisted methane ignition using plasma energy fraction manifolds

Phenomenological modeling of plasma-assisted methane ignitio...

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AIAA science and Technology Forum and Exposition, AIAA sciTech Forum 2025

作者： Noah Johnson, Praise Taneja, Taaresh sanjeev sun, Zongxuan Yang, suo Kim, Kenneth s. Kweon, Chol-Bum M. Department of Mechanical Engineering University of Minnesota - Twin Cities MinneapolisMN55455 United States U.S. Army Combat Capabilities Development Command Army Research Laboratory Aberdeen Proving GroundMD21005 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The present work advances a phenomenological plasma-assisted combustion model by implementing spatiotemporally varying plasma energy fractions for fast gas heating, vibrational energy excitation, and fast chemical dissociation of gas species. This is achieved through a 2D manifold of plasma energy fractions generated for varying reduced electric fields (E/N) and the thermochemical state of the gas mixture, represented by a progress variable, forming a 2D manifold of energy fractions. The manifold is generated using 0D calculations with a detailed chemical mechanism for plasma-assisted combustion of methane at E/N ranging from 100 to 500 Td and progress variable ranging from 0 (fresh gas mixture) to 1 (burned gas mixture), and is integrated into the phenomenological model. Additionally, a spatiotemporally evolving plasma power density (PPD) obtained from 2D detailed plasma simulations for an air discharge is accommodated in the phenomenological model to describe the evolution of the plasma streamer during discharge. This enhanced model is validated against experiments representing plasma discharge and post-discharge ignition kernel evolution. specifically, the present model is compared against experimental ultra-fast gas heating and O2 dissociation during plasma discharge, in addition to experimental pressure wave and heated channel radius. The present model is then utilized to investigate ignition kernel evolution for a methane-air discharge across a pin-pin discharge configuration. specifically, the plasma evolution and the evolution of the post-discharge ignition kernel for a stoichiometric methane-air plasma discharge that utilizes the accommodated spatiotemporal PPD are compared against the results obtained using the original phenomenological model. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Dissociation

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Bi-Fidelity Neural Network Model for Multi-fuel Capable Internal Combustion Engines

Bi-Fidelity Neural Network Model for Multi-fuel Capable Inte...

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AIAA science and Technology Forum and Exposition, AIAA sciTech Forum 2025

作者： Pavalavanni, Pradeep Kumar Narayanan, sai Ranjeet sun, Zongxuan Yang, suo Kim, Kenneth s. Kweon, Chol-Bum M. Department of Mechanical Engineering University of Minnesota - Twin Cities MinneapolisMN55455 United States U.S. Army Combat Capabilities Development Command Army Research Laboratory Aberdeen Proving GroundMD21005 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Energy Assisted Compression Ignition (EACI) has garnered significant attention in recent years due to its potential applications in aviation propu.s.on. To enhance combustion and facilitate ignition with low cetane number (CN) jet fuels, a ceramic-based glow plug is utilized as an ignition assistant (IA). Control systems are essential for internal combustion (IC) engines to optimize performance, and ensure durability. They manage various parameters like fuel injection (timing, duration, and amount), ignition timing, and after-treatment systems, making the engine efficient, responsive, and environmentally friendly. A reliable engine control system requires a su.s.antial amount of pressure-crank-angle signals spread across a wide range of operating conditions to effectively train the controller software. For engines operating under a wide range of conditions and control parameters, traditional data collection through experimental methods is both time-consuming and expensive, especially under challenging conditions, such as low CNs. Computational Fluid Dynamics (CFD) can be supplemented to the experimental data. However, the CFD simulation time to generate the wide range of data points for the control systems requires a huge number of CPu hou.s. In this study, we propose a bi-fidelity regression problem, where we integrate the high-fidelity, expensive experimental data with "low-fidelity", cost-effective CFD data. We employed an artificial neural network (ANN) based methodology for multi-fidelity regression, utilizing two separate ANNs for each level of fidelity. This approach demonstrated effectiveness by achieving physically acceptable error levels in pressure prediction across various operating conditions and engine control parameters, highlighting its potential for efficient and accurate engine control system training in complex IC engine operations. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Glow plugs

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Iso-surface sampler for developing high-dimensional engine controllers using high-fidelity CFD models

Iso-surface sampler for developing high-dimensional engine c...

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AIAA science and Technology Forum and Exposition, AIAA sciTech Forum 2025

作者： Narayanan, sai Ranjeet Govind Raju, sathya Aswath sun, Zongxuan Yang, suo Kim, Kenneth s. Kweon, Chol-Bum M. Department of Mechanical Engineering University of Minnesota Twin Cities MinneapolisMN55455 United States U.S. Army Combat Capabilities Development Command Army Research Laboratory Aberdeen Proving Ground MN21005 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Developing controllers solely based on experimental testing is an industrious task as it is time-consuming and can be quite expensive. Instead, model-based controller development relies on a model, and hence the controller’s performance relies on the model’s accuracy. In the case of complex physical systems, physics-based high-fidelity models can be developed but cannot be directly used in controllers due to their complexity and computational overhead. Consequently, the use of data-driven models has garnered significant interest as they require minimal knowledge of the underlying physics for model development. However, these models heavily rely on the data based on which they are built. This paper discu.s.s a method for developing reliable controllers by sampling data from high-fidelity computational fluid dynamics (CFD) models developed for internal combustion engine simulations. The developed iso-surface sampler (Iss) methodology samples data at high dimensions automatically in two stages, namely iso-surface generation and infilling. The framework and sampled data was then validated by performing simulations to develop a controller for the combustion phasing of a multi-fuel capable compression ignition engine. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Internal combustion engines

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Lightweight Low-Cost Multifunctional Composite for structure

Lightweight Low-Cost Multifunctional Composite for Structure

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AIAA science and Technology Forum and Exposition, AIAA sciTech Forum 2025

作者： Haynes, Robert A. Luzetsky, Harry R. Phifer, Ellen M. U.S. Army Combat Capabilities Development Command Aviation and Missile Center Fort EustisVA23604 United States Research and Analysis Group SURVICE Engineering Company LLC BelcampMD21017 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The requirement for improved performance in air vehicles necessitates technological advancements in the materials and structures used in those vehicles to achieve weight savings and improved durability and damage tolerance. simultaneously, vehicle development programs are demanding reductions in cost and fabrication and assembly time. This article presents work to develop a multifunctional composite material technology that saves weight through incorporating electromagnetic shielding into the laminate, improves durability and damage tolerance through use of thermoplastic composite materials, and reduces cost and manufacturing time through continuous compression molding and automated laser in-situ tape placement fabrication techniqu.s. A comparative analysis is conducted with other widely used aircraft designs to quantify the improvements of the multifunctional composite. A representative section of an aircraft tailcone structure is designed, fabricated, and tested to demonstrate the advancements. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Compression molding

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Robust Error state sage-Husa Adaptive Kalman Filter for uWB Localization

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IEEE sensors Journal 2025年第9期25卷 16034-16049页

作者： Juston, Marius Gupta, soumil Mathur, shrey Norris, William R. Nottage, Dustin soylemezoglu, Ahmet University of Illinois Urbana-Champaign The Grainger College of Engineering Industrial and Enterprise Systems Engineering Department UrbanaIL61801 United States University of Illinois Urbana-Champaign The Grainger College of Engineering Electrical and Computer Engineering Department UrbanaIL61801 United States Construction Engineering Research Laboratory U.S. Army Corps of Engineers Engineering Research and Development Center ChampaignIL61822 United States

Given the sensors’ path and interference mitigation capabilities, ultra-wideband (uWB)-based positioning systems have demonstrated high accuracy and reliability. This work aims to improve the sage-Husa fuzzy adaptive filter (sHFAF) proposed in previous works by modifying the motion model to a 3-D ground-based differential drive robot using IMu and wheel encoder kinematic fused control inpu.s. In addition to the changed motion model kinematics, this article improved the positive definite constraint on P and R during dynamic estimations, thus making the filter more robust to outliers. An improvement to the computation and derivation of the fuzzy logic system for the sHFAF based on the adaptive neuro-fuzzy inference system (ANFIs) structure was developed, and training the fuzzy system using gradient descent was applied to improve the system’s accuracy. Experimental validation was conducted using real-world data from a Clearpath Jackal robot equipped with Qorvo uWB sensors and static nodes. Regarding localization accuracy, the proposed velocity-based sHFAF (VelsHFAF) system outperformed the previous sHFAF implementation by approximately 30%–25% across two test cou.s.s, demonstrating its enhanced performance and reliability. © 2001-2012 IEEE.

关键词： Adaptive filters

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Local-Transfer Gau.s.an Process (LTGP) Learning for Multi-fuel Capable Engines

Local-Transfer Gaussian Process (LTGP) Learning for Multi-fu...

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AIAA science and Technology Forum and Exposition, AIAA sciTech Forum 2025

作者： Narayanan, sai Ranjeet sun, Zongxuan Yang, suo Miller, John J. Mak, simon Kim, Kenneth s. Kweon, Chol-Bum M. Department of Mechanical Engineering University of Minnesota Twin Cities MinneapolisMN55414 United States Department of Statistical Science Duke University DurhamNC27708 United States U.S. Army Combat Capabilities Development Command Army Research Laboratory Aberdeen Proving GroundMD21005 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Data-driven engine surrogate models have been widely used to emulate in-cylinder trends of pressure and heat release rate for a wide variety of applications. For example, engines using multi-fuels, e.g., varying fuel cetane number (CN) or different sustainable aviation fuel (sAF) blends, require optimization of input parameters related to fuel injection and ignition assistance to achieve maximum combustion efficiency. such an optimization task requires building an accurate surrogate model for the engine. Gau.s.an processes (GPs) are a popular choice: they provide accurate predictions as well as efficient uncertainty quantification to guide decision-making. One challenge, however, is the costly nature of engine combustion experiments, which results in limited data for surrogate training with many input parameters, i.e., with significant variability in engine parameters and conditions. To address this, we present a new local transfer learning Gau.s.an process (LTGP) surrogate, which transfers knowledge from CFD simulations to learn the expensive combustion response surface, on which limited data is available. A key novelty of the LTGP is the use of a carefully-integrated classifier that regulates when learning should be transferred using ignition misfire data from CFD simulations. Compared to the standard GP surrogate, we show that the proposed model achieves superior prediction performance for engine combustion modeling. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Gau.s.an distribution

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