Showing papers by "Wright-Patterson Air Force Base published in 2021"

Autonomous experimentation systems for materials development: A community perspective

Abstract: Summary Solutions to many of the world's problems depend upon materials research and development. However, advanced materials can take decades to discover and decades more to fully deploy. Humans and robots have begun to partner to advance science and technology orders of magnitude faster than humans do today through the development and exploitation of closed-loop, autonomous experimentation systems. This review discusses the specific challenges and opportunities related to materials discovery and development that will emerge from this new paradigm. Our perspective incorporates input from stakeholders in academia, industry, government laboratories, and funding agencies. We outline the current status, barriers, and needed investments, culminating with a vision for the path forward. We intend the article to spark interest in this emerging research area and to motivate potential practitioners by illustrating early successes. We also aspire to encourage a creative reimagining of the next generation of materials science infrastructure. To this end, we frame future investments in materials science and technology, hardware and software infrastructure, artificial intelligence and autonomy methods, and critical workforce development for autonomous research.

Post-viral effects of COVID-19 in the olfactory system and their implications.

Abstract: Summary Background The mechanisms by which any upper respiratory virus, including SARS-CoV-2, impairs chemosensory function are not known. COVID-19 is frequently associated with olfactory dysfunction after viral infection, which provides a research opportunity to evaluate the natural course of this neurological finding. Clinical trials and prospective and histological studies of new-onset post-viral olfactory dysfunction have been limited by small sample sizes and a paucity of advanced neuroimaging data and neuropathological samples. Although data from neuropathological specimens are now available, neuroimaging of the olfactory system during the acute phase of infection is still rare due to infection control concerns and critical illness and represents a substantial gap in knowledge. Recent developments The active replication of SARS-CoV-2 within the brain parenchyma (ie, in neurons and glia) has not been proven. Nevertheless, post-viral olfactory dysfunction can be viewed as a focal neurological deficit in patients with COVID-19. Evidence is also sparse for a direct causal relation between SARS-CoV-2 infection and abnormal brain findings at autopsy, and for trans-synaptic spread of the virus from the olfactory epithelium to the olfactory bulb. Taken together, clinical, radiological, histological, ultrastructural, and molecular data implicate inflammation, with or without infection, in either the olfactory epithelium, the olfactory bulb, or both. This inflammation leads to persistent olfactory deficits in a subset of people who have recovered from COVID-19. Neuroimaging has revealed localised inflammation in intracranial olfactory structures. To date, histopathological, ultrastructural, and molecular evidence does not suggest that SARS-CoV-2 is an obligate neuropathogen. Where next? The prevalence of CNS and olfactory bulb pathosis in patients with COVID-19 is not known. We postulate that, in people who have recovered from COVID-19, a chronic, recrudescent, or permanent olfactory deficit could be prognostic for an increased likelihood of neurological sequelae or neurodegenerative disorders in the long term. An inflammatory stimulus from the nasal olfactory epithelium to the olfactory bulbs and connected brain regions might accelerate pathological processes and symptomatic progression of neurodegenerative disease. Persistent olfactory impairment with or without perceptual distortions (ie, parosmias or phantosmias) after SARS-CoV-2 infection could, therefore, serve as a marker to identify people with an increased long-term risk of neurological disease.

Evolving Trust in Robots: Specification Through Sequential and Comparative Meta-Analyses:

Abstract: ObjectiveThe objectives of this meta-analysis are to explore the presently available empirical findings on the antecedents of trust in robots and use this information to expand upon a previous meta...

Multiple Pursuer Multiple Evader Differential Games

Abstract: In this article an $N$ -pursuer versus $M$ -evader team conflict is studied. This article extends classical differential game theory to simultaneously address weapon assignments and multiplayer pursuit-evasion scenarios. Saddle-point strategies that provide guaranteed performance for each team regardless of the actual strategies implemented by the opponent are devised. The players’ optimal strategies require the codesign of cooperative optimal assignments and optimal guidance laws. A representative measure of performance is employed and the Value function of the attendant game is obtained. It is shown that the Value function is continuously differentiable and that it satisfies the Hamilton–Jacobi–Isaacs equation—the curse of dimensionality is overcome and the optimal strategies are obtained. The cases of $N=M$ and $N>M$ are considered. In the latter case, cooperative guidance strategies are also developed in order for the pursuers to exploit their numerical advantage. This article provides a foundation to formally analyze complex and high-dimensional conflicts between teams of $N$ pursuers and $M$ evaders by means of differential game theory.

3D Printing of Supramolecular Polymer Hydrogels with Hierarchical Structure.

Abstract: Liquid crystalline hydrogels are an attractive class of soft materials to direct charge transport, mechanical actuation, and cell migration. When such systems contain supramolecular polymers, it is possible in principle to easily shear align nanoscale structures and create bulk anisotropic properties. However, reproducibly fabricating and patterning aligned supramolecular domains in 3D hydrogels remains a challenge using conventional fabrication techniques. Here, a method is reported for 3D printing of ionically crosslinked liquid crystalline hydrogels from aqueous supramolecular polymer inks. Using a combination of experimental techniques and molecular dynamics simulations, it is found that pH and salt concentration govern intermolecular interactions among the self-assembled structures where lower charge densities on the supramolecular polymers and higher charge screening from the electrolyte result in higher viscosity inks. Enhanced hierarchical interactions among assemblies in high viscosity inks increase the printability and ultimately lead to greater nanoscale alignment in extruded macroscopic filaments when using small nozzle diameters and fast print speeds. The use of this approach is demonstrated to create materials with anisotropic ionic and electronic charge transport as well as scaffolds that trigger the macroscopic alignment of cells due to the synergy of supramolecular self-assembly and additive manufacturing.

Digital logic gates in soft, conductive mechanical metamaterials.

Abstract: Integrated circuits utilize networked logic gates to compute Boolean logic operations that are the foundation of modern computation and electronics. With the emergence of flexible electronic materials and devices, an opportunity exists to formulate digital logic from compliant, conductive materials. Here, we introduce a general method of leveraging cellular, mechanical metamaterials composed of conductive polymers to realize all digital logic gates and gate assemblies. We establish a method for applying conductive polymer networks to metamaterial constituents and correlate mechanical buckling modes with network connectivity. With this foundation, each of the conventional logic gates is realized in an equivalent mechanical metamaterial, leading to soft, conductive matter that thinks about applied mechanical stress. These findings may advance the growing fields of soft robotics and smart mechanical matter, and may be leveraged across length scales and physics.

Toward autonomous additive manufacturing: Bayesian optimization on a 3D printer

Abstract: Materials exploration and development for three-dimensional (3D) printing technologies is slow and labor-intensive. Each 3D printing material developed requires unique print parameters be learned for successful part fabrication, and sub-optimal settings often result in defects or fabrication failure. To address this, we developed the Additive Manufacturing Autonomous Research System (AM ARES). As a preliminary test, we tasked AM ARES with autonomously modulating four print parameters to direct-write single-layer print features that matched target specifications. AM ARES employed automated image analysis as closed-loop feedback to an online Bayesian optimizer and learned to print target features in fewer than 100 experiments. In due course, this first-of-its-kind research robot will be tasked with autonomous multi-dimensional optimization of print parameters to accelerate materials discovery and development in the field of AM. The combining of open-source ARES OS software with low-cost hardware makes autonomous AM highly accessible, promoting mainstream adoption and rapid technological advancement. The discovery and development of new materials and processes for three-dimensional (3D) printing is hindered by slow and labor-intensive trial-and-error optimization processes. Coupled with a pervasive lack of feedback mechanisms in 3D printers, this has inhibited the advancement and adoption of additive manufacturing (AM) technologies as a mainstream manufacturing approach. To accelerate new materials development and streamline the print optimization process for AM, we have developed a low-cost and accessible research robot that employs online machine learning planners, together with our ARES OS software, which we will release to the community as open-source, to rapidly and effectively optimize the complex, high-dimensional parameter sets associated with 3D printing. In preliminary trials, the first-of-its-kind research robot, the Additive Manufacturing Autonomous Research System (AM ARES), learned to print single-layer material extrusion specimens that closely matched targeted feature specifications in under 100 iterations. Delegating repetitive and high-dimensional cognitive labor to research robots such as AM ARES frees researchers to focus on more creative, insightful, and fundamental scientific work and reduces the cost and time required to develop new AM materials and processes. The teaming of human and robot researchers begets a synergy that will exponentially propel technological progress in AM.

Vitrimer Transition Temperature Identification: Coupling Various Thermomechanical Methodologies

Abstract: Vitrimers hold great promise as adaptive materials capable of shape reconfigurability, welding, and self-healing due to dynamic covalent reactions occurring above the vitrimer transition temperatur...

Improving cell-free glycoprotein synthesis by characterizing and enriching native membrane vesicles

Abstract: Cell-free gene expression (CFE) systems from crude cellular extracts have attracted much attention for biomanufacturing and synthetic biology. However, activating membrane-dependent functionality of cell-derived vesicles in bacterial CFE systems has been limited. Here, we address this limitation by characterizing native membrane vesicles in Escherichia coli-based CFE extracts and describing methods to enrich vesicles with heterologous, membrane-bound machinery. As a model, we focus on bacterial glycoengineering. We first use multiple, orthogonal techniques to characterize vesicles and show how extract processing methods can be used to increase concentrations of membrane vesicles in CFE systems. Then, we show that extracts enriched in vesicle number also display enhanced concentrations of heterologous membrane protein cargo. Finally, we apply our methods to enrich membrane-bound oligosaccharyltransferases and lipid-linked oligosaccharides for improving cell-free N-linked and O-linked glycoprotein synthesis. We anticipate that these methods will facilitate on-demand glycoprotein production and enable new CFE systems with membrane-associated activities.

Broadband infrared photodetection using a narrow bandgap conjugated polymer.

Abstract: Photodetection spanning the short-, mid-, and long-wave infrared (SWIR-LWIR) underpins modern science and technology. Devices using state-of-the-art narrow bandgap semiconductors require complex manufacturing, high costs, and cooling requirements that remain prohibitive for many applications. We report high-performance infrared photodetection from a donor-acceptor conjugated polymer with broadband SWIR-LWIR operation. Electronic correlations within the π-conjugated backbone promote a high-spin ground state, narrow bandgap, long-wavelength absorption, and intrinsic electrical conductivity. These previously unobserved attributes enabled the fabrication of a thin-film photoconductive detector from solution, which demonstrates specific detectivities greater than 2.10 × 109 Jones. These room temperature detectivities closely approach those of cooled epitaxial devices. This work provides a fundamentally new platform for broadly applicable, low-cost, ambient temperature infrared optoelectronics.

Benchmarking Machine Learning Models for Polymer Informatics: An Example of Glass Transition Temperature.

Abstract: In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the glass transition temperature Tg and other properties of polymers has attracted extensive attention. This data-centric approach is much more efficient and practical than the laborious experimental measurements when encountered a daunting number of polymer structures. Various ML models are demonstrated to perform well for Tg prediction. Nevertheless, they are trained on different data sets, using different structure representations, and based on different feature engineering methods. Thus, the critical question arises on selecting a proper ML model to better handle the Tg prediction with generalization ability. To provide a fair comparison of different ML techniques and examine the key factors that affect the model performance, we carry out a systematic benchmark study by compiling 79 different ML models and training them on a large and diverse data set. The three major components in setting up an ML model are structure representations, feature representations, and ML algorithms. In terms of polymer structure representation, we consider the polymer monomer, repeat unit, and oligomer with longer chain structure. Based on that feature, representation is calculated, including Morgan fingerprinting with or without substructure frequency, RDKit descriptors, molecular embedding, molecular graph, etc. Afterward, the obtained feature input is trained using different ML algorithms, such as deep neural networks, convolutional neural networks, random forest, support vector machine, LASSO regression, and Gaussian process regression. We evaluate the performance of these ML models using a holdout test set and an extra unlabeled data set from high-throughput molecular dynamics simulation. The ML model's generalization ability on an unlabeled data set is especially focused, and the model's sensitivity to topology and the molecular weight of polymers is also taken into consideration. This benchmark study provides not only a guideline for the Tg prediction task but also a useful reference for other polymer informatics tasks.

Final Design of the Boundary Layer Transition (BOLT) Flight Experiment

Abstract: To improve scientific knowledge of boundary-layer transition physics on increasingly complex geometries, the Air Force Research Laboratory/Air Force Office of Scientific Research has sponsored the ...

A Pearl Spectrometer

Abstract: Information recovery from incomplete measurements, typically performed by a numerical means, is beneficial in a variety of classical and quantum signal processing. Random and sparse sampling with nanophotonic and light scattering approaches has received attention to overcome the hardware limitations of conventional spectrometers and hyperspectral imagers but requires high-precision nanofabrications and bulky media. We report a simple spectral information processing scheme in which light transport through an Anderson-localized medium serves as an entropy source for compressive sampling directly in the frequency domain. As implied by the "lustrous" reflection originating from the exquisite multilayered nanostructures, a pearl (or mother-of-pearl) allows us to exploit the spatial and spectral intensity fluctuations originating from strong light localization for extracting salient spectral information with a compact and thin form factor. Pearl-inspired light localization in low-dimensional structures can offer an alternative of spectral information processing by hybridizing digital and physical properties at a material level.

Experiments on a Thin Panel Excited by Turbulent Flow and Shock/Boundary-Layer Interactions

Abstract: An experimental methodology was designed to study the effects of turbulent flow and shock/boundary-layer interaction (SBLI) on the postflutter response of a thin, buckled panel. The approach combin...

Solution-processable infrared photodetectors: Materials, device physics and applications

Abstract: This review is written to introduce infrared photon detectors based on solution-processable semiconductors. A new generation of solution-processable photon detectors have been reported in the past few decades based on colloidal quantum dots, two-dimensional materials, organics semiconductors, and perovskites. These materials offer sensitivity within the infrared spectral regions and the advantages of ease of fabrication at low temperature, tunable materials properties, mechanical flexibility, scalability to large areas, and compatibility with monolithic integration, rendering them as promising alternatives for infrared sensing when compared to vacuum-processed counterparts that require rigorous lattice matching during integration. This work focuses on infrared detection using disordered semiconductors so as to articulate how the inherent device physics and behaviors are different from conventional crystalline semiconductors. The performance of each material family is summarized in tables, and device designs unique to solution-processed materials, including narrowband photodetectors and pixel-less up-conversion imagers, are highlighted in application prototypes distinct from conventional infrared cameras. We share our perspectives in examining open challenges for the development of solution-processable infrared detectors and comment on recent research directions in our community to leverage the advantages of solution-processable materials and advance their implementation in next-generation infrared sensing and imaging applications.

A perspective on the electro-thermal co-design of ultra-wide bandgap lateral devices

Abstract: Fundamental research and development of ultra-wide bandgap (UWBG) semiconductor devices are under way to realize next-generation power conversion and wireless communication systems. Devices based on aluminum gallium nitride (AlxGa1−xN, x is the Al composition), β-phase gallium oxide (β-Ga2O3), and diamond give promise to the development of power switching devices and radio frequency power amplifiers with higher performance and efficiency than commercial wide bandgap semiconductor devices based on gallium nitride (GaN) and silicon carbide (SiC). However, one of the most critical challenges for the successful deployment of UWBG device technologies is to overcome adverse thermal effects that impact the device performance and reliability. Overheating of UWBG devices originates from the projected high power density operation and poor intrinsic thermal properties of AlxGa1−xN and β-Ga2O3. This Perspective delineates the need and process for the “electro-thermal co-design” of laterally configured UWBG electronic devices and provides a comprehensive review of current state-of-the-art thermal characterization methods, device thermal modeling practices, and both device- and package-level thermal management solutions.

Violet phosphorus surface chemical degradation in comparison to black phosphorus

Abstract: Bulk growth methods have made it possible to synthesize several allotropes of phosphorus such as black, white, red, and violet phosphorus. However, unlike exfoliated black phosphorus (BP), which has been extensively studied, much of the optoelectronic properties and stability of the other allotropes have yet to be comprehensively investigated. Here, we study the chemical degradation of exfoliated violet phosphorus (VP) in comparison to BP under ambient conditions using nanoscale infrared spectroscopy and imaging. We identify oxidized phosphorus species that result from chemical reaction processes on the surfaces of these phosphorus allotropes. We have found that VP exhibits a noticeably different and slower degradation process when compared to BP, establishing it as the more stable of the two allotropes. A better understanding of the stability of VP could lead to the further fundamental study of its monolayer form for potential future applications.

Bio-Organic Chiral Nematic Materials with Adaptive Light Emission and On-Demand Handedness.

Abstract: Real-time active control of the handedness of circularly polarized light emission requires sophisticated manufacturing and structural reconfigurations of inorganic optical components that can rarely be achieved in traditional passive optical structures. Here, robust and flexible emissive optically-doped biophotonic materials that facilitate the dynamic optical activity are reported. These optically active bio-enabled materials with a chiral nematic-like organization of cellulose nanocrystals with intercalated organic dye generated strong circularly polarized photoluminescence with a high asymmetric factor. Reversible phase-shifting of the photochromic molecules intercalated into chiral nematic organization enables alternating circularly polarized light emission with on-demand handedness. Real-time alternating handedness can be triggered by either remote light illumination or changes in the acidic environment. This unique dynamic chiro-optical behavior presents an efficient way to design emissive bio-derived materials for dynamic programmable active photonic materials for optical communication, optical coding, visual protection, and visual adaptation.

Toward Architected Nanocomposites: MXenes and Beyond.

Abstract: Achieving excellent electromagnetic interference (EMI) shielding combined with mechanical flexibility, optical transparency, and environmental stability is vital for the future of coatings, electrostatic discharge, electronic displays, and wearable and portable electronic devices. Unfortunately, it is challenging to engineer materials with all of these desired properties due to a lack of understanding of the underlying materials physics and structure-property relationships. Nature has provided numerous examples of a combination of properties through precision engineering of hierarchical structures at multiple length scales with selectively chosen ingredients. This inspiration is reflected in a wide range of synthetic architected nanocomposites. In this Perspective, we provide a brief overview of recent advances in the role of hierarchical architectures in MXene-based thin-film nanocomposites in the quest to achieve multiple functionalities, especially focusing on a combination of excellent EMI shielding, transparency, and mechanical robustness. We also discuss key opportunities, challenges, and prospects.

Switchable Photonic Bio-Adhesive Materials.

Abstract: A soft photonic bio-adhesive material is designed with real-time colorimetrical monitoring of switchable adhesion by integrating a responsive bio-photonic matrix with mobile hydrogen-binding networking. Synergetic materials sequencing creates a unique iridescent appearance directly coupled with both adhesive ability and shearing strength, in a highly reversible manner. The responsive photonic materials, having a physically hydrogen-bonded chiral nematic organization, vary their adhesion strength due to a transition in cohesive and interfacial failure mechanism in humid surroundings. The bright color appearance shifts from blue to red to transparent and back due to a change in pitch length of the chiral helicoidal organization that also triggers coupled changes in both mechanical strength and interfacial adhesion. Such reversible strength-adhesion-iridescence triple-coupling phenomenon is further explored for design of super-strong switchable bio-adhesives for synthetic/biological surfaces with quick remotely triggered sticky-to-nonsticky transitions, removable conformal soft stickers, and wound dressings with visual monitoring of the healing process, to colorimetric stickers for contaminated respiratory masks.

Multiple, comparative heat treatment and aging schedules for controlling the microstructures and mechanical properties of laser powder bed fusion fabricated AlSi10Mg alloy

Abstract: In this investigation, the optimization of mechanical properties with thermal post-processing treatments was analyzed across a wide range of variants. A major aspect of additive manufacturing is the correlation between heat treatments and the effects on the mechanical properties and microstructure of the printed materials. Therefore, the present paper describes a comprehensive overview of post-process heat treatments for Laser Powder Bed Fusion fabricated AlSi10Mg alloy consisting of stress relief anneals at 190 C and 285 C for 2 h, hot isostatic pressing at 515 C for 3 h, hot isostatic pressing + T6 treatment for 6 h, and final aging of each of these conditions at 177 C for up to 1000 h. This has resulted in 40 experimental variants: 20 in the vertical and 20 in the horizontal tensile direction. After tensile testing, the resulting mechanical properties (ultimate tensile strength, yield strength, and elongation) and stress–strain curves are analyzed for comparison between all variants. Ultra-fine cellular, micro dendritic structures (0.6–1.2 μm) along with melt-band structures dominated the asbuilt and stress relief anneal conditions. In contrast, hot isostatic pressing and hot isostatic pressing + T6 conditions were dominated by ~10 μm, equiaxed, recrystallized grain structures and pseudo-eutectic silicon particles with varying sizes and size distributions. Microhardness and fractography results also corresponded to their specific heat treatment and microstructure. The comparison and correlation of the heat treatments are presented to help advance the selection of design strategies for high performance applications.

Flutter and Limit Cycle Oscillations of Cantilevered Plate in Supersonic Flow

Abstract: Research interest is growing for theoretical models of highly deflected structures in aeroelastic settings. Presented here is a model of a cantilevered plate subjected to axial supersonic flow to d...

Thermal Conductivity of β-Phase Ga2O3 and (AlxGa1-x)2O3 Heteroepitaxial Thin Films.

Abstract: Heteroepitaxy of β-phase gallium oxide (β-Ga2O3) thin films on foreign substrates shows promise for the development of next-generation deep ultraviolet solar blind photodetectors and power electronic devices. In this work, the influences of the film thickness and crystallinity on the thermal conductivity of (201)-oriented β-Ga2O3 heteroepitaxial thin films were investigated. Unintentionally doped β-Ga2O3 thin films were grown on c-plane sapphire substrates with off-axis angles of 0° and 6° toward ⟨1120⟩ via metal-organic vapor phase epitaxy (MOVPE) and low-pressure chemical vapor deposition. The surface morphology and crystal quality of the β-Ga2O3 thin films were characterized using scanning electron microscopy, X-ray diffraction, and Raman spectroscopy. The thermal conductivities of the β-Ga2O3 films were measured via time-domain thermoreflectance. The interface quality was studied using scanning transmission electron microscopy. The measured thermal conductivities of the submicron-thick β-Ga2O3 thin films were relatively low as compared to the intrinsic bulk value. The measured thin film thermal conductivities were compared with the Debye-Callaway model incorporating phononic parameters derived from first-principles calculations. The comparison suggests that the reduction in the thin film thermal conductivity can be partially attributed to the enhanced phonon-boundary scattering when the film thickness decreases. They were found to be a strong function of not only the layer thickness but also the film quality, resulting from growth on substrates with different offcut angles. Growth of β-Ga2O3 films on 6° offcut sapphire substrates was found to result in higher crystallinity and thermal conductivity than films grown on on-axis c-plane sapphire. However, the β-Ga2O3 films grown on 6° offcut sapphire exhibit a lower thermal boundary conductance at the β-Ga2O3/sapphire heterointerface. In addition, the thermal conductivity of MOVPE-grown (201)-oriented β-(AlxGa1-x)2O3 thin films with Al compositions ranging from 2% to 43% was characterized. Because of phonon-alloy disorder scattering, the β-(AlxGa1-x)2O3 films exhibit lower thermal conductivities (2.8-4.7 W/m·K) than the β-Ga2O3 thin films. The dominance of the alloy disorder scattering in β-(AlxGa1-x)2O3 is further evidenced by the weak temperature dependence of the thermal conductivity. This work provides fundamental insight into the physical interactions that govern phonon transport within heteroepitaxially grown β-phase Ga2O3 and (AlxGa1-x)2O3 thin films and lays the groundwork for the thermal modeling and design of β-Ga2O3 electronic and optoelectronic devices.

Gold Nanorod Size-Dependent Fluorescence Enhancement for Ultrasensitive Fluoroimmunoassays.

Abstract: Plasmon-enhanced fluorescence (PEF) is a simple and highly effective approach for improving the signal-to-noise ratio and sensitivity of various fluorescence-based bioanalytical techniques. Here, we show that the fluorescence enhancement efficacy of gold nanorods (AuNRs), which are widely employed for PEF, is highly dependent on their absolute dimensions (i.e., length and diameter). Notably, an increase in the dimensions (length × diameter) of the AuNRs from 46 × 14 to 120 × 38 nm2 while holding the aspect ratio constant leads to nearly 300% improvement in fluorescence enhancement efficiency. Further increase in the AuNR size leads to a decrease of the fluorescence enhancement efficiency. Through finite-difference time-domain (FDTD) simulation, we reveal that the size-dependent fluorescence enhancement efficiency of AuNR stems from the size-dependent electromagnetic field around the plasmonic nanostructures. AuNRs with optimal dimensions resulted in a nearly 120-fold enhancement in the ensemble fluorescence emission from molecular fluorophores bound to the surface. These plasmonic nanostructures with optimal dimensions also resulted in a nearly 30-fold improvement in the limit of detection of human interleukin-6 (IL-6) compared to AuNRs with smaller size, which are routinely employed in PEF.

Ab initio simulation of hypersonic flows past a cylinder based on accurate potential energy surfaces

Abstract: For the first time in the literature, we present 2D simulations of hypersonic flows around a cylinder obtained from accurate ab initio potential energy surfaces (PESs). We compare results obtained from a low fidelity (empirical) and a high fidelity (ab initio) PES, thus demonstrating the impact of PES accuracy on the entire aerothermodynamic field around the body. We observe that the empirical PES is not adequate to accurately reproduce rotational and vibrational relaxation in the hypersonic flow, both in the compression and expansion regions of the flow field. This approach, enabled by advancements in large-scale computing, paves the way to the direct simulation of hypersonic flows where the only modeling input is the PES that describes molecular interactions between the various air constituents. Such flow field simulations provide benchmark solutions for the validation of reduced-order models.