A. James Clark School of Engineering

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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

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13C and 15N Metabolic Flux Analysis on the Marine Diatom Phaeodactylum tricornutum to Investigate Efficient Unicellular Carbon and Nitrogen Assimilation Mechanisms
(2013) Zheng, Yuting; Sriram, Ganesh; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Photosynthesis is indispensable in carbon cycling and obtaining renewable carbon. Operated by cyanobacteria, algae and plants, this process provides reduced carbon and molecular oxygen, consumes atmospheric CO₂ and harnesses solar energy. Photosynthesis is also central to the production of biofuels. Diatoms, a class of marine algae, contribute 20% to 40% of global photosynthetic productivity despite surviving in CO₂-depleted and nitrogen-limited environments. This makes diatoms ideal models to study efficient photosynthetic, specifically carbon concentrating mechanisms (CCM). It has been long debated that whether the unicellular marine diatom Phaeodactylum tricornutum operates a CCM, and whether the CCM is biophysical or biochemical (C4) in nature, with existing (circumstantial) experimental evidence divided amongst the two possibilities. Through isotope labeling experiments (ILE) and metabolic flux analysis (MFA), we provide for the first time significant, direct evidence for a biochemical CCM and the potential combined operation of a biochemical and a biophysical CCM. Additionally, we shed light on how genes regulating this complex process respond to critical environmental variables. Furthermore, we report the use of isotope-assisted metabolic flux analysis to study organic carbon (especially glucose) assimilation in P. tricornutum. Our steady state ILEs reveal glucose assimilation under light and potentially which genes may be responsible for glucose metabolism. We then studied nitrogen (mainly urea) assimilation through instationary 15N and 13C labeling experiments, to find indications of an unusual pathway of urea assimilation. Gene expression trends under various environmental conditions suggest the possible participation of the urea cycle in assimilating nitrogen in P. tricornutum, and how this metabolically differs from nitrate and ammonium assimilation. We anticipate that this work will not only improve understanding of unicellular C4 CCMs, but provide insights to explain the ecological success of diatoms in adapting to challenging environments.
13C Metabolic Flux Analysis Indicates Endothelial Cells Attenuate Metabolic Perturbations by Modulating TCA Activity
(MDPI, 2021-04-07) Moiz, Bilal; Garcia, Jonathan; Basehore, Sarah; Sun, Angela; Li, Andrew; Padmanabhan, Surya; Albus, Kaitlyn; Jang, Cholsoon; Sriram, Ganesh; Clyne, Alisa Morss
Disrupted endothelial metabolism is linked to endothelial dysfunction and cardiovascular disease. Targeted metabolic inhibitors are potential therapeutics; however, their systemic impact on endothelial metabolism remains unknown. In this study, we combined stable isotope labeling with 13C metabolic flux analysis (13C MFA) to determine how targeted inhibition of the polyol (fidarestat), pentose phosphate (DHEA), and hexosamine biosynthetic (azaserine) pathways alters endothelial metabolism. Glucose, glutamine, and a four-carbon input to the malate shuttle were important carbon sources in the baseline human umbilical vein endothelial cell (HUVEC) 13C MFA model. We observed two to three times higher glutamine uptake in fidarestat and azaserine-treated cells. Fidarestat and DHEA-treated HUVEC showed decreased 13C enrichment of glycolytic and TCA metabolites and amino acids. Azaserine-treated HUVEC primarily showed 13C enrichment differences in UDP-GlcNAc. 13C MFA estimated decreased pentose phosphate pathway flux and increased TCA activity with reversed malate shuttle direction in fidarestat and DHEA-treated HUVEC. In contrast, 13C MFA estimated increases in both pentose phosphate pathway and TCA activity in azaserine-treated cells. These data show the potential importance of endothelial malate shuttle activity and suggest that inhibiting glycolytic side branch pathways can change the metabolic network, highlighting the need to study systemic metabolic therapeutic effects.
1D-CROSSPOINT ARRAY AND ITS CONSTRUCTION, APPLICATION TO BIG DATA PROBLEMS, AND HIGHER DIMENSION VARIANTS
(2022) An, Taeyoung; Oruc, Yavuz A; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Increased chip densities offer massive computation power to deal with fundamental bigdata operations such as sorting. At the same time the proliferation of processing elements (PEs) in settings such as High Performance Computers(HPCs) or servers together with the employment of more aggressive parallel algorithms cause the interprocessor communications to dominate the overall computation time, potentially resulting in reduced computational efficiency. To overcome this issue, this dissertation introduces a new architecture that uses simple crosspoint switches to pair PEs instead of a complex interconnection network. This new architecture may be viewed as a “quadratic” array of processors as it uses O(n^2) PEs rather than O(n) as in linear array processor models. In addition, three different models for sorting big data in a distributed com- puting environment such as Cloud computing are presented. With the most realistic model of the three, we demonstrate that the high parallelism made possible by the simple communication channels overcomes the seemingly excessive hardware complexity and performs comparable to or better than existing algorithms. Furthermore, two additional algorithms of matrix multiplica- tion and triangle counting for the 1D-Crosspoint Array are introduced and analyzed. Lastly, two higher dimensional variants, 2D- and 3D-Crosspoint Array are also proposed with a construction method, which succeeds in reducing the number of PEs required by utilizing the communication channels in the added dimensions.
2-DIMENSIONAL ZEOLITES FOR ADSORPTIVE DESULFURIZATION
(2018) Fang, Jingyu; Liu, Dongxia; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The removal of sulfur-containing compounds from transportation fuels is of growing urgency due to the increasingly government stringent regulations. Adsorptive desulfurization at ambient conditions is a promising strategy for sulfur-containing compound removal compared to traditional hydrodesulfurization (HDS) that requires high temperature and pressure. In this thesis, we studied zeolite adsorbents for adsorptive desulfurization of model fuels. Three zeolite frameworks (MFI, MWW and FAU) in both 2-dimensional (2D) and 3D structures were synthesized and ion-exchanged to both proton-form and Ag+-form. The adsorption of thiophene and benzothiophene, respectively, in n-octane was done using both H+- and Ag+-form zeolites in both 2D and 3D structures. The results show that 2D zeolites have high adsorption capacity than 3D analogues in removal of benzothiophene. The Ag+-form zeolites increase the adsorption capacity compared with that of H+-form. In terms of zeolite framework effects, MWW zeolites possesses the highest adsorption capacity.
2021 Annual Report - Center for Engineering Concepts Development
(2021-10-01) Anand, Davinder; Hazelwood, Dylan
CECD is twenty-one years old and continues to be a platform for experimenting with new ideas in engineering research and education with special attention to the impact of engineering on society. I’m pleased to report that we continue to be supported by ARL, NSWC-IHEODTD, the State of Maryland, and the Neilom Foundation. One hundred guests helped us celebrate our twenty years of activities highlighting innovative activities of contemporary interest that benefit the economic welfare of the State of Maryland and the Nation. This report provides a brief overview of those accomplishments as well as ongoing activities that bring great credit to our faculty and students that comprise CECD.
3-(4-Amino-1,2,5-oxadiazol-3-yl)-4-(4-nitro-1,2,5-oxadiazol-3-yl)-1,2,5-oxadiazole
(MDPI, 2014-05-22) Pagoria, Philip; Zhang, Maoxi; Racoveanu, Ana; DeHope, Alan; Tsyshevsky, Roman V.; Kuklja, Maija M.
The title compound 3-(4-amino-1,2,5-oxadiazol-3-yl)-4-(4-nitro-1,2,5-oxadiazol-3-yl)-1,2,5-oxadiazole (ANFF-1) was synthesized by: (1) by reaction of 3,4-bis(4-nitro-1,2,5-oxadiazol-3-yl)-1,2,5-oxadiazole (BNFF-1) with gaseous ammonia in toluene and (2) by partial oxidation of 3,4-bis(4-amino-1,2,5-oxadiazol-3-yl)-1,2,5-oxadiazole (BAFF-1) with 35% H2O2 in concentrated H2SO4.
3D ENGINEERING OF VIRUS-BASED PROTEIN NANOTUBES AND RODS: A TOOLKIT FOR GENERATING NOVEL NANOSTRUCTURED MATERIALS
(2018) Brown, Adam Degen; Culver, James N; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Technological innovation at the nanometer scale has the potential to improve a wide range of applications, including energy storage, sensing of environmental and medical signals, and targeted drug delivery. A key challenge in this area is the ability to create complex structures at the nanometer scale. Difficulties in meeting this challenge using traditional fabrication methods have prompted interest in biological processes, which provide inspiration for complex structural organization at nanometer to micrometer length scales from self-assembling components produced inexpensively from common materials. From that perspective, a system of targeted modifications to the primary amino acid structure of Tobacco mosaic virus (TMV) capsid protein (CP) has been developed that induces new self-assembling behaviors to produce nanometer-scale particles with novel architectures. TMV CPs contain several negatively charged carboxylate residues which interact repulsively with those of adjacent CP subunits to destabilize the assembled TMV particle. Here, the replacement of these negatively charged carboxylate residues with neutrally charged or positively charged residues results in the spontaneous assembly of bacterially expressed CP into TMV virus-like particles (VLPs) with a range of environmental stabilities and morphologies and which can be engineered to attach perpendicularly to surfaces and to display functional molecular patterns such as target-binding peptide chains or chemical groups for attachment of functional targets. In addition, the distinct electrostatic surface charges of these CP variants enable the higher-level coassembly of TMV and VLP into continuous rod-shaped nanoparticles with longitudinally segregated distribution of functionalities and surface properties. Furthermore, the unique, novel, environmentally responsive assembly and disassembly behaviors of the modified CPs are shown to act as simple mechanisms to control the fabrication of these hierarchically structured functional nanoparticles.
3D Fast Geometric Collision Avoidance Algorithm (FGA) and Decision-Making Approach based on the Balance of Safety and Cost for UAS
(2021) lin, zijie; Xu, Mumu; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Unmanned Aircraft System (UAS) is a fast-growing industry with extensive economic implications and would be integrated into the national airspace system (NAS), which requires UAS to have the efficient sense and avoidance capability. This thesis develops a fast geometry-based algorithm FGA which shortens the collision avoidance computation time, path length and balances the probability of safety and energy cost by calculating and giving UAS proper selective avoidance starting time tc, meaning the last possible point for the UAS to avoiding the potential threaten and itis based on the UAS kinematic, conflicts likelihood map, and navigation constraints. This operation enables the update path to be as close as possible to the UAVs resume designed path, decreasing the length of path variation and the corresponding time cost. In comparison to a current geometry method, the sampling-based method and the search algorithm, the FGA algorithm shows 40% to 90% of reduction in computational time and length of path for the same obstacle avoidance scenarios. Quantitative analysis of the efficiency by different avoiding trigger times is also provided. FGA with critical avoidance time tc not only could improve the geometry methods, but also could be used for (1) research on the bounds of general geometry based collision avoidance, and (2) solving the multiple obstacles avoidance problem.For a scenarios with crowded obstacles which cannot be avoided at the same time, an applicable algorithm for obstacles classification is provided. It divides the obstacles into small groups with different urgent levels by their critical avoidance trigger time tc, and then avoids them in sequence. Simulation validates the efficiency of this application. Extremely difficult obstacle avoidance such as the UAV working under maneuver limitation and the obstacles are time-variant are discussed and solved in the following chapters. Monte Carlo simulation, statistical method and Machine learning algorithms especially the supervised logistic learning methods are implemented later to analyze the weight of the factors such as sensor detection distance, ratio of the speed, number of obstacles, which have impacts on the geometric based obstacle avoidance methods. Finally, flight missions in an aircraft simulator and the hardware fixed-wing aircraft experiments validate the algorithm effectiveness with successful results.
3D IMAGE ANALYSIS OF CT DATA OF CONCRETE CYLINDER SUNDERGOING DELAYED ETTRINGTIE FORMATION
(2019) Shi, Kuo; Amde, Amde M; Livingston, Richard A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The strains in a concrete caused by delayed ettringite formation (DEF) are conventionally expressed in terms of the one-dimensional linear expansion. However, concrete is not a homogeneous material, and differences in the volume change between the inert aggregates and the reactive cement paste will produce variations in local displacements that cannot be detected by the linear expansion variable. With CT slices offered by Simultaneous neutron and X-ray computed tomography (SNXCT), this thesis applies image analysis algorithms to quantify the distortion of cylinder over time due to delayed ettringite formation. The research reported in this thesis concerned the development of several MATLAB programs to apply image analysis algorithms to quantify the distortion of cylinder over time in terms of summary variables. These included mean radial expansion, deviation from circularity, vertical tilt angle and rotation, void area fraction and the displacement of microbead internal reference points.
3D Integration, Temperature Effects, and Modeling
(2005-05-02) Parker, Latise; Goldsman, Neil; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Practical limits to device scaling are threatening the growth of integrated circuit (IC) technology. A breakthrough architecture is needed in order to realize the increased device density and circuit functionality that future high performance ICs demand. 3D integration is being considered as this breakthrough architecture. In this thesis, the limits to scaling are noted and the feasibility of overcoming these limits using 3D integration is presented. The challenges and considerations, most notably dangerously high chip temperatures, are provided. To address the temperature concern, a mixed-mode simulator that calculates temperature as a function of position on chip is detailed. The simulator captures the important link between individual device and full chip heating. Lastly, circuit simulations and lab experiments are performed to experimentally validate the claims that differences in device activity on chip leads to dangerously high local and overall chip temperatures.
3D Multimodal Image Registration: Application to equine PET and CT images
(2017) Regani, Sai Deepika; Chellappa, Rama; Beylin, David; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Positron Emission Tomography (PET) is being widely used in veterinary medicine in recent years. Although it was limited to small animals because of its classical design and the large amount of radionuclide doses required, PET imaging in horses became possible with the introduction of a portable PET scanner developed by Brain Biosciences Inc. It was observed that this new modality could capture abnormalities like lesions that Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and other modalities could not. Since 2016, PET imaging in horses is being studied and analysed. While PET provides functional information characterizing the activity of lesions, it is useful to combine information from other modalities like CT and match the structural information to develop an accurate spatial representation of the data. Since biochemical changes occur much earlier than structural changes, this helps detect lesions and tumours during the early stages. Multimodal image registration is used to achieve this goal. A series of steps are proposed to automate the process of registration of equine PET and CT images. Multimodal image registration using landmark-based and intensity-based techniques are studied. It is observed that a few tissues are not imaged in the PET, which makes image segmentation, an important preprocessing step in the registration process. A study of the segmentation algorithms relevant to the field of medical imaging is presented. The performance of segmentation algorithms improved with the extent of manual interaction and intensity-based registration gave the smallest time complexity with reasonable accuracy.
3D PRINTED MULTILAYERED / INTERFACIAL SCAFFOLDS FOR OSTEOCHONDRAL REGENERATION
(2021) choe, Robert; Fisher, John P; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Osteoarthritis is a highly prevalent rheumatic musculoskeletal disorder that primarily affects the knee joint. This disease is characterized by the progressive breakdown of the articular cartilage and remodeling of the subchondral bone in the synovial joint. Repetitive overloading perpetuates the damage to the affected cartilage and undermines the structural integrity of the osteochondral unit. Despite much research in osteochondral tissue engineering, no particular strategy has stood out as a potential alternative to conventional treatment options. One major issue that arises during osteochondral regeneration is that the defect site is exposed to a significant physiological load. To overcome these challenges, various tissue engineering strategies have been employed to design multiphasic osteochondral scaffolds that recapitulate layer-specific biomechanical properties. However, multilayered scaffolds have failed to fully satisfy the mechanical requirements to persists within the osteochondral defect. Through the use of extrusion-based bioprinting, we attempt to fabricate a biphasic osteochondral scaffold with improved load-bearing properties and a mechanically strong interface.
3D-Printed Microinjection Needle Arrays via a Hybrid DLP-Direct Laser Writing Strategy
(Wiley, 2023-02-05) Sarker, Sunandita; Colton, Adira; Wen, Ziteng; Xu, Xin; Erdi, Metecan; Jones, Anthony; Kofinas, Peter; Tubaldi, Eleonora; Walczak, Piotr; Janowski, Miroslaw; Liang, Yajie; Sochol, Ryan D.
Microinjection protocols are ubiquitous throughout biomedical fields, with hollow microneedle arrays (MNAs) offering distinctive benefits in both research and clinical settings. Unfortunately, manufacturing-associated barriers remain a critical impediment to emerging applications that demand high-density arrays of hollow, high-aspect-ratio microneedles. To address such challenges, here, a hybrid additive manufacturing approach that combines digital light processing (DLP) 3D printing with “ex situ direct laser writing (esDLW)” is presented to enable new classes of MNAs for fluidic microinjections. Experimental results for esDLW-based 3D printing of arrays of high-aspect-ratio microneedles—with 30 µm inner diameters, 50 µm outer diameters, and 550 µm heights, and arrayed with 100 µm needle-to-needle spacing—directly onto DLP-printed capillaries reveal uncompromised fluidic integrity at the MNA-capillary interface during microfluidic cyclic burst-pressure testing for input pressures in excess of 250 kPa (n = 100 cycles). Ex vivo experiments perform using excised mouse brains reveal that the MNAs not only physically withstand penetration into and retraction from brain tissue but also yield effective and distributed microinjection of surrogate fluids and nanoparticle suspensions directly into the brains. In combination, the results suggest that the presented strategy for fabricating high-aspect-ratio, high-density, hollow MNAs could hold unique promise for biomedical microinjection applications.
3D-PRINTED POLYSTYRENE FOR CELL CULTURE
(2019) Lerman, Max Jonah; Fisher, John P; Gillen, Greg; Material Science and Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Efficient methods to expand stem cells ex vivo hold significant promise in many clinical applications. For example, hematological malignancies account for nearly 10% of cancer related deaths in the United States of America and frequently require a transplant to successfully treat the disease. Ex vivo expanded hematopoietic stem cells (HSCs) could help narrow treatment gaps; however, generating viable dosages of HSCs currently fall short of expectations with difficulties in expanding HSCs and the loss of cellular multipotency. Coculture with mesenchymal stem cells (MSCs) aims to provide the necessary intercellular signaling to counteract monoculture deficiencies. Typically, achieving these and other clinical goals have relied on 2D polystyrene (PS) as the fundamental substrate for cell culture. With the emergence of 3D printing, improved biomimicry with 3D culture models are becoming widely available. In this dissertation, we develop a 3D PS culture substrate for adherent and non-adherent cells, working towards a model for the bone marrow niche. To achieve this goal, the objectives of the work were to: (1) develop a 3D printing method for PS and surface functionalization strategy to facilitate extracellular matrix protein and MSC adhesion, (2) assess the effects of the underlying surface functionality on osteogenic differentiation under static and dynamic conditions, and (3) validate the culture model successfully cultures multiple cell types with a model non-adherent cell line, demonstrating validity and translatability as a bone marrow niche model. In converting PS from a 2D culture platform to a 3D printed one, we take steps to increase the biomimicry of in vitro cell culture without sacrificing fundamental PS properties (e.g. optical clarity, cost-effectiveness, disposability). Continued development and of the model would see an efficient method for studying the complex bone marrow niche with applications in pharmacology and cancer diagnostics.
A 1D Reduced-Order Model (ROM) for a Novel Latent Thermal Energy Storage System
(MDPI, 2022-07-14) Kailkhura, Gargi; Mandel, Raphael Kahat; Shooshtari, Amir; Ohadi, Michael
Phase change material (PCM)-based thermal energy storage (TES) systems are widely used for repeated intermittent heating and cooling applications. However, such systems typically face some challenges due to the low thermal conductivity and expensive encapsulation process of PCMs. The present study overcomes these challenges by proposing a lightweight, low-cost, and low thermal resistance TES system that realizes a fluid-to-PCM additively manufactured metal-polymer composite heat exchanger (HX), based on our previously developed cross-media approach. A robust and simplified, analytical-based, 1D reduced-order model (ROM) was developed to compute the TES system performance, saving computational time compared to modeling the entire TES system using PCM-related transient CFD modeling. The TES model was reduced to a segment-level model comprising a single PCM-wire cylindrical domain based on the tube-bank geometry formed by the metal fin-wires. A detailed study on the geometric behavior of the cylindrical domain and the effect of overlapped areas, where the overlapped areas represent a deviation from 1D assumption on the TES performance, was conducted. An optimum geometric range of wire-spacings and size was identified. The 1D ROM assumes 1D radial conduction inside the PCM and analytically computes latent energy stored in the single PCM-wire cylindrical domain using thermal resistance and energy conservation principles. The latent energy is then time-integrated for the entire TES, making the 1D ROM computationally efficient. The 1D ROM neglects sensible thermal capacity and is thus applicable for the low Stefan number applications in the present study. The performance parameters of the 1D ROM were then validated with a 2D axisymmetric model, typically used in the literature, using commercially available CFD tools. For validation, a parametric study of a wide range of non-dimensionalized parameters, depending on applications ranging from pulsed-power cooling to peak-load shifting for building cooling application, is included in this paper. The 1D ROM appears to correlate well with the 2D axisymmetric model to within 10%, except at some extreme ranges of a few of the non-dimensional parameters, which lead to the condition of axial conduction inside the PCM, deviating from the 1D ROM.
A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics
(MDPI, 2019-12-27) Verstraete, David; Droguett, Enrique; Modarres, Mohammad
Multi-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system’s reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.
A lionfish-inspired predation strategy in planar structured environments
(Institute of Physics, 2023-06-30) Thompson, Anthony A.; Peterson, Ashley N.; McHenry, Matthew J.; Paley, Derek A.
This paper investigates a pursuit-evasion game with a single pursuer and evader in a bounded environment, inspired by observations of predation attempts by lionfish (Pterois sp.). The pursuer tracks the evader with a pure pursuit strategy while using an additional bioinspired tactic to trap the evader, i.e. minimize the evader’s escape routes. Specifically, the pursuer employs symmetric appendages inspired by the large pectoral fins of lionfish, but this expansion increases its drag and therefore its work to capture the evader. The evader employs a bioinspired randomly-directed escape strategy to avoid capture and collisions with the boundary. Here we investigate the trade-off between minimizing the work to capture the evader and minimizing the evader’s escape routes. By using the pursuer’s expected work to capture as a cost function, we determine when the pursuer should expand its appendages as a function of the relative distance to the evader and the evader’s proximity to the boundary. Visualizing the pursuer’s expected work to capture everywhere in the bounded domain, yields additional insights about optimal pursuit trajectories and illustrates the role of the boundary in predator-prey interactions.
A novel approach to inverse design of wind turbine airfoils using tandem neural networks
(Wiley, 2024-05-30) Anand, Apurva; Marepally, Koushik; Safdar, M Muneeb; Baeder, James D.
The performance of a wind turbine and its efficiency majorly depends on wind-to-rotor efficiency. The aerodynamic design of the wind turbine blades using high-fidelity tools such as adjoint-computational fluid dynamics (CFD) is accurate but computationally expensive. It becomes impractical when the number of design variables increases for multidisciplinary optimization (MDO). Low-fidelity tools are computationally cheaper but are not accurate, especially in regions of adverse pressure gradient and reverse flows. Surrogate modeling has been used in many aerodynamic problems. We develop and apply a recent architecture of the deep learning module, tandem neural networks (T-NNs) for the inverse design of wind turbine airfoils. The T-NNs trained on CFD data for fully turbulent cases predict not only the performance parameters for the given airfoil geometry but also the airfoil geometry for a given design objective. This framework uses the entire performance polar for inverse design which ensures that the airfoil optimization is not a single-point optimization problem which is essential for practical design problems. The T-NNs are also optimized to include multiple constraints like maximum thickness and trailing edge (TE) thickness which is a novel contribution in the field of inverse design using surrogate models. A statistical analysis is also performed to predict a family of airfoil geometries.
A Novel Framework for Sustainable Traffic Safety Programs Using the Public as Sensors of Hazardous Road Information
(MDPI, 2018-10-26) Chung, Younshik; Won, Minsu
Traditionally, traffic safety improvement programs (TSIPs) have been based on the number of crashes at a specific location or their severity. However, the crash datasets used for such programs are obtained from the police and include two limitations: not all crashes are collected by the police (most minor and near-miss crashes are not reported), and the traditional process uses crash data recorded for the past two or three years (meaning most data inevitably include a time lag). To overcome these limitations, this study proposes a new approach for a TSIP based on citizen participation through an online survey that is broadcasted through social media. The method uses the public as sensors of hazardous road information, which means that information can be collected on individual experiences of minor crashes and latent risk factors, such as near misses and traffic conflicts. To demonstrate this approach, a case study was carried out in a small district in the city of Goyang, Korea, which has one of the highest usage rates of social media technologies. The proposed method and a traditional method were both assessed.
A Path Dependent Approach for Characterizing the Legal Governance of Autonomous Systems
(IEEE, 2022-11-10) Borson, Joseph E.; Xu, Huan
Autonomous systems promise significant improvements in many fields. These systems will be subject to legal governance requirements. The literature has largely focused on “autonomous governance” as a framework that is broadly applicable to autonomous devices regardless of the type of system (e.g., aviation or motor vehicles) at issue. While there are regulatory principles applicable to autonomous systems generally, an “autonomy-focused” approach is an inadequate lens to consider the governance of these systems. Rather, because autonomous systems are improvements of currently regulated complex systems, the regulation of autonomous elements will occur within those systems’ preexisting regulatory framework. Accordingly, the nature of future autonomous regulation will likely depend on the preexisting features of that substantive system, rather than on an optimal approach divorced from that history, an attribute known in the social science literature as path dependency. In order to characterize diverse regulated systems with an eye toward assessing future autonomous developments, we develop a framework of regulatory approaches to identify specific features of the preexisting regulatory scheme for a given system. We then analyze that approach by examining three different regulatory regimes (aviation, motor vehicles, and medical devices), across two different continents, and consider how the same type of requirement, e.g., fail-safe systems, can lead to different types of regulations depending on the differing baseline framework.

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