<p>Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) have not only revolutionized engineering but also the way humanity foresees the future with machines. From self-driving cars to large language models and ChatGPT, AI and ML will continue to redefine the boundaries of innovations and reshape the way we interact with the world. The anticipated benefits are transformative, enabling enhanced productivity, improved decision-making, and the potential for significant cost savings. These developments in AI/ML and the promise for improved reliability, anomaly detection, efficient operation, etc., have unavoidably caught the attention of nuclear engineers. Advancing nuclear predictive models and providing real-time support with regard to operation and maintenance are just a few of the potential tasks AI/ML could provide assistance. Microreactors is just one example of future nuclear systems where semi-autonomous operation and fully digital instrumentation and control with AI/ML-based decision support would be required for cost-effective deployment in remote areas.</p><p>However, the world of nuclear engineering is skeptical of the direct application of AI/ML at nuclear facilities mostly due to limited past experience, potential high risk for false negatives, and limited amount of available data to demonstrate widespread applicability with high confidence. In order to curb these worries and take advantage of recent public interest in AI/ML, publicly available, real-time datasets need to be created. In this thesis, a universal AI/ML dataset is developed takes advantage of the recent digitization of Purdue University Reactor One (PUR-1) and using real-time data directly from PUR-1. The expectation is to follow the paradigm of the AI/ML community where open datasets (e.g., Kaggle, ImageNet, etc.) were the stepping stone towards new algorithms, facilitating collaborative problem-solving, and driving breakthroughs in the field of AI/ML through open competitions and knowledge sharing.</p><p>PUR-1 is capable of providing real-time research data to the second for over 2000 different parameters ranging from physical components such as neutron flux and control rod positions to calculated signals such as the system change rate. The proposed Purdue Reactor Integrated Machine Learning dataset (PRIMaL), as described in the thesis herein, includes ten signals handpicked to create simple and of various degree of complexity AI/ML benchmarks related directly to the nuclear field, with the goal of kickstarting both a new-founded interest in the nuclear field by AI/ML professionals and building faith in AI/ML amongst nuclear engineers. To the best of our knowledge, PRIMaL is the first curated AI/ML benchmark based on real reactor data and focused on nuclear applications, aiming to advance safety, efficiency, and innovation in the nuclear industry while promoting the responsible and secure use of AI/ML technologies.</p><p>To confirm the validity of the dataset and provide a simple example on how to use the dataset for AI/ML benchmarking, an example problem of classifying shutdown data as gang lowers or SCRAM was performed using three ML algorithms: support vector machine, random forest, and logistic regression. This binary classification problem was repeated 288 times for each algorithm, varying the balance ratio of the SCRAMs to gang lowers, the time prior to the shutdown, and the time after the shutdown the algorithms have access to. The sample problem was a success, as the algorithms were able to distinguish SCRAMs and gang lowers with reasonable accuracy in all cases. Future work would include gathering more data from PUR-1 for the database, as further testing with different sized balanced datasets lead to unusually high accuracy due to the smaller sample size.</p>

As the world population keeps increasing, ammonia-based fertilizers like urea are essential to provide food security. However, the current fertilizer industry is based on fossil fuel feedstock (mainly natural gas), making the production process CO2 emission-intensive. More specifically, besides the CO2 emitted during the process, the CO2 captured in urea is also released into the atmosphere after the fertilizer is applied to agricultural soils. Thus, positioning the fertilizer industry among the top four industrial emitters globally. Hence, in order to meet the target of limiting global warming to 1.5 ºC and achieve net-zero emissions by 2050, it is necessary to strengthen the carbon mitigation efforts in the current fertilizer industry. This can be achieved in different ways, such as using renewable biofuels and implementing technologies that can lead to zero/negative CO2 emissions. For that reason, the present study presents pathways to achieve a more environmentally friendly fertilizer production process. An overall analysis is performed if negative emissions can be achieved by replacing different fractions of natural gas (used as both feedstock and fuel) with biogas and biomethane and by capturing and storing the CO2 emitted from the process using chemical solvents as activated MDEA and MEA. The results obtained from the study revealed that negative emissions in fertilizer plant can be achieved by retrofitting an existing ammonia plant with a MEA based CO2 capture system (with a carbon capture rate of 90%) for the SMR burner flue gas, and by introducing 50% of biogas in the feedstock (alongside Natural gas), and 75% of biogas in the SMR burner fuel (alongside Natural gas). This initial approach would result in net negative emissions from urea's production and application and require approximately 0.5 kg of biogas per kg of urea produced in this case. Furthermore, the equivalent energy intensity for the negative emission urea plant would be 0.32% and 3.37% lower compared to the fossil fuel-based case without/with CCS, respectively. Ultimately, it is even possible to produce approximately 6% more urea product by replacing a particular fraction of natural gas with biogas. The reason for this increased production is due to the surplus of carbon dioxide by the introduction of biogas. It can be used along with the ammonia product going to storage in the fossil fuel-based case, where there was not enough CO2 to keep the feedstock molar ratio at the urea plant's inlet.

<p dir="ltr">The demand and utilization of fiber-reinforced composites are increasing in various sectors, including aerospace, civil engineering, and automotive industries. Non-destructive methods are necessary for monitoring fiber-reinforced composites due to their complex and often visually undetectable failure modes. An emerging method for monitoring composite structures is through the integration of self-sensing capabilities. Self-sensing in nanocomposites can be achieved through nanofiller modifications, which involve introducing an adequate amount of nanofillers into the matrix, such as carbon nanotubes (CNTs) and carbon nanofillers (CNFs). These fillers form an electrically well-connected network that allows the electrical current to travel through conductive pathways. The disruption of connectivity of these pathways, caused by mechanical deformations or damages, results in a change in the overall conductivity of the material, thereby enabling intrinsic self-sensing.</p><p dir="ltr">Currently, the majority of predictive modeling attempts in the field of self-sensing nanocomposites have been dedicated to microscale piezoresistivity. There has been a lack of research conducted on the modeling of strain-induced resistivity changes in macroscale fiber-matrix material systems. As a matter of fact, no analytical macroscale model that addresses the impact of continuous fiber reinforcement in nanocomposites has been presented in the literature. This gap is significant because it is impossible to make meaningful structural condition predictions without models relating observed resistivity changes to the mechanical condition of the composite. Accordingly, this dissertation presents a set of three research contributions. The overall objective of these contributions is to address this knowledge gap by developing and validating an analytical model. In addition to advancing our theoretical understanding, this model provides a practical methodology for predicting the piezoresistive properties of continuous fiber-reinforced composites with integrated nanofillers.</p><p dir="ltr">To bridge the above-mentioned research gap, three scholarly contributions are presented in this dissertation. The first contribution proposes an analytical model that aims to predict the variations in resistivity within a material system comprising a nanofiller-modified polymer and continuous fiber reinforcement, specifically in response to axial strain. The fundamental principle underlying our methodology involves the novel use of the concentric cylindrical assembly (CCA) homogenization technique to model piezoresistivity. The initial step involves the establishment of a domain consisting of concentric cylinders that represent a continuous reinforcing fiber phase wrapped around by a nanofiller-modified matrix phase. Subsequently, the system undergoes homogenization to facilitate the prediction of changes in the axial and transverse resistivity of the concentric cylinder as a consequence of longitudinal deformations. The second contribution investigates the effect of radial deformations on piezoresistivity. Here, we demonstrate yet another novel application of the CCA homogenization technique to determine piezoresistivity. This contribution concludes by presenting closed-form analytical relations that describe changes in axial and transverse resistivity as functions of externally applied radial strain. The third contribution involves computationally analyzing piezoresistivity in fiber-reinforced laminae by using three-dimensional representative volume elements (RVE) with a CNF/epoxy matrix. By comparing the single-fiber-based analytical model with the computational model, we can investigate the impact of interactions between multiple adjacent fibers on the piezoresistive properties of the material. The study revealed that the differences between the single-fiber CCA analytical model and the computational model are quite small, particularly for composites with low- to moderate-fiber volume fractions that undergo relatively minor deformations. This means that the analytical methods herein derived can be used to make accurate predictions without resorting to much more laborious computational methods.</p><p dir="ltr">In summary, the impact of this dissertation work lies in the development of novel analytical closed-form nonlinear piezoresistive relations. These relations relate the electrical conductivity/resistivity changes induced by axial or lateral mechanical deformations in directions parallel and perpendicular to the reinforcing continuous fibers within fiber-reinforced nanocomposites and are validated against in-depth computational analyses. Therefore, these models provide an important and first-ever bridge between simply observing electrical changes in a self-sensing fiber-reinforced composite and relating such observations to the mechanical state of the material.</p>

<p>3D character modeling is a crucial process of asset creation in the entertainment industry, particularly for animation and games. A fully automated pipeline via sketch-based 3D modeling (SBM) is an emerging possibility, but development is stalled by unrefined outputs and a lack of character-centered tools. This thesis proposes an improved method for constructing 3D character models with minimal user input, using only two sketch inputs  i.e., a front and side unshaded sketch. The system implements a deep convolutional neural network (CNN), a type of deep learning algorithm extending from artificial intelligence (AI), to process the input sketch and generate multi-view depth, normal and confidence maps that offer more information about the 3D surface. These are then fused into a 3D point cloud, which is a type of object representation for 3D space. This point cloud is converted into a 3D mesh via an occupancy network, involving another CNN, for a more precise 3D representation. This reconstruction step contends with non-deep learning approaches such as  Poisson reconstruction. The proposed system is evaluated for character generation on standardized quantitative metrics (i.e., Chamfer Distance [CD], Earth Mover’s Distance [EMD], F-score and Intersection of Union [IoU]), and compared to the base framework trained on the same character sketch and model database. This implementation offers a  significant improvement in the accuracy of vertex positions for the reconstructed character models. </p>

I jämförelse med sina föregångare, har moderna lastbilsmotorer genomgått en betydandeutveckling och har utvecklats till effektiva kraftmaskiner med låga utsläpp genom införandet avavancerade avgasbehandlingssystem. Trots att de framsteg som gjorts under utvecklingen av lastbilsmotorer har varit betydande, så framhäver de framtida förväntningarna vad gällerprestanda, bränsleförbrukning och emissioner behovet av snabba samt storskaliga förbättringar av dessa parametrar för att förbränningsmotorn ska fortsätta att vara konkurrenskraftig och hållbar. Utmaningen i att uppfylla dessa till synes enkla krav är den invecklade, ogynnsammabalansgång som måste göras mellan parametrarna. Förbränningsmotorns kärna är förbränningsprocessen, som i sin tur är kopplad till motorns luftbehandlings- och bränsleregleringssystem. I denna studie undersöks Millercykeln som en potentiell lösning till att nå de motstridiga kraven för framtida lastbilsmotorer, framförallt med fokus på potentialen att förbättra prestandan samtidigt som NOx-emissionerna hålls på konstantnivå. Traditionellt har utvärderingen av Millercykeln utförts på encylindriga forskningsmotorer, vilket också har utgjort utgångspunkten i denna studie. Även om studier på flercylindriga simuleringsmodeller och forskningsmotorer har gjorts med konstanta inställningar för Millercykeln, så utförs de inte i samband med undersökningar av encylindriga motorer. Dessutom så möts inte kraven från insugssystemet på samma sätt mellan de olika motorkonfigurationerna. Denna studie undersöker och jämför potentialen för ökad prestanda med Miller-cykeln mellan encylindrig och flercylindrig motorkonfiguration för en lastbilsmotor med ett tvåstegs turboladdningssystem, som representerar ett realistiskt insugssystem som möjliggör implementeringen av Millercykeln. För att undersöka motorprestationen så används i denna studie den kommersiella mjukvaran GT-Power. Ytterligare resultat från studien innefattar kvantifiering av prestandakraven för ett högeffektivt tvåstegs turboladdningssystem och dess inverkan på temperaturen i inloppet till avgasbehandlings-systemet. En kvalitativ förståelse av betydelsen av interaktionen mellan cylindrar och effekten på cylinder-cylinder variationer med Millercykel utfördes också i simuleringar med flercylindrig motorkonfiguration. Studien utvärderade Millertiming inom ett intervall på -90 till +90 graders vev vinkel från utgångsvinkeln för stängning av insugsventilen. Utvärderingen utfördes vid systemjämvikt vid en fullastpunkt (1000RPM), där basfallet för både encylindrig och flercylindrig motor för utvärdering av Millercykeln var det välkända fallet med konstant specifik NOx. Ett ytterligare fall framhäver NOx-reduktionspotentialen med Miller vid konstant EGR-flöde på en encylindrig konfiguration. Fallen med ökad prestation realiserades genom att öka lufttillförseln, bränslemängden och det geometriska kompressionsförhållandet. Maximal prestandaökning observerades i fallet med ökad bränslemängd, och endast i detta fall utvärderades även konfigurationen med fler cylindrar för jämförelse av prestationsförbättringen med en encylindrig motsvarighet med Millertiming. Den flercylindriga motorn innefattade EGR som en lågtryckskrets, och medan detta antagande förenklade i avseende på modellering och kontroll, så var det till fördel för konfigurationen meden flercylindrig motor (jämfört med encylindrig) på grund av reducerade pumpförluster. Som påföljd gjordes en jämförande undersökning med encylinder-modellen med motsvarande mottryck för flercylinder-modellen inställt som gränsvärde. Resultaten visar att encylindermodellen representerar medelvärdet för cylindrarna i flercylinder-motorn när lämpligagränsvillkor tillämpas som kontrollparametrar. Studien ger en grund för jämförelse av Millertiming på encylindrig samt flercylindriga konfigurationer, samtidigt som kraven på insugssystemet fastställs och utgör en utgångspunkt föratt utvärdera Millercykeln och bestämma insugssystemets krav för hela motorns arbetsområde. / Modern heavy-duty engines have undergone considerable development over their predecessors and have evolved into efficient performance machines with a reducing emission footprint through the incorporation of advanced aftertreatment systems. Although, the progress achieved in heavy-duty engine development has been significant, the future expectation from heavy-duty engines in terms of performance, fuel consumption and emissions stresses the need for rapid large-scale improvements of these metrics to keep the combustion engine competitive and sustainable. The challenges in resolving these apparently straightforward demands are the intricate unfavourable trade-off that exists among the target metrics. The core of the combustion engine lies in the combustion process which is inherently linked to the air handling and fuel regulating systems of the engine. This study explores adopting the Miller cycle as a potential solution to the conflicting demands placed on future heavy-duty engines with an emphasis on the performance enhancement potential while keeping the specific NOX emission consistent. Traditionally, evaluation of the Miller cycle is performed on single-cylinder research engines and formed the starting point in this study. While studies on full-engine simulation models and test engines with fixed Miller timing have been evaluated, they appear to be performed in isolation of the favoured single-cylinder approach. Additionally, the charging system requirements are not consistently addressed between the two approaches. This study investigates and contrasts the performance enhancement potential of the Miller cycle on single-cylinder and serial enginemodels of a heavy-duty engine along with a two-stage turbocharging system to represent a realistic charging system that enables implementation of Miller timing. The commercial engine performance prediction tool GT-Power was used in this study. Additional outcomes of the study included quantifying the performance demands of a high efficiency two-stage turbocharging system and its impact on the inlet temperature of the exhaust aftertreatment system. A qualitative understanding of the significance of cylinder interaction effects on cylinder-cylinder variations with Miller timing was also performed on the serial engine cases. The study evaluated Miller timing within a range of -90 to +90 CAD from the baseline intake valve close angle. The evaluation was performed at steady-state operation of the engine at one full load point (1000RPM) wherein both the single-cylinder and serial engine Miller evaluation included a base case which characterises the Miller effect for constant specific NOX. An additional case highlights the NOX reduction potential with Miller for a constant EGR rate on the single-cylinder configuration. The performance enhancement cases were realised by increasingthe air mass, fuel mass and the geometric compression ratio. Maximum performance increase was observed in the increased fuel mass case and only this case was evaluated on the serial engine for contrasting single-cylinder and serial engine performance enhancement with Miller timing. The serial engine incorporated EGR as a low-pressure circuit and while this simplified modelling and controller considerations, it led to biasing of results in favour of the serial engine configuration (over the single-cylinder) due to reduced pumping loss. A subsequent comparison case was evaluated on the single-cylinder model with backpressure settings from the serial engine model. The results show that the single-cylinder model is representative of the cylinder averaged responses of the serial engine when appropriate boundary conditions are imposed as controller targets. The study provides a basis for contrasting Miller timing on single-cylinder and serial configurations while determining the charging system requirements and presents a starting point to evaluate Miller timing and determine air system demands over the entire engine operating range.

Current-to-pressure (I/P) converters are pneumatic devices which provide precise control of pressure in various industries – for example these devices are often used in valve positioner systems (typically found in the oil and gas industry) and tensioning systems (typically used in the packaging industry). With an increasing demand for such devices to operate in harsh environments all by delivering acceptable performance means that Current-to-pressure converters need to be carefully designed such that environmental factors have no or minimal effects on its performance. This work presents an investigation of the principles of operation of an existing I/P converter through mathematical modelling. A simulation model has been created and which allows prediction of performance of the I/P converter. This tool has been used to identify areas of poor performances through theoretical analysis and consequently led to optimisation of certain areas of the I/P converter through a design change to deliver improved performances, for instance the average percentage shift in gain at 1mA input signal (over the temperature range of -40°C to 85°C) on the new I/P converter is 2.13% compared to the average gain of 4.24% on the existing I/P converter, which represents an improvement of almost two fold. Experimental tests on prototypes have been carried out and tests results showed that improved linearity and temperature sensitivity can be expected from the new design.

<p dir="ltr">This work introduces an innovative algorithm for co-simulating time-dependent Drift Diffusion (DD) equations with Maxwell\textquotesingle s equations to characterize semiconductor devices. Traditionally, the DD equations, derived from the Boltzmann transport equations, are used alongside Poisson\textquotesingle s equation to model electronic carriers in semiconductors. While DD equations coupled with Poisson\textquotesingle s equation underpin commercial TCAD software for micron-scale device simulation, they are limited by electrostatic assumptions and fail to capture time dependent high-frequency effects. Maxwell\textquotesingle s equations are fundamental to classical electrodynamics, enabling the prediction of electrical performance across frequency range crucial to advanced device fabrication and design. However, their integration with DD equations has not been studied thoroughly. The proposed method advances current simulation techniques by introducing a new broadband patch-based method to solve time-domain 3-D Maxwell\textquotesingle s equations and integrating it with the solution of DD equations. This technique is free of the low-frequency breakdown issues prevalent in conventional full-wave simulations. Meanwhile, it enables large-scale simulations with reduced computational complexity. This work extends the simulation to encompass the complete device, including metal contacts and interconnects. Thus, it captures the entire electromagnetic behavior, which is especially critical in electrically larger systems and high-frequency scenarios. The electromagnetic interactions of the device with its contacts and interconnects are investigated, providing insights into performance at the chip level. Validation through numerical experiments and comparison with results from commercial TCAD tools confirm the effectiveness of the proposed method. </p>

<p dir="ltr">The U.S. food supply chain, particularly the beef industry, faces growing demands for transparency and traceability due to increased consumer awareness of environmental and social impacts. A lack of transparency and access to information, along with misinformation, pose challenges to brand trust and supply chain efficiency. Emerging information technologies like RFID and blockchain are being explored to enhance traceability and information transparency, forming the topic of this dissertation.</p><p dir="ltr">This study aims to understand how the growing demand for transparency in the U.S. beef industry could reshape its supply chain structure and dynamics. It comprehensively examines the potential of information technologies like blockchain to enhance traceability, reduce transaction costs and information asymmetry and shift the supply chain structure to vertical coordination.</p><p dir="ltr">To achieve these objectives, we employed System Dynamics (SD) to model the U.S beef supply chain from 2013 to 2022, leveraging on existing literature and statistical data. This methodology was selected because of its unique ability to capture dynamic complexities and feedback among variables, allowing us to assess market dynamics and evaluate potential changes in the beef supply chain under different information technology scenarios. The model was evaluated through a series of tests and demonstrated its efficacy in simulating and analyzing the dynamics of the beef supply chain.</p><p dir="ltr">We simulated a wide range of key policy initiatives on both supply and demand sides of the beef supply chain. Our findings reveal that blockchain adoption is influenced by various factors such as market dynamics, consumer preferences, and existing power imbalances within the supply chain. Scenario analyses suggest that larger firms may be less incentivized to adopt blockchain if the market isn't ready for transparency, due to high implementation costs. Conversely, smaller firms could benefit from reduced transaction costs. Also, our results show an increased willingness to pay for transparent beef boosts the market for smaller firms and raises beef prices. Regulatory intervention may be necessary to balance the power dynamic within the supply chain, especially considering the market power held by packers.</p><p dir="ltr">This study fills existing knowledge gaps and provides valuable insights for beef supply chain stakeholders. It organizes complex data into clear, communicable causal loop diagrams and then introduces a comprehensive U.S. beef supply stock-and-flow diagram, grounded in literature, data, and trends. Finally, by synthesizing complex data and providing practical tools for decision-making, this research offers a foundation for future studies and policy recommendations in the field of supply chain transparency.</p>

<p dir="ltr">Path-tracking control is an integral part of motion planning in autonomous vehicles, in which the vehicle's lateral and longitudinal positions are controlled by a control system that will provide acceleration and steering angle commands to ensure accurate tracking of longitudinal and lateral movements in reference to a pre-defined trajectory. Extensive research has been conducted to address the growing need for efficient algorithms in this area. In this dissertation, a scenario and machine learning-based data-driven control approach is proposed for a path-tracking controller. Firstly, a Deep Reinforcement Learning model is developed to facilitate the control of longitudinal speed. A Deep Deterministic Policy Gradient algorithm is employed as the primary algorithm in training the reinforcement learning model. The main objective of this model is to maintain a safe distance from a lead vehicle (if present) or track a velocity set by the driver. Secondly, a lateral steering controller is developed using Neural Networks to control the steering angle of the vehicle with the main goal of following a reference trajectory. Then, a path-planning algorithm is developed using a hybrid A* planner. Finally, the longitudinal and lateral control models are coupled together to obtain a complete path-tracking controller that follows a path generated by the hybrid A* algorithm at a wide range of vehicle speeds. The state-of-the-art path-tracking controller is also built using Model Predictive Control and Stanley control to evaluate the performance of the proposed model. The results showed the effectiveness of both proposed models in the same scenario, in terms of velocity error, lateral yaw angle error, and lateral distance error. The results from the simulation show that the developed hybrid A* algorithm has good performance in comparison to the state-of-the-art path planning algorithms.</p>

<p>Soft robotics is concerned with the modeling and designing of devices fabricated from materials with low Young’s moduli—much less than that of metal— that mimic the input/output operation and physical task utility of robotics.  The inherent compliance of soft robots lends these devices an adaptability and a capacity for human-machine interaction beyond that of conventional robotics. Multistable soft robotic grippers are a subset of the technology at the intersection of soft robotics and multistable structures. Multistable structures are continuum systems that exhibit more than one statically stable state, each associated with a strain energy minimum. The existence of these energetic minima allows the structures to adopt different stable configurations that can provide a reference point for open loop control schemes. Multistable soft robotics takes advantage of both the adaptability of soft robotics and the potential for simplified control of multistable structures.</p> <p>Achieving simplified control for soft robotics is a necessary milestone in creating functional and applied soft robots. </p> <p>This work presents a means for simple open-loop control of a multistable soft robotic gripper that is adaptable, controllable, and robust. The behavior is illustrated through a gripper geometry described by specific design parameters resulting in a near infinite design space. An analytical model based on lumped parameter springs is derived, allowing us to search the design space in a tractable fashion. Specifically, we predict the system’s stable states for any given design instance by searching for local minima in the energy landscape formed by a spring lattice representation of our device. The lattice is composed of linear, bistable, and torsional springs—each of which contributes to the energy landscape of the system. We validate our model against Finite Element simulations of our device, showing good agreement with the proposed model. The aptitude of the model sheds light on the fundamental mechanics of our soft robotic gripper topology, laying the foundation for efficient design optimization and simplified control of soft robots.</p>