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This paper presents a review of recent advances in modeling and simulation of conventional metal machining processes, which continue to dominate a significant part of all machining processes, and in recent years, the need for predictive models for machining processes has grown in importance in the digital manufacturing age. Significant advances have been made in modeling the mechanics of cutting in conventional machining, driven by industrial need and enabled by rapid advances in computational power. The paper surveys the state-of-the-art in analytical and numerical modeling of conventional metal machining processes with a focus on their ability to predict useful performance attributes including chip geometry, forces, temperatures, tool wear, residual stress, and microstructure. Also included in the review is a discussion of the industrial use of modeling and simulation tools for conventional machining. Additionally, the practical applicability, implementation benefits, and methodological limitations of conventional machining process modeling have been examined. The paper concludes with a summary of future research directions in modeling and simulation of conventional metal machining processes. [ABSTRACT FROM AUTHOR]

The extended meniscus and the intermolecular and capillary forces that govern its behavior and connection to change-of-phase heat transfer have been the subject of an increasing body of research over the past 50 years. We have been fortunate to be at the forefront of this effort starting from the development of a capillary feeder, in Earth's gravity, to stabilize film boiling to running a series of transparent heat pipe experiments aboard the International space station hoping to better understand the role of intermolecular forces in microgravity. The use of ellipsometry and interferometry to highlight the location and state of the vapor-liquid interface have been key to these studies and have helped to uncover many new, interesting, and sometimes unexpected phenomena associated with fluid flow and change-of-phase heat transfer. [ABSTRACT FROM AUTHOR]

Origami is the art of creating a three-dimensional (3D) shape by folding paper. It has drawn much attention from researchers, and the designs that origami has inspired are used in various engineering applications. Most of these designs are based on familiar origami patterns and their known deformations, but origami patterns were originally intended for materials of near-zero thickness, primarily paper. To use the designs in engineering applications, it is necessary to simulate origami in a way that enables designers to explore and understand the designs while taking the thickness of the material to be folded into account. Because origami is primarily a problem in geometric design, this paper develops a geometric simulation for thick origami. The actuation, constraints, and assignment of mountain and valley folds in origami are also incorporated into the geometric formulation. The experimental results show that the proposed method is efficient and accurate. The method can successfully simulate a flat-foldable degree-four vertex, two different action origami, the bistable property of a waterbomb base, and the elasticity of non-rigid origami panels. [ABSTRACT FROM AUTHOR]

Brittleness in paper is one of the primary reasons library books are removed from circulation, digitized, or have their access limited. Yet, paper brittleness is difficult to characterize as it has multiple definitions and no single measurable physical or chemical property associated with it. This study reevaluates the cantilever test as applied to aged papers. In this nondestructive test, the deflection of a strip of paper held horizontally is measured across its length. The deflection data are then fit to nonlinear bending theories assuming large deflection of a cantilever beam under a combined uniform and concentrated load. Fitting the shape of the deflection profiles provides bending and elastic moduli, the bending length, and confirms that the paper sheets respond linearly. The results are compared to those calculated from a simplified single point measurement of the maximum deflection of the cantilevered sample. Young's modulus measured by the cantilever test is lower for paper-based materials than that measured by tensile testing, and the bending modulus was found to correlate with the destructive Massachusetts Institute of Technology (MIT) fold endurance test. [ABSTRACT FROM AUTHOR]

It was an honor to receive the Max Jakob Memorial Award for 2018. This invited paper is based on the award lecture and is written in an informal style expanding on the lecture and focusing on work in which I have been involved over the past 60-years. Early experimental and theoretical work was published only in conference proceedings when this was regarded an alternative to a journal and not, as seems to be the case nowadays, prior to journal publication or for incomplete work. I welcome this opportunity to clarify and make some of this earlier work more readily accessible. My comments on more recent work of others are also included. [ABSTRACT FROM AUTHOR]

Oil-free microturbomachinery (OFT) implements compliant foil bearings because of their minute drag and ability to operate in extreme (high or low) temperature. Prominent to date, bump-foil thrust bearings integrate an underspring thin metal structure that provides resilience and material damping, and while the rotor is airborne, it acts in series with the stiffness and damping of the gas film. The design and manufacturing of foil bearings remain costly as it demands extensive engineering and actual experience. Alternative foil bearing configurations, less costly and easier to manufacture, are highly desirable to enable widespread usage of OFT. This paper details the design and manufacturing of a novel Rayleigh-step metal mesh foil thrust bearing (MMFTB) as well as its testing on a dedicated rig. Metal mesh structures offer significant material structural damping and can be easily procured at a fraction of the cost of a typical bump-foil strip layer. The MMFTB consists of a solid carrier, a number of stacked annular copper mesh sheets (wire diameter = 0.25, 0.3, and 0.41 mm), and a steel top foil (0.127 mm thick) that makes six pads (ID = 50.8 mm, OD =2 ID), each 45 deg in extent. A 3 μm polymer coats each pad, and a photochemical process etches a step 20 μm in height. Static and dynamic load measurements (without rotor speed) demonstrate that the MMFTB has structural stiffness and material damping similar to that of a publicized bump-type foil thrust bearing. A maiden test of the MMFTB with rotor speed of Ω = 15 krpm (~80 m/s at bearing outer diameter (OD)) proved briefly the bearing operation when applying a tiny thrust load. Further tests with ambient air, a rotor speed of 40 krpm (~212 m/s at bearing OD), and a very light load/area <7 kPa failed several of the prototype bearings, all exhibiting significant wear on one or more pads. The source of the failure is the inherent unevenness of the metal mesh stacked substructures, thus causing the pads to bulge toward the rotor collar surface before a load applies. A deficient anchoring method exacerbates the unevenness. Incidentally, a high rotor speed induced large windage that lifted the top foils pushing them against the spinning collar. As the bearing moved toward the rotating collar to begin applying thrust, the local high spots rubbed against the collar before a hydrodynamic wedge could form to separate the surfaces. Without a robust sacrificial coating, metal-to-metal contact quickly disfigured the contacting top foil pads, erasing the etched step, and leading to failure. In concept, and on paper, the mesh sheets and the top foil lay flat against the bearing carrier, giving a false sense of uniformity in the design process. In actuality, a designer must consider the manufactured states of the individual components and how they assemble. A redesign of the bearing intends to overcome the existing flaws (highlighted herein) by incorporating a thicker top foil that is well anchored (to better withstand the effects of windage), a robust sacrificial coating, and a hydrodynamic wedge accomplished via a circumferential taper on each pad. [ABSTRACT FROM AUTHOR]

Geothermal resources represent a valuable option to reduce fossil fuel-based power production because they feature an unmatched capacity factor among other renewable energy sources (RES). Geothermal resource availability reduces with the temperature. Therefore, developing cost-effective solutions to exploit low-temperature geothermal energy is mandatory to expand technology utilization. The standard solution for converting low-temperature thermal sources into power is organic rankine cycles (ORCs). ORC basic layout (subcritical) is well-known, but the more advanced alternatives, such as transcritical and two-pressure level cycles, are much less widespread, and it is unclear whether the higher efficiency justifies the higher capital cost. The paper focuses on the exploitation with ORC of geothermal resources (hot water) with a temperature lower than 200 °C and mass flow rates between 200 and 1400 m³/h for a target power production ranging from 3 to 30 MW. The paper compares three ORC layouts, subcritical, transcritical, and two pressure-level, from thermodynamic and economic points of view to map the most cost-effective solutions in the investigated size ranges. The techno-economic comparison considers the impact of the operating conditions and fluid on the machine's expected performance and the heat exchangers' size. As expected, more complicated layouts yield higher conversion efficiencies, with the two pressure-level cycles achieving the highest power output for the same geothermal source conditions. However, the economic analysis showed that the most efficient solutions are not always preferable when considering the cost-efficiency tradeoff. [ABSTRACT FROM AUTHOR]

Electrified Aircraft Propulsion (EAP) holds great potential for reducing aviation emissions and fuel burn. A variety of EAP architectures have been proposed including partially turbo-electric configurations that combine turbofan engines with motor-driven propulsors. Such architectures exhibit coupling between subsystems and thus require an integrated control solution. To address this need, this paper presents an integrated control design strategy for a commercial single-aisle partially turbo-electric aircraft concept consisting of two wing-mounted turbofan engines and an electric motor driven tailfan propulsor. Within this architecture the turbofans serve the dual purpose of generating thrust and supplying mechanical offtake power used to generate electricity for the tailfan motor. The propulsion control system is tasked with coordinating turbofan and tailfan operation under both steady-state and transient scenarios. The paper introduces a linear state-space representation of the architecture reflecting the coupling between the turbofan and tailfan subsystems along with loop transfer functions reflecting open- and closed-loop system dynamics. Also discussed is an applied strategy for scheduling the tailfan setpoint command based on the average sensed fan speed of the two turbofans. This approach ensures synchronized operation of the turbofan and tailfan subsystems while also allowing the turbofan fuel control design to be simplified. Performance of the integrated control design is assessed through a real-time hardware-in-the-loop test conducted at the NASA Electric Aircraft Testbed. During this test a scaled version of the electrical system and turbomachinery shaft dynamics were implemented in electrical machine hardware and evaluated under closed-loop control. Results from this facility test are presented to illustrate the efficacy of the applied integrated control design approach under steady-state and transient scenarios including a full-flight mission profile. [ABSTRACT FROM AUTHOR]

This paper provides a specific analysis strategy for tensegrity prism units with different complexities and connectivity. Through the nodal coordinate matrix and connectivity matrix, we can establish the equilibrium equation of the structure in the self-equilibrium state, and the equilibrium matrix can be obtained. The Singular Value Decomposition (SVD) method can find the self-equilibrium configuration. The torsional angle formula between the upper and bottom surfaces of the prismatic tensegrity structure, which includes complexity and connectivity, can be obtained through the SVD form-finding method. According to the torsional angle formula of the self-equilibrium configuration, we carry out the mechanical analysis of the single node, and the force density relationship between elements is gained. As one of the standards, the mass is used to evaluate the light structure. This paper also studied the minimal mass of the self-equilibrium tensegrity structure with the same complexity in different connectivity and got the minimal mass calculation formula. A six-bar tensegrity prism unit is investigated in this paper, which shows the feasibility of systematic analysis of prismatic structures. This paper provides a theoretical reference for prismatic tensegrity units. [ABSTRACT FROM AUTHOR]

This paper illustrates how particle size affects the initial yield stress of particle-reinforced composites. A formulation in a closed form is presented to demonstrate the size effect of yielding of the composites. This paper also demonstrates that there is an upper bound and a lower bound for the size-dependent yield stress with the change of particle size. This means that decreasing particle size increases its yield stress up to an upper bound. Similarly, increasing particle size decrease its yield stress up to a lower bound. In this paper the asymptotic homogenization method is used in framework of the Cosserat elasticity. A virtual "unreinforced matrix" is introduced as a reference configuration. As a numerical example, the size effect of yielding of Sicp/Al is predicted. [ABSTRACT FROM AUTHOR]

Considering the load uncertainty and unmodeled dynamics in multicylinder hydraulic systems, this paper proposes a balance control algorithm based on safe reinforcement learning to release the restrictions of classical model-based control methods that depend on fixed gain. In this paper, the hydraulic press is controlled by a trained agent that directly maps the system states to control commands in an end-to-end manner. By introducing an action modifier into the algorithm, the system states are kept within security constraints from the beginning of training, making safe exploration possible. Furthermore, a normalized exponential reward function has been proposed. Compared with a quadratic reward function, the precision is greatly improved under the same training steps. The experiment shows that our algorithm can achieve high precision and fast balance for multicylinder hydraulic presses while being highly robust. To the best of our knowledge, this research is the first to attempt the application of a reinforcement learning algorithm to multi-execution units of hydraulic systems. [ABSTRACT FROM AUTHOR]

Understanding fluid movement in multi-pored materials is vital for energy security and physiology. For instance, shale (a geological material) and bone (a biological material) exhibit multiple pore networks. Double porosity/permeability models provide a mechanics-based approach to describe hydrodynamics in aforesaid porous materials. However, current theoretical results primarily address steady-state response, and their counterparts in the transient regime are still wanting. The chief aim of this paper is to fill this knowledge gap. We present three principal properties--with rigorous mathematical arguments--that the solutions under the double porosity/permeability model satisfy in the transient regime: backward-in-time uniqueness, reciprocity, and a variational principle. We employ the "energy method"--exploiting the physical total kinetic energy of the flowing fluid--to establish the first property and Cauchy-Riemann convolutions to prove the next two. The results reported in this paper--qualitatively describe the dynamics of fluid flow in double-pored media--have (a) theoretical significance, (b) practical applications, and (c) considerable pedagogical value. In particular, these results will benefit practitioners and computational scientists in checking the accuracy of numerical simulators. The backward-in-time uniqueness lays a firm theoretical foundation for pursuing inverse problems in which one predicts the prescribed initial conditions based on data available about the solution at a later instance. [ABSTRACT FROM AUTHOR]

The following paper presents breakthrough experimental results for a new hermetic squeeze film damper (HSFD) concept that is integrally designed within an externally pressurized tilting-pad radial gas bearing support. The flexibly damped gas bearing module was designed for a 7.2 (183 mm) diameter shaft and fabricated using direct metal laser melting (DMLM); also known as additive manufacturing. The bearing and HSFD were sized based on ongoing studies for oil-free supercritical carbon dioxide (sCO2) power turbines in the 8.5 MW-10 MW power range. The development of the new damper concept was motivated by past dynamic testing on HSFD, which generated frequency-dependent stiffness and damping force coefficients. In efforts to eliminate the frequency dependency, a new HSFD architecture was conceived that adds accumulator volumes and a pass-through channel to previously conceived HSFD flow network designs. The other motivation for the work is the need to develop a cost-effective and reliable oil-free bearing technology that is scalable to large power turbomachinery applications. There were several objectives for the following work. The first objective was to successfully design and fabricate a single piece bearing-damper using additive manufacturing, while dimensionally controlling critical design features. The paper discusses the manufacturing steps and shows cut-ups that reveal adequate clearance control capability with internal damper clearances. The second objective was to perform experimental testing with the new HSFD design in efforts to extract stiffness and damping coefficients for excitation frequencies within 20-160 Hz and peak vibration amplitudes between 0.25 mils (6.35 microns) to 1 mil (25.4 microns). The test results for a single HSFD bearing module indicated that the design modifications to the HSFD architecture were successful in eliminating nearly all the frequency dependencies for the stiffness force coefficient. The dynamic tests yielded a stiffness coefficient that varied between 112 klb/in. (19.6 MN/m) and 96 klb/in. (16.8 MN/m). The damping force coefficient however, exhibited relatively more variation with frequency with values residing between 175 lb-s/in. (31 kN-s/m) to 214 lb-s/in. (37 kN-s/m). Finally, the paper advances a three-dimensional fluid-structure interaction (FSI) model using transient finite element analysis (FEA) coupled to a computational fluid dynamics (CFD) model. The FSI analysis performed between 20 Hz and 80 Hz was used to predict the stiffness and damping of the HSFD using a quarter-section model of the damper. The FSI analysis was able to support test results by showing only a 6-7.4% change in the magnitude of force coefficients. Stiffness predictions agree reasonably well with experiments whereas damping is underpredicted. [ABSTRACT FROM AUTHOR]

This paper presents a novel real-time kinematic simulation algorithm for planar N-bar linkage mechanisms, both single- and multi-degrees-of-freedom, comprising revolute and/or prismatic joints and actuators. A key feature of this algorithm is a reinterpretation technique that transforms prismatic elements into a combination of revolute joint and links. This gives rise to a unified system of geometric constraints and a general-purpose solver which adapts to the complexity of the mechanism. The solver requires only two types of methods--fast dyadic decomposition and relatively slower optimization-based--to simulate all types of planar mechanisms. From an implementation point of view, this algorithm simplifies programming without requiring handling of different types of mechanisms. This versatile algorithm can handle serial, parallel, and hybrid planar mechanisms with varying degrees-of-freedom and joint types. Additionally, this paper presents an estimation of simulation time and structural complexity, shedding light on computational demands. Demonstrative examples showcase the practicality of this method. [ABSTRACT FROM AUTHOR]

Design space exploration and margin analysis can inform critical decisions early in engineering design, helping to handle the uncertainties of early design while ensuring design performance. In practice, the complexity of many products makes such decision-making challenging. This paper addresses the challenge with a new design framework that relies on the margin value method to evaluate sets of concepts that are combinatorially generated from an enhanced function-means tree. The basis for concept comparison is the margin value in each design alternative. The margin value method is expanded to address a broad class of design problems by using surrogate models and novel metrics for evaluating different conceptual alternatives. Visualization tools are introduced to support the evaluations. The efficacy of the framework is demonstrated using the design of a structural aero-engine component involving simulation models and uncertain load specifications. Overall, this paper shows how design concepts can be compared objectively and distilled to a set of alternatives that would retain their values throughout product development. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for designing estimators to achieve consensus in uncertain multi-agent systems (MAS), even when various fault conditions are present and communication is assumed to be undirected and connected. The method includes an adaptive fault detection technique to detect faults and a unique adaptation in the unscented Kalman filter (UKF) to adjust noise covariance matrices and reconstruct uncertain states in the MAS is proposed in the framework of Q-learning. Additionally, it involves training neural network internal parameters using previous measurements. A Chebyshev neural network (CNN) is employed to model the uncertain plant, and a hyperbolic tangent-based robust control term is used to mitigate neural network approximation errors. This novel approach is known as reinforced UKF (RUKF). The paper also discusses the asymptotic stability of the proposed method and presents numerical simulations to demonstrate its effectiveness with reduced computational load. [ABSTRACT FROM AUTHOR]

In this paper, we address the following question: How can multi-robot self-organizing systems be designed so that they show the desired behavior and are able to perform tasks specified by the designers? Multi-robot self-organizing systems, e.g., swarm robots, have great potential for adapting when performing complex tasks in a changing environment. However, such systems are difficult to design due to the stochasticity of system performance and the non-linearity between the local actions/interaction and the desired global behavior. In order to address this, in this paper, we propose a framework for designing self-organizing systems using Multi-Agent Reinforcement Learning (MARL) and the compromise Decision-Support Problem (cDSP) construct. The proposed framework consists of two stages, namely, preliminary design followed by design improvement. In the preliminary design stage, MARL is used to help designers train the robots so that they show stable group behavior for performing the task. In the design improvement stage, the cDSP construct is used to explore the design space and identify satisfactory solutions considering several performance indicators. Surrogate models are used to map the relationship between local parameters and global performance indicators utilizing the data generated in the preliminary design. These surrogate models represent the goals of the cDSP. Our focus in this paper is to describe the framework. A multi-robot box-pushing problem is used as an example to test the framework's efficacy. This framework is general and can be extended to design other multi-robot self-organizing systems. [ABSTRACT FROM AUTHOR]

Many complex engineering systems can be represented in a topological form, such as graphs. This paper utilizes a machine learning technique called Geometric Deep Learning (GDL) to aid designers with challenging, graph-centric design problems. The strategy presented here is to take the graph data and apply GDL to seek the best realizable performing solution effectively and efficiently with lower computational costs. This case study used here is the synthesis of analog electrical circuits that attempt to match a specific frequency response within a particular frequency range. Previous studies utilized an enumeration technique to generate 43,249 unique undirected graphs presenting valid potential circuits. Unfortunately, determining the sizing and performance of many circuits can be too expensive. To reduce computational costs with a quantified trade-off in accuracy, the fraction of the circuit graphs and their performance are used as input data to a classification-focused GDL model. Then, the GDL model can be used to predict the remainder cheaply, thus, aiding decision-makers in the search for the best graph solutions. The results discussed in this paper show that additional graph-based features are useful, favorable total set classification accuracy of 80% in using only 10% of the graphs, and iteratively built GDL models can further subdivide the graphs into targeted groups with medians significantly closer to the best and containing 88.2 of the top 100 best-performing graphs on average using 25% of the graphs. [ABSTRACT FROM AUTHOR]

Many design problems involve reasoning about points in high-dimensional space. A common strategy is to first embed these high-dimensional points into a low-dimensional latent space. We propose that a good embedding should be isometric--i.e., preserving the geodesic distance between points on the data manifold in the latent space. However, enforcing isometry is non-trivial for common neural embedding models such as autoencoders. Moreover, while theoretically appealing, it is unclear to what extent is enforcing isometry necessary for a given design analysis. This paper answers these questions by constructing an isometric embedding via an isometric autoencoder, which we employ to analyze an inverse airfoil design problem. Specifically, the paper describes how to train an isometric autoencoder and demonstrates its usefulness compared to non-isometric autoencoders on the UIUC airfoil dataset. Our ablation study illustrates that enforcing isometry is necessary for accurately discovering clusters through the latent space. We also show how isometric autoencoders can uncover pathologies in typical gradient-based shape optimization solvers through an analysis on the SU2-optimized airfoil dataset, wherein we find an over-reliance of the gradient solver on the angle of attack. Overall, this paper motivates the use of isometry constraints in neural embedding models, particularly in cases where researchers or designers intend to use distance-based analysis measures to analyze designs within the latent space. While this work focuses on airfoil design as an illustrative example, it applies to any domain where analyzing isometric design or data embeddings would be useful. [ABSTRACT FROM AUTHOR]

Incorporating style-related objectives into shape design has been centrally important to maximize product appeal. However, algorithmic style capture and reuse have not fully benefited from automated data-driven methodologies due to the challenging nature of design describability. This paper proposes an AI-driven method to fully automate the discovery of brand-related features. First, to tackle the scarcity of vectorized product images, this research proposes two data acquisition workflows: parametric modeling from small curve-based datasets, and vectorization from large pixel-based datasets. Second, this study constructs BIGNet, a two-tier Brand Identification Graph Neural Network, to learn from both scalar vector graphics' curve-level and chunk-level parameters. In the first case study, BIGNet not only classifies phone brands but also captures brand-related features across multiple scales, such as lens' location, as confirmed by AI evaluation. In the second study, this paper showcases the generalizability of BIGNet learning from a vectorized car image dataset and validates the consistency and robustness of its predictions given four scenarios. The results match the difference commonly observed in luxury versus economy brands in the automobile market. Finally, this paper also visualizes the activation maps generated from a convolutional neural network and shows BIGNet's advantage of being a more explainable style-capturing agent. [ABSTRACT FROM AUTHOR]

Models of long-term product innovation depict the trajectory of products through an evolutionary selection metaphor in which product designs converge toward a dominant design. The product innovation literature favors trajectory descriptions based on the physical architecture of products while neglecting to account for the functional architecture. This paper offers a new way to explain the life cycle of product innovation by identifying motifs that describe a product's functions. Functional motifs are recurrent function blocks across multiple generations of designs for a product. A collection of functional motifs defines the functional architecture of the product. Using some key examples from innovations in sewing machines, the paper illustrates the occurrence of motifs as the basis for detecting the emergence of a dominant design. Patents related to the sewing machine over 177 years are analyzed to identify functional motifs characterizing the evolution and convergence toward a dominant design. Results show that motifs do not change over long periods once a dominant design emerges, even though components continue to change. This observation confirms a view of dominant designs as a technological frame but refutes the notion that design no longer matters in the era of incremental change. These motifs refine our understanding of how designs evolve along a particular path over the course of product innovation. [ABSTRACT FROM AUTHOR]

In engineering-design problems, usually, there are multiple goals with different units, continuous and discrete variables, nonlinear equations, nonconvex equations, and coupled decisions. Ideally, all goals' target are reached simultaneously within the feasible space. However, the optimal solution may not be available. To deal with all those complexities, a modeling strategy named "satisficing" was proposed in the 1980s. The satisficing strategy allows designers to find "good enough" but may not be optimal solutions. In this paper, we review the publications applying the satisficing strategy on engineering-design problems, and categorize the methods regarding the design stages they manage. We define the methods dealing with all four design stages--formulation, approximation, solution, and evaluation--as the whole process satisficing methods. We review the publications using the whole process satisficing strategy in great detail. In the past 30 years, the whole process satisficing strategy has been improved and applied to a wide variety of engineering-design problems, based on which derived methods, concepts, and platforms are developed. We generalize the specialties, advantages, and scope of applications of the methods in the whole process satisficing strategy. We expect this paper provides information on when and how designers may apply satisficing for their problems. [ABSTRACT FROM AUTHOR]

Compares two advanced gas turbine types integrated with the combined cycle, steam injected gas turbine (STIG) and evaporative gas turbine (EvGT), in pulp and paper mills for increased power generation. STIG Cycle and its application in pulp and paper industry; EvGT Cycle; Reference pulp and paper mills; Modeling of advanced gas turbine systems.

Mathematical modeling of bearing faults, worn tool holder taper contact interface, and unbalance are presented and integrated into a digital dynamic model of spindles in Part I of this paper. These faults lead to changes in preload and dynamic stiffness over time, consequently resulting in observable vibrations. This paper predicts the vibrations of a spindle at a particular measurement location by simulating the presence of a specific fault or multiple faults during spindle rotation. The vibration spectra generated by the digital spindle model at the spindle speed and its harmonics, the changes in the natural frequencies, and dynamic stiffnesses are correlated to faults with experimental validations. The simulated vibration spectrums are later used in training an artificial neural network for fault condition monitoring presented in Part III of the paper. [ABSTRACT FROM AUTHOR]

The primary goal of the paper is to monitor the health of the spindle in machine tools to ensure optimal performance and reduce costly downtimes. Spindle health monitoring is essential to detect wear and cracks in spindle bearings, which can be challenging due to their gradual development and hidden locations. The proposed approach combines physics-based modeling and data-driven techniques to monitor spindle health effectively. In Part I and Part II of the paper, mathematical models of bearing faults and spindle imbalance are integrated into the digital model of the spindle. This allows for simulating the operation of the spindle both with and without faults. The integration of fault models enables the generation of vibrations at sensor locations along the spindle shaft. The generated vibration data from the physics-based model are used to train a recurrent neural network-based (RNN) fault detection algorithm. The RNN learns from the labeled vibration spectra to identify different fault conditions. Bayesian optimization is used to automatically tune the hyperparameters governing the accuracy and efficiency of the learning models during the training process. The RNN classifiers are further fine-tuned using a small set of experimentally collected data for the generalization of the model on real-world data. Once the RNN classifier is trained, it can distinguish between different types of damage and identify their specific locations on the spindle assembly. The proposed algorithms achieved an accuracy of 98.43% on experimental data sets that were not used in training the network. [ABSTRACT FROM AUTHOR]

Presents information on a study which analyzed the heating and drying process of moist paper with infrared radiation energy. Set-up of the experiment; Description of paper samples; Error and theoretical analyses; Results of the study; Comparison of numerical and experimental results.

In this paper, a technique is developed for determining the nonstationary response statistics of linear oscillators endowed with fractional derivative elements. Notably, fractional operators are particularly effective in modeling solid mechanics problems as they offer the option of influencing both the elasticity and the energy dissipation capacity of the system. In this paper, particular attention is devoted to the case of fractional derivatives of rational order that approximates reasonably well any real order model. The oscillators are subjected to stationary stochastic excitations, and the pertinent nonstationary response statistical moments are determined by first introducing a finite number of oscillator response related states; this is afforded by the rational number order of the fractional operator. Next, the technique involves proceeding to treating the problem in the Laplace transform domain. This leads to multiple convolution integrals determined by representing the transfer function of the oscillator in a partial fraction form by a pole-residue formulation. In this manner, the response evolutionary power spectral density of the fractional oscillator is derived in a closed form, while nonstationary second-order statistics can be obtained by mundane numerical integration in the frequency domain. Applications to oscillators comprising one or two fractional derivative elements are presented, considering the case of a white noise excitation and of a random process possessing the classical Kanai-Tajimi spectrum. Reliability of the developed technique is assessed by juxtaposing its analytical results with pertinent Monte Carlo simulation data. [ABSTRACT FROM AUTHOR]

Robotic devices are capable of reducing the physical burden on rehabilitation therapists and providing training programs of good repeatability, high efficiency, and high precision. When designing the kinematic structure for rehabilitation robots, there has been a growing interest toward one-degree-of-freedom (DOF) end-effector mechanisms due to their simpler structure and less complicated control algorithms. Compared with current one-DOF mechanism designs that are mainly customized for multi-joint robotic training, spherical coupled serial chain (SCSC) mechanisms are proposed in this paper to specifically deliver the single-joint robotic training, which is of equal importance to the effective physical recovery. Using kinematic mapping theory, the end-effector motion of SCSC mechanisms can be naturally transformed to two trigonometric curves composed of finite Fourier series in two separate planes of the image space. This novel formulation helps to establish an analytical and direct relationship between the design parameters of the SCSC mechanism and the harmonic parameters of the image-space representation of the task rehabilitation motion. The result is a simple and effective method for kinematic synthesis of SCSC mechanisms for generation of single-joint motion with an arbitrary number of spherical poses. An example of designing SCSC mechanisms for shoulder-joint rehabilitation is presented at the end of this paper to illustrate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This paper develops a method to integrate user knowledge into the optimization process by simultaneously modelling feasible design space and optimizing an objective function. In engineering, feasible design space is a constraint similar to those in optimization problems. However, not all constraints can be explicitly written as mathematical functions. This includes manufacturing concerns, ergonomic issues, complex geometric considerations, or exploring material options for a particular application. There needs to be a way to integrate designer knowledge into the design process and, preferably, use that to guide an optimization problem. In this research, these constraints are modeled using classification surrogate models and incorporated with Bayesian optimization. By suggesting design options to a user and allowing them to box off areas of feasible and infeasible designs, the method models both the feasible design space and an objective function probability of new design targets that are more optimal and have a high probability of being feasible. This proposed method is first proven with test optimization problems to show viability then is extended to include user feedback. This paper shows that by allowing users to box off areas of feasible and infeasible designs, it can effectively guide the optimization process to a feasible solution. [ABSTRACT FROM AUTHOR]

3D/4D printing offers significant flexibility in manufacturing complex structures with a diverse range of mechanical responses, while also posing critical needs in tackling challenging inverse design problems. The rapidly developing machine learning (ML) approach offers new opportunities and has attracted significant interest in the field. In this perspective paper, we highlight recent advancements in utilizing ML for designing printed structures with desired mechanical responses. First, we provide an overview of common forward and inverse problems, relevant types of structures, and design space and responses in 3D/4D printing. Second, we review recent works that have employed a variety of ML approaches for the inverse design of different mechanical responses, ranging from structural properties to active shape changes. Finally, we briefly discuss the main challenges, summarize existing and potential ML approaches, and extend the discussion to broader design problems in the field of 3D/4D printing. This paper is expected to provide foundational guides and insights into the application of ML for 3D/4D printing design. [ABSTRACT FROM AUTHOR]

This paper discusses a study performed by SoftInWay as part of a Phase II SBIR project funded by NASA. In contrast with the Phase I project (Burlaka and Moroz, 2023, "Axial Compressor Map Generation Leveraging Autonomous Self-Training Artificial Intelligence," ASME J. Eng. Gas Turbines Power, 145(1), p. 011001) where three discrete compressors were considered, the Phase II study was focused on addressing the problem of axial compressor long development time and cost with the use of AI models capable of predicting the geometry and performance of various multistage axial compressors with multiple variable vanes. The applicability of the AI models to various compressors enables the opportunity to avoid iterations between engine cycle analysis and compressor design. In this paper, automated compressor design and performance generation workflows are described. The approach for autonomous selection of the architectures and hyperparameters of Machine Learning (ML) models is explained. The uncertainty quantification techniques are considered. The developed ML-powered methods for compressor geometry prediction are discussed. The ML models' accuracy values and representations of typical geometry and performance predictions are given. The utilization of the ML models in engine cycle analysis is discussed. [ABSTRACT FROM AUTHOR]

Subsea centrifugal compressors commonly using labyrinth seals (LS) and pocket damper seals (PDS) must withstand the flow of oil in gas mixtures with a liquid volume fraction (LVF) up to 5%. This paper presents experimental results for the leakage and rotordynamic coefficients of a uniform clearance PDS and a similar size LS both supplied with wet gas. The shaft diameter D = 127 mm and the seal axial length L = 48 mm. In the tests, the seals' pressure ratio (inlet/exit) = 2.5, and the shaft speed reaches 5.25 krpm (surface speed = 35 m/s). The LS has a 17% larger radial clearance (Cr, LS = 0.230 mm > Cr, PDS = 0.196 mm) due to a manufacturing error. This paper extends prior work by Torres et al. (2022, "A Stepped Shaft Labyrinth Seal versus a Pocket Damper Seal: Leakage and Dynamic Force Coefficients Under Wet Gas Operation," ASME J. Eng. Gas Turbines Power, 145(1), p. 011006) for the same two seal types with a stepped clearance configuration that promotes damping. When supplied with a dry gas, the LS leaks more because of its larger clearance. A loss coefficient (cd) is a fraction of the physical clearance that characterizes a seal's effectiveness in reducing leakage. The cd of both seals is nearly identical for operation with pure gas; the small differences are well within the experimental uncertainty. For operation with wet gas, the PDS cd decreases as the LVF increases whereas the LS cd increases, thus indicating the PDS is more effective to restrict wet gas leakage. When operating with pure gas, the direct stiffness (K) and effective damping (Ceff) of both seals are small in magnitude (K < 0.5 MN/m, Ceff < 2 kN-s/m) and often less than the experimental uncertainty. For wet gas operation and with shaft speed = 3 krpm and 5.25 krpm, the PDS produces Ceff < 0 for whirl frequencies below 50 Hz. In contrast, the LS Ceff > 0, although small in magnitude. The experimental results under wet gas operation are similar for operation with LVF equal to 3% and 5%. Unexpected low-frequency broadband motions appear when supplying the PDS with a wet gas. Although small in amplitude (<5 µm), the motions increase in severity as the mixture inlet LVF and shaft speed increase. The motions are entirely absent for tests with the LS. Experiments in which the mixture is drawn from the PDS cavities rule out liquid accumulation as the cause of the observed motions. The current test results serve as a reference for turbomachinery design engineers and aid in the validation of analytical predictive tools. [ABSTRACT FROM AUTHOR]

Current U.S. government policy seeks to achieve a completely carbon-free economy by 2050, with a carbon-free electricity sector by 2035 (per executive orders #14008 and #14057). To address these goals, the U.S. Department of Energy is evaluating strategies and technologies that support the production, utilization, transport, and storage of hydrogen (via initiatives such as Department of Energy's (DOE) Energy Earthshot--Hydrogen and various DOE funding opportunity announcements). A carbon-free fuel such as hydrogen cannot be overvalued in a dynamic electric energy sector seeking to decarbonize. One of the most important technologies needed to achieve the goal of a carbon-free electricity sector is a 100% hydrogen-fueled gas turbine. Accommodating hydrogen-based fuels has been a key goal for various original engine manufacturers (OEMs) for many years, but much more research and development (R&D) is needed. The purpose of this paper is to highlight the current state-of-the-art of hydrogen turbine technology, especially regarding nitrogen oxide (NOX) emissions compared to natural gas-fueled turbines. NOX is the primary criteria pollutant from thermally driven combustion turbines and should be controlled to levels that are equivalent to or below existing standards (as reported "existing standards" for hydrogen-fueled gas turbines may need to be rebaselined). This paper will provide an overview of hydrogen as a fuel and various NOX emissions control techniques that are relevant for hydrogen-based fuels. A conclusion from this overview is that, with some level of R&D, NOX emissions from hydrogen-fueled gas turbines can be controlled to levels similar to those produced by state-of-the-art (SOTA) natural gas-fueled combustion turbines while remaining competitive in terms of performance and efficiency. [ABSTRACT FROM AUTHOR]

Bioprinting using algae cells has many potential applications including tissue engineering, environmental engineering, contaminant removal from water, and establishing space habitats. In extrusion-based bioprinting, bioink needs to be crosslinked after being extruded from the nozzle for printed constructs to first achieve and then maintain adequate shape fidelity. Crosslinking methods used in reported studies on algae-contained bioinks include both photo-crosslinking and ionic crosslinking. This paper reports a preliminary study where the coaxial nozzle-based in situ ionic crosslinking method was used in bioprinting of algae cells without additional crosslinking of printed samples for the first time. In comparison with photo-crosslinking, in situ ionic crosslinking can minimize bioink preparation time and complexity, eliminate cells' exposure to ultraviolet radiation, and reduce the number of post-printing steps. In this preliminary study, the bioink was an alginate solution containing algae (Chlorella vulgaris) cells, and the crosslinking solution was a calcium chloride solution. The coaxial nozzle had two nozzles: inner and outer nozzles. In printing, the bioink was delivered through the outer nozzle while the crosslinking solution was delivered through the inner nozzle. The shape of the printed samples was a square block with dimensions of 30 × 30 × 10 mm. It was observed that, 9 days after printing, the algae cells grew within the printed samples, and the samples could keep their shapes relatively well. Many knowledge gaps exist regarding the effects of input variables in bioprinting of algae cells using this method. This paper discusses future research directions to fill these knowledge gaps. [ABSTRACT FROM AUTHOR]

Design education has traditionally relied heavily on physical integration as it involves a lot of hands-on work, group critiques, and collaborative projects, but the COVID-19 pandemic has fundamentally shifted the way teaching is done, which resulted in many institutions adapting to remote teaching and learning environments. This has created challenges for design educators who have had to find ways to evaluate students' progress in the absence of in-person interactions. In this paper, we are proposing a dashboard visualization approach that helps educators monitor the progression of the entire class of students using artificial intelligence (AI) by tracking a time-based evolution of a design statement. This approach uses various natural language processing (NLP) models to produce stock-like charts, which represent students' and student groups' progression through a series of divergence and convergence phases. These charts become a form of design artifact that allows educator(s) to gain a bird's-eye view of the class and react to groups that may require assistance; at the same time, it becomes a qualitative means of evaluation and comparison across students and groups. Toward the end, this paper also showcases a web-based platform that is publicly available using such methodology, a case study that applied so methodology and recommendations of future works possible. [ABSTRACT FROM AUTHOR]

Digital tools for sketching, such as tablets, have become popular for streamlining design work and keeping a large quantity of sketches in one place. However, their impact on design creativity, novelty, and concept evolution is not yet well understood. Here, we present a controlled human subjects study that assesses the influence of tablets (iPads) on concept novelty and evolution in the context of an engineering design concept generation exercise. We expect that iPad use will not influence concept novelty due to its similar speed of use as pen and paper sketching. We expect to see different patterns in concept evolution between the two types of tools, namely, that iPad users will demonstrate more iteration on a concept (concept evolution) than pen and paper users due to the fact that iPad features make it easy to copy and paste previous sketches and then modify them. We find that the tool used is not correlated with concept novelty. Additionally, we find no strong differences in overall concept evolution quantities between the two tools, though we see that iPad sketches exhibited more cases of consecutive concept evolution than nonconsecutive whereas paper and pen sketches showed an equal amount of both consecutive and nonconsecutive concept evolution. Results indicate that overall, iPads may not significantly inhibit designers' creative skills and thus could be a reasonable replacement for pen and paper sketching, which has implications for both design education and practice. [ABSTRACT FROM AUTHOR]

In the coming years, decentralized power generation systems with renewables are expected to take a leading role, and micro gas turbines will serve as backup sources to compensate for times of low inputs from other sources. In order to deal with the unpredictable energy inputs from renewables, the micro gas turbine must be capable of running under varying load conditions and making fast transitions between them. The operation of a micro gas turbine in an integrated microgrid (MG) has the potential to reduce operational costs and ensure the delivery of demanded heat and power to consumers. This paper investigates the operation of a micro gas turbine in a MG, serving as a supplementary power source for a municipal building. The building's required energy is initially provided by wind turbine power, and the micro gas turbine serves as a backup source during times of wind power deficiency. The micro gas turbine can operate using a natural gas/hydrogen fuel blend ranging from zero to 100% hydrogen. Furthermore, a water electrolyzer with a hydrogen tank is available to operate as a storage system within the MG. The study's results demonstrate the economic and environmental benefits of using hydrogen storage and optimizing operational planning in the MG. The primary objective of the paper is to highlight the feasibility and benefits of employing micro gas turbines and hydrogen storage systems within a MG as a renewable energy backup power source. [ABSTRACT FROM AUTHOR]

Origami structures, which were inspired by traditional paper folding arts, have been applied for engineering problems for the last two decades. Origami-based thin-wall tubes have been extensively investigated under axial loadings. However, less has been done with radial stiffness as one of the critical mechanical properties of a tubular structure working under lateral loadings. In this study, the radial stiffness of novel thin-wall tubular structures based on origami patterns have been studied with compression tests and finite element analysis (FEA) simulations. The results show that the radial stiffness of an origami-inspired tube can achieve about 27.1 times that of a circular tube with the same circumcircle diameter (100 mm), height (60 mm), and wall thickness (2 mm). Yoshimura, Kresling, and modified Yoshimura patterns are selected as the basic frames, upon which the influences of different design parameters are tested and discussed. Given that the weight can vary due to different designs, the stiffness-to-weight ratio is also calculated. The origami-inspired tubular structures with superior stiffness performances are obtained and can be extended to crashworthy structures, functional structures, and stiffness enhancement with low structural weight. [ABSTRACT FROM AUTHOR]

Fluid injection is a common practice in the oil and gas industry found in many applications such as waterflooding and disposal of produced fluids. Maintaining high injection rates is crucial to guarantee the economic success of these projects; however, there are geomechanical risks and difficulties involved in this process that may threat the viability of fluid injection projects. Near wellbore reduction of permeability due to pore plugging, formation failure, out of zone injection, sand production, and local compaction are challenging the effectiveness of the injection process. Due to these complications, modeling and simulation has been used as an effective tool to assess injectors' performance; however, different problems have yet to be addressed. In this paper, we review some of these challenges and the solutions that have been proposed as a primary step to understand mechanisms affecting well performance. [ABSTRACT FROM AUTHOR]

Over the past few decades, folding paper has extended beyond the origami deployable applications to reach the engineering field. Nevertheless, mechanical information about paper behavior is still lacking, especially during folding/unfolding. This article proposes an approach to characterize the paper fold behavior in order to extract the material data that will be needed for the simulation of folding and to go a step further the single kinematics of origami mechanisms. The model developed herein from simple experiments for the fold behavior relies on a macroscopic local hinge with a nonlinear torsional spring. Though validated with only straight folds, the model is still applicable in the case of curved folds thanks to the locality principle of the mechanical behavior. The influence of both the folding angle and the fold length is extracted automatically from a set of experimental values exhibiting a deterministic behavior and a variability due to the folding process. The goal is also to propose a methodology that may extend the simple case of the paper crease, or even the case of thin material sheets, and may be adapted to other identification problems. [ABSTRACT FROM AUTHOR]

Aerodynamic stability enhancement is crucial for the stable operation of supercritical carbon dioxide (SCO2) centrifugal compressors. This paper investigates the mechanism of aerodynamic instability of shrouded SCO2 compressors and accordingly proposes a new method for stability enhancement via the casing treatment in terms of shroud riblets. First, the experimentally validated computational fluid dynamic (CFD) method is employed to investigate the flow mechanism of the compressor under near-surge condition. The significant backflow phenomena within the impeller were revealed. Further analysis indicated that the imbalance of the Coriolis force and pressure gradient in blade-to-blade direction pushed the low-momentum fluid toward the shroud suction side. Additionally, higher Reynolds number resulted in thinner SCO2 boundary layer at the inlet near end-wall, increasing passage vorticity and further intensifying the aggregation of low-energy fluid on the shroud suction side. Based on the flow mechanisms, the streamwise riblets on shroud were designed to impede the migration of low-energy fluid. The CFD results revealed that under low-flow condition, riblets inhibit the formation of inducer vortices and backflow, thereby enhancing impeller aerodynamic stability and reducing the surge mass-flowrate. Further research indicated that riblets obstruct the migration of low-energy fluid toward shroud suction side, reducing the accumulation of low-energy fluid and blockage, thereby increasing the flow area and aerodynamic stability. Moreover, additional riblets wake and friction losses contributed to the deterioration of compressor performance at middle/large mass-flowrate conditions. Specifically, riblets reduced the flow area between blades at near choke mass-flowrate, leading to more pronounced shock structures and compressor earlier choke. [ABSTRACT FROM AUTHOR]

Crosswind can reduce the operability of an aeroengine significantly at static or near static operating conditions. Computational fluid dynamics predictions of the flow at crosswind conditions will play an important part in future designs, however, accurate numerical predictions of the flow within the intake remain challenging even for simulations of cases with intake only. The main objective of this paper is to demonstrate the importance of numerical setup and to determine an optimal computational model for crosswind investigations that can be used by other researchers. By considering the flow to be inherently unsteady, the influence of inlet and exit boundary conditions, and grid sensitivity is studied by using unsteady Reynolds-average Navier-Stokes (URANS) simulations. Numerical predictions of the time-averaged intake pressure recovery (IPR) and the motion of the vortex on the ground are compared against the existing experimental data. The results show that for an intake under crosswind, the ground vortex that forms under the intake and the in-duct separation, when present, exhibit unsteady behavior that become stronger as the crosswind velocity is increased. The steady-state simulation is only representative at lower crosswinds. The intake flow separation and ground vortex predictions are influenced by the inlet boundary layer profiles. Moreover, acoustic reflection was observed at the intake exit boundary which propagates upstream, creating artificial unsteady frequencies in IPR. The reflection is generated from the use of uniform boundary conditions at the intake exit and is not dissipated in the grid due to the long wavelength; this can be mitigated by using a choked nozzle at the intake exit. Acoustic reflection was also observed at the far-field exit boundary. These reflections are caused from interaction of trailing vortex and far-field boundary. [ABSTRACT FROM AUTHOR]

As an extremely valuable and reliable energy source, the renewable energy is developing around the world at an unprecedented pace. By the end of 2022, the global installed capacity of offshore wind turbines is expected to reach 46.4 GW, among which 33.9 GW in Europe. Efficiencies in Operations and Maintenance (O&M) offer potential to achieve significant cost savings as it accounts for around 20%-30% of overall offshore wind farm costs. A recent study of approximately 350 offshore wind turbines indicates that gearboxes might have to be replaced as early as 6.5 years. Therefore sensing and condition monitoring (CM) systems are needed in order to obtain reliable information on the state and condition of different critical parts. The development and use of such technologies will allow companies to schedule actions at the right time. The reduced costs of O&M enable the wind energy at a competitive price thus strengthening productivity of the wind energy sector. At the academic level, a plethora of methodologies have been proposed during the last decades focusing toward early and accurate fault detection. Among others, Envelope Analysis is one of the most important methodologies, where the envelope of the vibration signal is estimated, usually after filtering around a selected frequency band excited by impacts due to the faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclostationary analysis and corresponding methodologies, i.e., the cyclic spectral correlation and the cyclic spectral coherence, have been proved as powerful tools for CM. On the other hand the application, test, and evaluation of such tools in general industrial cases is still rather limited. Therefore the main aim of this paper is the application and evaluation of advanced diagnostic techniques and diagnostic indicators, including the Enhanced Envelope Spectrum and the Spectral Flatness on real-world vibration data collected from vibration sensors on gearboxes in multiple wind turbines over an extended period of time of nearly four years. The diagnostic indicators are compared with classical statistic time and frequency indicators, i.e., Kurtosis, Crest Factor, etc. and their effectiveness is evaluated based on the successful detection of two failure events. [ABSTRACT FROM AUTHOR]

The concept of electrified aircraft propulsion (EAP) has garnered substantial attention and investigation due to its potential for mitigating fuel consumption, emissions, and noise. Present studies mainly concentrate on point design rather than systematic design space exploration. This paper considers the attainment of prescribed mission objectives as a paramount evaluation criterion and proposed a mission-oriented design and verification method based on model-based systems engineering (MBSE). Instead of using a general modeling language, this method develops a domain-specific metamodel library for EAP based on six meta-metamodels. A Mission-Operational-Functional-Logical-Physical (MOFLP) modeling methodology is provided to standardize EAP design process. Furthermore, the modeling process is integrated with the verification process by executable verification script. A case study about skydiving mission is conducted to verify the effectiveness of this method. The case results corroborate the utility of this method in the generation of an initial EAP solution. Such initial solution can serve as a fundamental benchmark for iterative design. [ABSTRACT FROM AUTHOR]

This paper expands the one-engine-inoperative conventional oversizing consideration to account for aircraft propulsion systems with multiple energy sources and thrust-generating media. Components in a generic hybrid propulsion system are categorized into power-generation, power-transmission, and thrust-generation. For a given architecture, each possible single component failure is simulated to identify elements affected or eliminated by the respective loss of power, through the use of connection matrices. Failures are linked to losses in supplied and propulsive power, creating a list of oversizing factors for all individual components. Each element is oversized according to its corresponding maximum oversizing rate, defining the ideally redundant propulsion system. Case studies for conventional, all-electric, and hybrid-electric powertrains highlight the need for balancing the number of components between minimizing excess power and increasing the probability of a failure. Additionally, it is shown that asymmetrical configurations should not have a major imbalance of power to avoid significant oversizing. The proposed methodology is applied to a 19-passenger, commuter aircraft. Increasing oversizing rate close to ideal leads to lower optimum energy consumption and boosts redundancy. However, payload capacity penalties are required, up to four passengers for ideal oversizing. Heavier variants without penalties are up to 4% more efficient in terms of energy-per-weight in their carrying capacity against counterparts of the same oversize rate with reduced payload capacity. The proposed method maintains the principles of the conventional oversizing process and highlights the tradeoffs needed between redundancy and performance in sizing novel propulsion systems. [ABSTRACT FROM AUTHOR]