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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]

The growing share of renewable energy sources in the energy mix and the liberalization of electricity markets has drastically affected the operation of electricity generators. Especially, in the last decade, fossil fuel-based generators have shifted their role from providing continuous base load to covering the peak demand and providing backup capacity to stabilize the grid. At the same time, a large amount of storage capacity is foreseen to be integrated into electricity grids in the coming years to shave demand peaks, mitigate price volatility, and provide services to the grid. In such a situation, in order to properly manage these crucial technologies, and thus guarantee the economic viability of the operation, it is essential to properly optimize the dispatch and define the best scheduling. This paper considers a gas turbine combined cycle and battery energy storage to study the problem of dispatch optimization of both generators and storage technologies. Different optimization algorithms have been considered and mixed integer linear programming is selected for its ability to identify the global optimum and the reduced optimization time. The impact of optimization windows (i.e., the forecast horizon of electricity prices) is also investigated. It is highlighted that an increase in forecasting ability, at least up to 36 h, guarantees more effective scheduling; on the other hand, it may require a significantly longer time. Subsequently, different approaches to account for the operation and maintenance costs at the optimization stage are assessed, and, finally, a sensitivity analysis is carried out with respect to market parameters (price average and variability) and technology features (conversion efficiency, cycle cost, etc.). [ABSTRACT FROM AUTHOR]

This paper examines the problem of simultaneously optimizing the spooling and cross-current flight trajectory of a tethered marine hydrokinetic kite using an analytic solution of its inverse dynamics. Tethered kites hold considerable promise for energy production, especially when undergoing cross-current motion. The novelty of this work lies in the use of an analytic solution of the inverse dynamics of the kite to solve the trajectory optimization problem. The term "inverse dynamics" refer to the process of obtaining an exact solution for the actuator inputs from the position, velocity, and acceleration of the kite. While the literature on tethered kites explores trajectory optimization in great detail, most of the work exploits the forward dynamics of the kite, and does not simultaneously optimize the spooling motion and cross-current trajectory. This paper formulates the co-optimization of the kite spooling and cross-current trajectory using a three degrees-of-freedom kite model, paired with an inelastic tether model. The analytic solution of the inverse dynamics is solved in terms of the roots of a fourth-order polynomial in terms of the angle of attack. A simulation study validates the optimization approach and shows that the kite is able to achieve significant energy production. [ABSTRACT FROM AUTHOR]

Higher education has been crucially impacted by the pandemic during the past years. Despite the associated challenges, a wide portfolio of digital literacies has been developed for the delegates. This work evaluates the introduction of digital tools into in-person education. The "Process Optimization" course at Mälardalen University (MDU) is reformed to operate in a digitally enhanced classroom mode. The course covers a variety of optimization methods applied on thermal and fluid machinery such as systems of compressors, pumps and heat exchangers, heat and power plants, aircraft trajectories, and propulsion systems. The constructive alignment is presented to illustrate links between learning objectives, learning activities, and assessment tasks. A series of digital tools is introduced to elevate learning experience prior, during, and after class time. Those comprise digital quizzes, a video channel, polls, a digital whiteboard, and a digital forum. The course is systematically instrumented, yielding a vast set of statistics for evaluating the effectiveness of digital tools as well as engagement levels for learners. The contribution of digitalization into standardizing the formative and summative assessment is discussed. It is observed that digital tools complement the participation into pre- and postclassroom activities. An interactive and digitalized course evaluation activity is also designed. This allowed learners and educators to productively exchange feedback in an inclusive manner. The accrued data provide insight into the impact of digitalization on the delivery of an applied engineering course. Lessons learnt comprise quantitative and qualitative outcomes arising from the perspectives of both learners and teachers. Guidelines and recommended practices are provided for the penetration of digital tools into synchronous and asynchronous learning activities. This paper identifies opportunities as well as space for improvement arising from the penetration of digital tools into the new era for education. [ABSTRACT FROM AUTHOR]

This paper develops a discrete constrained controller for electrohydraulic system (EHS) controlled by proportional directional control (PDC) valve while taking parametric uncertainties into account. The proposed controller solves the discrete barrier Lyapunov function via convex optimization while taking into consideration the system constraints such as state and actuator constraints. The proposed control law successfully tracks the reference trajectory while satisfying the state and actuator constraints. It is proven that the tracking error always converges to an arbitrary small neighborhood which is validated using Lyapunov stability analysis. The effectiveness of the proposed discrete barrier Lyapunov function based constrained controller was verified in a simulation environment and also through experimentation. From the simulation and experimental results, it is clear that the proposed controller drives the load to accurately track the reference trajectory in the presence of uncertainty and unknown disturbances. [ABSTRACT FROM AUTHOR]

The concept of variable rotor speed (VRS) has been recognized as an efficient means to improve rotorcraft operational performance and environmental impact, with electrification being a potential technology to further contribute to that. This paper explores the impact of optimal implementation and scheduling of VRS and power management strategy for conventional and hybrid-electric rotorcraft on energy, fuel, and emissions footprint. A multidisciplinary simulation framework for rotorcraft performance combined with models for engine performance and gaseous emissions estimation is deployed. A holistic optimization approach is developed for the derivation of globally optimal schedules for combined rotor speed and power split targeting minimum energy consumption. Application of the derived optimal schedules at mission level resulted to a 6% improvement in range capability for the VRS tilt-rotor relative to its conventional counterpart. For the hybrid-electric tilt-rotor, combined optimization of VRS and power management leads to an increase in range to 18.4% at 40% and 25% reduced payload for current (250 Wh/kg) and future (450 Wh/kg) battery technology, respectively. For representative urban air mobility (UAM) scenarios, it is demonstrated that the VRS concept resulted in up to 10% and 14% reductions in fuel burn and NOx relative to the nominal rotor speed case, respectively. The utilization of the combined optimum VRS and power split schedules can boost performance with reductions of the order of 20% and 25% in mission fuel/CO2 and NOx at a reduced payload relative to the conventional tilt-rotor. [ABSTRACT FROM AUTHOR]

This paper aims at introducing an original, simulation based, methodology to optimize air charging system of a medium speed engine in order to improve fuel consumption/NOx tradeoff, over a given load profile. A preliminary study is performed on an engine originally equipped with fixed geometry turbocharger (TC) matched at full load. It shows that improving the TC efficiency has a positive impact at matching point but an adverse effect at lower loads. Therefore, matching the TC at part load is more promising when the whole engine operating range is considered. However, this requires using a relief system to avoid unacceptable peak firing pressure (PFP) at full load, and may alter regulated NOx emission. A new, original methodology is then proposed. Using an advanced existing algorithm, it allows for simultaneous optimization of injection timing, main TC matching and relief system design. The objective is to minimize fuel consumption averaged over several operating points (corresponding to actual load profile). At the same time, NOx emission, evaluated on regulatory cycle, is kept below a given limit and others reliability constrains are fulfilled. The methodology is assessed with several case studies: • First, various relief systems (blow-off valve, waste gate (WG) and parallel sequential turbocharger (PTC)) are compared on a given load profile. • Then, the dimensioning of a TC + WG system is performed for two different load profiles. In both cases, the proposed methodology leads to a charging system design customized for a given user profile, which enables a significant fuel consumption reduction. [ABSTRACT FROM AUTHOR]

The paper describes the study performed by SoftInWay in the scope of the Phase I SBIR project funded by the National Aeronautics and Space Administration (NASA). The project was dedicated to a study of optimization of the variable geometry reset angle schedules with the use of innovative autonomous artificial intelligence (AI) technology. In the scope of the project, an automated compressor performance data generation workflow was developed. Three highly loaded multistage axial compressors were designed. The developed workflow was used to generate the training, validation, and test data sets for all three compressors. Multiple different architectures of artificial neural networks were studied, and parametric models for the representation of performance speedlines were developed. Utilizing the developed approaches, artificial neural networks were trained for all three compressors to predict their performance with a relative error below 3%. The trained neural networks were successfully used in the optimization of the variable inlet guide vanes and variable stator vanes reset angle schedules with a relative error of total-to-total pressure ratio prediction below 2% for most of the points and relative error of total-to-total efficiency prediction below 1% for all the points of the operational line. The capability of the developed AI models to accurately predict the optimal combination of reset angles and efficiency of the axial compressor with multiple vanes controlled independently allowed doing quick evaluations of efficiency and stability margins. The availability of such information enables the opportunity to make technical-economical decisions about the reasonability of implementation of independent variable vanes and their number during engine system analysis. [ABSTRACT FROM AUTHOR]

This paper presents an optimization-driven controller design for smooth and accurate position control of a single-rod electrohydrostatic actuator. The design approach uses logically guided iterative runs of the electrohydrostatic actuator to determine the optimal gain and poles' locations of a low-bandwidth controller. The optimization algorithm used in the paper is the globalized bounded Nelder-Mead algorithm with deterministic restarts for improved globalization and lower numerical cost. The design also incorporates a prefilter to ensure minimum jerk in the system's step input response in the beginning and while approaching steady-state. The step response of the filter is a seventh-deg polynomial curve that ensures the minimum change in acceleration in both states. Experimental results reveal that the addition of the proposed prefilter reduces jerk in the system by up to 90%. Results also indicate that the controller performs very well in all quadrants with external load uncertainty of up to 367 kg and thus proves the effectiveness of the design approach. [ABSTRACT FROM AUTHOR]

This paper presents a two-step optimization-based design method for iterative learning control and applies it onto the quadrotor unmanned aerial vehicles (UAVs) trajectory tracking problem. Iterative learning control aims to improve the tracking performance through learning from errors over iterations in repetitively operated systems. The tracking errors from previous iterations are injected into a learning filter and a robust filter to generate the learning signal. The design of the two filters usually involves nontrivial tuning work. This paper presents a new two-optimization design method for the iterative learning control, which is easy to obtain and implement. In particular, the learning filter design problem is transferred into a feedback controller design problem for a purposely constructed system, which is solved based on H-infinity optimal control theory thereafter. The robust filter is then obtained by solving an additional optimization to guarantee the learning convergence. Through the proposed design method, the learning performance is optimized and the system's stability is guaranteed. The proposed two-step optimization-based design method and the regarding iterative learning control algorithm are validated by both numerical and experimental studies. [ABSTRACT FROM AUTHOR]

The selection of component parameters plays an important role in the energy optimization of dual-motor drive electric buses (DMDEBs). To design a set of parameters that can be robust to different road slopes, passenger masses, and bus routes, a Taguchi-based robust design optimization method is proposed in this paper for DMDEBs. First, the dynamic programming (DP) is designed as a part of the Taguchi method to explore the optimal solution (electricity consumption) corresponding to each set of component parameters. Then, by taking the optimal electricity consumption obtained by DP as the response, the Taguchi method is utilized to find the robust component parameters that are insensitive to the noise factors. To evaluate the robustness and reliability of the designed component parameters, a six sigma analysis based on Monte Carlo sampling is employed considering three kinds of stochastic disturbances. Finally, simulation results demonstrate that the proposed robust design optimization method is effective and the designed component parameters have better robustness than the initial component parameters. [ABSTRACT FROM AUTHOR]

This paper presents an efficient stochastic model predictive control (SMPC) framework for quasi-linear parameter varying (qLPV) systems. The framework applies to general nonlinear systems that are driven by stochastic additive disturbances and subject to chance constraints. The qLPV form is featured by a composition of a set of linear time-invariant (LTI) models with state-/control-dependent scheduling variables, which can be obtained by the spatial-temporal filtering-based system identification approach developed in our earlier work. The overall framework can then be transformed into a tube-based MPC optimization problem which can be efficiently handled by a series of quadratic programing (QP) problems. A case study on automotive engine control is presented as a pilot demonstration of the proposed qLPV-SMPC where we show its advantage over the zone-based MPC, much greater computational efficiency than nonlinear MPC (NMPC) and less conservativeness of the proposed method as compared to its robust MPC (RMPC) counterpart. [ABSTRACT FROM AUTHOR]

This paper is concerned with solving, from the learning-based decomposition control viewpoint, the problem of output tracking with nonperiodic tracking-transition switching. Such a nontraditional tracking problem occurs in applications where sessions for tracking a given desired trajectory are alternated with those for transiting the output with given boundary conditions. It is challenging to achieve precision tracking while maintaining smooth tracking-transition switching, as postswitching oscillations can be induced due to the mismatch of the boundary states at the switching instants, and the tracking performance can be limited by the nonminimum-phase (NMP) zeros of the system and effected by factors such as input constraints and external disturbances. Although recently an approach by combining the system-inversion with optimization techniques has been proposed to tackle these challenges, modeling of the system dynamics and complicated online computation are needed, and the controller obtained can be sensitive to model uncertainties. In this work, a learning-based decomposition control technique is developed to overcome these limitations. A dictionary of input-output bases is constructed offline a priori via data-driven iterative learning first. The input-output bases are used online to decompose the desired output in the tracking sessions and design an optimal desired transition trajectory with minimal transition time under input-amplitude constraint. Finally, the control input is synthesized based on the superpositioning principle and further optimized online to account for system variations and external disturbance. The proposed approach is illustrated through a nanopositioning control experiment on a piezoelectric actuator. [ABSTRACT FROM AUTHOR]

Asymmetric flexible machining system has been widely used in numerical control machining. In traditional milling dynamics model, the cutter feed direction is usually defined as parallel to its vibration DOF, while the nonparallel condition and its induced milling dynamics response are not deeply considered. This paper presents a general dynamics modeling method for asymmetric flexible machining systems. First, to the best of the author's knowledge, a new dimension named feed direction is proposed, which is used to establish the generalized coupling relationship between the vibration displacement and the regenerative milling force, thus improve the applicability of the milling dynamics model and reduce the experimental workload compared with the traditional modeling. Second, through the theoretical and experimental research, it is shown that the asymmetric flexible machining system has a significant feed direction-dependent characteristics, and implied the existence of high performance machining region with higher stability and lower surface location error (SLE) by contrast with the symmetric milling system and the traditional models. Finally, by controlling the feed direction angle, the milling parameters in roughing and finishing operations are optimized, and the machining efficiency has been greatly improved on the premise of stable cutting and machining accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a series of deployable mechanisms based on n-UU (universal joint) Wren parallel mechanism (PM) units, which undergo one degree-of-freedom (DOF) Borel-Bricard motion. First, the PM unit is developed into ortho-planar mechanisms by adopting an R-R joint. The link parameters of the mechanism are optimized to maximize the folding ratio while avoiding interference. Then, the optimized PM units are piled up to construct a novel 1DOF multilayer mechanism that has the largest folding ratio among similar structures in the literature. Moreover, polyhedral deployable mechanisms are obtained by connecting the PM unit using U joint or UU joint. Apart from saving space, the polyhedral mechanisms can transform among different shapes of polyhedrons. Finally, variations of the n-UU PM are investigated with different shapes of platforms, and each mechanism has its unique movement characteristics. [ABSTRACT FROM AUTHOR]

Nanoparticle drug delivery better targets neoplastic lesions than free drugs and thus has emerged as a safer form of cancer therapy. Nanoparticle design variables are important determinants of efficacy as they influence the drug biodistribution and pharmacokinetics. Previously, we determined optimal designs through mechanistic modeling and optimization. However, the numerical nature of the tumor model and numerous candidate nanoparticle designs hinder hypothesis generation and treatment personalization. In this paper, we utilize the parallel coordinates technique to visualize high-dimensional optimal solutions and extract correlations between nanoparticle design and treatment outcomes. We found that at optimality, two major design variables are dependent, and thus the optimization problem can be reduced. In addition, we obtained an analytical relationship between optimal nanoparticle sizes and optimal distribution, which could facilitate the utilization of tumors models in preclinical studies. Our approach has simplified the results of the previously integrated modeling and optimization framework developed for nanotherapy and enhanced the interpretation and utilization of findings. Integrated mathematical frameworks are increasing in the medical field, and our method can be applied outside nanotherapy to facilitate the clinical translation of computational methods. [ABSTRACT FROM AUTHOR]

Capacitor discharge (CD) stud welding is a common and fast connection technology. This paper presents an experimental and simulation study of the CD stud weld joint of 20 carbon steel plates. An optimized stud welding process was proposed based on microstructure, microhardness, and residual stresses of CD stud welded joint. The results show that a narrow weld seam with a widmanstaten structure is formed because of quick cooling. For the longer stud extension length, the width of the weld zone becomes wider and the microstructure becomes more uniform. With the increase of welding voltage and stud extension length, the microhardness increases and then decreases. The residual stresses are increased with the increase of welding voltage, while they are decreased with the increase of stud extension length. The optimized welding voltage and stud extension length should be designed to 90 V and 5 mm, respectively. This study will provide a great significance to the stud welding on site. [ABSTRACT FROM AUTHOR]

This work considers the cooperative trajectory-planning problem along a double lane change scenario for autonomous driving. In this paper, we develop two frameworks to solve this problem based on distributed model predictive control (MPC). The first approach solves a single nonlinear MPC problem. The general idea is to introduce a collision cost function in the optimization problem at the planning task to achieve a smooth and bounded collision function, and thus to prevent the need to implement tight hard constraints. The second method uses a hierarchical scheme with two main units: a trajectory-planning layer based on mixed-integer quadratic program (MIQP) computes an on-line collision-free trajectory using simplified motion dynamics, and a tracking controller unit to follow the trajectory from the higher level using the nonlinear vehicle model. Connected and automated vehicles (CAVs) sharing their planned trajectories lay the foundation of the cooperative behavior. In the tests and evaluation of the proposed methodologies, matlab-carsim cosimulation is utilized. carsim provides the high-fidelity model for the multibody vehicle dynamics. matlab-carsim conjoint simulation experiments compare both approaches for a cooperative double lane change maneuver of two vehicles moving along a one-way three-lane road with obstacles. [ABSTRACT FROM AUTHOR]

Geometrical variation is one of the sources of quality issues in a product. Spot welding is an operation that impacts the final geometrical variation of a sheet metal assembly considerably. Evaluating the outcome of the assembly, considering the existing geometrical variation between the components, can be achieved using the method of influence coefficients (MICs), based on the finite element method (FEM). The sequence with which the spot welding operation is performed influences the final geometrical deformations of the assembly. Finding the optimal sequence that results in the minimum geometrical deformation is a combinatorial problem that is experimentally and computationally expensive. Traditionally, spot welding sequence optimization strategies have been to simulate the geometrical variation of the spot-welded assembly after the assembly has been positioned in an inspection fixture. In this approach, the calculation of deformation after springback is one of the most time-consuming steps. In this paper, a method is proposed where the springback calculation in the inspection fixture is bypassed during the sequence evaluation. The results show a significant correlation between the proposed method of weld relative displacements evaluation in the assembly fixture and the assembly deformation in the inspection fixture. Evaluating the relative weld displacement makes each assembly simulation less time-consuming, and thereby, sequence optimization time can be reduced by up to 30%, compared to the traditional approach. [ABSTRACT FROM AUTHOR]