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Design of next-generation membranes requires a nanoscopic understanding of the effect of biologically inspired heterogeneous surface chemistries and topologies (roughness) on local water and solute behavior. In particular, the rejection of small, neutral solutes, such as boric acid, poses a heretofore unsolved challenge. In prior work, a computational inverse design technique using an evolutionary optimization successfully uncovered new surface design strategies for optimized transport of water over solutes in smooth, model pores consisting of two surface chemistries. However, extending such an approach to more complex (and realistic) scenarios involving many surface chemistries as well as surface roughness is challenging due to the expanded design space. In this work, we develop a new approach that uses active learning to optimize in a reduced feature space of surface group interactions, finding parameters that lead to their assembly into ordered, optimal patterns. This approach rapidly identifies novel surface functionalizations that maximize the difference in water and boric acid transport through the nanopore. Moreover, we find that the roughness of the nanopore wall, independent of its chemistry, can be leveraged to enhance transport selectivity: oscillations in the pore wall diameter optimally inhibit boric acid transport by creating energetic wells from which the solute must escape to transport down the pore. This proof-of-concept demonstrates the potential for active learning strategies, in concert with molecular simulations, to rapidly navigate complex design spaces of aqueous interfaces and is promising as a tool for engineering water-mediated surface interactions for a broad range of applications. [ABSTRACT FROM AUTHOR]

Directional fluid flow in perivascular spaces surrounding cerebral arteries is hypothesized to play a key role in brain solute transport and clearance. While various drivers for a pulsatile flow, such as cardiac or respiratory pulsations, are well quantified, the question remains as to which mechanisms could induce a directional flow within physiological regimes. To address this question, we develop theoretical and numerical reduced-order models to quantify the directional (net) flow induceable by peristaltic pumping in periarterial networks. Each periarterial element is modeled as a slender annular space bounded internally by a circular tube supporting a periodic traveling (peristaltic) wave. Under reasonable assumptions of a small Reynolds number flow, small radii, and small-amplitude peristaltic waves, we use lubrication theory and regular perturbation methods to derive theoretical expressions for the directional net flow and pressure distribution in the perivascular network. The reduced model is used to derive closed-form analytical expressions for the net flow for simple network configurations of interest, including single elements, two elements in tandem, and a three element bifurcation, with results compared with numerical predictions. In particular, we provide a computable theoretical estimate of the net flow induced by peristaltic motion in perivascular networks as a function of physiological parameters, notably, wave length, frequency, amplitude, and perivascular dimensions. Quantifying the maximal net flow for specific physiological regimes, we find that vasomotion may induce net pial periarterial flow velocities on the order of a few to tens of μ m/s and that sleep-related changes in vasomotion pulsatility may drive a threefold flow increase. [ABSTRACT FROM AUTHOR]

The innovative combination of additive manufacturing (AM) and continuous fiber‐reinforced polymer composites (CFRPCs) confers products with the dual advantages of integrated manufacturing and designability of properties, but lack an efficient and reliable method for property prediction. This study presents a materials informatics framework using reduced‐order models and machine learning (ML) to extract the structure–property (SP) linkages between microstructures and macroscopic elastic properties of AM‐CFRPCs. The initial step involves generating microstructural 2D cross‐sections and representative volume elements (RVEs) with random fiber and pore distributions based on the minimum potential method. Then, finite element (FE) calculations are performed on RVEs to obtain nine macroscopic elastic properties. Following that, the quantification and dimensionality reduction of the 2D cross‐sectional dataset are conducted separately using two‐point spatial correlations and principal component analysis (PCA). Finally, a Bayesian optimized composite kernel support vector regression (CK‐SVR) algorithm is used to effectively establish complex mapping relationships between the reduced‐order representations of the microstructures and the mechanical properties. Despite the reduced‐order dataset containing only 3–6 variables, the framework generates an interpretable model exhibiting excellent accuracy with all predicted R2 values surpassing 0.91. Therefore, this framework presents a prospective solution for expediting the design and optimization of AM‐CFRPCs. Highlights: A materials informatics scheme is proposed to predict the 9 elastic properties of AM‐CFRPCs.Microstructures are quantified and dimensionally reduced by two‐point statistics and PCA.SP linkages are established between 2D cross‐sections and 3D macromechanical properties.Modified CK‐SVR exhibits higher prediction accuracy compared to conventional models. [ABSTRACT FROM AUTHOR]

Most models employed for fluid forces in drill-string dynamics are of reduced order. The simplified nature of these models often fails to describe the complex behavior of the fluid force, in particular, when the drill-string movement is non-trivial or even when non-Newtonian behavior is predominant. In this work, the fluid forces are estimated by modeling the dynamic of the drilling fluid for both Newtonian and non-Newtonian fluids, and compared with reduced-order models. To achieve it, the lattice-Boltzmann method is implemented to solve the fluid model, while prescribed movements are set for the tool-joint. With the obtained fields, the forces are calculated and compared with recurrent models. Finally, it is observed that some models are capable of describing the interaction as long as the dynamic of the tool-joint is simple. In the presence of other trajectories--e.g., bouncing and chaotic--the models fail to describe the details of the dynamic. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, affecting over 1% of the population. It is usually triggered by irregular electrical impulses that cause the atria to contract irregularly and ineffectively. It increases blood stasis and the risk of thrombus formation within the left atrial appendage (LAA) and aggravates adverse atrial remodeling. Despite recent efforts, LAA flow patterns representative of AF conditions and their association with LAA stasis remain poorly characterized. To develop reduced-order data-driven models of LAA flow patterns during atrial remodeling in order to uncover flow disturbances concurrent with LAA stasis that could add granularity to clinical decision criteria. We combined a geometric data augmentation process with projection of results from 180 CFD atrial simulations on a universal LAA coordinate (ULAAC) system. The projection approach enhances data visualization and facilitates direct comparison between different anatomical and functional states. ULAAC projections were used as input for a proper orthogonal decomposition (POD) algorithm to build reduced-order models of hemodynamic metrics, extracting flow characteristics associated with AF and non-AF anatomies. We verified that the ULAAC system provides an adequate representation to visualize data distributions on the LAA surface and to build POD-based reduced-order models. These models revealed significant differences in LAA flow patterns for atrial geometries that underwent adverse atrial remodeling and experienced elevated blood stasis. Together with anatomical morphing-based patient-specific data augmentation, this approach could facilitate data-driven analyses to identify flow features associated with thrombosis risk due to atrial remodeling. • Novel Universal Left Atrial Appendage Coordinates (ULAAC) system. • Assessing the influence of geometric and flow parameters on LAA blood stasis. • Developing reduced-order data-driven models of hemodynamic metrics. • Identification of non-AF features by proper orthogonal decomposition (POD). [ABSTRACT FROM AUTHOR]

This article introduces the concept of Pulley-Driven Clustered Tensegrity Structures (PD-CTS) and develops nonlinear and linearized static and dynamic equations using the Lagrangian method. The generalized coordinates utilized in this framework comprise the nodal coordinates of the tensegrity structure and the string sliding distances. The governing equations are specifically constructed, considering situations with and without boundary constraints. The developed method can be applied to nonlinear and linearized static and dynamic analysis of any PD-CTS, covering various research domains such as load analysis, form-finding, large deformation analysis, modal analysis, and dynamic response to various inputs. The substructure method is used to lower the order of the equations to reduce computational costs and facilitate the study of actuation strategies of tensegrity structures. The effectiveness of the suggested methods is validated through the analysis of two case studies: a 2D T-bar and a 3D tensegrity tower. Furthermore, the methodologies established in this paper can also be employed to examine deployable cable net structures and pulley-driven robotic systems. • The concept of pulley-driven clustered tensegrity structures (PD-CTS) is proposed. • Nodal coordinates and sliding distances of cables are the generalized coordinates. • Explicit equations of nonlinear & linearized statics and dynamics are derived. • Substructure method is used to study the reduced-order model & actuation strategy. [ABSTRACT FROM AUTHOR]

This paper considers model order reduction based on low-rank decomposition of the cross Gramian for linear systems. The proposed approach uses the low-rank factors of the cross Gramian to generate approximate balanced model for the large-scale system. Then, the reduced-order model is obtained by truncating the states corresponding to the small approximate Hankel singular values. The low-rank factors are directly constructed from the expansion coefficients of impulse responses in the space spanned by Legendre polynomials by solving block tridiagonal linear systems. In contrast to balanced truncation related approaches which require to solve Sylvester equation to compute the full cross Gramian, the proposed method needs to solve sparse linear equations, and only one singular value decomposition is applied to a low-dimensional matrix. The stability preservation of the reduced model is briefly discussed. And in combination with the dominant subspace projection method, the reduction procedure is modified to alleviate the shortcoming, which may unexpectedly lead to unstable systems even though the original one is stable. Finally, numerical experiments are given to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

As the percentage of renewable energy source within the energy production mix has expanded, it is become gradually difficult to determine the appropriate values for controller gains and model parameters. Controlling system frequency of interconnected power system during sudden load disturbance requires careful consideration of the controller gain values and small signal stability model parameters. This study proposes an innovative methodology for ascertaining controller gain values and model parameters for plug-in electric vehicles (PEVs) in load frequency control (LFC) applications. Proposed method uses the root locus (RL) approach to find the suitable controller gain values and model parameters for PEVs. This paper offers a thorough mathematical description of the proposed RL approach. Routh approximation method is used for reduced-order modelling (ROM), which comprises thermal and PEV systems, to reduce complexity of higher-order system while designing controllers. Fractional-order proportional-integral-derivative controller (FO-PID) is proposed, and its parameters are adjusted using particle swarm optimization (PSO) tool. To validate the efficacy of suggested method, a comprehensive comparison of time response parameters and performance indices (PI) is carefully carried out. Also, the various PEVs state of charge (SOC) levels is investigated, and effects of these levels are studied in LFC with robustness analysis of controller. The proposed method for determining gain value is highly reliable and efficient, outperforming existing methodologies in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a novel order reduction technique is suggested for constructing an accurate and less complex reduced order controller of a higher order system model. This reduction strategy is based on a mixed integrated Routh and differential approach that can produce the reduced-order model with the same stability properties as the real system. In this procedure, a simplified Automatic Voltage Regulator (AVR) plant's reduced transfer function (RTF) is created by reducing the numerator and denominator using a mixed Routh-differential technique. A controller for the large-order system is subsequently developed using the calculated lower-order model. The higher order AVR system's PID controller is developed using the lower-order model of the original system. Different case studies are used to study both the original higher order and the newly designed lower order AVR systems with and without the reduced controller. With the use of step responses, various error indices, time-domain responses, and sinusoidal input, the models' performances for each of the case studies are assessed. Additionally, the proposed reduced order model and developed controller's efficacy and accuracy are contrasted with those of other existing methods. [ABSTRACT FROM AUTHOR]

It is known that the widely studied Bray–Liebhafsky reaction typically exhibits complex chemical behaviour. Numerous mathematical systems have been proposed to describe the iodine oscillations that occur during this process. Recently, a four-variable model of the Bray–Liebhafsky reaction has been proposed and analytical and numerical investigations suggested that chaotic solutions may exist. We revisit this four-variable model here and perform what appears to be the first detailed work on this system. We suggest that this model is perhaps not chaotic after all. Informed by these fresh insights, we propose a reduced two-variable model based upon the four-variable system. This model is created with the twin goals of enabling simpler mathematical analysis while retaining the underlying chemical mechanisms. We are able to demonstrate that our reduced problem performs very well when compared with the full model for realistic parameter values. In particular, key regions of parameter space are identified within which temporal oscillations can occur. Moreover, these persistent oscillations are consistent with the available qualitative experimental observations. [ABSTRACT FROM AUTHOR]

The licensing of new reactors implies the use of verified and validated neutronic codes. Numerical validation can rely on sensitivity and uncertainty studies, but they require repeated execution of time-consuming neutron flux and depletion calculations. The computational costs can be reduced by using perturbation theories. However, the uncoupled Depletion Perturbation Theory is restricted to single integral values such as nuclide density. Relying on reduced-basis approaches, which reconstruct all nuclide densities at once, is one way to get around this restriction. Furthermore, the adjoint-based reduced-order model uses the direct and adjoint equations for projection. For diffusion or transport calculations, the Exact-to-Precision Generalized Perturbation Theory was developed. Still, no models for depletion calculations are readily available. Therefore, this paper describes a novel adjoint-based reduced-order model for the Bateman Equation. It uses a range-finding algorithm to create the basis and the uncoupled Depletion Perturbation Theory for the reconstruction of the first order replaced by with a first order formulation. Our paper shows that for several perturbed cases, the depletion reduced-order model successfully reconstructs the nuclide densities. As a result, this serves as a proof of concept for our adjoint-based reduced-order model, which can perform sensitivity and uncertainty burn-up analysis in a shorter time. [ABSTRACT FROM AUTHOR]

We present convergence estimates of two types of greedy algorithms in terms of the entropy numbers of underlying compact sets. In the first part, we measure the error of a standard greedy reduced basis method for parametric PDEs by the entropy numbers of the solution manifold in Banach spaces. This contrasts with the classical analysis based on the Kolmogorov n -widths and enables us to obtain direct comparisons between the algorithm error and the entropy numbers, where the multiplicative constants are explicit and simple. The entropy-based convergence estimate is sharp and improves upon the classical width-based analysis of reduced basis methods for elliptic model problems. In the second part, we derive a novel and simple convergence analysis of the classical orthogonal greedy algorithm for nonlinear dictionary approximation using the entropy numbers of the symmetric convex hull of the dictionary. This also improves upon existing results by giving a direct comparison between the algorithm error and the entropy numbers. [ABSTRACT FROM AUTHOR]

Aeroelastic flutter is a dynamically complex phenomenon that has adverse and unstable effects on elastic structures. It is crucial to better predict the phenomenon of flutter within the scope of aircraft structures to improve the design of their wings. This review aims to establish fundamental guidelines for flutter analysis across subsonic, transonic, supersonic, and hypersonic flow regimes, providing a thorough overview of established analytical, numerical, and reduced-order models as applicable to each flow regime. The review will shed light on the limitations and missing components within the previous literature on these flow regimes by highlighting the challenges involved in simulating flutter. In addition, popular methods that employ the aforementioned analyses for optimizing wing structures under the effects of flutter--a subject currently garnering significant research attention--are also discussed. Our discussion offers new perspectives that encourage collaborative effort in the area of computational methods for flutter prediction and optimization. [ABSTRACT FROM AUTHOR]

The purpose of this research is to establish the generalised fractional Bloch-Torrey equation for better simulating anomalous diffusion in heterogeneous biological tissues. The introduction of the distributed-order time fractional derivative allows for an improved interpretation of the complex diffusion behaviours with multi-scale effects. The use of variable coefficients in the model increases its applicability for describing the spatial heterogeneity evident in the cellular structures. The proposed distributed-order time and space fractional Bloch-Torrey equation is discretised in time and space by the L 2-1 σ formula and the finite element method, respectively. The stability and convergence analyses of these numerical methods are provided. To further improve the computational efficiency, a reduced-order extrapolation scheme is developed. We verify the effectiveness of the proposed methods by numerical examples. Moreover, the coupled fractional dynamic system solution behaviour is explored on a human brain-like domain divided into the white matter and grey matter regions. Compared with the model having constant coefficients, solution behaviours suggest that variable diffusion coefficients offer an effective way to differentiate the distinct diffusion phenomena evolving in different tissue micro-environments. Furthermore, to evaluate the impacts of the weight function in the distributed-order operator, we choose three types of beta distributions with the same mean but different values of the variance. The results indicate that the larger value of the variance leads to a more remarkable fluctuation and a slower decay of the transverse magnetisation. This generalised distributed-order fractional model may provide further insights into capturing anomalous diffusion in heterogeneous media. • A distributed-order time-space FBTE with variable coefficients is developed. • The fractional dynamic system is solved by the L 2 − 1 σ formula and FEM. • The stability and convergence of numerical methods are provided. • An improved fast algorithm reduces the computational cost. • This research provides more insights into capturing the diffusion complexity. [ABSTRACT FROM AUTHOR]

This paper deals with the constrained state regulation problem (CSRP) of descriptor fractional-order linear continuous-time systems (DFOLCS) with order 0 < α < 1 . The objective is to establish the existence of conditions for a linear feedback control law within state constraints and to propose a method for solving the CSRP of DFOLCS. First, based on the decomposition and separation method and coordinate transformation, the DFOLCS can be transformed into an equivalent fractional-order reduced system; hence, the CSRP of the DFOLCS is equivalent to the CSRP of the reduced system. By means of positive invariant sets theory, Lyapunov stability theory, and some mathematical techniques, necessary and sufficient conditions for the polyhedral positive invariant set of the equivalent reduced system are presented. Models and corresponding algorithms for solving the CSRP of a linear feedback controller are also presented by the obtained conditions. Under the condition that the resulting closed system is positive, the given model of the CSRP in this paper for the DFOLCS is formulated as nonlinear programming with a linear objective function and quadratic mixed constraints. Two numerical examples illustrate the proposed method. [ABSTRACT FROM AUTHOR]

This work is concerned with an adaptive reduced order model of modular structures assembled from parameter-dependent substructures. The substructures are reduced by proper orthogonal decomposition (POD) and connected by means of a tied contact formulation. We present a method to adapt the matrices of the substructures to parameter changes. We employ interpolation on Grassmann manifolds for the parametric adaption of the projection matrices. For the adaptation of the stiffness matrices, we use the direct empirical interpolation method (DEIM). Manifold interpolation of the reduced stiffness matrices, cannot be applied here since it would require semi-positive definiteness, which is here not fulfilled because of necessary rigid body motion modes. The novelty of this work is the application of these interpolation methods to the special problem class of POD-based tied contact model order reduction. Furthermore, we show a methodology to compute significant snapshots on the substructure level to compute a POD basis that can be used in different global structures. [ABSTRACT FROM AUTHOR]

The real-time computation of a three-dimensional pipe network flow is crucial for both pipe design and operational maintenance. This study devises a novel reduced-order configuration approach that combines the advantages of the acceleration characteristics of the reduced-order model and the structural applicability of the configuration model. First, a configuration model is established by categorizing sub-pipes extracted from a pipe network into sets based on the sub-pipes' type. Subsequently, reduced-order configurations are realized by a reduced-order model established for each type of configuration, enabling real-time computation of individual sub-pipes. Thus, the concatenation of sub-pipes allows the computation of an entire pipe network. A complex boundary–deep learning–reduced-order configuration model and a complex boundary–deep learning–reduced-order configuration–multi-source data–reduced-order configuration model integrated with a local multi-physical–discrete empirical interpolation method and a multi-source data fusion model are devised. These models were employed for the real-time computation and prediction of a three-dimensional velocity field for 300 snapshots composed of one to four sub-pipes extrapolated from a dataset of 294 pipe network snapshots composed of one to three sub-pipes. The maximum relative errors for snapshots from the dataset were similar to the limit precision of the proper orthogonal decomposition, with more precise accuracy than the relevant studies, indicating the excellent performance of our reduced-order configuration approach. [ABSTRACT FROM AUTHOR]

The fuel rod bundles are the core part of pressurized water reactors (PWRs), and its heat transfer characteristics directly impact the safety of PWRs. A computational fluid dynamics (CFD) model of 5 × 5 fuel rod bundles with a spacer grid is established, and the numerical simulation results are in excellent agreement with the experimental results. Then, the effects of four turbulence models, namely shear stress transport model, standard k–ε model, re-normalization group k–ε model, and realizable k–ε model on the thermal-hydraulic characteristics of the 5 × 5 fuel rod bundles are systematically investigated. Furthermore, two data-driven methods, namely proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), are used to analyze the flow fields of the 5 × 5 fuel rod bundles. The two methods can extract key modes or features to enhance the comprehension and description of the dynamic behaviors within the flow fields of 5 × 5 fuel rod bundles. Finally, two reduced-order models (ROMs), called the POD-radial basis function neural network surrogate model and DMD method, are constructed, which can enable rapid prediction of the flow fields for 5 × 5 fuel rod bundles with high accuracy. The CFD simulation results presented in this paper can provide valuable insights for studying the thermal-hydraulic characteristics of the 5 × 5 fuel rod bundles. The two ROMs proposed in this paper can significantly reduce the computational costs associated with studying the thermal-hydraulic characteristics of 5 × 5 fuel rod bundles. [ABSTRACT FROM AUTHOR]

Selective laser melting (SLM) technology has the advantage of quickly producing complex-shaped parts. To achieve good mechanical properties, it's vital to minimize defects that can occur because of high residual porosity if incorrect processing techniques are used. One effective way to prevent defects is by using computer simulations of underlying processes before printing in the industry. This paper presents a reduced-order numerical model of SLM processing that accurately predicts material porosity by focusing on the key mechanisms that affect the melting and consolidation processes. The focus is on the formation of defects and the expected time that is required until the consolidation of a powder bed is completed. Then the elasticity of the SLM processed materials near defects is analyzed. The modeling results for powder consolidation are shown for comparison with experimental data on stainless steel 316L powder during SLM. This information can be further used for proper selection of SLM parameters such as the scanning speed and the power of the laser source. [ABSTRACT FROM AUTHOR]

We develop a two-timing perturbation analysis to provide quantitative insights on the existence of temporal ratchets in an exemplary system of a particle moving in a tank of fluid in response to an external vibration of the tank. We consider two-mode vibrations with angular frequencies ω and α ω , where α is a rational number. If α is a ratio of odd and even integers (e.g., 2 1 , 3 2 , 4 3 ), the system yields a net response: here, a nonzero time-average particle velocity. Our first-order perturbation solution predicts the existence of temporal ratchets for α = 2 . Furthermore, we demonstrate, for a reduced model, that the temporal ratcheting effect for α = 3 2 and 4 3 appears at the third-order perturbation solution. More importantly, we find closed-form formulas for the magnitude and direction of the induced net velocities for these α values. On a broader scale, our methodology offers a new mathematical approach to study the complicated nature of temporal ratchets in physical systems. [ABSTRACT FROM AUTHOR]

This paper presents an interpolatory framework for model order reduction of linear time-invariant (LTI) systems over limited time intervals of the form $ [0,\tau ] $ [ 0 , τ ] , with $ \tau \lt \infty $ τ < ∞. We give a new proof for deriving interpolation-based first-order necessary conditions for time-limited $ H_2 $ H 2 optimality. Based on these optimality conditions, we propose a time-limited rational Krylov framework for time-limited rational interpolation. The interpolatory framework is used to present an iterative algorithm that yields reduced-order models satisfying the optimality conditions approximately. The distance to optimality is quantified in terms of the interpolation errors. The errors depend primarily on the interpolating model's poles and the time interval size. We test the proposed algorithm in three numerical examples and compare its performance with various time-limited model reduction algorithms available in the literature. [ABSTRACT FROM AUTHOR]

Conceptual design studies performed with reduced-order approaches allow feasibility and sizing considerations of air-breathing engines to be configured at an affordable computational cost. The present study is devoted to exploring the design space of a dual-mode ramjet engine operating in scramjet mode by means of reduced-order analysis to assess the effects of propulsive system design configurations on component level and overall performance characteristics. The approach proposed in this work combines axisymmetric flow configuration used for the design of supersonic/hypersonic intakes and solutions of one-dimensional flow governing equations coupled with finite-rate chemistry and thermophysical properties tables in the numerical domains of the combustor and nozzle components. The scramjet design space is generated by varying parameters which are flight Mach number and altitude, intake truncation angle, intake exit Mach number and equivalence ratio. Performance outputs of total pressure recovery factor, compression ratio, captured air mass flow rate, intake startability index, thrust, specific impulse, fuel consumption and overall efficiency are computed for each design scenario. The generated database is visualized via performance maps and analyzed in terms of propulsive characteristics. A feature importance study is also conducted to quantify the effects of design parameters on the propulsive performance. • Reduced-order modeling of propulsion systems decreases the complexity of analysis. • Scramjet design space consists of thermal choking, non-ignition, and propulsive zones. • Uninstalled thrust strongly depends on combustor inlet and cruise Mach numbers. • Total pressure recovery is the dominant factor dictating the intake efficiency. • Low speed flow at burner inlet yields autoignition but may cause thermal choking. [ABSTRACT FROM AUTHOR]

Neutron flux modal decomposition is a key tool for analytical and reduced order modeling of boiling water reactor (BWR) stability and oscillations. As a minimum, the fundamental flux mode is used for representing global oscillations while the addition of at least one azimuthal harmonic is needed for simulating the regional out-of-phase mode. Unlike the fundamental and first azimuthal modes, the excitation of an axial flux mode alters the axial power shape but not the total power in the channel and therefore cannot be self-sustained when coupled to density wave–generated reactivity, presumably explaining why it has not been explicitly included in previously published models. Although not self-sustained, the axial mode excitation driven by density wave propagation and interactions with other spatial modes play important roles in interpreting observed BWR stability and oscillations particularly in the nonlinear regime when the oscillation magnitude is large. In this paper, the characteristics of the steady-state axial modes are presented, and their impact on oscillation dynamics for small and large amplitudes of both the global and the regional oscillations is studied using reduced order analytical tools. Aside from the oscillating component, our research results identify an average nonzero axial mode component to develop during limit cycle oscillations that causes the average axial power profile to shift toward the bottom of the core and thus contributes a negative reactivity component. The emergence of this nonzero average axial mode component and the associated negative reactivity were found to diminish the power increase due to global mode power oscillations and contribute to nonlinear stabilization of regional oscillations. The physical interpretation of nonlinear power oscillations with the inclusion of the axial mode component resolves previously unexplained results obtained from high-fidelity numerical models. [ABSTRACT FROM AUTHOR]

In this paper, a non-intrusive reduced-order model (ROM) for parametric reactor kinetics simulations is presented. Time-dependent ROMs are notoriously data intensive and difficult to implement when nonlinear multiphysics phenomena are considered. These challenges are exacerbated when parametric dependencies are included. The proper orthogonal decomposition mode coefficient interpolation (POD-MCI) ROM presented in this work can be constructed directly from lower-dimensional full-order model (FOM) outputs and is independent of the underlying model. This greatly alleviates the data requirement of many existing ROMs and can be used without modification on arbitrarily complex models or experimental data. The POD-MCI ROM is demonstrated on a number of examples and yields accurate characterizations of the parametric behaviors of both FOM outputs and derived quantities of interest within the selected parameter spaces, at extremely attractive computational speedup factors relative to FOMs. [ABSTRACT FROM AUTHOR]

This work constructs a rovibrational state-to-state model for the O2 + O2 system leveraging high-fidelity potential energy surfaces and quasi-classical trajectory calculations. The model is used to investigate internal energy transfer and nonequilibrium reactive processes in a dissociating environment using a master equation approach, whereby the kinetics of each internal rovibrational state is explicitly computed. To cope with the exponentially large number of elementary processes that characterize reactive bimolecular collisions, the internal states of the collision partner are assumed to follow a Boltzmann distribution at a prescribed internal temperature. This procedure makes the problem tractable, reducing the computational cost to a comparable scale with the O2 + O system. The constructed rovibrational-specific kinetic database covers the temperature range of 7500–20 000 K. The reaction rate coefficients included in the database are parameterized in the function of kinetic and internal temperatures. Analysis of the energy transfer and dissociation process in isochoric and isothermal conditions reveals that significant departure from the equilibrium Boltzmann distribution occurs during the energy transfer and dissociation phase. Comparing the population distribution of the O2 molecules against the O2 + O case demonstrates a more significant extent of nonequilibrium characterized by a more diffuse distribution whereby the vibrational strands are more clearly identifiable. This is partly due to less efficient mixing of the rovibrational states, which results in more diffuse rovibrational distributions in the quasi-steady-state distribution of O2 + O2. A master equation analysis for the combined O2 + O and O2 + O2 system reveals that the O2 + O2 system governs the early stage of energy transfer, whereas the O2 + O system takes control of the dissociation dynamics. The findings of the present work will provide a strong physical foundation that can be exploited to construct an improved reduced-order model for oxygen chemistry. [ABSTRACT FROM AUTHOR]

The concentration of residual O2 in oxy-fuel combustion flue gas needs to be reduced before CO2 transportation, utilization, or storage. An original application of the printed circuit heat exchanger (PCHE) for catalytic combustion with natural gas (catalytic deoxygenation) is described for reducing the residual O2 concentration. The PCHE design features multiple adiabatic packed beds with interstage cooling and fuel injection, allowing precise control over the reaction extent and temperature within each reaction stage through the manipulation of fuel and utility flow rates. This work describes the design of a PCHE for methane–oxygen catalytic combustion where the catalyst loading is minimized while reducing the O2 concentration from 3 vol% to 100 ppmv, considering a maximum adiabatic temperature rise of 50 °C per stage. Each PCHE design differs by the number of reaction stages and its individual bed lengths. As part of the design process, a one-dimensional transient reduced-order reactor model (1D ROM) was developed and compared to temperature and species concentration axial profiles from 3D CFD simulations. The final design consists of five reaction stages and four heat exchanger sections, providing a PCHE length of 1.09 m at a processing rate of 12.3 kg/s flue gas per m3 PCHE. [ABSTRACT FROM AUTHOR]

It is observed that the reduced-order models (ROMs) by some existing limited Gramians-based techniques deviate significantly from the high-order model, resulting in a large approximation inaccuracy. Therefore, this paper introduces a novel solution for finite-frequency model reduction using new frequency-weighted Gramians by employing balanced truncation. This work provides a novel structure of fictitious input and output matrices, resulting in the proposed continuous-time system Gramians. The suggested strategy provides stable ROMs even when input and output weightings are incorporated into the system. Furthermore, the proposed approach is extended with the frequency-interval Gramians, ensuring stable ROMs. The proposed simulation outcomes are compared with other well-known frequency-weighted and frequency-interval Gramians-based techniques using numerical examples to demonstrate the efficacy of the suggested strategies. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the topic of model order reduction (MOR) for coupled systems. At first, an approximation via Laguerre polynomials expansions to controllability and observability gramians for such systems are presented, which provides a low-rank decomposition form whose factors are constructed from a recurrence formula instead of Lyapunov equations. Then, in combination of balanced truncation and dominant subspace projection method, a series of MOR algorithms are proposed that preserve the coupled structures. What's more, some main properties of reduced-order models, such as stability preservation, are well discussed. Finally, three numerical simulations are provided to illustrate the effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

A reduced-order model based on the proper orthogonal decomposition (POD) method is used to assess whether it can predict the highly unsteady flow in turbomachinery cascades. Three cases are considered: a transonic fan rotor called the National Aeronautics and Space Administration Rotor 67, a cascade of oscillating compressor blades called the 10th Standard Configuration, and a cascade of oscillating turbine blades called the 11th Standard Configuration. The governing equations used in this POD model, called zeta-POD, are written using the specific volume instead of density. This formulation of the governing equations allows the precomputation of the coefficients of the system of ordinary differential equations prior the flow simulation. Consequently, a significant speedup is obtained compared to the full-order model and a traditional, density-based POD. In addition, the accuracy of the zeta-POD model is compared against the full-order model and the density-based POD. [ABSTRACT FROM AUTHOR]

Reduced-order models (ROMs) have been extensively employed to understand complex systems efficiently and adequately. In this study, a novel parametric ROM framework is developed to produce Eulerian–Lagrangian simulations. This study employs two typical parametric strategies to reproduce the physical phenomena of a gas–solid flow by predicting the adequate dynamics of modal coefficients in the ROM: (i) based on the radial-basis function (RBF) interpolation, termed ROM-RBF and (ii) based on a long–short term memory (LSTM) neural network, termed ROM-LSTM. In the ROM, an advanced technique, namely, Lanczos-based proper orthogonal decomposition (LPOD), is employed to efficiently transform numerical snapshots into the modal coefficients. Validation tests are conducted in a typical gas–solid flow system such as a spouted bed. The coherent structures of the gas–solid flows are shown to be captured by the LPOD technique. Besides, in comparison with the high-fidelity simulations, our proposed ROMs are shown to simulate the gas–solid flows by significantly reducing the calculation time by several orders of magnitude and faithfully predicting the macroscopic properties. In particular, compared to the ROM-RBF, the ROM-LSTM can capture the flow fields more accurately within the gas–solid flows. [ABSTRACT FROM AUTHOR]

Two-body aerodynamic analysis is crucial whenever there is a store that separates from its parent body, i.e., the aircraft. Any time that a new aircraft is developed or an existing one undergoes some modification with its associated store, it has to undergo a meticulous analysis to predict the path of the separated store across a range of operating parameters (freestream conditions). The present work demonstrates an efficient albeit approximate semi-empirical technique that employs a reduced-order model based on proper orthogonal decomposition in conjunction with a multi-domain-decomposition approach for predicting the flow field around a store-aircraft dyad. Encouraging preliminary results are obtained in the verification that is pursued on a two-dimensional problem for simplicity. The approach can be readily extended to three-dimensional problems as well. [ABSTRACT FROM AUTHOR]

Pressure gain combustion cycles are under the spotlight due to their higher theoretical cycle thermal efficiency compared to conventional machines. Under this prism, a constant-volume combustor (CVC) prototype supplied with a mixture of air and liquid iso–octane was developed. The efforts of the current study were focused on both creating a 1D model of the experimental test rig for the CVC analysis and a 3D numerical simulation of the exhaust system. The goal of the study was to retrieve the total outlet quantities of the combustor, which would otherwise be difficult to assess experimentally, and to investigate the pulsating flow field at the outlet. First, a thorough description of the reduced order model was accompanied with the model's validation using the available experimental data of the chamber. Then, the resulting outlet stagnation properties of the CVC were imposed as spatially averaged transient boundary conditions to the 3D exhaust flow domain. The unsteady Reynolds–averaged Navier–Stokes equations were solved for a sufficient number of periods, and the assessment of the out-take system in terms of losses and attenuation was conducted. In conclusion, the analysis of the combustor's outflow will pave the way for an effective future design of the CVC exhaust system. [ABSTRACT FROM AUTHOR]

The interaction between streamwise vortices and fuel plumes was investigated through numerical simulations and experiments conducted in a supersonic combustion chamber. Different interactions between vortices and plumes were generated by varying the position of fuel injection on the alternating-wedge struts. This study also involved the development of a reduced-order model to predict the development of fuel plumes and elucidate the mixing mechanism of streamwise vortices. To validate the accuracy of the reduced-order model, Mie scattering and Acetone-PLIF measurements were employed to observe changes in plume morphology downstream of the struts. By utilizing this model, the influence of fuel injection on the development of streamwise vortices can be assessed effectively. Furthermore, the reduced-order model exhibits reasonable predictive capabilities concerning the evolution of streamwise vortices and fuel plumes, thus shedding light on the underlying interaction mechanism between fuel injection and streamwise vortices. • Study on fuel/air mixing mechanism downstream of alternating-wedge struts • Two approaches were employed: optical measurement experiments and a prediction model • The influence of vortex deformation is introduced into the prediction model • The predicted results showed excellent agreement with those measured by the experiment [ABSTRACT FROM AUTHOR]

This paper presents a reduced-order multi-scale simulation approach for nonlinear analysis of structures with local nonlinearities. Using it, the traditional global structural analysis can be changed to that of a reduced-order system where only nodal displacements of nonlinear elements are solved using the Newton–Raphson method in a single iterative procedure. Meanwhile, two nonlinear force/stiffness correctors are introduced based on the stress decomposition to describe material nonlinearities of frame members. In addition, two simulation ways are used to deal with the nonlinearities of the yielding frame members. Finally, the static and transient structural systems are simulated to verify the approach. [ABSTRACT FROM AUTHOR]

We present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the data through a general representation learning problem. The proposed approach is driven by embeddings of low-order polynomial form. A projection onto the nonlinear manifold reveals the algebraic structure of the reduced-space system that governs the problem of interest. The matrix operators of the reduced-order model are then inferred from the data using operator inference. Numerical experiments on a number of nonlinear problems demonstrate the generalizability of the methodology and the increase in accuracy that can be obtained over reduced-order modeling methods that employ a linear subspace approximation. [ABSTRACT FROM AUTHOR]

We develop a novel computational framework to approximate solution operators of evolution partial differential equations (PDEs). By employing a general nonlinear reduced-order model, such as a deep neural network, to approximate the solution of a given PDE, we realize that the evolution of the model parameters is a control problem in the parameter space. Based on this observation, we propose to approximate the solution operator of the PDE by learning the control vector field in the parameter space. From any initial value, this control field can steer the parameter to generate a trajectory such that the corresponding reduced-order model solves the PDE. This allows for substantially reduced computational cost to solve the evolution PDE with arbitrary initial conditions. We also develop comprehensive error analysis for the proposed method when solving a large class of semilinear parabolic PDEs. Numerical experiments on different high-dimensional evolution PDEs with various initial conditions demonstrate the promising results of the proposed method. [ABSTRACT FROM AUTHOR]

A new variable Eddington factor (VEF) model is presented for nonlinear problems of thermal radiative transfer (TRT). The VEF model is data-driven and acts on known (a-priori) radiation-diffusion solutions for material temperatures in the TRT problem. A linear auxiliary problem is constructed for the radiative transfer equation (RTE) whose emission source and opacities are evaluated at these known material temperatures. The solution to this RTE approximates the specific intensity distribution in phase-space and time. It is applied as a shape function to define the Eddington tensor for the presented VEF model. The shape function computed via the auxiliary RTE problem will capture some degree of transport effects within the TRT problem. The VEF moment equations closed with this approximate Eddington tensor will thus carry with them these captured transport effects. In this study, the temperature data comes from multigroup P 1 , P 1 / 3 , and flux-limited diffusion radiative transfer models. The proposed VEF model can be interpreted as a transport-corrected diffusion reduced-order model. Numerical results are presented on the Fleck-Cummings test problem which models a supersonic wavefront of radiation. The VEF model is shown to improve accuracy by 1–2 orders of magnitude compared to the considered radiation-diffusion model solutions to the TRT problem. [ABSTRACT FROM AUTHOR]

The orthogonal experimental method is currently the most commonly used tool for determining an appropriate composition of modified asphalt binders. However, it cannot automatically output a reasonable composition when dealing with multi objective optimization and must go through the process of repeated comparison manually. Obviously, the accuracy and efficiency of the orthogonal method are easily affected by subjective factors, which grow tremendously as the sizes of the experiments and data increase. In order to enrich the design method, the objective of this study is to develop a new framework to determine reasonable compositions of modified asphalt binder automatically, accurately, and efficiently. Hence, a data-driven multiobjective optimization method is established based on the experimental data, reduced order model (ROM), and simplicial homology global optimization (SHGO). The ROM is utilized to characterize the complex relationship between material composition and the corresponding performance. The average relative errors between experimental results and prediction values of the ROM are less than 1.5%, and the maximum relative errors are less than 8.7%. The SHGO is selected to determine the reasonable compositions in the global space. Finally, the properties and costs of the SBS-modified asphalt binders, orthogonal-based modified asphalt binder, and ROM-SHGO-based modified asphalt binder are tested to verify the feasibility of the developed method. The ROM-SHGO-based modified asphalt binder has the lowest cost under the premise of similar performance when taking the cost as the constraint. The developed ROM-SHGO method provides feasible, useful, and promising tools for composition design of modified asphalt binder based on laboratory test data. [ABSTRACT FROM AUTHOR]

In this paper, we construct a reduced-order finite element (ROFE) method holding seldom unknowns for the parabolic optimal control problem. We apply the proper orthogonal decomposition (POD) technique to develop two unsteady systems about state and co-state approximations, which efficiently reduces the number of unknowns and computational costs. Optimal a priori error estimates for the state, co-state and control approximations are derived. Finally, numerical examples are presented to verify that the ROFE method is accurate and efficient for solving the parabolic optimal control problem. [ABSTRACT FROM AUTHOR]

Although strain engineering and soft-clamping techniques for attaining high Q-factors in nanoresonators have received much attention, their impact on nonlinear dynamics is not fully understood. In this study, we show that nonlinearity of high-Q Si3N4 nanomechanical string resonators can be substantially tuned by support design. Through careful engineering of support geometries, we control both stress and mechanical nonlinearities, effectively tuning nonlinear stiffness of two orders of magnitude. Our approach also allows control over the sign of the Duffing constant resulting in nonlinear softening of the mechanical mode that conventionally exhibits hardening behavior. We elucidate the influence of support design on the magnitude and trend of the nonlinearity using both analytical and finite element-based reduced-order models that validate our experimental findings. Our work provides evidence of the role of soft-clamping on the nonlinear dynamic response of nanoresonators, offering an alternative pathway for nullifying or enhancing nonlinearity in a reproducible and passive manner. In the design optimization of resonance frequencies and Q-factor of nanomechanical resonators, the influence of geometric design on the nonlinear dynamics has been rarely investigated. Here, the authors tune the stress field via soft-clamping, simultaneously increasing both the Q-factor and the onset of nonlinearity of a Si3N4 string resonator. [ABSTRACT FROM AUTHOR]

In part I of the article, we demonstrated that a variant of the dynamic mode decomposition (DMD) algorithm based on variable projection optimization, called optimized DMD (OPT-DMD), enables a robust identification of the dominant spatiotemporally coherent modes underlying the data across various test cases representing different physical parameters in an E × B simulation configuration. We emphasized that the OPT-DMD significantly improves the analysis of complex plasma processes, revealing information that cannot be derived using conventionally employed analyses such as the fast Fourier transform. As the OPT-DMD can be constrained to produce stable reduced-order models (ROMs) by construction, in this paper, we extend the application of the OPT-DMD and investigate the capabilities of the linear ROM from this algorithm toward forecasting in time of the plasma dynamics in configurations representative of the radial-azimuthal and axial-azimuthal cross-sections of a Hall thruster and over a range of simulation parameters in each test case. The predictive capacity of the OPT-DMD ROM is assessed primarily in terms of short-term dynamics forecast or, in other words, for large ratios of training-to-test data. However, the utility of the ROM for long-term dynamics forecasting is also presented for an example case in the radial-azimuthal configuration. The model's predictive performance is heterogeneous across various test cases. Nonetheless, a remarkable predictiveness is observed in the test cases that do not exhibit highly transient behaviors. Moreover, in all investigated cases, the error between the ground-truth and the reconstructed data from the OPT-DMD ROM remains bounded over time within both the training and the test window. As a result, despite its limitation in terms of generalized applicability to all plasma conditions, the OPT-DMD is proven as a reliable method to develop low computational cost and highly predictive data-driven ROMs in systems with a quasi-periodic global evolution of the plasma state. [ABSTRACT FROM AUTHOR]

The advent of data-driven/machine-learning based methods and the increase in data available from high-fidelity simulations and experiments has opened new pathways toward realizing reduced-order models for plasma systems that can aid in explaining the complex, multi-dimensional phenomena and enable forecasting and prediction of the systems' behavior. In this two-part article, we evaluate the utility and the generalizability of the dynamic mode decomposition (DMD) algorithm for data-driven analysis and reduced-order modeling of plasma dynamics in cross-field E × B configurations. The DMD algorithm is an interpretable data-driven method that finds a best-fit linear model describing the time evolution of spatiotemporally coherent structures (patterns) in data. We have applied the DMD to extensive high-fidelity datasets generated using a particle-in-cell (PIC) code based on the cost-efficient reduced-order PIC scheme. In this part, we first provide an overview of the concept of DMD and its underpinning proper orthogonal and singular value decomposition methods. Two of the main DMD variants are next introduced. We then present and discuss the results of the DMD application in terms of the identification and extraction of the dominant spatiotemporal modes from high-fidelity data over a range of simulation conditions. We demonstrate that the DMD variant based on variable projection optimization (OPT-DMD) outperforms the basic DMD method in identification of the modes underlying the data, leading to notably more reliable reconstruction of the ground-truth. Furthermore, we show in multiple test cases that the discrete frequency spectrum of OPT-DMD-extracted modes is consistent with the temporal spectrum from the fast Fourier transform of the data. This observation implies that the OPT-DMD augments the conventional spectral analyses by being able to uniquely reveal the spatial structure of the dominant modes in the frequency spectra, thus, yielding more accessible, comprehensive information on the spatiotemporal characteristics of the plasma phenomena. [ABSTRACT FROM AUTHOR]

In the pursuit of rare earth-lean permanent magnets for green technologies, microstructural optimisation offers a promising strategy to enhance coercivity while minimising critical element content. For this approach, the combination of experimental work on hard magnetic films and numerical investigations is necessary. However, computational limitations restrict micromagnetics to small systems, motivating the development of a reduced order model for investigating large multigrain systems. The model is based on the embedded Stoner-Wohlfarth method and is used to investigate the influence of a nonmagnetic grain boundary phase thickness and the aspect ratio of the magnetic grains on the overall coercivity. It is possible to simulate large NdFeB multigrain structures which can be compared to hard magnetic films. We derive design recommendations to increase coercivity by increasing the grain boundary phase thickness and the aspect ratio of the grains in hard magnetic materials. [ABSTRACT FROM AUTHOR]

The present work presents a stable proper orthogonal decomposition (POD)-Galerkin based reduced-order model (ROM) for two-dimensional Rayleigh–Bénard convection in a square geometry for three Rayleigh numbers: 10 4 (steady state), 3 × 10 5 (periodic), and 6 × 10 6 (chaotic). Stability is obtained through a particular (staggered-grid) full-order model (FOM) discretization that leads to a ROM that is pressure-free and has skew-symmetric (energy-conserving) convective terms. This yields long-time stable solutions without requiring stabilizing mechanisms, even outside the training data range. The ROM's stability is validated for the different test cases by investigating the Nusselt and Reynolds number time series and the mean and variance of the vertical temperature profile. In general, these quantities converge to the FOM when increasing the number of modes, and turn out to be a good measure of accuracy. However, for the chaotic case, convergence with increasing numbers of modes is relatively difficult and a high number of modes is required to resolve the low-energy structures that are important for the global dynamics. [ABSTRACT FROM AUTHOR]

To meet the increasing demand for trajectory tracking accuracy and high-efficiency requirements in modern mobile machinery, this paper proposes a practical adaptive robust control method based on the dual-valve parallel electro-hydraulic servo system. Existing tracking control strategies for such servo systems rely on a backstepping method and assume that full states are known, which is stringent in practice. To cope with the problem, we reduce the mathematical model order of the studied system using singular perturbation theory in this paper, and only the position feedback is required for the control implementation. Then, to achieve high accuracy tracking control, a direct adaptive robust control scheme combing with a dynamic flow allocation layer is adopted to synthesize the controller. With this method, parameter uncertainties and load disturbance are rejected, and valve characteristics are considered in the dynamic flow allocation layer to solve the flow redundancy problem, respectively. Convergence of the simplified system tracking results is proved theoretically. Extensive co-simulations and experimental tests are carried out to illustrate the effectiveness of the proposed strategy, both tracking precision and flow distribution can be achieved efficiently by simple parameter adjustments in the field. [ABSTRACT FROM AUTHOR]

Bifurcations organize the dynamics of many natural and engineered systems. They induce qualitative and quantitative changes to a system's dynamics, which can have catastrophic consequences if ignored during design. In this paper, we propose a general computational method to control the local bifurcations of dynamical systems by optimizing design parameters. We define an objective functional that enforces the appearance of local bifurcation points at targeted locations or even encourages their disappearance. The methodology is an efficient alternative to bifurcation tracking techniques capable of handling many design parameters (>10²). The method is demonstrated on a Duffing oscillator featuring a hardening cubic nonlinearity and an autonomous van der Pol-Duffing oscillator coupled to a nonlinear tuned vibration absorber. The finiteelement model of a clamped-free Euler--Bernoulli beam, coupled with a reduced-order modelling technique, is also used to show the extension to the shape optimization of more complicated structures. Results demonstrate that several local bifurcations of various types can be handled simultaneously by the bifurcation control framework, with both parameter and state target values. [ABSTRACT FROM AUTHOR]

High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly limit the number of parameter configurations considered and/or time window evaluated. Multi-fidelity surrogate modelling aims to leverage less accurate, lower-fidelity models that are computationally inexpensive in order to enhance predictive accuracy when high-fidelity data are scarce. However, low-fidelity models, while often displaying the qualitative solution behaviour, fail to accurately capture fine spatio-temporal and dynamic features of high-fidelity models. To address this shortcoming, we present a data-driven strategy that combines dimensionality reduction with multi-fidelity neural network surrogates. The key idea is to generate a spatial basis by applying proper orthogonal decomposition (POD) to high-fidelity solution snapshots, and approximate the dynamics of the reduced states--time-parameter-dependent expansion coefficients of the POD basis--using a multi-fidelity long short-term memory network. By mapping low-fidelity reduced states to their high-fidelity counterpart, the proposed reduced-order surrogate model enables the efficient recovery of full solution fields over time and parameter variations in a non-intrusive manner. The generality of this method is demonstrated by a collection of PDE problems where the low-fidelity model can be defined by coarser meshes and/or time stepping, as well as by misspecified physical features. [ABSTRACT FROM AUTHOR]

The concept of Advanced Air Mobility involves utilizing cutting-edge transportation platforms to transport passengers and cargo efficiently over short distances in urban and suburban areas. However, using simplified atmospheric models for aircraft simulations can prove insufficient for modeling large disturbances impacting low-altitude flight regimes. Due to the complexities of operating in urban environments, realistic wind modeling is necessary to ensure trajectory planning and control design can maintain high levels of safety. In this study, we simulate the dynamic response of a representative advanced air mobility platform operating in wing-borne flight through an urban wind field generated using Large Eddy Simulations (LES) and a wind field created using reduced-order models based on full-order computational solutions. Our findings show that the longitudinal response of the aircraft was not greatly affected by the fidelity of the LES models or if the spatial variation was considered while evaluating the full-order wind model. This is encouraging as it indicates that the full LES generation of the wind field may not be necessary, which decreases the complexity and time needed in this analysis. Differences are present when comparing the lateral response, owing to the differences in the asymmetric loading of the planform in the full and reduced order models. These differences seen in the lateral responses are expected to increase for planforms with smaller wing loadings, which could pose challenges. Additionally, the response of the aircraft to the mean wind field, the temporal average of the full order model, was misrepresentative in the longitudinal response and greatly under-predicted control surface activity, particularly in the lateral response. [ABSTRACT FROM AUTHOR]

The complexity of hydrogen leakage at different pressures leads to the challenge of developing a prediction method for both low pressure and supercritical pressure hydrogen leakage. In this paper, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) is developed to rapidly predict the hydrogen dispersion caused by leakage in small holes at different pressures. The subsonic and underexpanded hydrogen jets are simulated and verified with experimental data. Numerical results of some pressures are collected as POD snapshots and POD interpolation methods are performed to predict the velocity and hydrogen concentration distributions at desired unsampled pressures. Different sample plans with different sampling intervals and sample numbers are investigated. POD-linear, POD-spline, and POD-RBF (radial basis function) interpolations are compared. The results show that the POD-linear and POD-spline methods can accurately predict the velocity and hydrogen fraction at different pressures, and both are better than the POD-RBF method. The relative errors in predicting the hydrogen mole fraction between the POD-linear and POD-spline methods and the simulation results are 2.26% and 2.37%, respectively. Best of all, the POD interpolation method proposed in this work significantly reduces the time required for prediction, costing only 3.3 ms. • A fast POD-interpolation simulation method for hydrogen leakage from 1.1 to 10 bar pressure is developed. • Subsonic and underexpanded hydrogen jets are simulated and verified with experimental data. • Three POD-interpolation methods, POD-linear, POD-spline and POD-RBF, are investigated. • The influence of POD snapshot sample interval and sample number are investigated. [ABSTRACT FROM AUTHOR]