Design Parameter Space Research Articles

Three-dimensional cohesive finite element simulations coupled with machine learning to predict mechanical properties of polymer-bonded explosives

Developing multifactorial predictive models for the design of polymer-bonded explosives (PBXs) is of importance for their further application in insensitive munition fields. As a popular method, finite element simulations can provide a reliable prediction, but are laborious and expensive if considering the extensive design parameter space. In light of this challenge, we proposed a coupled strategy that includes machine learning (ML) and three-dimensional cohesive finite element simulation for effciently predicting the mechanical properties of PBXs. The strain rate, particle volume fraction, interface strength, fracture energy, and the binders are considered as the main factors of tailoring the tensile strength of PBXs. To improve the prediction performance, an augmented database of 2500 data sets utilizing GANs neural network were established and then processed to train and test six ML models. The results show the accuracy and generalizability of the low-computational-cost ML models in predicting the mechanical properties of PBX composites. The predicted values from these models are in good agreement with the experimental ones. Feature contribution analysis demonstrates that the tensile modulus and failure strain are most affected by the binders, while the tensile strength are most affected by the fracture energy. Using the above conclusions as design guidelines, we can develop the new PBX formulations according to different mechanical property requirements for their optimal use across insensitive ammunitions. This strategy can be a viable machine-learning-assisted solution to designing PBXs.

Twisted hyperbolic van der Waals crystals for chip-scale full Stokes mid-infrared polarization detection

Abstract Investigating the polarization properties of light in the mid-infrared (mid-IR) spectrum is crucial for molecular sensing, biomedical diagnostics, and IR imaging system technologies. Traditional methods, limited by bulky size and complicated fabrication process, utilize large rotating optics for full Stokes polarization detection, impeding miniaturization and accuracy. Naturally occurring hyperbolic van der Waals (vdW) material based devices can address these challenges due to their lithography-free fabrication, ease of integration with chip-scale platforms and room-temperature operation. This study designs a chip-integrated polarimeter by performing multi-objective optimization for efficient exploration of the design parameter space. The spatial division measurement scheme used incorporates six precisely designed linear and circular polarization filters, achieving high extinction ratios exceeding 30 dB and transmittance surpassing 50%, with fabrication tolerance of film thickness up to 100 nm. The proposed device represents a significant advancement in polarimetric detection, providing a compact, cost-effective solution and opens new avenues for on-chip mid-IR polarimetric detection in next-generation ultra-compact optical systems.

High-throughput optimized prime editing mediated endogenous protein tagging for pooled imaging of protein localization.

The subcellular organization of proteins carries important information on cellular state and gene function, yet currently there are no technologies that enable accurate measurement of subcellular protein localizations at scale. Here we develop an approach for pooled endogenous protein tagging using prime editing, which coupled with an optical readout and sequencing, provides a snapshot of proteome organization in a manner akin to perturbation-based CRISPR screens. We constructed a pooled library of 17,280 pegRNAs designed to exhaustively tag 60 endogenous proteins spanning diverse localization patterns and explore a large space of genomic and pegRNA design parameters. Pooled measurements of tagging efficiency uncovered both genomic and pegRNA features associated with increased efficiency, including epigenetic states and interactions with transcription. We integrate pegRNA features into a computational model with predictive value for tagging efficiency to constrain the design space of pegRNAs for large-scale peptide knock-in. Lastly, we show that combining in-situ pegRNA sequencing with high-throughput deep learning image analysis, enables exploration of subcellular protein localization patterns for many proteins in parallel following a single pooled lentiviral transduction, setting the stage for scalable studies of proteome dynamics across cell types and environmental perturbations.

Growth of VO2-ZnS thin film cavity for adaptive thermal emission

Low-weight, passive, thermal-adaptive radiation technologies are needed to maintain an operable temperature for spacecraft while they experience various energy fluxes. In this study, we used a thin film coating with the Fabry–Pérot (FP) effect to enhance emissivity contrast (Δε) between VO2 phase-change states. This coating utilizes a hybrid material architecture that combines VO2 with a mid- and long-wave infrared transparent chalcogenide, zinc sulfide (ZnS), as a cavity spacer layer. We simulated the design parameter space to obtain a theoretical maximum Δε of 0.63 and grew prototype devices. Using x-ray diffraction, Raman spectroscopy, and Fourier transform infrared spectroscopy (FTIR), we determined that an intermediate buffer layer of TiO2 is necessary to execute the crystalline growth of monoclinic VO2 on ZnS. Through temperature-dependent FTIR measurements, our fabricated devices demonstrated FP-cavity enhanced adaptive thermal emittance.

A computational framework for aesthetic preferences in architecture using computer vision and artificial neural networks

This paper introduces a computational aesthetics framework utilizing computer vision (CV) and artificial neural networks (ANN) to predict the aesthetic preferences of groups of people for architecture. It relies on part-to-whole theories from aesthetics and cognitive psychology. A survey of a group of people on preferences of images is held to record an average hedonic response (AHR). CV algorithms MSER and SAM recognize parts in images. Birkhoff’s aesthetic measure formula is adapted by employing the number of parts and their connections. These quantities are used as input layers of an ANN, and the AHR is the target output. The ANN evaluates images to output a predicted hedonic response (PHR), which is tested as a criterion in parametric design space navigation and in mapping the latent space of GANs. We conclude that such a framework is a heuristic method for better understanding the design and latent spaces and exploring designs.

A Computational Framework to Optimize the Mechanical Behavior of Synthetic Vascular Grafts.

The failure of synthetic small-diameter vascular grafts has been attributed to a mismatch in the compliance between the graft and native artery, driving mechanisms that promote thrombosis and neointimal hyperplasia. Additionally, the buckling of grafts results in large deformations that can lead to device failure. Although design features can be added to lessen the buckling potential, the addition is detrimental to decreasing compliance (e.g., reinforcing coil). Herein, we developed a novel finite element framework to inform vascular graft design by evaluating compliance and resistance to buckling. A batch-processing scheme iterated across the multi-dimensional design parameter space, which included three parameters: coil thickness, modulus, and spacing. Three types of finite element models were created in FEBio for each unique coil-reinforced graft parameter combination to simulate pressurization, axial buckling, and bent buckling, and results were analyzed to quantify compliance, buckling load, and kink radius, respectively, from each model. Importantly, model validation demonstrated that model predictions agree qualitatively and quantitatively with experimental observations. Subsequently, data for each design parameter combination were integrated into an optimization function for which a minimum value was sought. The optimization values identified various candidate graft designs with promising mechanical properties. Our investigation successfully demonstrated the model-directed framework identified vascular graft designs with optimal mechanical properties, which can potentially improve clinical outcomes by addressing device failure. In addition, the presented computational framework promotes model-directed device design for a broad range of biomaterial and regenerative medicine strategies.

EFFECTS IN DISCRETE AND CONTINUOUS STRENGTHENING OF MACHINES ELEMENTS

The work describes approaches to problem solving of operational and effective improvement of the technical and tactical characteristics of military and civilian machines: tanks, armored vehicles, diesel locomotives, turbo-expander power plants, autonomous power plants of strategic objects, etc. For this purpose, fundamentally new methods of strengthening of contacting machines elements are being developed. These methods combine the advantages of discrete and continuous strengthening methods, which have largely exhausted their capabilities by now. Qualitative features and advantages of proposed discrete-continuous strengthening methods require effective implementation. We are talking about the justification of progressive technical solutions based on the criteria of strength and durability. For this purpose, the methodology of multi-criteria synthesis in the expanded space of design and technological parameters will be improved. In addition, as a basis for substantiating advanced technological solutions, a set of related problems of analysis of the stress-strain state, friction and wear is formed. Therefore, based on the improved system approach, a much higher level of strength and durability of the reinforced structural parts is achieved than with traditional methods. The results of test problems solving are illustrated. In the result, micro-macromodels are generalized to determine the joint influence of structural and technological factors, microstructure, physical and mechanical properties of materials of reinforced layers on the contact interaction and stress-strain state of representative fragments of the system of discrete-continuous reinforced bodies. The results of analysis of combined influence of structural and technological factors, microstructure, physical and mechanical properties of materials reinforced layers have been established on the contact interaction and stress-strain state of test representative fragments of the system of discrete-continuously reinforced bodies. At the same time, a significant influence of reinforced layers properties was established on strength of contacting bodies.

Soft pneumatic actuators for pushing fingers into extension

BackgroundCompliant pneumatic actuators possess many characteristics that are desirable for wearable robotic systems. These actuators can be lightweight, integrated with clothing, and accommodate uncontrolled degrees of freedom. These attributes are especially desirable for hand exoskeletons, where the soft actuator can conform to the highly variable digit shape. In particular, locating the pneumatic actuator on the palmar side of the digit may have benefits for assisting finger extension and resisting unwanted finger flexion, but this configuration requires suppleness to allow digit flexion while retaining sufficient stiffness to assist extension.MethodsTo meet these needs, we designed an actuator consisting of a hollow chamber long enough to span the joints of each digit while sufficiently narrow not to inhibit finger adduction. We explored the geometrical design parameter space for this chamber in terms of shape, dimensions, and wall thickness. After fabricating an elastomer-based prototype for each actuator design, we measured active extension force and passive resistance to bending for each chamber using a mechanical jig. We also created a finite element model for each chamber to enable estimation of the impact of chamber deformation, caused by joint rotation, on airflow through the chamber. Finally, we created a prototype hand exoskeleton with the chamber parameters yielding the best outcomes.ResultsA rectangular cross-sectional area was preferable to a semi-obround shape for the chamber; wall thickness also impacted performance. Extension joint torque reached 0.33 N-m at a low chamber pressure of 48.3 kPa. The finite element model confirmed that airflow for the rectangular chamber remained high despite deformation resulting from joint rotation. The hand exoskeleton created with the rectangular chambers enabled rapid movement, with a cycle time of 1.1 s for voluntary flexion followed by actuated extension.ConclusionsThe developed soft actuators are feasible for use in promoting finger extension from the palmar side of the hand. This placement utilizes pushing rather than pulling for digit extension, which is more comfortable and safer. The small chamber volumes allow rapid filling and evacuation to facilitate relatively high frequency finger movements.

Searching for a new light gauge boson with axial couplings in muon beam dump experiments

We present a formalism for new U(1) interactions involving weak hypercharge, baryon, and lepton numbers, and a possible axial symmetry generator FA in the presence of a second Brout-Englert-Higgs doublet. The resulting U boson, after mixing with the Z, interpolates between a generalised dark photon, a dark Z, and an axially coupled gauge boson. We especially focus on the axial couplings originating from FA or from mixing with the Z, determined by the scalar sector via parameters like tan β and the v.e.v. of an extra dark singlet.We explore the distinctive features of axially coupled interactions, especially in the ultrarelativistic limit, where the U boson behaves much as an axion-like particle, with enhanced interactions to quarks and leptons. This enhancement is particularly relevant for future muon beam dump experiments, since the muon mass considerably increases the effective coupling, proportional to 2mμ/mU, compared to analogous experiments with electrons.We also analyse the shape of the expected beam dump exclusion or discovery regions, influenced by U boson interactions and the experiment geometry. Different situations are considered, limited in particular by cases for which the U decays before reaching the detector, or has too small couplings to produce detectable events. We also compare to vectorially coupled bosons and axion-like pseudoscalars, highlighting the importance of understanding the parameter space for future experiment design and optimisation.

Distant retrograde orbit baseline generation considering solar eclipse mitigation

Mitigating solar eclipses presents a formidable challenge in the design of spacecraft orbits, particularly in the case of a distant retrograde orbit (DRO), which is susceptible to significant solar eclipse threats. This paper undertakes a comprehensive analysis of the occurrence characteristics and patterns of both Moon and Earth eclipses on a DRO. The proposed approach involves a two-stage design procedure, encompassing initial orbit selection and orbit extension. The initial orbit selection method primarily focuses on mitigating Earth eclipses by employing the out-of-plane motion inherent in a DRO. The study meticulously explores the influence of three pivotal elements — amplitude, period, and initial phase — on the efficacy of Earth eclipse avoidance. Concurrently, when faced with unavoidable solar eclipses of the initial orbit, a series of tiny maneuvers are systematically applied to extend the baseline orbit. The parameter design space is discretized, and a tree search method is employed to identify viable maneuver sequences. The research successfully achieves the long-term stabilization of the baseline DRO, ensuring that solar eclipses do not exceed 2 h in 10 years. The proposed method’s robustness and applicability are substantiated through validation under the ephemeris model.

Stabilizing regions of dominant pole placement for second order lead processes with time delay using filtered PID controllers.

In order to handle second order lead processes with time delay, this paper provides a unique dominant pole placement based filtered PID controller design approach. This method does not require any finite term approximation like Pade to obtain the quasi-polynomial characteristic polynomial, arising due to the presence of the time delay term. The continuous time second order plus time delay systems with zero (SOPTDZ) are discretized using a pole-zero matching method with specified sampling time, where the transcendental exponential delay terms are converted into a finite number of poles. The pole-zero matching discretization approach with a predetermined sampling period is also used to discretize the continuous time filtered PID controller. As a result, it is not necessary to use any approximate discretization technique, such as Euler or Tustin, to derive the corresponding discrete time PID controller from its continuous time counterpart. The analytical expressions for discrete time dominant pole placement based filtered PID controllers are obtained using the coefficient matching approach, while two distinct kinds of non-dominant poles, namely all real and all complex conjugate, have been taken into consideration. The stabilizable region in the controller and design parameter space for the chosen class of linear second order time delay systems with lead is numerically approximated using the particle swarm optimization (PSO) based random search technique. The efficacy of the proposed method has been validated on a class of SOPTDZ systems including stable, integrating, unstable processes with minimum as well as non-minimum phase zeros.

Robust design space of compression parameters for initial and reprocessed granules

The design of experiments has been widely used for product and process development based on Quality by design (QbD) approach to achieve quality product. Sources of variability are identified and controlled during manufacturing. However, uncertainty due to uncontrollable variables in the process leads to quality issues and the need for reprocessing in some cases. This study proposes a systematic approach to determine the robust design space for process parameters embraced with the variation of initial and reprocessed materials to ensure that the tablet will meet the specification. The Taguchi L36 experiment design is applied to determine suitable tableting parameters for different granules to ensure physical specifications. Four types of calcium carbonate granules were produced with different moisture contents (ZM) and manufacturing conditions (ZT). Additionally, three critical parameters of tablet compression process—filling volume (XV), compression force (XF), and turret speed (XS)—each encompassing three levels, were selected as input variables. The responses were derived from the product specification, namely weight (YW), tensile strength (YTS), and disintegration time (YDT). The experiment results were embedded into two models: the single independence response model (SRM) and the multiple dependent response model (MRM). After a confirmation experiment using predicted parameters, a comparison of their %MAPE (mean average percentage error) revealed that MRM has lower accuracy in terms of YTS but dominates SRM in terms of the region of design space. This finding relates to the effect of data transformation capturing non-linear relationships between these factors and responses. The design space derived from the models allows manufacturers to adjust critical process parameters for getting desired specification tablets and also provides a framework for continuous improvement by enhancing their original design space and providing a rigorous reprocessing plan.

Visual analysis of fitness landscapes in architectural design optimization

In architectural design optimization, fitness landscapes are used to visualize design space parameters in relation to one or more objective functions for which they are being optimized. In our design study with domain experts, we developed a visual analytics framework for exploring and analyzing fitness landscapes spanning data, projection, and visualization layers. Within the data layer, we employ two surrogate models and three sampling strategies to efficiently generate a wide array of landscapes. On the projection layer, we use star coordinates and UMAP as two alternative methods for obtaining a 2D embedding of the design space. Our interactive user interface can visualize fitness landscapes as a continuous density map or a discrete glyph-based map. We investigate the influence of surrogate models and sampling strategies on the resulting fitness landscapes in a parameter study. Additionally, we present findings from a user study (N = 12), revealing how experts’ preferences regarding projection methods and visual representations may be influenced by their level of expertise, characteristics of the techniques, and the specific task at hand. Furthermore, we demonstrate the usability and usefulness of our framework by a case study from the architecture domain, involving one domain expert.

Investigation on the thermal and hydrodynamic performance of a microchannel heat sink partially filled with porous medium

Microchannel heat sinks with porous media are promising solutions for high heat flux applications. This work presents a numerical study on the evaluation and optimization of microchannel heat sinks partially filled with porous medium. The thermal and hydrodynamic performance of the microchannel heat sinks are first systematically investigated and compared with those of the fully filled and unfilled counterparts. The optimization of the partially filled microchannel heat sinks is then conducted by using the response surface method, focusing on the effects of five parameters, including the height and width of the porous medium at the inlet, the height and width of the porous medium at the outlet, and the Reynolds number, on the thermal and hydrodynamic performance of the microchannel heat sinks. Furthermore, the optimization problem with two objective functions is analyzed by calculating the Pareto optimal set within the considered design parameter space. Depending on the available pumping power, the design and operation parameters could be selected from the Pareto optimal set to achieve the best heat transfer performance.

Deep learning-based predictive models for laser direct drive at the Omega Laser Facility

The rich and complex physics of inertial confinement fusion provides a unique and challenging space for high-fidelity first-principles modeling. Consequently, simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this article, we present two deep-learning-based predictive models intended to address these difficulties. The first model (TL DNN) acts as a fast emulator of simulations as well as experiments at the Omega Laser Facility. This model is trained on a simulation database and subsequently calibrated on experimental data using transfer learning. To facilitate the development of this model, an autoencoder is developed to reduce the dimensionality of the input space by compressing the laser pulse input. The model predicts key experimental scalar observables of Omega experiments with high accuracy and minimal computational cost. This deep neural net enables rapid exploration of a high-dimensional input parameter space for an optimal implosion design. The second model (DNN SM+) aims to extend the statistical modeling work of Lees et al. [Phys. Rev. Lett. 127, 105001 (2021)], by increasing the complexity of the model space and allowing for coupling between degradation terms. Since the model capacity of DNN SM+ is higher than the model of Lees et al., DNN SM+ can potentially provide an improvement in predictive capability, and we use this model to provide insight into complicated degradation dependencies.

Multi-Objective Optimization of Yokeless and Segmented Armature Machine for In-Wheel Traction Applications Based on the Taguchi Method

For electrical machines with complex structures, the design space of parameters can be large with high dimensions during optimization. Considering the calculation cost and time consumption, it is hard to optimize all the design parameters at the same time. Therefore, in that situation, sensitivity analysis of these design parameters is usually used to sort out crucial parameters. In this paper, the sensitivity analysis-based Taguchi method is applied to optimize the axial-flux permanent magnet (AFPM) machine with yokeless and segmented armature (YASA) topology for an in-wheel traction system. According to the key parameters and their sensitivity analysis, the optimal machine scheme to meet the performance requirements can be formed. In this case study, the machine performance is improved significantly after optimization. Lastly, the experimental results verify the accuracy of the model used in this study.

Fin-Bayes: A Multi-Objective Bayesian Optimization Framework for Soft Robotic Fingers.

Computational design is a critical tool to realize the full potential of Soft Robotics, maximizing their inherent benefits of high performance, flexibility, robustness, and safe interaction. Practically, computational design entails a rapid iterative search process over a parameterized design space, with assessment using (frequently) computational modeling and (more rarely) physical experimentation. Bayesian approaches work well for these expensive-to-analyze systems and can lead to efficient exploration of design space than comparative algorithms. However, such computational design typically entails weaknesses related to a lack of fidelity in assessment, a lack of sufficient iterations, and/or optimizing to a singular objective function. Our work directly addresses these shortcomings. First, we harness a sophisticated nonlinear Finite Element Modeling suite that explicitly considers geometry, material, and contact nonlinearity to perform rapid accurate characterization. We validate this through extensive physical testing using an automated test rig and printed robotic fingers, providing far more experimental data than that reported in the literature. Second, we explore a significantly larger design space than comparative approaches, with more free variables and more opportunity to discover novel, high performance designs. Finally, we use a multiobjective Bayesian optimizer that allows for the identification of promising trade-offs between two critical objectives, compliance and contact force. We test our framework on optimizing Fin Ray grippers, which are ubiquitous throughout research and industry due to their passive compliance and durability. Results demonstrate the benefits of our approach, allowing for the optimization and identification of promising gripper designs within an extensive design space, which are then 3D printed and usable in reality.

Enabling massively parallel, ad hoc exploration of the design space for simulation models within a serverless environment

This paper presents a massively parallel, cloud-computing framework for the ad hoc evaluation of discrete-event simulation (DES) models to enable broad exploration of the design space for model parameters. Parallel evaluation is enabled through use of a serverless computing environment allowing thousands of simultaneous experiments, on demand, without the need to explicitly provision or manages hardware. A standard Simulation Evaluation application programming interface (API) was designed for evaluating simulation functions that enables language independence between client application and simulation model, encouraging reuse of simulation models for multiple purposes (what-if analysis, ranking and selection, sensitivity analysis, or optimization). Extensions to the Java Simulation Library (JSL)27 enable rapid deployment of models built with the JSL as parameterized serverless functions implementing the Simulation Evaluation API. New Java packages facilitate the calling of any serverless functions that implement the Simulation Evaluation API.

Finite element models for radiation effects in nuclear fusion applications

Deuterium-tritium fusion reactions produce energy in the form of 14.1 MeV neutrons, and hence fusion reactor components will be exposed to high energy neutron irradiation while also being subjected to thermal, mechanical and magnetic loads. Exposure to neutron irradiation has numerous consequences, including swelling and dimensional changes, comparable in magnitude to the peak transient thermal deformations occurring in plasma-facing components. Irradiation also dynamically alters the various thermo-mechanical properties, relating temperature, stress and swelling in a strongly non-linear way. Experimental data on the effect of neutron exposure spanning the design parameter space are very sparse and this highlights the relevance of computer simulations. In this study we explore the equivalence between the body force/surface traction approach and the eigenstrain formalism for treating anisotropic irradiation-induced swelling. We find that both commercial and massively parallelised open source software for finite element method (FEM) simulations are suitable for assessing the effect of neutron exposure on the mechanically loaded reactor components. We demonstrate how two primary effects of irradiation, radiation swelling and the degradation of thermal conductivity, affect the distributions of stress and temperature in the divertor of the ITER tokamak. Significant uncertainties characterising the magnitude of swelling and models for treating it suggest that on the basis of the presently available data, only an order of magnitude estimate can be given to the stress developing in reactor components most exposed to irradiation during service.

Design and analysis of a contact-aided flexure hinge (CAFH) with variable stiffness

This paper presents a novel contact-aided flexure hinge (CAFH) with variable stiffness, which consists of a contact-aided segment, a flexible segment and a rigid part. The proposed CAFH can facilitate a compact design and provide an alternative for stiffness-variable designs under any loading conditions. With a mortise-tenon structure, the CAFH is trivially affected by friction. The design and deformation procedures of the CAFH are described in detail, followed by its theoretical kinetostatic modeling using the chained beam-constraint model. The deformation of all segments is considered in the kinetostatic model, which expands the space of design parameters for stiffness-variable designs. Then, the accuracy of the theoretical model and the variable stiffness design are verified by nonlinear finite element analysis (FEA) and experimental tests. In term of stiffness, the maximum relative errors of the theoretical model are 0.76% in Stage 1 and 0.70% in Stage 2, as compared with FEA, respectively. Further, the parameter sweep is carried out, followed by sensitivity analysis to identify the main test error sources. Finally, the multi-material scenarios are investigated preliminarily, and some outlooks are discussed.