Parallel Domain Research Articles

Numerical study of turbulent eddy self-interaction in tokamaks with low magnetic shear. Part I: Linear simulations

Abstract This study examines the impact of low and zero magnetic shear, along with rational values of safety factor, on linear mode behavior in tokamak plasmas. We focus on how magnetic field line topology and parallel domain length influence linear mode structures and growth rates as magnetic shear decreases. Our results
show that as magnetic shear is reduced to low values, linear modes can transition between different branches, significantly affecting their characteristics. At zero magnetic shear, accurate modeling requires precisely capturing magnetic topology, as small deviations near rational surfaces can greatly impact growth rates. These findings are extended to nonlinear simulations in a companion paper (Volčokas, 2024), revealing that long turbulent eddies and magnetic field line topology play a crucial role in turbulence self-interaction and plasma transport.

High-throughput affinity measurements of direct interactions between activation domains and co-activators.

Sequence-specific activation by transcription factors is essential for gene regulation1,2. Key to this are activation domains, which often fall within disordered regions of transcription factors3,4 and recruit co-activators to initiate transcription5. These interactions are difficult to characterize via most experimental techniques because they are typically weak and transient6,7. Consequently, we know very little about whether these interactions are promiscuous or specific, the mechanisms of binding, and how these interactions tune the strength of gene activation. To address these questions, we developed a microfluidic platform for expression and purification of hundreds of activation domains in parallel followed by direct measurement of co-activator binding affinities (STAMMPPING, for Simultaneous Trapping of Affinity Measurements via a Microfluidic Protein-Protein INteraction Generator). By applying STAMMPPING to quantify direct interactions between eight co-activators and 204 human activation domains (>1,500 K ds), we provide the first quantitative map of these interactions and reveal 334 novel binding pairs. We find that the metazoan-specific co-activator P300 directly binds >100 activation domains, potentially explaining its widespread recruitment across the genome to influence transcriptional activation. Despite sharing similar molecular properties (e.g. enrichment of negative and hydrophobic residues), activation domains utilize distinct biophysical properties to recruit certain co-activator domains. Co-activator domain affinity and occupancy are well-predicted by analytical models that account for multivalency, and in vitro affinities quantitatively predict activation in cells with an ultrasensitive response. Not only do our results demonstrate the ability to measure affinities between even weak protein-protein interactions in high throughput, but they also provide a necessary resource of over 1,500 activation domain/co-activator affinities which lays the foundation for understanding the molecular basis of transcriptional activation.

Probing intrinsic magnetic domain in bare CrI3 bulk with a magnetic force microscope

Two-dimensional magnetism in CrI3 links to its stacking structural order. In bulk CrI3, coexistence of monoclinic and rhombohedral phases has been observed across wide temperature range. Direct characterization of the magnetic textures on the exposed surface of bulk CrI3 has been challenging but meaningful. Here, we have employed a custom glovebox-assisted magnetic force microscope to observe the evolution of magnetic domains on the bare CrI3 surface under variable temperatures and magnetic fields for the first time. Below the Curie temperature, robust parallel stripe magnetic domains spontaneously emerge. The root mean square values of the localized weak pinning domain images at various temperatures indicate a critical transition behavior consistent with 3D Ising model with Tc ∼ 59 K. Images obtained by varying the magnetic field at low temperatures, showing magnetization saturation-reentry cycles, reveal larger hysteresis and stronger disorder compared to the VSM measurement, indicating local structural phase inhomogeneity. ​Two in-plane stripe magnetic domains oriented locally at an angle of 51-degrees compete under an out-of-plane magnetic field, presumably due to different sliding orientations induced by the magnetic field during the transition from a monoclinic to a rhombohedral phase. This study provides an experimental reference for future spin density wave manipulation in CrI3.

Domain composition and attention network trained with synthesized unlabeled images for generalizable medical image segmentation

Despite that deep learning models have achieved remarkable performance in medical image segmentation, their performance is often limited on testing images from new centers with a domain shift. To achieve Domain Generalization (DG) for medical image segmentation, we propose a Domain Composition and Attention-based Network (DCA-Net) combined with structure- and style-based data augmentation that generates unlabeled synthetic images for training. First, DCA-Net represents features in one certain domain by a linear combination of a set of basis representations that are learned by parallel domain preceptors with a divergence constraint. The linear combination is used to calibrate the feature maps of an input image, which enables the model to generalize to unseen domains. Second, considering the number of domains and images for training is limited, we employ generative models to synthesize images with a higher structure diversity, and to leverage the unlabeled synthetic images, we introduce a consistency constraint for their predictions under style augmentation based on frequency amplitude mixture. Additionally, a Test-Time Frequency Augmentation (TTFA) is proposed to neutralize the domain shift from the target to source domains. Experimental results on two multi-domain datasets for fundus structure and nasopharyngeal carcinoma segmentation showed that: (1) our method significantly outperformed several existing DG methods, and (2) the model’s generalizability was largely improved by domain composition and attention modules; (3) by leveraging the unlabeled synthetic images and the TTFA, the model could better deal with images from unseen domains.

Domain decomposition methods and acceleration techniques for the phase field fracture staggered solver

AbstractThe phase field modeling of fracture is able to simulate the nucleation and the propagation of complex crack patterns. However, the relatively small internal lengths that are required usually lead to very fine meshes and high computational costs, especially for three‐dimensional applications. In the present work, additional cost also comes from the implicit dynamics regularization of unstable crack propagations which potentially leads to a large variation of time steps when switching from quasi‐static to dynamic regimes. To reduce the time to solution in this context, this study proposes a domain decomposition framework and acceleration techniques for the phase field fracture staggered solver. The displacement subproblem and the phase field one are solved with parallel domain decomposition solvers. Dual domain decomposition methods provide low cost preconditioner well adapted to the phase field subproblem. For displacement subproblems undergoing unstable crack propagations, primal domain decomposition methods are preferred to be less sensitive to the treatment of floating substructures. Preconditioners performances are assessed and scalability studies over academic test cases, up to 324 subdomains, are presented. Finally, the robustness of the approach is illustrated on two semi‐industrial simulations.

Variability in adolescent reception of parental support: Testing the domain-matching hypothesis.

The present study investigated matches and mismatches between adolescent and parent socialization domains (i.e., protection, guidance) as related to adolescent reception of parental support during a laboratory-based social evaluation challenge. Participants were 80 early adolescents (Mage = 12.36 years, SD = 1.33, 55% males, 55% Black, 42.5% White, and 2.5% other races or ethnicities) and one parent or guardian per adolescent. Observational measures of parent socialization domains assessed sensitivity to adolescents' thoughts and feelings (protection domain) and prosocial behavioral advice (guidance domain). Measures of parallel adolescent socialization domains included self-reported discomfort during a social evaluation challenge (protection domain) and desire to continue the social evaluation challenge (guidance domain). Adolescent reception of parental support was assessed using an observational measure of adolescent attentiveness and responsiveness to the parent during a parent-adolescent discussion about how to approach the social evaluation challenge. Analyses of interactions between measures of parent and adolescent socialization domains revealed: (a) higher levels of adolescent-reported discomfort during the social evaluation challenge interfered with their reception of parental prosocial behavioral advice but did not enhance their reception of parental sensitivity, and (b) higher levels of adolescent-reported desire to continue the social evaluation challenge interfered with their reception of parental sensitivity but did not enhance their reception of parental prosocial behavioral advice. This study advances socialization research by identifying conditions under which adolescents are more and less receptive to supportive communication from parents. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An investigation into the reduction mechanism of temperature-magnetic stress relief based on DO3 crystal

This study assesses a novel method proposed by the research group for reducing residual stress through temperature-magnetic stress relief (TMSR). Experimental findings indicate that this approach yields significant reductions in residual stress. Microscopic analysis reveals that following TMSR treatment, the labyrinthine domains within the material diminish while the parallel domains increase, prompting atomic migration and dislocation in regions of elevated stress. Moreover, utilizing density functional theory, this paper investigates the intrinsic strengthening mechanism of TMSR. The findings indicate that the atomic magnetic moment of the Fe3C (DO3) crystal exhibits an approximately increasing trend within a specific temperature range. This observation underscores the thermo-magnetic coupling strengthening effect inherent in the DO3 ferromagnetic crystal, which constitutes a pivotal element in the enhancement effect of TMSR. Furthermore, an examination of changes in the energy band distribution and electronic density of states at varying temperatures sheds light on the underlying cause of fluctuations in the atomic magnetic moment. The research outcomes indicate that upon surpassing a certain temperature threshold, the crystal phase undergoes changes, leading to fluctuations in the atomic magnetic moment. This study contributes novel insights aimed at enhancing comprehension of the TMSR method.

A computational framework for pharmaco‐mechanical interactions in arterial walls using parallel monolithic domain decomposition methods

AbstractA computational framework is presented to numerically simulate the effects of antihypertensive drugs, in particular calcium channel blockers, on the mechanical response of arterial walls. A stretch‐dependent smooth muscle model by Uhlmann and Balzani is modified to describe the interaction of pharmacological drugs and the inhibition of smooth muscle activation. The coupled deformation‐diffusion problem is then solved using the finite element software FEDDLib and overlapping Schwarz preconditioners from the Trilinos package FROSch. These preconditioners include highly scalable parallel GDSW (generalized Dryja–Smith–Widlund) and RGDSW (reduced GDSW) preconditioners. Simulation results show the expected increase in the lumen diameter of an idealized artery due to the drug‐induced reduction of smooth muscle contraction, as well as a decrease in the rate of arterial contraction in the presence of calcium channel blockers. Strong and weak parallel scalability of the resulting computational implementation are also analyzed.

Enhanced insulation and ferro/piezo-electric properties of BiFeO3–BaTiO3via synchronous B-site co-doping and quenching treatment

Lead-free piezoelectric ceramics 0.7BiFe1-x(Zn0.5Sn0.5)xO3-0.3BaTiO3 [BF(ZS)x-BT] were successfully prepared using the solid-state reaction method. The dual effects of B-site Zn/Sn co-doping and quenching on the structure and piezoelectricity of BF(ZS)x-BT were investigated. All samples showed a distinct perovskite structure, characterized by the simultaneous presence of rhombohedral (R) and pseudo-cubic (pC) phases. The insulation and electrical properties were significantly increased by appropriate Zn/Sn co-doping and quenching treatment. The long parallel strip domains and enhanced lattice distortion synergistically contribute to the outstanding piezoelectricity (d33 = 203 pC/N) and a high Curie temperature (Tc = 493 °C) in quenched BF(ZS)0.003-BT. Furthermore, the quenching treatment was found to further improve the piezoelectric property and enhance its high-temperature stability, as demonstrated by the in-situ electromechanical coupling coefficient (kp) and thermal depolarization test. The d33 of the quenched BF(ZS)0.003-BT was maintained at 186 pC/N around 400 °C, highlighting its promising applications in medium-high temperature conditions.

A Parallel Domain Decomposition Method for the Fully-Mixed Stokes-Dual-Permeability Fluid Flow Model with Beavers-Joseph Interface Conditions

A Parallel Domain Decomposition Method for the Fully-Mixed Stokes-Dual-Permeability Fluid Flow Model with Beavers-Joseph Interface Conditions

RUL prediction of rolling bearings across working conditions based on multi-scale convolutional parallel memory domain adaptation network

Rolling bearings are widely used in mechanical equipment, effectively determining the failure time of rolling bearings is particularly significant to ensure the safe performance of mechanical equipment. However, in industrial scenarios, the machine mainly works in the normal state for a long time, it is difficult to accumulate the same distribution of the whole life data, but the use of different distribution of data for forecasting will reduce the performance of deep learning-based prediction methods. Therefore, in order to tackle this problem, a multi-scale convolutional parallel memory domain adaptation network is investigated to forecast the residual useful life (RUL) of bearings across working conditions. Firstly, a new characteristic extractor—multi-scale convolutional parallel memory network is designed to extract spatial and temporal characteristics of bearing degradation data. At the same time, in order to minimize the distribution difference between source domain and target domain, a temporal-spatial feature alignment strategy is proposed to obtain domain invariable characteristics by combining maximum mean difference and domain adversarial learning. Finally, the availability of the proposed approach is verified using two rolling bearing data sets. The results reveal that it can efficiently forecast the RUL of rolling bearings across working conditions.

Research on the Application and Performance Optimization of GPU Parallel Computing in Concrete Temperature Control Simulation

With the development of engineering technology, engineering has higher requirements for the accuracy and the scale of simulation calculation. The computational efficiency of traditional serial programs cannot meet the requirements of engineering. Therefore, reducing the calculation time of the temperature control simulation program has important engineering significance for real-time simulation of temperature field and stress field, and then adopting more reasonable temperature control and crack prevention measures. GPU parallel computing is introduced into the temperature control simulation program of massive concrete to solve this problem and the optimization is carried out. Considering factors such as GPU clock rate, number of cores, parallel overhead and Parallel Region, the improved GPU parallel algorithm analysis indicator formula is proposed. It makes up for the shortcomings of traditional formulas that focus only on time. According to this formula, when there are enough threads, the parallel effect is limited by the size of the parallel domain, and when the parallel domain is large enough, the efficiency is limited by the parallel overhead and the clock rate. This paper studies the optimal Kernel execution configuration. Shared memory is utilized to improve memory access efficiency by 155%. After solving the problem of bank conflicts, an accelerate rate of 437.5× was realized in the subroutine of the matrix transpose of the solver. The asynchronous parallel of data access and logical operation is realized on GPU by using CUDA Stream, which can overlap part of the data access time. On the basis of GPU parallelism, asynchronous parallelism can double the computing efficiency. Compared with the serial program, the accelerate rate of inner product matrix multiplication of the GPU asynchronous parallel program is 61.42×. This study further proposed a theoretical formula of data access overlap rate to guide the selection of the number of CUDA streams to achieve the optimal computing conditions. The GPU parallel program compiled and optimized by the CUDA Fortran platform can effectively improve the computational efficiency of the simulation program for concrete temperature control, and better serve engineering computing.

A parallel domain decomposition method for identifying the space-time dependent diffusion coefficients of 3D parabolic problems

A parallel domain decomposition method for identifying the space-time dependent diffusion coefficients of 3D parabolic problems

Temperature-drift effect analysis of microstrip filters based on parallel high-order DGTD and FETD method with memory reduction technique

The temperature-drift effect of microstrip filters is analyzed by the proposed electromagnetic-thermal co-simulation method based on parallel high-order discontinuous Galerkin time domain (DGTD) and finite-element time-domain (FETD) algorithms with a memory reduction technique. The element matrices of DGTD method are factorized into the product of coefficient matrices and universal matrices. The coefficient matrices are different for each tetrahedral element and need to be stored for all the elements. While the universal matrices are the same for all the tetrahedral elements and only need to be stored for one element. Since the sizes of the coefficient matrices are much smaller than the element matrices of DGTD method, the proposed method avoids storing the large element matrices and greatly reduce the memory requirement of the DGTD method. Thus it reduces the memory requirement of the whole electromagnetic-thermal co-simulation which is dominated by the memory consumption of the DGTD method. Large-scale parallel technique is adopted to accelerate the process of electromagnetic-thermal co-simulation. The proposed method provides a very powerful tool for temperature-drift effect analysis of microstrip filters.

Nuclear and chromatin rearrangement associate to epigenome and gene expression changes in a model of in vitro adipogenesis and hypertrophy

Hypertrophy of adipocytes represents the main cause of obesity. We investigated in vitro the changes associated with adipocyte differentiation and hypertrophy focusing on the nuclear morphometry and chromatin epigenetic remodelling. The 3 T3-L1 pre-adipocytes were firstly differentiated into mature adipocytes, then cultured with long-chain fatty acids to induce hypertrophy. Confocal and super-resolution stimulation emission depletion (STED) microscopy combined with ELISA assays allowed us to explore nuclear architecture, chromatin distribution and epigenetic modifications. In each condition, we quantified the triglyceride accumulation, the mRNA expression of adipogenesis and dysfunction markers, the release of five pro-inflammatory cytokines. Confocal microscopy revealed larger volume and less elongated shape of the nuclei in both mature and hypertrophic cells respect to pre-adipocytes, and a trend toward reduced chromatin compaction. Compared to mature adipocytes, the hypertrophic phenotype showed larger triglyceride content, increased PPARγ expression reduced IL-1a release, and up-regulation of a pool of genes markers for adipose tissue dysfunction. Moreover, a remodelling of both epigenome and chromatin organization was observed in hypertrophic adipocytes, with an increase in the average fluorescence of H3K9 acetylated domains in parallel with the increase in KAT2A expression, and a global hypomethylation of DNA. These findings making light on the nuclear changes during adipocyte differentiation and hypertrophy might help the strategies for treating obesity and metabolic complications.

Influence of the grain boundary phase characteristics on the magnetic properties of Nd-Fe-B magnets

The effects of the grain boundary phase (GBP) thickness, ferromagnetism and orientation with the c-axis of the Nd2Fe14B phase on the magnetic properties of Nd-Fe-B magnets were investigated by micromagnetic simulation. Increasing the thickness of the non-ferromagnetic GBP parallel to the c-axis decreases the demagnetization field at the corners of magnets which act as the nucleation sites of initial reversed magnetic domain. However, the demagnetization field at the corners of magnets increases with the increased thickness of the non-ferromagnetic GBP perpendicular to the c-axis. This results in the high coercivity increasing rate with the same non-ferromagnetic GBP thickness increasing parallel to the c-axis than that perpendicular to the c-axis. For the magnet models with ferromagnetic GBP the initial nucleation of reversed magnetic domain starts at the ferromagnetic GBP. And the nucleation field of the reversed magnetic domain in the ferromagnetic GBP perpendicular to the c-axis is far higher than that in the ferromagnetic GBP parallel to the c-axis. Moreover, it is much easier for the reversed magnetic domain in the ferromagnetic GBP parallel to the c-axis to propagate to the neighboring Nd2Fe14B grains than that in the ferromagnetic GBP perpendicular to the c-axis. This leads to the much more substantial deteriorating coercivity with the increased thickness of ferromagnetic GBP parallel to the c-axis than that perpendicular to the c-axis. This work clarifies the influence mechanism of the GBP characteristics on the magnetic properties of Nd-Fe-B magnets.

Autonomous Decision-Making for Aerobraking via Parallel Randomized Deep Reinforcement Learning

Aerobraking is a process used to slow down and insert a spacecraft into a low orbit around a planet. It is composed of many orbital passages into the complex atmosphere of the planet, which is used for braking. The aerobraking atmospheric passages are challenging because of the high variability of the atmospheric environment. For this reason, autonomous aerobraking planning is essential for safety and mission performance. This paper develops a parallel domain randomized deep reinforcement learning architecture for autonomous decision-making in a stochastic environment, such as aerobraking atmospheric passages. In this context, the architecture is used for planning aerobraking maneuvers to avoid the occurrence of thermal violations during the atmospheric aerobraking passages and target a final low-altitude orbit. The parallel domain randomized deep reinforcement learning architecture is designed to account for large variability of the physical model, as well as uncertain conditions. Also, the parallel approach speeds up the training process for simulation-based applications, and the domain randomization improves resultant policy generalization. To use this architecture, a Markov-Decision process framework is developed for a general aerobraking-type mission. A three-dimensional running reward function, expressed in spacecraft state and action, is designed. This framework is applied to the 2001 Mars Odyssey aerobraking campaign, which is also used to verify the performance of the parallel domain randomized deep reinforcement learning architecture. With respect to the 2001 Mars Odyssey mission flight data and a Numerical Predictor Corrector (NPC)-based state-of-the-art heuristic for autonomous aerobraking, the proposed architecture outperforms the state-of-the-art heuristic algorithm with an average increase of 87.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in the cumulative reward and a decrease of 97.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in the number of thermal violations. Specifically, the proposed architecture is able to predict and avoid thermal violations while requiring fewer computational resources. Furthermore, it yields a decrease of 98.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> in the number of thermal violations with respect to the Mars Odyssey mission flight data and requires 13.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> fewer orbits, with a comparable aerobraking duration and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Delta$</tex-math></inline-formula> V budget. Results also show that the proposed architecture can also learn a generalized policy in the presence of strong uncertainties, such as aggressive atmospheric density perturbations, different atmospheric density models, and a different simulator maximum step size and error accuracy. To this end, a generalization analysis is performed using out-of-distribution generalization environments. Results of the generalization analysis show that the architecture can perform safe aerobraking campaigns with only a maximum increase of 2 in the number of thermal violations for cases in which the simulator accurately described the physical model.

Massively parallel numerical simulation of 3D shock wave propagation based on JASMIN framework

An efficient massively parallel computing program for shock wave propagation named Shock3d is developed for shock wave damage mechanism in complex scenes. With the finite volume method, the program is built upon JASMIN (J parallel Adaptive Structured Mesh applications INfrastructure) and employs a component-based hierarchical organization structure. Using the patch-based core algorithm, Shock3d enables high parallel efficiency and strong parallel scalability with a parallel domain decomposition method and an image domain-based communication mode. It not only considers gravity, real gas effects, and boundaries in complex scenes, but also supports dynamic load balancing (DLB) and three-dimensional structured meshes and allows the parallel solution of hundreds of millions of mesh problems. Typical examples were used to verify the validity, parallelism, and high-resolution simulation results of the program developed. In the scalability test, where the propagation of shock waves through hundreds of millions of mesh elements was simulated on 300,000 CPU cores, the program delivered parallel efficiency of 60.84%.

Anterior and posterior imaging with hyperparallel OCT.

Hyperparallel OCT (HP-OCT) is a parallel spectral domain imaging technology particularly well-suited to the anterior segment. It uses a 2-dimensional grid of 1008 beams to simultaneously image across a wide area of the eye. In this paper we demonstrate that sparsely sampled volumes captured at 300 Hz can be registered without the need for active eye tracking to produce 3-dimensional (3D) volumes free from motion artefacts. The anterior volume provides complete 3D biometric information, including lens position, curvature, epithelial thickness, tilt, and axial length. We further demonstrate that, with the change of a detachable lens, we can capture high resolution anterior volumes and importantly, posterior volume images for preoperative assessment of the posterior segment. Advantageously, the retinal volumes have the same 11.2 mm Nyquist range as the anterior imaging mode.

A Dimension-Oblivious Domain Decomposition Method Based on Space-Filling Curves

In this paper we present an algebraic dimension-oblivious two-level domain decomposition solver for discretizations of elliptic partial differential equations. The proposed parallel domain decomposition solver is based on a space-filling curve partitioning approach that is applicable to any discretization, i.e., it directly operates on the assembled matrix equations. Moreover, it allows for the effective use of arbitrary processor numbers independent of the dimension of the underlying partial differential equation while maintaining its optimal convergence behavior. This is the core property required to attain a combination technique solver with extreme scalability which can utilize exascale parallel systems efficiently. Moreover, this approach provides a basis for the development of a fault-tolerant solver for the numerical treatment of high-dimensional Problems. To achieve the required data redundancy we are therefore concerned with large overlaps of our domain decomposition, which we construct in any dimension via space-filling curves. In this paper, we propose a space-filling curve based domain decompositoin solver and present its convergence properties and scaling behavior. The results of our numerical experiments clearly show that our approach provides optimal convergence and scaling behavior in an arbitrary dimension utilizing arbitrary processor numbers.