Membrane Shell Reflector Segment Antenna

The mesh reflector is the only type of large, in-space deployable antenna that has successfully flown in space. However, state-of-the-art large deployable mesh antenna systems are RF-frequency-limited by both global shape accuracy and local surface quality. The limitations of mesh reflectors stem from two factors. First, at higher frequencies, the porosity and surface roughness of the mesh results in loss and scattering of the signal. Second, the mesh material does not have any bending stiffness and thus cannot be formed into true parabolic (or other desired) shapes. To advance the deployable reflector technology at high RF frequencies from the current state-of-the-art, significant improvements need to be made in three major aspects: a high-stability and highprecision deployable truss; a continuously curved RF reflecting surface (the function of the surface as well as its first derivative are both continuous); and the RF reflecting surface should be made of a continuous material. To meet these three requirements, the Membrane Shell Reflector Segment (MSRS) antenna was developed

Shape Memory Composite Hybrid Hinge

There are two conventional types of hinges for in-space deployment applications. The first type is mechanically deploying hinges. A typical mechanically deploying hinge is usually composed of several tens of components. It is complicated, heavy, and bulky. More components imply higher deployment failure probability. Due to the existence of relatively moving components among a mechanically deploying hinge, it unavoidably has microdynamic problems. The second type of conventional hinge relies on strain energy for deployment. A tape-spring hinge is a typical strain energy hinge. A fundamental problem of a strain energy hinge is that its deployment dynamic is uncontrollable. Usually, its deployment is associated with a large impact, which is unacceptable for many space applications. Some damping technologies have been experimented with to reduce the impact, but they increased the risks of an unsuccessful deployment. Coalescing strain energy components with shape memory composite (SMC) components to form a hybrid hinge is the solution. SMCs are well suited for deployable structures. A SMC is created from a high-performance fiber and a shape memory polymer resin. When the resin is heated to above its glass transition temperature, the composite becomes flexible and can be folded or packed. Once cooled to below the glass transition temperature, the composite remains in the packed state. When the structure is ready to be deployed, the SMC component is reheated to above the glass transition temperature, and it returns to its as-fabricated shape. A hybrid hinge is composed of two strain energy flanges (also called tape-springs) and one SMC tube. Two folding lines are placed on the SMC tube to avoid excessive strain on the SMC during folding. Two adapters are used to connect the hybrid hinge to its adjacent structural components. While the SMC tube is heated to above its glass transition temperature, a hybrid hinge can be folded and stays at folded status after the temperature is reduced to below its glass transition temperature. After the deployable structure is launched in space, the SMC tube is reheated and the hinge is unfolded to deploy the structure. Based on test results, the hybrid hinge can achieve higher than 99.999% shape recovery. The hybrid hinge inherits all of the good characteristics of a tape-spring hinge such as simplicity, light weight, high deployment reliability, and high deployment precision. Conversely, it eliminates the deployment impact that has significantly limited the applications of a tape-spring hinge. The deployment dynamics of a hybrid hinge are in a slow and controllable fashion. The SMC tube of a hybrid hinge is a multifunctional component. It serves as a deployment mechanism during the deployment process, and also serves as a structural component after the hinge is fully deployed, which makes a hybrid hinge much stronger and stiffer than a tape-spring hinge. Unlike a mechanically deploying hinge that uses relatively moving components, a hybrid hinge depends on material deformation for its packing and deployment. It naturally eliminates the microdynamic phenomenon

Search Personalization: Knowledge-Based Recommendation in Digital Libraries

Recommendation engines have made great strides in understanding and implementing search personalization techniques to provide interesting and relevant documents to users. The latest research effort advances a new type of recommendation technique, Knowledge Based (KB) engines, that strive to understand the context of the user’s current information need and then filter information accordingly. The KB engine proposed in this paper requires less effort from the user in representing the search task and is the first of its kind implemented in a digital library setting. The KB engine performance was compared with Content Based (CB) and Collaborative Filtering (CF) recommendation techniques and the text search engine Lucene by asking sixty subjects to perform two different tasks to find relevant documents in a database of 212,000 documents from 22 National Science Digital Library (NSDL) collections. Our KB engine design outperforms CB, CF, and text search techniques in nearly all areas of evaluation

Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin- Hebei region in China

Accurate prediction of spatiotemporal ozone concentration is of great significance to effectively establish advanced early warning systems and regulate air pollution control. However, the comprehensive assessment of uncertainty and heterogeneity in spatiotemporal ozone prediction remains unknown. Here, we systematically analyze the hourly and daily spatiotemporal predictive performances using convolutional long short term memory (ConvLSTM) and deep convolutional generative adversarial network (DCGAN) models over the Beijing-Tianjin-Hebei region in China from 2013 to 2018. In extensive scenarios, our results show that the machine learning-based (ML-based) models achieve better spatiotemporal ozone concentration prediction performance with multiple meteorological conditions. A further comparison to the air pollution model-Nested Air Quality Prediction Modelling System (NAQPMS) and monitoring observations, the ConvLSTM model demonstrates the practical feasibility of identifying high ozone concentration distribution and capturing spatiotemporal ozone variation patterns at a high spatial resolution (here 15 km × 15 km)

A domain decomposition non-intrusive reduced order model for turbulent flows

In this paper, a new Domain Decomposition Non-Intrusive Reduced Order Model (DDNIROM) is developed for turbulent flows. The method works by partitioning the computational domain into a number of subdomains in such a way that the summation of weights associated with the finite element nodes within each subdomain is approximately equal, and the communication between subdomains is minimised. With suitably chosen weights, it is expected that there will be approximately equal accuracy associated with each subdomain. This accuracy is maximised by allowing the partitioning to occur through areas of the domain that have relatively little flow activity, which, in this case, is characterised by the pointwise maximum Reynolds stresses.A Gaussian Process Regression (GPR) machine learning method is used to construct a set of local approximation functions (hypersurfaces) for each subdomain. Each local hypersurface represents not only the fluid dynamics over the subdomain it belongs to, but also the interactions of the flow dynamics with the surrounding subdomains. Thus, in this way, the surrounding subdomains may be viewed as providing boundary conditions for the current subdomain.We consider a specific example of turbulent air flow within an urban neighbourhood at a test site in London and demonstrate the effectiveness of the proposed DDNIROM

Ensemble Kalman filter for GAN-ConvLSTM based long lead-time forecasting

Data-driven machine learning techniques have been increasingly utilized for accelerating nonlinear dynamic system prediction. However, machine learning-based models for long lead-time forecasts remain a significant challenge due to the accumulation of uncertainty along the time dimension in online deployment. To tackle this issue, the ensemble Kalman filter (EnKF) has been introduced to machine learning-based long-term forecast models to reduce the uncertainty of long lead-time forecasts of chaotic dynamic systems. Both the deep convolutional generative adversarial network (DCGAN) and convolutional long short term memory (ConvLSTM) are used for learning the complex nonlinear relationships between the past and future states of dynamic systems. Using an iterative Multi-Input Multi-Output (MIMO) algorithm, the two-hybrid forecast models (DCGAN-EnKF and ConvLSTM-EnKF) are able to yield long lead-time forecasts of dynamic states. The performance of the hybrid models has been demonstrated by one-level and two-level Lorenz 96 models. Our results show that the use of EnKF in ConvLSTM and DCGAN models successfully corrects online model errors and significantly improves the real-time forecasting of dynamic systems for a long lead-time

A real-time flow forecasting with deep convolutional generative adversarial network: Application to flooding event in Denmark

Real-time flood forecasting is crucial for supporting emergency responses to inundation-prone regions. Due to uncertainties in the future (e.g., meteorological conditions and model parameter inputs), it is challenging to make accurate forecasts of spatiotemporal floods. In this paper, a real-time predictive deep convolutional generative adversarial network (DCGAN) is developed for flooding forecasting. The proposed methodology consists of a two-stage process: (1) dynamic flow learning and (2) real-time forecasting. In dynamic flow learning, the deep convolutional neural networks are trained to capture the underlying flow patterns of spatiotemporal flow fields. In real-time forecasting, the DCGAN adopts a cascade predictive procedure. The last one-time step-ahead forecast from the DCGAN can act as a new input for the next time step-ahead forecast, which forms a long lead-time forecast in a recursive way. The model capability is assessed using a 100-year return period extreme flood event occurred in Greve, Denmark. The results indicate that the predictive fluid flows from the DCGAN and the high fidelity model are in a good agreement (the correlation coefficien

Machine learning-based rapid response tools for regional air pollution modelling

A parameterised non-intrusive reduced order model (P-NIROM) based on proper orthogonal decomposition (POD) and machine learning methods has been firstly developed for model reduction of pollutant transport equations. Our motivation is to provide rapid response urban air pollution predictions and controls. The varying parameters in the P-NIROM are pollutant sources. The training data sets are obtained from the high fidelity modelling solutions (called snapshots) for selected parameters (pollutant sources, here) over the parameter space . From these training data sets, the machine learning method is used to generate the relationship between the reduced solutions and inputs (pollutant sources) over . Furthermore a set of hyper-surface functions associated with each POD basis function is constructed for representing the fluid dynamics over the reduced space. The accuracy of the P-NIROM is highly dependent on the quality of the training set, here obtained from the high fidelity model. Over existing machine learning methods, the P-NIROM algorithm proposed here has the advantages that (1) it is combined with NIROM, thus providing rapid and reasonably accurate solutions; and (2) it is a robust and efficient approach for representation of any parametrised partial differential equations as the model parameters/inputs vary. In this study, we demonstrate the way how to implement the P-NIROM for the pollutant transport equation (but not limited to due to its robustness). Its predictive capability is illustrated in a three-dimensional (3-D) simulation of power plant plumes over a large region in China, where the varying parameters are the emission intensity at three locations. Results indicate that in comparison to the high fidelity model, the CPU cost is reduced by factor up to five orders of magnitude while reasonable accuracy remains

Galaxy-scale Star Formation on the Red Sequence: the Continued Growth of S0s and the Quiescence of Ellipticals

This paper examines star formation (SF) in relatively massive, primarily early-type galaxies (ETGs) at z~0.1. A sample is drawn from bulge-dominated GALEX/SDSS galaxies on the optical red sequence with strong UV excess and yet quiescent SDSS spectra. High-resolution far-UV imaging of 27 such ETGs using HST ACS/SBC reveals structured UV morphology in 93% of the sample, consistent with low-level ongoing SF (~0.5 Ms/yr). In 3/4 of the sample the SF is extended on galaxy scales (25-75 kpc), while the rest contains smaller (5-15 kpc) SF patches in the vicinity of an ETG - presumably gas-rich satellites being disrupted. Optical imaging reveals that all ETGs with galaxy-scale SF in our sample have old stellar disks (mostly S0 type). None is classified as a true elliptical. In our sample, galaxy-scale SF takes the form of UV rings of varying sizes and morphologies. For the majority of such objects we conclude that the gas needed to fuel current SF has been accreted from the IGM, probably in a prolonged, quasi-static manner, leading in some cases to additional disk buildup. The remaining ETGs with galaxy-scale SF have UV and optical morphologies consistent with minor merger-driven SF or with the final stages of SF in fading spirals. Our analysis excludes that all recent SF on the red sequence resulted from gas-rich mergers. We find further evidence that galaxy-scale SF is almost exclusively an S0 phenomenon (~20% S0s have SF) by examining the overall optically red SDSS ETGs. Conclusion is that significant number of field S0s maintain or resume low-level SF because the preventive feedback is not in place or is intermittent. True ellipticals, on the other hand, stay entirely quiescent even in the field.Comment: Accepted for publication in ApJ. Contains color figures, but compatible with non-color printer