The determinations of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, which also wastes too much time and manpower. To address this problem, we propose machine learning models including artificial neural networks (ANNs) and support vector machines (SVM) to predict the heat collection rate and heat loss coefficient without a direct determination. Parameters that can be easily obtained by “portable test instruments” were set as independent variables, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, final temperature and angle between tubes and ground, while the heat collection rate and heat loss coefficient determined by the detection device were set as dependent variables respectively. Nine hundred fifteen samples from in-service water-in-glass evacuated tube solar water heaters were used for training and testing the models. Results show that the multilayer feed-forward neural network (MLFN) with 3 nodes is the best model for the prediction of heat collection rate and the general regression neural network (GRNN) is the best model for the prediction of heat loss coefficient due to their low root mean square (RMS) errors, short training times, and high prediction accuracies (under the tolerances of 30%, 20%, and 10%, respectively).

Accurate prediction of the particles’ temperature distribution and the time required to heat up the particles is important to maintain good quality products and economical processes for several industrial processes that involve thermal treatment. However, we do not have quantitative models to predict the average temperature or particles’ temperature distribution accurately. In this article, we carry out DEM simulations and compute the temporal and spatial evolution of the distribution of the particles’ temperature in rotating cylinders. We present typical examples for different particle properties and operating conditions. The temperature distribution follows what is referred to as a uniform distribution with well defined mean and standard deviation values. Our analysis of these statistical parameters can assist in the prediction of the time required to heat up granular materials and the design of efficient processes.

Theoretical perspectives on anticipatory planning of object manipulation have traditionally been informed by studies that have investigated kinematics (hand shaping and digit position) and kinetics (forces) in isolation. This poses limitations on our understanding of the integration of such domains, which have recently been shown to be strongly interdependent. Specifically, recent studies revealed strong covariation of digit position and load force during the loading phase of two-digit grasping. Here, we determined whether such digit force-position covariation is a general feature of grasping. We investigated the coordination of digit position and forces during five-digit whole-hand manipulation of an object with a variable mass distribution. Subjects were instructed to prevent object roll during the lift. As found in precision grasping, there was strong trial-to-trial covariation of digit position and force. This suggests that the natural variation of digit position that is compensated for by trial-to-trial variation in digit forces is a fundamental feature of grasp control, and not only specific to precision grasp. However, a main difference with precision grasping was that modulation of digit position to the object’s mass distribution was driven predominantly by the thumb, with little to no modulation of finger position. Modulation of thumb position rather than fingers is likely due to its greater range of motion and therefore adaptability to object properties. Our results underscore the flexibility of the central nervous system in implementing a range of solutions along the digit force-to-position continuum for dexterous manipulation.

Neutron production methods are an integral part of research and analysis for an array of applications. This paper examines methods of neutron production, and the advantages of constructing a radioisotopic neutron irradiator assembly using ²⁵²Cf. Characteristic neutron behavior and cost-benefit comparative analysis between alternative modes of neutron production are also examined. The irradiator is described from initial conception to the finished design. MCNP modeling shows a total neutron flux of 3 × 10⁵ n/(cm²·s) in the irradiation chamber for a 25 μg source. Measurements of the gamma-ray and neutron dose rates near the external surface of the irradiator assembly are 120 μGy/h and 30 μSv/h, respectively, during irradiation. At completion of the project, total material, and labor costs remained below $50,000.

The objective of this paper is to design miniaturized narrow- and dual-band filters for WLAN application using zero order resonators by the method of least squares. The miniaturization of the narrow-band filter is up to 70% and that of the dual-band filter is up to 64% compared to the available models in the literature. Two prototype models of the narrow-band and dual-band filters are fabricated and measured, which verify the proposed structure for the filter and its design by the presented method, using an equivalent circuit model.

1,1,1,2,3,3,3-Heptafluoropropane (R227ea) is a good refrigerant that reduces greenhouse effects and ozone depletion. In practical applications, we usually have to know the compressed liquid densities at different temperatures and pressures. However, the measurement requires a series of complex apparatus and operations, wasting too much manpower and resources. To solve these problems, here, Song and Mason equation, support vector machine (SVM), and artificial neural networks (ANNs) were used to develop theoretical and machine learning models, respectively, in order to predict the compressed liquid densities of R227ea with only the inputs of temperatures and pressures. Results show that compared with the Song and Mason equation, appropriate machine learning models trained with precise experimental samples have better predicted results, with lower root mean square errors (RMSEs) (e.g., the RMSE of the SVM trained with data provided by Fedele et al. [1] is 0.11, while the RMSE of the Song and Mason equation is 196.26). Compared to advanced conventional measurements, knowledge-based machine learning models are proved to be more time-saving and user-friendly.

Compaction waves traveling through porous cyclotetramethylene-tetranitramine (HMX) are computationally modeled using the Eulerian hydrocode CTH and validated with gas gun experimental data. The method employed use of a newly generated set of P-α parameters for granular HMX in a Mie-Gruneisen equation of state. The P-α model adds a separate parameter to differentiate between the volume changes of a solid material due to compression from the volume change due to compaction, void collapse in a granular material. Computational results are compared via five validation schema for two different initial-porosity experiments. These schema include stress measurements, velocity rise times and arrival times, elastic sound speeds though the material and final compaction densities for a series of two different percent Theoretical Maximum Density (TMD) HMX sets of experimental data. There is a good agreement between the simulations and the experimental gas gun data with the largest source of error being an 11% overestimate of the peak stress which may be due to impedance mismatch on the experimental gauge interface. Determination of these P-α parameters are important as they enable modeling of porosity and are a vital first step in modeling of precursory hotspots, caused by hydrodynamic collapse of void regions or grain interactions, prior to deflagration to detonation transition of granular explosives.

The exploration of environmentally friendly energy resources is one of the major challenges facing society today. The last decade has witnessed rapid developments in renewable energy engineering. Wind and solar power plants with increasing sizes and technological sophistication have been built. Amid this development, meteorological modeling plays an increasingly important role, not only in selecting the sites of wind and solar power plants but also in assessing the environmental impacts of those plants. The permanent land-use changes as a result of the construction of wind farms can potentially alter local climate (Keith et al. [1], Roy and Traiteur [2]). The reduction of wind speed by the presence of wind turbines could affect the preconstruction estimate of wind power potential (e.g., Adams and Keith [3]). Future anthropogenic greenhouse gas emissions are expected to induce changes in the surface wind and cloudiness, which would affect the power production of wind and solar power plants. To quantify these two-way relations between renewable energy production and regional climate change, mesoscale meteorological modeling remains one of the most efficient approaches for research and applications.

Trip travel time reliability is an important measure of transportation system performance and a key factor affecting travelers’ choices. This paper explores a method for estimating travel time distributions for corridors that contain multiple bottlenecks. A set of analytical equations are used to calculate the number of queued vehicles ahead of a probe vehicle and further capture many important factors affecting travel times: the prevailing congestion level, queue discharge rates at the bottlenecks, and flow rates associated with merges and diverges. Based on multiple random scenarios and a vector of arrival times, the lane-by-lane delay at each bottleneck along the corridor is recursively estimated to produce a route-level travel time distribution. The model incorporates stochastic variations of bottleneck capacity and demand and explains the travel time correlations between sequential links. Its data needs are the entering and exiting flow rates and a sense of the lane-by-lane distribution of traffic at each bottleneck. A detailed vehicle trajectory data-set from the Next Generation SIMulation (NGSIM) project has been used to verify that the estimated distributions are valid, and the sources of estimation error are examined.

The centennial trends in the surface wind speed over North America are deduced from global climate model simulations in the Climate Model Intercomparison Project—Phase 5 (CMIP5) archive. Using the 21st century simulations under the RCP 8.5 scenario of greenhouse gas emissions, 5–10 percent increases per century in the 10 m wind speed are found over Central and East-Central United States, the Californian Coast, and the South and East Coasts of the USA in winter. In summer, climate models projected decreases in the wind speed ranging from 5 to 10 percent per century over the same coastal regions. These projected changes in the surface wind speed are moderate and imply that the current estimate of wind power potential for North America based on present-day climatology will not be significantly changed by the greenhouse gas forcing in the coming decades.