Abstract

     A major problem with deep generative models is verifying that the generated distribution resembles the target distribution while the individual generated sample is indistinguishable from the original data. In particular, for application in astrophysics we need to be sure that the generated data matches our prior knowledge and that the generated samples entail all object types with the correct frequency and diversity. We currently lack objective ways to systematically assess these quality aspects, where human inspection reaches its limits, as this requires detailed analysis of a large data volume. In this work, we identify reasonable metrics for the quality of galaxy image generators. To this end, we compare a small set of conditional image generators, trained on galaxy images with classification labels for visual morphology features. Our main contribution is a new set of cluster-based metrics for matching the generated distribution to the target distribution. Furthermore, we use the Wasserstein distance on proxies for galaxy morphology as well as a number of other metrics commonly used for image generators. The newly introduced cluster-based metrics are good proxies for the quality of the generated distribution and are suited for automatized identification of mode collapse. Furthermore, the cluster metrics allow for a qualitative interpretation of the generated distribution. The metrics based on morphological statistics provide a useful tool to probe the physical soundness of generated samples. Finally, we find that kernel inception distance used with an InceptionV3 model pre-trained on ImageNet is a good proxy for the overall quality of galaxy image generators, although it cannot be interpreted that easily.

Introduction

     Upcoming astronomical surveys with telescopes, such as Euclid (Laureijs et al., 2012) and LSST (Abell et al., 2009), will provide a wealth of data with billions of galaxy images. These hold unprecedented insight in highly researched astrophysical and cosmological questions, such as formation and evolution of galaxies, the cosmic distribution of dark matter, as well as the expansion history of the Universe (Laureijs et al., 2011). However, the huge amount of images is far too big to be investigated by astrophysicists on an individual basis. Instead, fast and systematic extraction of galaxy properties from their images is required in order to inform and constrain physical models. There are a number of computational tools that have been developed and are already in use by the astrophysical community (Rodriguez-Gomez et al., 2019, Boquien et al., 2019, Shamir, 2011). Though these allow for a systematic extraction of properties, they require too much computational resources to provide big collections of high quality mock images in a reasonable amount of time. Hence, it is necessary to replace these tools by faster methods, e. g. by using machine learning techniques, such as deep neural networks (Lovell et al., 2019, Ferreira et al., 2020, Walmsley et al., 2022 e. g.).

Conclusions

     In order to find suitable evaluation metrics to assess the quality of galaxy image generators, we investigate a number of evaluation metrics with the potential to measure different quality aspects. These metrics are probed with a small number of conditional generative models, some of which purposely are of worse quality than the others. We identified evaluation metrics that are good proxies for the quality of individual images and the resemblance of the target distribution. Our main results are:

• The newly introduced cluster-based metrics (Section 3.1) are a formidable new tool to assess the distribution of generated data.

• The KID metric on features of the pre-trained InceptionV3 model (Szegedy et al., 2015) provides a useful proxy on the overall quality of galaxy image generators.

• Using an ALReLU activation (Mastromichalakis, 2020) significantly enhances classification accuracy for rare object types. This is required to train conditional generators on the highly imbalanced dataset of galaxy images.

     We introduce a new set of cluster-based metrics (Section 3.1), which are good proxies for the distribution quality. In particular, they allow for a qualitative interpretation regarding the sample diversity and the amount of samples generated for different types. Thus, they provide an intuitive new tool to assess the resemblance of the target distribution in a qualitative way, which so far has been a rather illusive task. In addition, one of the cluster metrics, the cluster error provides a formidable tool to identify mode collapse. Moreover, the cluster metrics have the potential to be transformed into a loss function to train generative models to reproduce the distribution of training data exactly, or any other target distribution, e. g. balanced datasets. Together, the aforementioned metrics provide a good basis to assess the quality of trained image generators, which is not necessarily limited to the context of galaxy images. We are currently working on providing losses based on these cluster metrics, which have the potential to drastically improve training of generative models regarding distribution quality, which is especially valuable for GANs.

1- Introduction

2- Model and principle

3- Results and discussion

4- Conclusions

References

Abstract

As an indirect imaging technique, computational ghost imaging (GI) obtains the object information by calculating the intensity correlation between a series of computer-generated matrices and the corresponding bucket signals, which thereby offers a potential application in optical encryption. Here, we propose a new steganography scheme, called ghost steganography, based on the principle of computational GI. In our ghost steganography scheme, the bucket intensity signals of a secret image are concealed into the ones of a non-secret image by applying a non-conspicuous number integration process. To further increase the security, we introduce RSA cryptography to encode the integrated bucket signals after the steganography process. Simulation and experiment results fully demonstrate the feasibility of our optical ghost cryptography and steganography scheme. Our work paves a way to the application of GI in steganography and also enriches the knowledge of symmetric and asymmetric optical cryptography.

Introduction

Ghost imaging (GI), also known as correlated imaging, is an indirect imaging modality which obtains the object information from the intensity fluctuation correlation of two beams. One beam, called object beam, going through the object, is measured by a bucket detector. The other beam, called reference beam, interacting without the object, is detected by a spatially resolved camera. GI was firstly achieved with entangled photon pairs experimentally in 1995 [1], and later extended into the classical region with various thermal light sources [2–11]. Different from the two-detector GI, Shapiro proposed the computational GI in 2008 [12], which generated the active illumination patterns by using the spatial light modulator instead of passive measurements of reference beam. Similar to the single-pixel camera technique [13], computational GI can recover the object image with only a single-pixel detector [14,15], which largely simplifies the experimental setup. Meanwhile, the correlation between the computer-generated random matrix and the object beam intensity of computational GI offered a potential application in optical encryption [16-30]. To increase the efficiency and security of optical encryption based on GI [16], different methods are proposed and developed, including gray-scale and color encryption [17], multiple-image encryptions [18,26,29], specific phase masks schemes [22,25,27,30], XOR operation scheme [24].

1- Introduction

2- Theory and proposed scheme

3- Result and discussion

4- Conclusion

References

Abstract

This paper reveals an analytical perception of electro-optically tunable cascaded Kerr-lens using RbTiOPO4 (RTP) crystal where the advantageous properties of the crystal have been studied to mitigate the limitations of KTiOPO4 (KTP) and BaB2O4 (BBO) in intracavity high power laser generation. An external electric field application to the z-direction of RTP is introducing a phase mismatch condition leading to a nonlinear phase shift within propagating optical waves. Based on simulated endeavors, a maximum effective nonlinear refractive index of ± 1.4053 × 10−13 cm2 /W is calculated by developing a phase shift of ∓4.6 rad. In contrast, KTP and RTP both have almost similar optical properties but due to certain special features like higher damage threshold, less walk-off angle etc., RTP is firmly accepted for high power applications. Additionally, this article is augmented to analyze the self-focusing and defocusing phenomenon owing to the nonuniform distribution of radial intensity of the propagating Gaussian beam. To the best of author’s knowledge, the present analysis manifests the superior performance of RTP that may pave the way to devise a configurable lens analogous to Kerr-lens having an electro-optically tunable focal length.

Introduction

In the past several decades [1–5], cascaded second-order nonlinearity has played a vital role in the generation of cascaded Kerr lens mode-locking [6] that is nothing but a successive combination of two second harmonic generation (SHG) processes (χ(2):χ(2) development) that yields an intensity-dependent large phase mismatch in propagating fundamental waves. Accordingly, the nonlinear phase shift because of the non phasematched condition is also relative to the intensity of the incident beam that is analogous to Kerr medium (χ(3) nonlinearity) [6,7]. Moreover, the performance of this feature may enlarge the third-order nonlinearity of a medium having weak third-order coefficient [8] which is grander than the natural third-order nonlinearity [5] associated with the Kerr electro-optic effect as well. As a consequence of that, cascaded second-order nonlinearity equivalent to third-order nonlinearity [2] plays a significant role in several areas like transistor action [9], electro-optical detection [10], electro-optic switching [11], selffocusing and self-defocusing [9] etc. [12–15]. Although different techniques are emphasized to develop cascaded second-order nonlinearity using various types of crystals where problems like undesirable cavity loss, inaccuracy of incident beam angle, etc [5,16]. have occurred that deters a controllable desired output. In recent years [17,18], an analytical method has been introduced named “electro-optically tunable cascaded nonlinearity” in an exceedingly bulk anisotropic crystals like Potassium Titanyl Phosphate (KTiOPO4, KTP) and Beta Barium Borate (BaB2O4, BBO), etc.

Nomenclature

1. Introduction

2. Experimental section

3. Result and discussion

4. Conclusions

Acknowledgement

References

Abstract

The chemical looping process on honeycomb reactor for solar syngas is experimentally studied in this work, which is the key reaction of the liquid sunshine production process. The honeycomb reactor realizes the integration of oxygen carrier and reaction chamber. NiO is placed in the reactor as oxygen carrier and methane is introduced as fuel gas. The results show that, with the development of process, the major reaction in the reactor gradually changed from methane complete oxidation to methane partial oxidation. During the process, the methane conversion and outlet syngas concentration is affected by the methane flow and fractional oxidation. Under the optimal operating conditions, the methane conversion can be maintained more than 95% and the concentration of outlet syngas can be around 90%. Compared with non-honeycomb fixed bed reactor, the methane conversion increases by more than 20 percent point and the concentration of outlet syngas increases by about 10 percent point. In addition, oxygen carrier in honeycomb reactor shows excellent cyclic stability in 30 times experiments.

Introduction

When the consequences of the overuse of fossil fuels come, are we prepared to deal with them? Since the first industrial revolution, fossil fuels have been the primary source of global energy. Behind the exponential progress powered by fossil fuels are the potential of energy crisis and the environmental deterioration [1,2]. Nowadays, annual carbon dioxide emissions are about twice as much as that can be absorbed by nature [3]. If we continue to rely on fossil fuels, the resulting climate change and air pollution will threaten our life [4]. Sunlight is the most abundant source of energy on earth, and it can provide about 885 million TWh of energy in a year, surpassing other kinds of renewable energy by hundreds of times [5]. But it is hard to use it as easily as you flip a switch due to the decentralization and discontinuity of solar [6]. If we want to capture, store sunlight and supply it as energy source, the key process is to convert it into a stable, storable, highenergy-density chemical fuel.

The liquid sunshine vision is increasingly appealing to researchers [7]. Liquid fuels are not difficult to transport and store, and it can be widely used with some improvements to existing infrastructures. Liquid sunlight is designed to convert sunlight into liquid fuels such as methanol. Methanol is an attractive candidate because of its wide application. There are no C-C bonds in methanol which can effectively reduce the greenhouse gas emission due to its low carbon to hydrogen ratio [7]. As the raw material of methanol production, the preparation of solar syngas is a key process in current researches [8–11].

1- Introduction

2- Methodology

3- Results

4- Discussion

5- Conclusion

References

Abstract

Weather radar can provide spatially explicit precipitation grids. However interference, ground clutter and various causes of attenuation introduce uncertainty into the result. Typically, rain gauge observations, recognized as a precise measure of precipitation at point locations, are used to adjust weather radar grids to obtain more accurate precipitation maps. This adjustment involves one or more of various geostatistic techniques. Yet, since gauges are sparsely located, a geostatistic approach is sometimes limited or even not applicable.
This work adopts an alternative to radar adjustment by merging location-based variables with rain grids from weather radar. Recognizing that location-based variables: elevation, slope, aspect and distance from the coast all affect precipitation, these are applied to the original weather radar grid to produce an altered precipitation distribution.
The merging procedure presented here uses fuzzy logic, whereby all variables, as well as the original radar are assigned probabilities known as membership functions (MF), then a joint membership function (JMF) combines all MFs in the fuzzy set, each multiplied by its weight, to create a precipitation probability grid. This JMF probability grid is validated with gauge observation data. We show up to 30% higher correlation coefficients between gauges and the JMF grid than between gauges and the original radar. The improved correlation results from the flexibility of fuzzy logic in transforming location-based variables to probabilities.

Background

Estimating spatially distributed precipitation grids is a prerequisite to flood management and flood forecasting (Merz et al. (2014)). Hydrological models need basin-scale, spatially explicit precipitation data, among other inputs, to construct accurate flood forecasts (Todini et al. (2005)) for surface runoff management. Rain radar can produce such spatial precipitation distributions, however the challenges in calibrating and correcting for the various sources of error (detailed in Villarini et al. (2008)) create spatial and temporal uncertainty in the precipitation distribution (Cecinati et al. (2017), Krajewski and Smith, 2002). Nevertheless, the underlying motivation for research in improving precipitation maps rests in the needs of hydrological modeling and flood forecasting. Since weather radar became an accepted source of spatially distributed rainfall (Krajewski and Smith (2002), Morin et al. (2003)), extensive research has examined adjustment procedures to merge rain gauge observations with weather radar. Gauge data are accepted as reference observations (see for example Colli et al. (2013)), but represent point locations. Such point data can adjust weather radar grids through several geostatistic methods, reviewed and evaluated by Goovaerts (2000), Berndt et al. (2014) and McKee and Binns (2016). Kriging based methods have been examined by Kebaili Bargaoui and Chebbi (2009), Adhikary et al. (2017), and Ly et al. (2013). A comparison of various kriging methods where elevation was the secondary variable was done by Carrera-Hernández and Gaskin (2007). Another unique algorithm known as Conditional Merging, developed and evaluated by Sinclair and Pegram (2005), applies multiple kriging steps to achieve successful adjustment (Kim et al. (2007)) of weather radar grids.

Introduction

Material and methods

Results and discussion

Conclusions

References

Abstract

In nonlinear optics, the soliton transmission in different forms can be described with the use of nonlinear Schrödinger (NLS) equations. Here, the soliton transmission is investigated by solving the NLS equation with the reciprocal of the group velocity b1ðzÞ, the group velocity dispersion coefficient b2ðzÞ and nonlinear coefficient cðzÞ. Two-soliton solutions for the NLS equation are obtained through the Hirota method. According to the solutions obtained, b1ðzÞ and cðzÞ with different function forms are taken to study the characteristics of solitons. The effect of the phase shift on the soliton interaction is discussed, and the non-oscillating soliton amplification, which is transmitted in a bound state, is explored. Parabolic solitons with oscillations are analysed. Moreover, parabolic solitons can be reduced to dromion-like structures. Results indicate that the transmission of solitons can be adjusted with the group velocity dispersion and Kerr nonlinearity coefficients. The phase shift, amplification, oscillation and attenuation of solitons can also be controlled by other related parameters. This work accomplishes the theoretical study of transmission characteristics of optical solitons in spatially dependent inhomogeneous optical fibres. The conclusions of this research have theoretical guidance for the research of optical amplifier, all-optical switches and mode-locked lasers.

Introduction

Solitons have been investigated in such fields as mathematics and physics [1–6]. They can propagate in a long distance without changes in their waveform, velocity and amplitude [7–10]. The soliton phenomena are closely related to nonlinear evolution equation models [11–17]. Solitons are also applied in particle physics, fluid mechanics, Bose-Einstein condensation and nonlinear optics [18–20]. Some researchers have studied solitons by solving the nonlinear evolution equations and analysing their soliton solutions [21–25]. As one of the classic nonlinear evolution equations, the nonlinear Schrödinger (NLS) equation can be solved to obtain soliton solution, and has been widely investigated by using different methods [26–32]. With the differential quadrature method, the dynamic problems constructed by the NLS equation have been analysed [26]. The existence and stability of the standing wave solutions for the NLS equation in n-dimensional space have been studied [27]. Using the generalized exponential rational function, a new method to solve the exact special solutions of the NLS equation has been proposed [28]. The unified method has been used to acquire optical soliton solutions of the NLS equation [29]. Moreover, the stability of full dimensional KAM tori for the NLS equation has been proved [30]. Local Cauchy theory for the NLS equation has been discussed [31], and nonlinear instability of half-solitons has been analysed [32]. However, the soliton transmission process can be simulated more accurately with the variable coefficient NLS (vcNLS) equation, when the transmission medium or boundary condition is not uniform [33–40]. For the vcNLS equation, dynamics of solitons have been explored [33], and soliton interactions have been discussed [34,35]. In addition, the breather-to-soliton transitions for the vcNLS equation have been found [36], and nonautonomous multipeak solitons have been obtained [37].

Underwater imaging has always been a challenge due to limitations imposed by scattering and absorption nature of the underwater environment. The light would be highly degraded after reflection and propagation in the water medium. Being an advanced imaging technique, Single-pixel Imaging (SPI) is applicable to acquire object spatial information in low light, severe backscattering, and high absorption conditions. Combination of Compressive Sensing (CS) and SPI can overcome the limitation of SPI algorithms such as long data-acquisition time, low reconstruction efficiency and poor reconstruction quality. In the current research, an underwater SPI system based on CS is established to reconstruct our two-dimensional (2D) transparent object. We have systematically investigated the influence of water turbid degree, measurement pattern types and number of measurements on image reconstruction performance. The proposed system is capable to reconstruct the object even when the turbidity reaches up to 80 Nephelometric Turbidity Unit (NTU), where the conventional imaging systems are unusable. Proposed reconstruction method in our research can save more than 70% data acquisition time, compared to SPI algorithm. Our experimental setup has been compared to a conventional imaging system and an underwater ghost imaging system to show its efficiency in obtaining accurate results from turbid water conditions. Furthermore, various algorithm comparison and imaging enhancement studies demonstrates that our algorithm is superior in bringing highly convex optimization at a faster rate with a smaller number of measurements. This work creates new insight into the SPI application and generates a guideline for researchers to improve their applications.

Introduction

Imaging in an underwater environment is difficult particularly in turbid media due to backscattering and absorption effects. Various researches have been done aiming to capture the images of objects in underwater environments [1], [2] and underwater imaging enhancement [3], [4]. Due to the presence of faster, more dynamic computers and strong fundamental spatial light mapping mechanisms such as Spatial Light Modulator (SLM) and Digital Micromirror Device (DMD) developed in the last decade comprehensive investigations have been done in the field of imaging which employs only a single pixel detector noted as Single Pixel Imaging (SPI) [5]–[7]. Compared to the conventional method, SPI makes it possible for the imaging system to use a low-cost single-pixel photodetector rather than an expensive multi-pixel Complementary Metal Oxide Semiconductors (CMOS) or Charge-Coupled Devices (CCD). It is convenient and efficient to manufacture single-pixel detectors with a large active area, which makes SPI techniques more appropriate for imaging, especially in low illumination conditions (underwater and foggy medium, etc.) than traditional imaging techniques. The limited response of detection medium to a certain bandwidth has been a flaw of conventional imaging systems. The same drawback of the conventional system has become the major advantage of SPI because it can make measurement relatively in a much wider range of the electromagnetic spectrum.

Repetitive control is an effective method to eliminate the effects of a periodic disturbance on a control system. In some applications the period is not known with sufficient accuracy, or the period may fluctuate sufficiently to seriously compromise performance. Second order repetitive control has been developed in response to this issue. Existing methods of analyzing stability for such systems are either very difficult to use, or excessively conservative sufficient conditions. A novel second order repetitive control is proposed here which achieves the behavior of second order but uses data from only one period in the past. This better meets the requirements for real time storage, but more importantly, it allows us to develop a stability criterion that is much less conservative. The experimental results demonstrate that the suppression effect can achieve a high level under frequency fluctuation with the proposed control method.

Introduction

A novel form of second order repetitive control (RC) is proposed in this paper, which can greatly facilitate the stability analysis. The motivation of the study originates from the urgent requirements of micro-vibration spacecraft. Spacecraft often suffer from vibrations produced by the rotating wheels used as attitude control actuators, i.e., reaction wheels or control moment gyros [1]. This degrades the performance of fine pointing equipment on board. Passive vibration isolation methods have limited performance, hence active vibration cancellation strategies are considered [2]. Laser communications between spacecrafts or with ground offer a different option of canceling the influence of spacecraft jitter by control methods applied to pan and tilt of the outgoing mirror [3] that cancels vibrations in the beam. The hardware demonstration of the proposed second order repetitive control approach, considers magnetically suspended rotor (MSR) performance, which can have disturbances of rotor mass imbalance and sensor runout. The objective is to reduce the vibrations at their source in the spacecraft, instead of at the sensor location of fine pointing equipment by vibration isolation.

The goal of this study was to model the total leaf chlorophyll content (LCCtot) of Gannan navel orange leaves using a field imaging spectroscopy system in the visible and near-infrared domain. The spectral range from 400 to 1000 nm with 176 wavebands (a wavelength interval of 3.41 nm) or 360 wavebands (a wavelength interval of 1.67 nm), labeled as ‘‘Datasets_1.67’’ and ‘‘Datasets_3.41’’, respectively, were used. Although different spectral data types were used, better prediction results for LCCtot were based on Datasets_1.67 for LCCtot prediction. Several prediction models of LCCtot were built based on partial least squares regression (PLSR), artificial neural networks (ANN), ordinary least squares regression (OLSR), and stepwise linear regression (SLR) using full spectral and effective wavelength (EW) data (raw spectral (RS), first derivative spectral (FDS) and second derivative spectral (SDS) data). The determination coefficient (R2 ), the root mean square error (RMSE) and the residual predictive deviation (RPD) were used to evaluate the reliability and accuracy of the predicted LCCtot values. As a result, 14 (7 obtained from Datasets_1.67, 7 obtained from Datasets_3.41), 39 (21 obtained from Datasets_1.67, 18 obtained from Datasets_3.41) and 50 (27 obtained from Datasets_1.67, 23 obtained from Datasets_3.41) wavebands were selected from the RS data, FDS data and SDS data, respectively, as the EWs for LCCtot prediction of navel orange leaves. After that, PLSR and ANN predictive models were established using full spectra, and OLSR and SLR predictive models were built using the selected EWs. The experimental results demonstrated that these various regression methods were useful for estimating LCCtot in the order of PLSR models established using full spectra from RS data (F-RS-PLSR) > PLSR models established using full spectra from SDS data (F-SDS-PLSR) > PLSR models established using full spectra from FDS data (F-FDS-PLSR) > SLR models established using EWs by RS data (EWs-RS-SLR). However, models built with ANN and OLSR, where the RPD values were less than 3, cause the models to be inaccurate. Finally, in comparison, the F-RSPLSR model exhibited the best performance of LCCtot estimation; with the number of principal components (Pcs) = 5, this model provided high values of the R2 of calibration (C-R2 ) = 0.92 and the R2 of validation (V-R2 ) = 0.96, small values of the RMSE of calibration (C-RMSE)=0.05 mg/g and the RMSE of validation (V-RMSE) = 0.19 mg/g, and sufficient the RPD of calibration (C-RPD)=17.00 and the RPD of validation (V-RPD)=3.63 values. Overall, the best modeling method was PLSR. Hence, the PLSR applicability for assessing chlorophyll content in navel orange leaves was demonstrated.

Introduction

Chlorophyll is the main photosynthetic pigment present in green plants and plays an important role in controlling carbon exchange and plant productivity [1], [2]. The chlorophyll content increases in young expanding leaves, reaches the highest value at maturity, and then decreases significantly during senescence [3], [4]. Therefore, the chlorophyll content of plant leaves correlated with the nutritional status can theoretically be used as a marker of the growth status of plants. Measurements and estimates of chlorophyll content are regarded as a meaningful indicator of plant health, including nitrogen deficiency, water stress and certain diseases [2], which can provide theoretical guidance for crop nutrient diagnosis and field management. The traditional wet-chemical method for measuring chlorophyll is precise but costly, time-consuming and inapplicable to large-scale analysis. Hence, scientists have been developing convenient and rapid methods for the measurement of leaf chlorophyll utilizing its unique optical absorption feature. Extracting chlorophyll information from the spectral features of plants has become a major means of estimating chlorophyll contents because of its advantages of being fast, nondestructive and large-scale [1], [5]–[8]. Numerous studies have been conducted using spectral data to retrieve chlorophyll information as a function of time and space in environments such as the ground or airborne and spaceborne environments [9]–[16].

1- Introduction

2- Data acquisition and control system

3- Capabilities

4- Conclusion

References

Abstract

The application possibilities of the nuclear microprobe are constantly evolving which places new requirements on the data acquisition and microbeam control systems. MeV SIMS experiments for example, require the ability to pulse a continuous beam and steer it in the X-Y plane. For channeling RBS and STIM experiments, the sample needs to be rotated over two axes. Once it is positioned in the channeling direction, patterned irradiation may be needed. Additionally, with the evolution of digital electronics, it is now possible and sometimes preferred to perform signal processing in the digital domain as oppose to analogue signal chains in NIM modules.
In this work we present the most recent upgrades to the data acquisition and control system developed at Rudjer Boskovic Institute. A data acquisition/control system based on a Xilinx Virtex 6 FPGA was developed which can evolve with the microprobe and be reconfigured for various applications. The real time reprogrammable nature of the FPGA coupled with a modular design approach, allow for the ADCs, processing algorithms and communication protocols to be interchanged and upgraded while keeping a constant user interface through the SPECTOR software package.

Introduction

The modern nuclear microprobe is a very flexible platform on which a wide variety of experiments can be performed. To maintain this degree of flexibility, the data acquisition and control system has to be able to interconnect and synchronize the many different subsystems that make up a nuclear microprobe. Not every experiment performed on the microprobe is the same and therefore beam modification requirements, sample positioning and even the type of data acquisition varies. The standard solution is to use nuclear instrumentation modules (NIM) and manually connect only the modules that are required for the experiment. NIMs are the foundation of the nuclear data acquisition and control industry because they provide a flexible, reconfigurable, easy to use platform which can be adapted and used for a wide variety of experiments. However experiments today have become very complex and require precise control of many parameters. This in turn requires many interconnected modules, which limits the performance, increases complexity and introduces noise. Digital Field Programmable Gate Array (FPGA) technology has evolved significantly and can be used in conjunction with NIM for modern experiments. FPGA technology has been readily used for DAQ systems and many commercial solutions exist as described by Bettiol et al. [1]. However no ideal solution exists and there is always a balance between the complexity of the DAQ system and its usability. This paper builds upon the work of M. Bogovac et al. [2] by adapting more of a modular design philosophy and removing limitation of a closed system. The reprogrammable nature of FPGAs make it a versatile tool for a wide variety of applications and to fully take advantage of this, the hardware and software surrounding the FPGA has to be designed with this in mind. The implementation of this design philosophy to the upgrade of the data acquisition and control system for the nuclear microprobe and developments to the SPECTOR software package [3] are presented.

1- Introduction

2- Experimental

3- Model development

4- Results and discussion

5- Conclusion

References

Abstract

The effect of viscosity on heat transfer behaviour is analyzed for binary mixtures (ionic liquid + water and nanofluid + water) over the base fluid (water). Viscosities of 1-butyl-3-methylimidazolium bromide with water under ambient pressure are studied at different concentrations (0.1–0.6% w/w) and temperatures (296–336 K). Viscosities of nanofluid (γ-Al2O3/water) are estimated at the same concentration and temperature range and then compared with the ionic liquid solution. Viscosity of aqueous 1-butyl-3-methylimidazolium bromide solution increases with concentration and decreases with temperature. A similar trend is observed for γ-Al2O3/water. In addition, a model is developed with response surface methodology and artificial neural network to predict the viscosity of 1-butyl-3-methylimidazolium bromide with water.

Introduction

Viscosity is a property which describes the internal friction of a moving fluid. It is an important thermophysical property in fluid flow and heat transfer. In many fluid flow problems, viscosity is assumed to be a constant parameter. However, viscosity varies with temperature when the flow is associated with transfer of heat. To accurately model the flow behaviour and estimate heat transfer rates, it is essential to consider the variation of viscosity with temperature [1,2]. When compared with other thermophysical properties viscosity demonstrates a considerable variation. Hence it becomes necessary to incorporate the correction in the fluid flow model based on the behaviour of fluid with respect to temperature-dependent viscosity [3]. The variation in temperature-dependent viscosity plays a major role in laminar heat transfer and friction factor for non-circular conduits or ducts of different geometries [3–5]. It has been shown that the variation in temperaturedependent viscosity in a curved circular tube with water as a working fluid, plays a relatively major role on velocity and thermal profiles, compared to other thermophysical properties like specific heat, thermal conductivity and density [6]. In regards to viscosity variation, it results in a larger local Nusselt number when compared with that obtained from using constant viscosity [3,7]. The use of constant viscosity ignores the effect of decreased flow resistance leading to the generation of the secondary flow of fluid and its impact on convective heat transfer enhancement. Frictional losses in the fluid – the result of the microscopic interaction of molecules between layers – are governed by the viscosity of the fluid.