Key Physical Parameters Research Articles

Unsupervised classification of tissues composition for Monte Carlo dose calculation

The purpose of this study is to investigate the potential of k-means clustering to efficiently reduce the variety of materials needed in Monte Carlo (MC) dose calculation. A numerical phantom with 31 human tissues surrounded by water is created. K-means clustering is used to group the tissues in clusters of constant elemental composition. Four different distance measures are used to perform the clustering technique: Euclidean, Standardized Euclidean, Chi-Squared and Cityblock. Dose distributions are calculated with MC simulations for both low-kV photons and MeV protons using the clustered and reference elemental composition. Comparison between the dose distributions in the clustered and non-clustered phantom are made to assess the impact of clustering with each distance measure. The statistical significance of the differences observed between the four different metrics is determined by comparing the accuracy of energy absorption coefficients (EAC) of low-kV photons and proton stopping powers relative to water (SPR) for repeated clustering procedures. The performance of the proposed approach for a larger number of original materials is evaluated similarly by using a population of 62 000 statistically generated materials grouped into classes defined with supervised and unsupervised classification. In the phantom geometry, the Chi-Squared distance is the one introducing the smallest error on dose distribution and significant differences are observed between the EAC and SPR values predicted by each distance metric. The proposed approach is also shown to be equivalent to a state-of-the-art supervised classification method for proton therapy, but beneficial for low-kV photons applications. In conclusion, k-means clustering successfully reduces the variety of materials needed for accurate MC dose calculation. Based on the performance of four distance measures, we conclude that k-means clustering using the Chi-Squared distance introduces the smallest errors on dose distribution. The method is shown to yield similar or improved accuracy on key physical parameters compared to supervised classification.

Multi-objective optimisation and fast decision-making method for working fluid selection in organic Rankine cycle with low-temperature waste heat source in industry

In China, the utilisation of low-temperature waste heat (especially at temperatures lower than 100 °C) plays a significant role in increasing the energy-consumption efficiency in the industry. The organic Rankine cycle (ORC) is considered as a promising method to recover the aforementioned part of the waste heat. In the study, six potential candidates, namely R141b, R142b, R245ca, R245fa, R600a, and R601a were screened from 12 dry or adiabatic organic working fluids based on their thermodynamic performances in the ORC. A multi-objective optimisation (MOO) was performed for the thermodynamic performance (exergy efficiency, EXE) and economic performance (levelised energy cost, LEC) by using non-dominated sorting genetic algorithm-II (NSGA-II). The Pareto frontiers were obtained for the six candidates with the algorithm, and each optimal compromise solution was accurately obtained with the fuzzy set theory. Based on the EXE and LEC of the optimal compromise solution, the total cost and power generation efficiency for the six candidates were determined. This was used to obtain an explicit evaluation index in economic performance, namely static investment payback period (SIPP), to identify that the R245ca corresponded to the most cost-effective working fluid with the shortest SIPP. This suggests R245ca was the fastest to cover the investment and cost of the ORC system. Furthermore, a fast decision-making method was introduced to select the optimal working fluid based on the grey relational analysis (GRA) by considering key physical property parameters of the working fluids. The results suggest that any potential working fluid to recover low-temperature waste heat in the ORC can be evaluated by the simplified grey relational degree (SGRD) proposed in the study.

A Bayesian hierarchical model for glacial dynamics based on the shallow ice approximation and its evaluation using analytical solutions

Abstract. Bayesian hierarchical modeling can assist the study of glacial dynamics and ice flow properties. This approach will allow glaciologists to make fully probabilistic predictions for the thickness of a glacier at unobserved spatiotemporal coordinates, and it will also allow for the derivation of posterior probability distributions for key physical parameters such as ice viscosity and basal sliding. The goal of this paper is to develop a proof of concept for a Bayesian hierarchical model constructed, which uses exact analytical solutions for the shallow ice approximation (SIA) introduced by Bueler et al. (2005). A suite of test simulations utilizing these exact solutions suggests that this approach is able to adequately model numerical errors and produce useful physical parameter posterior distributions and predictions. A byproduct of the development of the Bayesian hierarchical model is the derivation of a novel finite difference method for solving the SIA partial differential equation (PDE). An additional novelty of this work is the correction of numerical errors induced through a numerical solution using a statistical model. This error-correcting process models numerical errors that accumulate forward in time and spatial variation of numerical errors between the dome, interior, and margin of a glacier.

Calculation method of ion mobility spectrum for single‐measurement‐electrode Gerdien tubes

The ion mobility and ion density are key physical parameters in research of ion flow field of high-voltage DC (HVDC) transmission lines. The Gerdien tube is one of the practical tools that can be utilised in situ measurement of ion mobility and ion density of coronated lines. The mobility spectrum, connecting the ion mobility and ion density, is an effective approach in analysis of the characteristics of corona ions and the effects of environmental factors on them. In previous studies, single measurement-electrode Gerdien tube is frequently used to get the mobility of one single ion via graphical method or the total ion density when the tube is operating as a counter. To enable it to obtain the ion mobility spectrum, a method is devised by the analysis of graphical method and the transition phenomenon in current–voltage characteristics of the tube. Furthermore, the method is applied to the raw data of current–voltage characteristics published in the literatures. The ion mobility spectra are obtained and compared with those having been reported. By doing this, the method is validated. This new approach makes applications of Gerdien tubes more advantages in experimental researches of ion flow field of HVDC transmission lines.

Propagation Modeling for Wireless Communications in the Terahertz Band

Terahertz band (0.1-10 THz) communication is envisioned as a key technology to support ultra-broadband wireless systems for beyond 5G. For realization of efficient wireless communication networks in the THz band, it is imperative to develop channel models that can accurately and efficiently characterize the THz spectrum peculiarities. This article provides an in-depth view of channel modeling in the THz band, based on the deterministic, statistical, and hybrid methods. The state-of-the-art THz channel models in single-antenna and ultra-massive MIMO systems are extensively reviewed, respectively. Furthermore, the open challenges and potential research directions are highlighted regarding THz propagation modeling. Associated with the channel models, key physical parameters of the THz channel and their implications for wireless communication design are analyzed. The provided analysis lays the foundation for reliable and efficient ultra-broadband wireless communications in the THz band.

Evolution simulation of lightning discharge based on a magnetohydrodynamics method

In order to solve the load problem for aircraft lightning strikes, lightning channel evolution is simulated under the key physical parameters for aircraft lightning current component C. A numerical model of the discharge channel is established, based on magnetohydrodynamics (MHD) and performed by FLUENT software. With the aid of user-defined functions and a user-defined scalar, the Lorentz force, Joule heating and material parameters of an air thermal plasma are added. A three-dimensional lightning arc channel is simulated and the arc evolution in space is obtained. The results show that the temperature distribution of the lightning channel is symmetrical and that the hottest region occurs at the center of the lightning channel. The distributions of potential and current density are obtained, showing that the difference in electric potential or energy between two points tends to make the arc channel develop downwards. The arc channel comes into expansion on the anode surface due to stagnation of the thermal plasma and there exists impingement on the copper plate when the arc channel comes into contact with the anode plate.

Non-equilibrium Thermodynamics of Fast Traveling Waves in a Catalytic Fixed Bed. Emergence of Near-equilibrium Spatiotemporal Dissipative Structure

AbstractThis work presents the results of the mathematical modeling of the fast traveling wave propagation phenomenon in the fixed-bed catalytic reactors according to a simple (basic) mathematical model with a reversible reaction. Qualitative and quantitative research is used to study the behavior of separatrices’ trajectories of the system’s non-linear ordinary differential equations. Special attention has been paid to the non-equilibrium thermodynamic methods. The entropy balance equation is constructed and analyzed under the assumption of the simple mathematical model of physical and chemical processes. The influence of key physical and chemical parameters on the fast traveling wave properties is studied. The phenomenon of fast traveling wave propagation in the fixed-bed catalytic reactors provides a vivid example of a spatiotemporal dissipative structure in active heterogeneous medium. These dissipative structures are shown to exist near the thermodynamic equilibrium.

Optical diagnostic systems based on Shenguang III

Optical diagnostic system plays an important role in inertial confinement fusion (ICF) experiment. Some key physical quantities and parameters in fusion can be diagnosed intuitively. In this review, three typical diagnostic systems on Shengang III facility are introduced, including imaging velocity interferometer system for any reflector (VISAR), fusion-reaction diagnostic system and backscatter light diagnostic system. VISAR can be used to measure velocity of free surface or shock wave traveling history in transparent media, which is an important diagnostic technique for shock wave velocity regulation, material compression characteristics and radiation temperature measurement. The fusion reaction-history diagnostic system can be used to accurately measure the fusion rate of ICF experiment. It’s a key diagnostic technique to study implosion dynamics and understand the mixed effect. Backscatter light diagnostic system is capable to measuring the energetic quotient stimulated by laser and plasma interaction (LPI), which produce important supports for calculating the coupling efficiency of driven laser and target.

Impacts of binary chemical reaction with activation energy on unsteady flow of magneto-Williamson nanofluid

The current review aims to study the combined effects of variable magnetic field and heat generation/absorption on unsteady flow of non-Newtonian Williamson fluid generated by a stretching cylinder in the presence of nanoparticles. An important prospective of this endeavour is to incorporate the impacts of binary chemical reaction and activation energy for revised Buongiorno's model of nanofluid in view of their improved heat transfer. The notion of Boussinesq-approximations is utilized to model the leading equations of momentum, thermal energy and nanoparticle concentration for Williamson fluid. We have employed suitable non-dimensional quantities to alter the leading partial differential equations (PDEs) into a set of ordinary differential equation (ODEs). The numerical simulation is conducted with the help of Runge-Kutta Fehlberg scheme coupled with shooting iteration procedure. The analysis of the obtained results revealed that the assumed physical model is significantly influenced by the key physical parameters, like, magnetic parameter, chemical reaction parameter, activation energy parameter, heat generation/absorption parameter, Brownian motion and thermophoresis parameter. The physical significance of above-mentioned physical parameters on nanofluid velocity, temperature and concentration is argued and exhibited through graphs. The cardinal physical intimation of obtained results is that the nanoparticles concentration enhances with higher activation energy parameter. Further, it is perceived from these results that the rate of heat transfer over the cylinder surface de-escalates with an increase in the reaction rate parameter. It is noted that an augmentation in thermophoresis parameter leads to a rise in nanofluid temperature.

Research on the fuel loading patterns of the initial core in Chinese pebble-bed reactor HTR-PM

Modular high-temperature gas-cooled reactor (HTGR) is a kind of safe and advanced nuclear energy system, which can provide electric power and high-temperature process-heat efficiently. At present, a 200 MWe demonstration plant, named high-temperature gas-cooled reactor – pebble-bed module (HTR-PM), is being built in China and is planned to be critical in one or two years. In this paper, the physical consideration on the fuel loading patterns of the initial core of HTR-PM is given, and the initial core of HTR-PM is proposed to be made up of low-enrichment fuel spheres and graphite spheres. Then several representative schemes are simulated using VSOP code. The relationship between the key physical parameters (fuel enrichment, flux distribution, temperature coefficient, power per fuel sphere, charging rate) and the volume fraction of fuel spheres in the initial core is analyzed. As a result, the feasible region of the volume fraction of fuel spheres in the initial core is determined as [5/15, 11/15]. The work of this paper can provide support for the fuel management of HTR-PM in the future.

Impact of city effluents on water quality of Indus River: assessment of temporal and spatial variations in the southern region of Khyber Pakhtunkhwa, Pakistan.

The impact of city effluents on water quality of Indus River was assessed in the southern region of Khyber Pakhtunkhwa, Pakistan. Water samples were collected in dry (DS) and wet (WS) seasons from seven sampling zones along Indus River and the physical, bacteriological, and chemical parameters determining water quality were quantified. There were marked temporal and spatial variations in the water quality of Indus River. The magnitude of pollution was high in WS compared with DS. The quality of water varied across the sampling zones, and it greatly depended upon the nature of effluents entering the river. Water samples exceeded the WHO permissible limits for pH, EC, TDS, TS, TSS, TH, DO, BOD, COD, total coliforms, Escherichia coli, Ca2+, Mg2+, NO3-, and PO42-. Piper analysis indicated that water across the seven sampling zones along Indus River was alkaline in nature. Correlation analyses indicated that EC, TDS, TS, TH, DO, BOD, and COD may be considered as key physical parameters, while Na+, K+, Ca2+, Mg2+, Cl-, F-, NO3-, PO42-, and SO42- as key chemical parameters determining water quality, because they were strongly correlated (r > 0.70) with most of the parameters studied. Cluster analysis indicated that discharge point at Shami Road is the major source of pollution impairing water quality of Indus River. Wastewater treatment plants must be installed at all discharge points along Indus River for protecting the quality of water of this rich freshwater resource in Pakistan.

Magnetic Nanotweezers for Interrogating Biological Processes in Space and Time.

The ability to sense and manipulate the state of biological systems has been extensively advanced during the past decade with the help of recent developments in physical tools. Unlike standard genetic and pharmacological perturbation techniques-knockdown, overexpression, small molecule inhibition-that provide a basic on/off switching capability, these physical tools provide the capacity to control the spatial, temporal, and mechanical properties of the biological targets. Among the various physical cues, magnetism offers distinct advantages over light or electricity. Magnetic fields freely penetrate biological tissues and are already used for clinical applications. As one of the unique features, magnetic fields can be transformed into mechanical stimuli which can serve as a cue in regulating biological processes. However, their biological applications have been limited due to a lack of high-performance magnetism-to-mechanical force transducers with advanced spatiotemporal capabilities. In this Account, we present recent developments in magnetic nanotweezers (MNTs) as a useful tool for interrogating the spatiotemporal control of cells in living tissue. MNTs are composed of force-generating magnetic nanoparticles and field generators. Through proper design and the integration of individual components, MNTs deliver controlled mechanical stimulation to targeted biomolecules at any desired space and time. We first discuss about MNT configuration with different force-stimulation modes. By modulating geometry of the magnetic field generator, MNTs exert pulling, dipole-dipole attraction, and rotational forces to the target specifically and quantitatively. We discuss the key physical parameters determining force magnitude, which include magnetic field strength, magnetic field gradient, magnetic moment of the magnetic particle, as well as distance between the field generator and the particle. MNTs also can be used over a wide range of biological time scales. By simply adjusting the amplitude and phase of the applied current, MNTs based on electromagnets allow for dynamic control of the magnetic field from microseconds to hours. Chemical design and the nanoscale effects of magnetic particles are also essential for optimizing MNT performance. We discuss key strategies to develop magnetic nanoparticles with improved force-generation capabilities with a particular focus on the effects of size, shape, and composition of the nanoparticles. We then introduce various strategies and design considerations for target-specific biomechanical stimulations with MNTs. One-to-one particle-receptor engagement for delivering a defined force to the targeted receptor and the small size of the nanoparticles are important. Finally, we demonstrate the utility of MNTs for manipulating biological functions and activities with various spatial (single molecule/cell to organisms) and temporal resolution (microseconds to days). MNTs have the potential to be utilized in many exciting applications across diverse biological systems spanning from fundamental biology investigations of spatial and mechanical signaling dynamics at the single-cell and systems levels to in vivo therapeutic applications.

Conduction of Metal–Thin Organic Film–Metal Junctions at Low Bias

A model for the low bias electric conductance of junctions, consisting of a thin organic film (TOF) positioned between two metallic electrodes (M), has been developed. In contrast with other theoretical studies, the proposed model relies on the energy band picture of M–TOF–M systems. Theoretical analysis of the band-like transport has shown that the electronic flow between metallic electrodes can exist in M–TOF–M junctions only if injected charge carriers are able to overcome the potential barrier with the thickness-dependent height. Such an obstacle to the motion of injected charges in the TOF conduction band arises due to the bending of this band caused by carriers localized in structural traps. Two regimes of the zero bias conductance of M–TOF–M junctions have been studied theoretically for situations, where charges overcome the thickness-dependent barrier undergoing either thermally activated or tunneling transitions. Analytical expressions derived for the zero bias conduction in these two regimes enable us to specify key physical parameters controlling charge transport across the film and provide results consistent with observations. On the basis of our findings, we infer that thermally activated and tunneling conductances can be distinguished by temperature and thickness dependencies. Theoretical results obtained for the electric conductance of M–TOF–M systems in the tunneling regime are compared with those obtained for assemblies in which TOF has been replaced by a single molecule. Distinctions between transport properties of these two systems and their similarities resulting from the present model are discussed.

Dispersive shock waves in systems with nonlocal dispersion of Benjamin–Ono type

We develop a general approach to the description of dispersive shock waves (DSWs) for a class of nonlinear wave equations with a nonlocal Benjamin–Ono type dispersion term involving the Hilbert transform. Integrability of the governing equation is not a pre-requisite for the application of this method which represents a modification of the DSW fitting method previously developed for dispersive-hydrodynamic systems of Korteweg-de Vries (KdV) type (i.e. reducible to the KdV equation in the weakly nonlinear, long wave, unidirectional approximation). The developed method is applied to the Calogero–Sutherland dispersive hydrodynamics for which the classification of all solution types arising from the Riemann step problem is constructed and the key physical parameters (DSW edge speeds, lead soliton amplitude, intermediate shelf level) of all but one solution type are obtained in terms of the initial step data. The analytical results are shown to be in excellent agreement with results of direct numerical simulations.

A new methodology for diesel surrogate fuel formulation: Bridging fuel fundamental properties and real engine combustion characteristics

Given the complexity and uncertainty of diesel compositions, it is of great challenges to study the fundamental combustion processes and mechanism of diesel fuel, in particular the gaseous and particulate pollutant formation mechanisms. A reasonably designed diesel surrogate is proved to be an effective way to study the fundamental combustion mechanism of diesel fuel. However, most existing diesel surrogates mainly adopt light hydrocarbon components, increasing the difficulty of accurately reflecting the physical and chemical properties of practical diesel fuel or the combustion and emissions characteristics of diesel engines. Therefore, in this study, a methodology to construct diesel surrogates with C10 ∼ C18 hydrocarbon components based on fuel properties and engine combustion and emissions characteristics was proposed. First, the key physical and chemical fuel properties that affect fuel injection, atomization, ignition, combustion, engine efficiency and emissions were discussed in detail. Second, 13 candidate components were chosen to represent n-alkanes, iso-alkanes, cycloparaffins and aromatics and blended with commercial diesel fuel in different proportions. Fuel injection, spray, ignition and combustion phase, engine efficiency and emissions versus changed blending component and proportion were systematically investigated. In particular, the effects of the cetane number and fuel volatility on combustion and emissions were investigated under fixed injection timing and fixed ignition timing conditions, respectively. Afterward, the key physical and chemical parameters of the surrogate fuels were defined, and the constraint equations were constructed for different parameters. Considered the uncertainties in the components and physicochemical properties of the actual diesel fuel, it is believed that the construction of surrogate fuel has only reasonable solutions and there is no optimal solution. Finally, three diesel surrogates were proposed from the feasible domains of constraint equations, including a 3-Components surrogate (41.3% n-hexadecane, 36.8% 2,2,4,4,6,8,8-heptamethylnonane (HMN), 21.9% 1-methylnaphthalene, by mol.), a 5-Components surrogate (21.6% n-hexadecane, 15.5% n-octadecane, 26.0% HMN, 20.7% 1-methylnaphthalene, 16.2% decalin, by mol.) and a 7-Components surrogate (21.5% n-hexadecane, 15.4% n-octadecane, 25.8% HMN, 13.7% 1-methylnaphthalene, 8.1% decalin, 8.1% n-butylbenzene, 7.4% n-butylcyclohexane, by mol.). It is found that the surrogates proposed in this study can accurately reproduce actual engine combustion characteristics. Also, the gaseous emissions of the 5-Components surrogate are closest to those of the target diesel fuel in the tested engine operation conditions.

Numerical investigation of thermal contact conductance between linear and curvilinear contacts

Heat transfer has considerable applications in different industries such as designing of heat exchanger, nuclear reactor cooling, control system for spacecraft and designing of microelectronics cooling. As the surfaces of two metals contact each other, this issue becomes so crucial. Thermal contact resistance is one of the key physical parameters in heat transfer of mentioned surfaces. Measuring the experimental value of thermal contact resistance in laboratory is highly expensive and difficult. As an alternative, numerical modeling methods could be engaged. In this study, Inverse problem method solution is utilized as a proper method for estimation of thermal contact resistance value. In this order, three different configurations (flat-flat, flat-cylinder, and cylinder-cylinder) were utilized in two steady and unsteady state conditions to predict the value of thermal contact resistance. In conclusion, the final results establish the fact that the inverse problem method solution can predict thermal contact resistance values between contacting surfaces.

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

Abstract. Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term (1966–2012) ground-based measurements from 1814 stations. Spatially, long-term (1971–2000) mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade−1 from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50° N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales.

Selected physical properties of various diesel blends

AbstractThe quality determination of biofuels requires identifying the chemical and physical parameters. The key physical parameters are rheological, thermal and electrical properties. In our study, we investigated samples of diesel blends with rape-seed methyl esters content in the range from 3 to 100%. In these, we measured basic thermophysical properties, including thermal conductivity and thermal diffusivity, using two different transient methods – the hot-wire method and the dynamic plane source. Every thermophysical parameter was measured 100 times using both methods for all samples. Dynamic viscosity was measured during the heating process under the temperature range 20-80°C. A digital rotational viscometer (Brookfield DV 2T) was used for dynamic viscosity detection. Electrical conductivity was measured using digital conductivity meter (Model 1152) in a temperature range from −5 to 30°C. The highest values of thermal parameters were reached in the diesel sample with the highest biofuel content. The dynamic viscosity of samples increased with higher concentration of bio-component rapeseed methyl esters. The electrical conductivity of blends also increased with rapeseed methyl esters content.

On-line Inertia Measurement of Unmanned Aerial Vehicles using on board Sensors and Bifilar Pendulum

Identification of a dynamical model and its parameter estimation is one of the fundamental problems in field of robotics and system dynamics modelling. For the general case of object motion with six degrees of freedom (6- DOF), such as in the case of Unmanned Aerial Vehicle (UAV), the key physical parameters are its mass and tensor of inertias. Even though UAV mass and its geometry/topology are easily obtainable, an identification of inertia tensor is difficult considering that is not measurable by static tests. This paper presents a simple and effective method for on-line estimation of a rigid-body inertia based on two-wire pendulum and integrated sensor system. Herein, the test subject (i.e. UAV) is suspended by two thin parallel wires in such a way to form a bifilar torsional pendulum about the vertical axis. Using on-board sensors from UAV flight controller (FC) unit, the pendulum oscillations are recorded and processed to obtain trend-free and noise-free signals used in the final inertia estimation phase based on inertia- related properties of pendulum oscillations. The proposed identification algorithm is verified experimentally for two typical cases of suspended objects related to UAV control box and full UAV configuration.

Electro-mechanical vibration analysis of functionally graded piezoelectric porous plates in the translation state

To provide reference for aerospace structural design, electro-mechanical vibrations of functionally graded piezoelectric material (FGPM) plates carrying porosities in the translation state are investigated. A modified power law formulation is employed to depict the material properties of the plates in the thickness direction. Three terms of inertial forces are taken into account due to the translation of plates. The geometrical nonlinearity is considered by adopting the von Kármán non-linear relations. Using the d’Alembert's principle, the nonlinear governing equation of the out-of-plane motion of the plates is derived. The equation is further discretized to a system of ordinary differential equations using the Galerkin method, which are subsequently solved via the harmonic balance method. Then, the approximate analytical results are validated by utilizing the adaptive step-size fourth-order Runge-Kutta technique. Additionally, the stability of the steady state responses is examined by means of the perturbation technique. Linear and nonlinear vibration analyses are both carried out and results display some interesting dynamic phenomenon for translational porous FGPM plates. Parametric study shows that the vibration characteristics of the present inhomogeneous structure depend on several key physical parameters.