Polynomial-based surrogate modeling of microwave structures in frequency domain exploiting the multinomial theorem

We propose a methodology for developing EM-based polynomial surrogate models exploiting the multinomial theorem. Our methodology is compared against four EM surrogate modeling techniques: response surface modeling, support vector machines, generalized regression neural networks, and Kriging. Results show that the proposed polynomial surrogate modeling approach has the best performance among these techniques when using a very small amount of learning base points. The proposed methodology is illustrated by developing a surrogate model for a T-slot PIFA antenna simulated on a commercially available 3D FEM simulator

Polynomial-based surrogate modeling of RF and microwave circuits in frequency domain exploiting the multinomial theorem

A general formulation to develop EM-based polynomial surrogate models in frequency domain utilizing the multinomial theorem is presented in this paper. Our approach is especially suitable when the number of learning samples is very limited and no physics-based coarse model is available. We compare our methodology against other four surrogate modeling techniques: response surface modeling, support vector machines, generalized regression neural networks, and Kriging. Results confirm that our modeling approach has the best performance among these techniques when using a very small amount of learning base points on relatively small modeling regions. We illustrate our technique by developing a surrogate model for an SIW interconnect with transitions to microstrip lines, a dual band T-slot PIFA handset antenna, and a high-speed package interconnect. Examples are simulated on a commercially available 3D FEM simulator

Proceedings of the 23rd annual Central Plains irrigation conference

Presented at Proceedings of the 23rd annual Central Plains irrigation conference held in Burlington, Colorado on February 22-23, 2011.Includes bibliographical references.Irrigation water management practices could greatly benefit from using soil moisture sensors that accurately measure soil water content or potential. Therefore, an assessment on soil moisture sensor reading accuracy is important. In this study, a performance evaluation of selected sensor calibration was performed considering factory- laboratory- and field-based calibrations. The selected sensors included: the Digitized Time Domain Transmissometry (TDT, Acclima, Inc., Meridian, ID) which is a volumetric soil water content sensor, and a resistance-based soil water potential sensor (Watermark 200, Irrometer Company, Inc., Riverside, CA). Measured soil water content/potential values, on a sandy clay loam soil, were compared with corresponding values derived from gravimetric samples. Under laboratory and field conditions, the factory-based calibrations for the TDT sensor accurately measured volumetric soil water content. Therefore, the use of the TDT sensor for irrigation water management seems very promising. Laboratory tests indicated that a linear calibration for the TDT sensor and a logarithmic calibration for the watermark sensor improved the factory calibration. In the case of the watermark, a longer set of field data is needed to properly establish its accuracy and reliability

Reliable full-wave EM simulation of a single-layer SIW interconnect with transitions to microstrip lines

We present a procedure to obtain reliable EM responses for a substrate integrated waveguide (SIW) interconnect with microstrip line transitions. The procedure focuses on two COMSOL configuration settings: meshing sizes and simulation bounding box. Once both are properly configured, the implemented structure is tested by perturbing the simulation bounding box to assure it has no effect on the EM responsesITESO, A.C

Surface energy balance model

Presented at the fifth international conference on irrigation and drainage, Irrigation and drainage for food, energy and the environment on November 3-6, 2009 in Salt Lake City, Utah.Includes bibliographical references.Remote sensing algorithms are currently being used to estimate regional surface energy fluxes [e.g., latent heat flux (LE) or evapotranspiration (ET)]. Many of these surface energy balance models use information derived from satellite imagery such as Landsat, AVHRR, ASTER, and MODIS to estimate ET. The remote sensing approach to estimating ET provides advantages over traditional methods. One of the most important advantages is that it can provide regional estimates of actual ET at low cost. Most conventional methods are based on point measurements (i.e., soil water sensors, lysimeters, weather station data, etc.), limiting their ability to capture the spatial variability of ET. Another advantage of remote sensing/surface energy balance ET models is that they are able to estimate the actual crop ET as a residual of the energy balance without the need of using reference crop ET and tabulated crop coefficients. This study focuses on the use of the energy balance-based model "Remote Sensing of ET" (ReSET) that uses an enhanced procedure to deal with the spatial and temporal variability of ET. ET was estimated for several years of data for the Arkansas River Basin, South Platte, and Palo Verde Irrigation District along with one day ET estimates for the Southern High Plains. Comparisons between the Remote Sensing ET values and ET values from more conventional ET methods [e.g., 2005 ASCE-EWRI Standardized Reference Evapotranspiration (Penman-Monteith) Equation] also are presented

Using remote sensing assessments to document historical and current saved consumptive use (CU) on alfalfa and grass hayfields managed under full and partial-season irrigation regimes

August 2017.Includes bibliographical references

Design Optimization of Full-Wave EM Models by Low-Order Low-Dimension Polynomial Surrogate Functionals

A practical formulation for EM-based design optimization of high-frequency circuits using simple polynomial surrogate functionals is proposed in this paper. Our approach starts from a careful selection of design variables and is based on a closed-form formulation that yields global optimal values for the surrogate model weighting factors, avoiding a large set of expensive EM model data, and resulting in accurate low-order low-dimension polynomials interpolants that are used as vehicles for efficient design optimization. Our formulation is especially suitable for EM-based design problems with no equivalent circuital models available. The proposed technique is illustrated by the EM-based design optimization of a Ka-band substrate integrated waveguide (SIW) interconnect with conductor-backed coplanar waveguide (CBCPW) transitions, a low crosstalk PCB microstrip interconnect structure with guard traces, and a 10-40 GHz SIW interconnect with microstrip transitions on a standard FR4-based substrate. Three commercially available full-wave EM solvers are used in our examples: CST, Sonnet and COMSOL

Surrogate-based Analysis and Design Optimization of Power Delivery Networks

As microprocessor architectures continue to increase computing performance under low-energy consumption, the combination of signal integrity, electromagnetic interference, and power delivery is becoming crucial in the computer industry. In this context, power delivery engineers make use of complex and computationally expensive models that impose time-consuming industrial practices to reach an adequate power delivery design. In this paper, we propose a general surrogate-based methodology for fast and reliable analysis and design optimization of power delivery networks (PDN). We first formulate a generic surrogate model methodology exploiting passive lumped models optimized by parameter extraction to fit PDN impedance profiles. This PDN modeling formulation is illustrated with industrial laboratory measurements of a 4th generation server CPU motherboard. We next propose a black box PDN surrogate modeling methodology for efficient and reliable power delivery design optimization. To build our black box PDN surrogate, we compare four metamodeling techniques: support vector machines, polynomial surrogate modeling, generalized regression neural networks, and Kriging. The resultant best metamodel is then used to enable fast and accurate optimization of the PDN performance. Two examples validate our surrogate-based optimization approach: a voltage regulator with dual power rail remote sensing intended for communications and storage applications, by finding optimal sensing resistors and loading conditions; and a multiphase voltage regulator from a 6th generation Intel® server motherboard, by finding optimal compensation settings to reduce the number of bulk capacitors without losing CPU performance.ITESO, A.C

Selecting Surrogate-Based Modeling Techniques for Power Integrity Analysis

In recent years, extensive usage of simulated power integrity (PI) models to predict the behavior of power delivery networks (PDN) on a chip has become more relevant. Predicting adequate performance against power consumption can yield to either cheap or costly design solutions. Since PI simulations including high-frequency effects are becoming more and more computationally complex and expensive, it is critical to develop reliable and fast models to understand system’s behavior to accelerate decision making during design stages. Hence, metamodeling techniques can help to overcome this challenge. In this work, a comparative study between different surrogate modeling techniques as applied to PI analysis is described. We model and analyze a PDN that includes two different power domains and a combination of remote sense resistors for communication and storage CPU applications. We aim at developing reliable and fast coarse models to make trade off decisions while complying with voltage levels and power consumption requirements

Combined use of the GGSFT data base and on Board Marine Collected Data to Model the Moho Beneath the Powell Basin, Antarctica

The Powell Basin is a small oceanic basin located at the NE end of the Antarctic Peninsula developed during the Early Miocene and mostly surrounded by the continental crusts of the South Orkney Microcontinent, South Scotia Ridge and Antarctic Peninsula margins. Gravity data from the SCAN 97 cruise obtained with the R/V Hespérides and data from the Global Gravity Grid and Sea Floor Topography (GGSFT) database (Sandwell and Smith, 1997) are used to determine the 3D geometry of the crustal-mantle interface (CMI) by numerical inversion methods. Water layer contribution and sedimentary effects were eliminated from the Free Air anomaly to obtain the total anomaly. Sedimentary effects were obtained from the analysis of existing and new SCAN 97 multichannel seismic profiles (MCS). The regional anomaly was obtained after spectral and filtering processes. The smooth 3D geometry of the crustal mantle interface obtained after inversion of the regional anomaly shows an increase in the thickness of the crust towards the continental margins and a NW-SE oriented axis of symmetry coinciding with the position of an older oceanic spreading axis. This interface shows a moderate uplift towards the western part and depicts two main uplifts to the northern and eastern sectors