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All sources NASA NTRS DOE OSTI

DOE OSTI2019

Optimal Voltage Reference for Droop-Based DERs in Distribution Systems

Due to the newly issued standards on distributed energy resources (DERs), the droop-based operation modes (Q-V and P-V droops) become a requirement for all the interconnected DER units at the distribution level. Voltage references of the DER units can be assigned to improve the operational behavior of the distribution system in the smart grid context. This paper presents a novel formulation for optimally setting voltage reference of the droop-based DER units in distribution systems with the objective of minimizing the system ohmic loss. This problem is formulated as a mixed-integer quadratic convex optimization problem in MATLAB and solved by the MATLAB and CPLEX solvers. The IEEE 123-bus system and the EPRI test system Ckt5 are used to validate the proposed formulation. The result obtained from the proposed formulation is compared with the result of the original problem solved by the heuristic method. Based on the numerical study results, system loss can be reduced after the proposed voltage reference assignment. The computational expense of the new formulation is also much lower than that of the heuristic approach.

distributed energy resource integration↗

DOE OSTI2018

Snorri Version 1.0

The software decomposes n-component time-series data into persistence vectors and provide visualizations of various summaries of this information. Persistence vectors are a type of vector, based on multidimensional persistent homology theory, which have magnitudes and directions that reflect dominant trends in the multi-component data. The software is a collection of MATLAB scripts, and thus MATLAB runs the scripts. The master script that calls other scripts is called Snorri. The purpose is thus analysis of time-series data to extract dominant trends or events and to classify the type of event. The software produces characteristic vectors (an average of the persistence vectors), which can have directions that reflect the overall dominant trend in a time-series data set. The MATLAB scripts have been partially converted to the PYTHON language and no do not have the full functionality of the MATLAB and are not fully tested; the python scripts are to be covered under this copyright assertion. Two versions of the PYTHON scripts are included, called: mph25.py and mph38.py, each with their own main.py file.

Callor, Nickolas↗

DOE OSTI2026

Data and scripts associated with a manuscript modeling microbial regulation of priming effects

This data package is associated with the publication “Modeling Microbial Regulatory Feedback in Organic Matter Decomposition Identifies Copiotrophic Traits as Key Drivers of Positive Priming” published as a preprint on BioRXiv by Ahamed et al. (2026); https://doi.org/10.1101/2024.08.11.607483. The package contains MATLAB scripts and saved simulation outputs used to implement a cybernetic model of microbial regulation during complex organic matter (OM) decomposition governing priming effects. It includes models of (i) single microbial functional groups (copiotrophic or oligotrophic degraders) and (ii) binary consortia composed of degraders and non-degraders with contrasting or common growth traits. Simulation results were generated using Monte Carlo analyses, with randomized key model parameters across a range of environmental mixing fractions of complex and labile OM. The dataset was created to provide a transparent and reusable computational framework for systematically exploring how microbial growth traits, metabolic regulation, and community composition influence OM decomposition dynamics and priming effects. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes the variable definitions. This package includes: (1) annotated MATLAB code implementing the system of ordinary differential equations and cybernetic control laws; (2) saved output files containing data (e.g., biomass, substrates, enzyme levels, priming metrics); and (3) scripts for processing saved outputs and regenerating figures. Specifically, the data package contains three main MATLAB scripts: runPrimingModel.m, runPlotData.m, and runPlotSuppFigS1.m, along with this readme and supporting documentation. Users should begin with runPrimingModel.m, which contains the annotated code implementing the system of ordinary differential equations and cybernetic control laws. This script runs the Monte Carlo simulations of microbial OM decomposition and allows users to modify microbial trait definitions, adjust parameter distributions, or define new community configurations. Simulation outputs are automatically saved as .mat files in the folder named SavedData, which stores all pre-generated results included in this package. The second script, runPlotData.m, reads files from the SavedData folder and processes them to regenerate the figures presented in the manuscript. The third script, runPlotSuppFigS1.m, specifically generates Figure S1 in the Supplementary Material of the manuscript. The package also includes the aforementioned files in non-proprietary .txt format. If users intend to use them, they should first save the files in their respective .m or .mat formats prior to execution in MATLAB.

Biomass concentration↗

DOE OSTI2019

Assessment of Long Term Creep Using Strain Rate Matching From the Stepped Isostress Method

Creep testing is an ongoing need, particularly with the development of new candidate alloy systems for advanced energy systems. The conventional creep test (CT) is regarded as a proven method to gather creep data however, the test is impractical due to being real-time: lasting up to 105 hours to characterize the service of long-lived turbomachinery components. Accelerated methods to gather the long-term creep properties of materials are needed to reduce the time to qualification of new materials. The time-temperature-stress-superposition principle (TTSSP) and the derivative time-temperature superposition principle (TTSP), time-stress superposition principle (TSSP), stepped isothermal method (SIM), and stepped isostress method (SSM) are accelerated creep tests (ACT) commonly used to predict the long-term creep behaviors of polymers and composites. The TTSP and TSSP tests require multiple specimen tested at various temperatures/stresses whereas the SIM and SSM tests employ a single specimen where temperature/stress are periodically step increased until rupture. The stepped creep deformation curve can then be time and strain shifted to produce a master creep curve. While these ACTs are useful tools to predict long-term creep, the drawback is the lack of mathematical laws to determine the virtual start time and time shift factors, especially for different materials. In this paper, a new self-calibration approach is developed and compared to existing SSM data for Kevlar 49. This new approach focuses on matching the creep strain rates between stress steps and fitting the data to a master curve using a modified theta projection model. This is performed using a MATLAB code consisting of five subroutines. The first subroutine takes the stress, time, and creep strain from SSM/SIM tests, and segregates the data intro arrays corresponding to each stress level. The second subroutine finds the constants for the modified theta projection model for each stress level. The third subroutine performs a time shift adjustment using creep strain rate matching. The fourth subroutine calculates the accelerated time of rupture. The last subroutine generates accelerated creep versus time plots. Kevlar 49 SSM data is gathered from literature and run through the MATLAB code. The master curves generated from the MATLAB are compared to the conventional creep curve of Kevlar 49 as well as the master curve gathered from literature in order to validate the feasibility of this new approach. The goal of this project is to vet if the self-calibration approach can produce results similar to the reference calibration approach.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Motion Tracking Above and Below Water Dataset

This data set includes one trial of above and under water motion tracking measurements from a Qualysis motion tracking system. The Qualisys native .qtm file can be opened by the Qualisys Track Manager software. Data from this file has been exported to a tab separated value (.tsv) file which is a generic ASCII file format that can be read by a text editor, MATLAB, Excel, etc... Also exported is a native MATLAB formatted file (.mat) which can be loaded directly into MATLAB. The trial is from a three body wave energy converter device, using the underwater system for the central nacelle data, and above water system for the fore and aft floats.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

HERO WEC 2024 - Electrical Configuration Deployment Data

The following submission includes raw and processed electrical configuration deployment data from the in water deployment of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of parquet files, TDMS files, CSV files, bag files, and MATLAB workspaces. This dataset was collected in April 2024 at the Jennette's pier test site in North Carolina. Raw data as TDMS, CSV, and bag files are provided here alongside processed data in the form of MATLAB workspaces and Parquet files. This dataset includes the Python code used to process the data and MATLAB scripts to visualize the processed data. All data types, calculations, and processing is described in the included "Data Descriptions" document. All files in this dataset are described in detail in the included README. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗

DOE OSTI2024

HERO WEC Belt Test Data

The following submission includes raw and processed data from the 2024 Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC) belt tests conducted using NREL's Large Amplitude Motion Platform (LAMP). A description of the motion profiles run during testing can be found in the run log document. Data was collected using NREL's Modular Ocean Data AcQuisition (MODAQ) system in the form of TDMS files. Data was then processed using Python and MATLAB and converted to MATLAB workspace, parquet, and csv file formats. During Data processing, a low pass filter was applied to each array and the arrays were then resampled to common 10Hz timestamps. A MATLAB data viewer script is provided to quickly visualize these data sets. The following arrays are contained in each test data file: - Time: Unix seconds timestamp - Test_Time: Time in seconds since beginning of test - POS_OS_1001: Encoder position in degrees (the encoder is located on the secondary shaft of the spring return and is driven by the winch after a 4.5:1 gear reduction) - LC_ST_1001: Anchor load cell data in lbf - PRESS_OS_2002: Air spring pressure in psi This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗

DOE OSTI2023

W-4 W88 Systems Engineering Summer 2023 Project [Slides]

The purpose is to create a simple, streamlined process to produce reports on out of tolerance conditions with CMM data. The methods are to develop the code or process within MATLAB to import the data (import tool can be used) and to develop the code to be able to plot the data of interest. The end states are to use MATLAB to analyze CMM data from the PA and document the process for a user with some knowledge of MATLAB and an engineering background.

42 ENGINEERING↗

DOE OSTI2021

Deadband Voltage Control and Power Buffering for Extreme Fast Charging Station

Voltage fluctuation is one of the most common challenges that electric vehicle charging station (EVCS) may introduce to the power grid. Local reactive power compensation (Q-compensation) capability of bi-directional electric vehicle (EV) chargers can mitigate the steady-state voltage violations caused by the EV charging itself or changes in the neighboring loads. Power buffering, using energy storage system (ESS), can be utilized to address the voltage transients (sags and swells) as a result of EV charging at the EVCS. To address PI controller’s ‘hunting’ issue, this paper proposes a Q-sign triggered deadband voltage control (V-control) method at the point of common coupling (PCC). In addition, to ensure the ramp rate of EV charging is within the allowable limits set forth by the grid code, a ramp rate control is proposed that uses the ESS as a ‘power buffer’. Lastly, different from most reported work in the literature where no explicit limit of the power electronic converters (PECs) is considered, this work considers a reasonable apparent power capacity limit of the PECs when achieving the V-control. This limit also affects the amount of active power that can be obtained from the grid, and subsequently may require ESS to function as ‘load sharing’ device to provide supplemental active power to satisfy EV load. A case study simulated in MATLAB (interfaced with PLECS) is presented to demonstrate the effectiveness of the proposed approaches for EVCS operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

Investigating ion recombination effects in a liquid-filled ionization chamber array used for IMRT QA measurements

Purpose: PTW’s Octavius 1000 SRS array performs IMRT quality assurance (QA) measurements with liquid-filled ionization chambers (LICs) to allow closer detector spacing and higher resolution, compared to air-filled QA devices. However, reduced ion mobility in LICs relative to air leads to increased ion recombination effects and reduced collection efficiencies that are dependent on Linac pulse frequency and pulse dose. These pulse parameters are variable during an IMRT delivery, which affects QA results. In this study, (1) 1000 SRS collection efficiencies were measured as a function of pulse frequency and pulse dose, (2) two methods were developed to correct changes in collection efficiencies during IMRT QA measurements, and the effects of these corrections on QA pass rates were compared. Methods: To obtain collection efficiencies, the OCTAVIUS 1000 SRS was used to measure open fields of varying pulse frequency, pulse dose, and beam energy with results normalized to air-filled chamber measurements. Changes in ratios of 1000 SRS to chamber measured dose were attributed to changing collection efficiencies, which were then correlated to pulse parameters using regression analysis. The usefulness of the derived corrections was then evaluated using 6 MV and 10FFF SBRT RapidArc plans delivered to the OCTAVIUS 4D system using a TrueBeam (Varian Medical Systems) linear accelerator equipped with a high definition multileaf collimator. For the first correction, MATLAB software was developed that calculates pulse frequency and pulse dose for each detector, using measurement and DICOM RT Plan files. Pulse information is converted to collection efficiency, and measurements are corrected by multiplying detector dose by ratios of calibration to measured collection efficiencies. For the second correction the MU/min in the daily 1000 SRS calibration was chosen to match the average MU/min of the volumetric modulated arc therapy plan. Effects of the two corrections on QA results were examined by performing 3D gamma analysis comparing predicted to measured dose, with and without corrections. Results: Collection efficiencies correlated linearly to pulse dose, while correlations with pulse frequency were less defined, generally increasing as pulse frequency decreased. After complex MATLAB corrections, average 3D gamma pass rates improved by [0.07%,0.40%,1.17%] for 6 MV and [0.29%,1.40%,4.57%] for 10FFF using [3%/3 mm,2%/2 mm,1%/1 mm] criteria. Maximum changes in gamma pass rates were [0.43%,1.63%,3.05%] for 6 MV and [1.00%,4.80%,11.2%] for 10FFF using [3%/3 mm,2%/2 mm,1%/1 mm] criteria. On average, pass rates of simple daily calibration corrections were within 1% of complex MATLAB corrections. Conclusions: OCTAVIUS 1000 SRS ion recombination effects have little effect on 6 MV measurements. However, the effect could potentially be clinically significant for higher pulse dose unflattened beams when using tighter gamma tolerances, especially when small aperture sizes are used, as is common for SRS/SBRT. In addition, ion recombination effects are strongly correlated to changing MU/min, therefore MU/min used in daily 1000 SRS calibrations should be matched to the expected average MU/min of the IMRT plan.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-T-584: Investigating Correction Methods for Ion Recombination Effects in OCTAVIUS 1000 SRS Measurements

Purpose: PTW’s Octavius 1000 SRS array performs IMRT QA measurements with liquid filled ionization chambers (LICs). Collection efficiencies of LICs have been shown to change during IMRT delivery as a function of LINAC pulse frequency and pulse dose, which affects QA results. In this study, two methods were developed to correct changes in collection efficiencies during IMRT QA measurements, and the effects of these corrections on QA pass rates were compared. Methods: For the first correction, Matlab software was developed that calculates pulse frequency and pulse dose for each detector, using measurement and DICOM RT Plan files. Pulse information is converted to collection efficiency and measurements are corrected by multiplying detector dose by ratios of calibration to measured collection efficiencies. For the second correction, MU/min in daily 1000 SRS calibration was chosen to match average MU/min of the VMAT plan. Usefulness of derived corrections were evaluated using 6MV and 10FFF SBRT RapidArc plans delivered to the OCTAVIUS 4D system using a TrueBeam equipped with an HD- MLC. Effects of the two corrections on QA results were examined by performing 3D gamma analysis comparing predicted to measured dose, with and without corrections. Results: After complex Matlab corrections, average 3D gamma pass rates improved by [0.07%,0.40%,1.17%] for 6MV and [0.29%,1.40%,4.57%] for 10FFF using [3%/3mm,2%/2mm,1%/1mm] criteria. Maximum changes in gamma pass rates were [0.43%,1.63%,3.05%] for 6MV and [1.00%,4.80%,11.2%] for 10FFF using [3%/3mm,2%/2mm,1%/1mm] criteria. On average, pass rates of simple daily calibration corrections were within 1% of complex Matlab corrections. Conclusion: Ion recombination effects can potentially be clinically significant for OCTAVIUS 1000 SRS measurements, especially for higher pulse dose unflattened beams when using tighter gamma tolerances. Matching daily 1000 SRS calibration MU/min to average planned MU/min is a simple correction that greatly reduces ion recombination effects, improving measurements accuracy and gamma pass rates. This work was supported by PTW.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Simit: A Language for Physical Simulation

With existing programming tools, writing high-performance simulation code is labor intensive and requires sacrificing readability and portability. Here, the alternative is to prototype simulations in a high-level language like Matlab, thereby sacrificing performance. The Matlab programming model naturally describes the behavior of an entire physical system using the language of linear algebra. However, simulations also manipulate individual geometric elements, which are best represented using linked data structures like meshes. Translating between the linked data structures and linear algebra comes at significant cost, both to the programmer and to the machine. High-performance implementations avoid the cost by rephrasing the computation in terms of linked or index data structures, leaving the code complicated and monolithic, often increasing its size by an order of magnitude. In this article, we present Simit, a new language for physical simulations that lets the programmer view the system both as a linked data structure in the form of a hypergraph and as a set of global vectors, matrices, and tensors depending on what is convenient at any given time. Simit provides a novel assembly construct that makes it conceptually easy and computationally efficient to move between the two abstractions. Using the information provided by the assembly construct, the compiler generates efficient in-place computation on the graph. We demonstrate that Simit is easy to use: a Simit program is typically shorter than a Matlab program; that it is high performance: a Simit program running sequentially on a CPU performs comparably to hand-optimized simulations; and that it is portable: Simit programs can be compiled for GPUs with no change to the program, delivering 4 to 20× speedups over our optimized CPU code.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

QCLAB v0.1

QCLAB is an object-oriented MATLAB package for creating and representing quantum circuits. QCLAB can be used for rapid prototyping and testing of quantum algorithms, and allows for fast algorithm development and discovery. QCLAB provides I/O through openQASM making it compatible with quantum hardware. It is uniquely targeted at MATLAB users who so far didn't have any native MATLAB options for developing quantum computing applications.

Van Beeumen, RoelMaria Franciscus↗

DOE OSTI2019

SWinzip v. 2.0

SWinzip is a Matlab and C++ library for scientific lossy data compression and reconstruction using compressed sensing and tree-wavelets transforms. These methods are known for their large compression and usefulness in data analytics such as features extraction. Compressed sensing and wavelets methods rely heavily on sparse and dense linear algebra operations implemented through the Boost codes in our library. SWinzip accommodates data represented on both regular grids (e.g. image data) and point-clouds (e.g. unstructured meshes). In SWinzip v2.0, there is more focus on wavelet compression and the following features are added: - The matrix-free (direct) wavelet transform is implemented in both C++ and Matlab. A threshold-based compression can now be used. Examples for distributed compression are also added. - Python implementation of Alpert wavelet transforms - Field data comparison using wavelets in Matlab with global and local metrics

Solloum, Maher↗

DOE OSTI2022

Transport and Retention of Particulate Organic Matter in Sand: Lab Experiments and Modelling

Flow-through reactor (FTR) experiments were conducted at three different downward vertical flow rates to study the transport and retention of particulate organic matter (Chlorella powder) in riverbed sediments. This data package includes data files (.csv) regarding effluent collected from the FTRs during the experiments (time, duration, volume of each sample collection; concentration of nonreactive bromide tracer; concentration of suspended Chlorella determined by absorbance measurements) and sediment (sand) slices collected from the FTRs after the experiments (mass of Chlorella retained in 1 cm depth intervals determined by loss-on-ignition method). More detailed descriptions of data are provided in the data dictionary, and detailed methods are available in the associated MSc thesis listed in ‘Related References.’ The data dictionary and file level meta data (FLMD) are included in the data package as both .csv and .xlsx files. This data package also includes timelapse videos (.mp4) of the experiments, and modelling scripts associated with the experiments. The modelling scripts are included as MATLAB ‘live scripts’ (.mlx, which can be run using MATLAB), and are also included as .pdf and .html files with output plots included (which can be viewed without a MATLAB license and installation). The data files and modelling scripts are organized into a .zip folder for each of the three FTR experiments (three flow rates).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

PETSc/TAO Users Manual (Rev. 3.19)

This manual describes the use of the Portable, Extensible Toolkit for Scientific Computation (PETSc) and the Toolkit for Advanced Optimization (TAO) for the numerical solution of partial differential equations and related problems on high-performance computers. PETSc/TAO is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all distributed memory communication. PETSc/TAO includes a large suite of parallel linear solvers, nonlinear solvers, time integrators, and opti mization that may be used in application codes written in Fortran, C, C++, and Python (via petsc4py; see Getting Started). PETSc provides many of the mechanisms needed within parallel application codes, such as parallel matrix and vector assembly routines. The library is organized hierarchically, enabling users to employ the level of abstraction that is most appropriate for a particular problem. By using techniques of object-oriented programming, PETSc provides enormous flexibility for users. PETSc is a sophisticated set of software tools; as such, for some users it initially has a much steeper learning curve than packages such as MATLAB or a simple subroutine library. In particular, for individuals without some computer science background, experience programming in C, C++, python, or Fortran and experience using a debugger such as gdb or lldb, it may require a significant amount of time to take full advantage of the features that enable efficient software use. However, the power of the PETSc design and the algorithms it incorporates may make the efficient implementation of many application codes simpler than “rolling them” yourself. For many tasks a package such as MATLAB is often the best tool; PETSc is not intended for the classes of problems for which effective MATLAB code can be written. There are several packages, built on PETSc, that may satisfy your needs without requiring directly using PETSc. We recommend reviewing these packages functionality before starting to code directly with PETSc. PETSc can be used to provide a “MPI parallel linear solver” in an otherwise sequential, or OpenMP parallel code. This approach cannot provide extremely large improvements in the application time by utilizing large numbers of MPI processes but can still improve the performance. Certainly all parts of a previously sequential code need not be parallelized but the matrix generation portion must be parallelized to expect true scalability to large numbers of MPI processes. See PCMPI for details on how to utilize the PETSc MPI linear solver server. Since PETSc is under continued development, small changes in usage and calling sequences of routines will occur. PETSc has been supported for twenty-five years; see mailing list information on our website for information on contacting support.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Gas-Gap Calorimeter Sizing and Rating Tools for Heat Pipe Experimentation

Effective gas-gap calorimeter sizing and rating tools are required to design calorimeters that enable well-defined operating conditions, high cooling powers, and accurate calorimetry. The present report describes two tools utilizing Microsoft Excel and MathWorks MATLAB that enable the sizing and rating of gas-gap calorimeters with a He-Ar binary gas mixture in the gap. The Excel tools are two easy-to-use spreadsheets for calculating heat pipe operating temperature or He-Ar mole fractions given the required inputs. The MATLAB tool includes similar models with more robust solution algorithms along with sub-options for full-vacuum, static gas-gap, and forced circulation in the gas-gap. The accuracy of the developed tools were demonstrated by comparing with existing work in literature. The MATLAB tool was used for a parametric study to determine the gas-gap thickness, coolant gap thickness, coolant flow rate, and coolant inlet temperatures for the testing of a 3/4 in outer diameter heat pipe up to powers of 10 kW and temperatures of 1,000°C. The effects of heat pipe and calorimeter inner shell surface emissivities were investigated, considering the inability to control or accurately measure surface emissivities in most applications. It was found that controlling the He-Ar mole fractions provides a wide operating range for gas-gap thicknesses around ~ 0.042--0.090 in (~ 1.07--2.29 mm) for a surface emissivity range of 0.4--0.8, since conduction heat transfer is a significant fraction of the heat transfer across the gas-gap. In addition, it was found that turbulent flow in the coolant gap is needed at high input powers to prevent shell temperatures from approaching the boiling temperature of the water coolant. The parametric study resulted in choosing stainless steel tubes with an outer diameter and thickness of 1 x 0.065 in as the inner shell, and a 1-1/2 x 0.156 in as the outer shell of the calorimeter. Overall, this report presents the necessary information for the sizing and rating of gas-gap calorimeters with He-Ar mixtures for the testing of high-temperature heat pipes (~ 500--1,000°C).

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1993

Simulation of linear mechanical systems

A dynamics and controls analyst is typically presented with a structural dynamics model and must perform various input/output tests and design control laws. The required time/frequency simulations need to be done many times as models change and control designs evolve. This paper examines some simple ways that open and closed loop frequency and time domain simulations can be done using the special structure of the system equations usually available. Routines were developed to run under Pro-Matlab in a mixture of the Pro-Matlab interpreter and FORTRAN (using the .mex facility). These routines are often orders of magnitude faster than trying the typical 'brute force' approach of using built-in Pro-Matlab routines such as bode. This makes the analyst's job easier since not only does an individual run take less time, but much larger models can be attacked, often allowing the whole model reduction step to be eliminated.

Sirlin, S. W.↗