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NASA NTRS2015

RACORO Continental Boundary Layer Cloud Investigations: 1. Case Study Development and Ensemble Large-Scale Forcings

Observation-based modeling case studies of continental boundary layer clouds have been developed to study cloudy boundary layers, aerosol influences upon them, and their representation in cloud- and global-scale models. Three 60 h case study periods span the temporal evolution of cumulus, stratiform, and drizzling boundary layer cloud systems, representing mixed and transitional states rather than idealized or canonical cases. Based on in situ measurements from the Routine AAF (Atmospheric Radiation Measurement (ARM) Aerial Facility) CLOWD (Clouds with Low Optical Water Depth) Optical Radiative Observations (RACORO) field campaign and remote sensing observations, the cases are designed with a modular configuration to simplify use in large-eddy simulations (LES) and single-column models. Aircraft measurements of aerosol number size distribution are fit to lognormal functions for concise representation in models. Values of the aerosol hygroscopicity parameter, kappa, are derived from observations to be approximately 0.10, which are lower than the 0.3 typical over continents and suggestive of a large aerosol organic fraction. Ensemble large-scale forcing data sets are derived from the ARM variational analysis, European Centre for Medium-Range Weather Forecasts, and a multiscale data assimilation system. The forcings are assessed through comparison of measured bulk atmospheric and cloud properties to those computed in "trial" large-eddy simulations, where more efficient run times are enabled through modest reductions in grid resolution and domain size compared to the full-sized LES grid. Simulations capture many of the general features observed, but the state-of-the-art forcings were limited at representing details of cloud onset, and tight gradients and high-resolution transients of importance. Methods for improving the initial conditions and forcings are discussed. The cases developed are available to the general modeling community for studying continental boundary clouds.

Boundary layers↗

NASA NTRS2015

Resolving Ice Cloud Optical Thickness Biases Between CALIOP and MODIS Using Infrared Retrievals

Despite its importance as one of the key radiative properties that determines the impact of upper tropospheric clouds on the radiation balance, ice cloud optical thickness (IOT) has proven to be one of the more challenging properties to retrieve from space-based remote sensing measurements. In particular, optically thin upper tropospheric ice clouds (cirrus) have been especially challenging due to their tenuous nature, extensive spatial scales, and complex particle shapes and light scattering characteristics. The lack of independent validation motivates the investigation presented in this paper, wherein systematic biases between MODIS Collection 5 (C5) and CALIOP Version 3 (V3) unconstrained retrievals of tenuous IOT (< 3) are examined using a month of collocated A-Train observations. An initial comparison revealed a factor of two bias between the MODIS and CALIOP IOT retrievals. This bias is investigated using an infrared (IR) radiative closure approach that compares both products with MODIS IR cirrus retrievals developed for this assessment. The analysis finds that both the MODIS C5 and the unconstrained CALIOP V3 retrievals are biased (high and low, respectively) relative to the IR IOT retrievals. Based on this finding, the MODIS and CALIOP algorithms are investigated with the goal of explaining and minimizing the biases relative to the IR. For MODIS we find that the assumed ice single scattering properties used for the C5 retrievals are not consistent with the mean IR COT distribution. The C5 ice scattering database results in the asymmetry parameter (g) varying as a function of effective radius with mean values that are too large. The MODIS retrievals have been brought into agreement with the IR by adopting a new ice scattering model for Collection 6 (C6) consisting of a modified gamma distribution comprised of a single habit (severely roughened aggregated columns); the C6 ice cloud optical property models have a constant g approx. = 0.75 in the mid-visible spectrum, 5-15% smaller than C5. For CALIOP, the assumed lidar ratio for unconstrained retrievals is fixed at 25 sr for the V3 data products.This value is found to be inconsistent with the constrained (predominantly nighttime) CALIOP retrievals. An experimental data set was produced using a modified lidar ratio of 32 sr for the unconstrained retrievals (an increase of 28%), selected to provide consistency with the constrained V3 results. These modifications greatly improve the agreement with the IR and provide consistency between the MODIS and CALIOP products. Based on these results the recently released MODIS C6 optical products use the single habit distribution given above, while the upcoming CALIOP V4 unconstrained algorithm will use higher lidar ratios for unconstrained retrievals.

Cloud↗

NASA NTRS2020

Assessing the Impact of Soil Layer Depth Specification on the Observability of Modeled Soil Moisture and Brightness Temperature

The utility of hydrologic land surface models (LSMs) can be enhanced by using information from observational platforms, but mismatches between the two are common.This study assesses the degree to which model agreement with observations (observability) is affected by two mechanisms in particular: 1) physical incongruities between the support volumes being characterized and 2) inadequateor inconsistent parameterizations of physical processes. The Noah and Noah-MP LSMs by default characterize surface soil moisture (SSM) in the top 10 cm of the soil column. This depth may be reasonable when comparing against soil moisture from in situ probes centered at 5 cm,but it is notably different from the 5 cm (or less) sensing depth of NASA’s Soil Moisture Active Passive (SMAP) satellite mission. These depth inconsistencies are examined by using thinner model layers in the Noah and Noah-MP LSMs and comparing resultant simulations to in situ and SMAP soil moisture. In addition, a forward radiative transfer model to simulate microwave brightness temperatures (Tbs) is used to facilitate direct comparisons of LSM-based and SMAP-based L-band Tb retrievals. Observability is quantified using Kolmogorov-Smirnov distance values, calculated from empirical cumulative distribution functions of SSM and Tb time series. Experiment results depend on the particular subspace being analyzed (SSM or Tb). This study concludes that therole of increasedsoil layer discretizationon LSM observability is secondary to the influence of component parameterizations, the effects of which dominate systematic differences with observations

SMAP↗

DOE OSTI2020

Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization

Abstract Within density functional theory (DFT), adding a Hubbard U correction can mitigate some of the deficiencies of local and semi-local exchange-correlation functionals, while maintaining computational efficiency. However, the accuracy of DFT+U largely depends on the chosen Hubbard U values. We propose an approach to determining the optimal U parameters for a given material by machine learning. The Bayesian optimization (BO) algorithm is used with an objective function formulated to reproduce the band structures produced by more accurate hybrid functionals. This approach is demonstrated for transition metal oxides, europium chalcogenides, and narrow-gap semiconductors. The band structures obtained using the BO U values are in agreement with hybrid functional results. Additionally, comparison to the linear response (LR) approach to determining U demonstrates that the BO method is superior.

Chemistry↗

DOE OSTI2020

One-step transformation of biomass to fuel precursors using a bi-functional combination of Pd/C and water tolerant Lewis acid

Direct one-pot transformation of lignocellulosic biomass has been developed as an effective and sustainable strategy to produce fuel blend stocks and high value chemical building blocks. In this wok, a bi-functional catalyst system consisting of palladium supported on carbon (Pd/C) and metal triflates (i.e., Sm(OTf) 3 , La(OTf) 3 , and Cu(OTf) 2 ) were shown to promote the biomass liquefaction in both hot-compressed water and supercritical ethanol medium, converting fir wood into oxygenated compounds. We report the highest bio-oil yield from hydrothermal liquefaction (HTL) was 10.47 wt% over Pd/C whereas the highest bio-oil yield of 49.71 wt% was achieved from supercritical ethanol liquefaction (SCEL) over the bi-functional catalyst system of Pd/C and La(OTf) 3 . Higher heating values, carbon recovered values and boiling point distributions were further determined for elucidating the physical properties of the bio-oils. Gas chromatography mass spectrometry (GC–MS) analysis of the bio-oils revealed the chemical composition of the bio-oils. Substituted phenols and cyclopentenone/cyclopentanone type compounds consisted of more than 60 area% of the total products from HTL, whereas phenol and esters represented the major products from SCEL. The major reaction pathways are proposed based on the GC–MS results, which include depolymerizaton, isomerization, dehydration, condensation, and hydrogenation.

09 BIOMASS FUELS↗

DOE OSTI2022

Fragility Functions Resource Report: Documented Sources for Electricity and Water Resilience Valuation

Fragility curves provide the vulnerability between hazard intensity and an asset. Federal installations may include many different electricity and water infrastructure types (or assets) such as generators, wind turbines, solar PV, switch yards, substations and power lines as well as water distribution systems that could be affected by different hazards. The vulnerability of each asset is a function of its age, type of materials and maintenance. In addition, the vulnerability changes with the hazards intensity and has a probability distribution function associated with it. The fragility functions are used in conjunction with hazard probability and consequence valuations to determine the values at risk for examination of investment grade analyses of alternative mitigation strategies. This document provides examples of fragility functions and links to their sources for different electricity and water infrastructure assets by hazard type.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Molecular dynamics simulations for glass transition temperature predictions of polyhydroxyalkanoate biopolymers

Polyhydroxyalkanoates (PHAs) represent an emerging class of biosynthetic and biodegradable polyesters that exhibit considerable potential to replace petroleum-based plastics towards a sustainable future. Despite the promise, general structure–property mappings within this class of polymers remain largely unexplored. An efficient exploration of this vast chemical space calls for the development and validation of predictive methods for accurate estimation of a diverse range of properties for PHA-based polymers. Towards this aim, we present and validate in this work the results of our molecular dynamics (MD) simulation based approach aimed at predicting glass transition temperatures (T g ) of PHA-based polymers. Since generally available and widely used polymer forcefields exhibit a relatively poor performance for T g predictions, we have developed a new forcefield by modifying the polymer consistent force field (PCFF) via refining a selected set of torsion potentials of the polymer backbone using accurate density functional theory (DFT) computations. After carefully assessing the dependence of critical simulation parameters, such as, polymer chain length, number of polymer chains, supercell size, and thermal quenching rate used in the simulation, the applicability and transferability of the modified PCFF (mPCFF) is demonstrated by directly comparing the computed T g predictions of various polymers with different chemistries, polymer side chain lengths and functional groups forming the polymer side chains against the respective experimentally measured values. Furthermore, the transport properties such as self-diffusion coefficient and viscosity are computationally determined and their well-known correlation with the target properties is demonstrated. Lastly, we have employed the developed approach to predict T g values for a number of yet-to-be-synthesized PHA-based polymers with a diverse set of functional groups in the polymer side chains. The results are further rationalized by correlating the predicted T g values with the inter-chain H-bond formation tendencies of the different side chain functional groups. This work represents an important first step towards computationally guided design of PHA-based functional polymers and opens up new directions for a systematic investigation of composition- and configuration-dependent structure–property relationships in more complex binary and ternary copolymer systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

An efficient random-sampling method for calculating double occupancy of Gutzwiller wave function in single-band 1D and 2D lattices

In this paper, we report a random sampling method for computing the expectation value of physical quantities based on the Gutzwiler variational wave function. As the first application, we calculated the double occupancy, which is a critical quantity for under- standing the correlation effects in many-body systems, for single-band 1D and 2D lattices. We demonstrated that the random sampling scheme is more efficient than an existing Metropolis Monte-Carlo algorithm. For the 1D Hubbard model with only nearest-neighbor hopping, our results are almost identical to the exact analytic solution. We have also studied systems to which analytic solutions are not available, including the 1D lattices with next-nearest-neighbor hopping and 2D lattices. In addition, constraints on real-space con gurations can be easily implemented in the current scheme to further improve the Gutzwiller wave function. As an example, we calculated the double occupancy for 1D Hubbard model by applying the constraint that all double-occupied sites are paired with an empty site. With enhanced correlation between double-occupied and empty sites, the constraint results in much improved ground-state energy for 1D Hubbard model with strong on-site repulsion.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Magnetic exchange interactions in binuclear and tetranuclear iron(III) complexes described by spin‐flip DFT and Heisenberg effective Hamiltonians

Abstract Low‐energy spectra of single‐molecule magnets (SMMs) are often described by Heisenberg Hamiltonians. Within this formalism, exchange interactions between magnetic centers determine the ground‐state multiplicity and energy separation between the ground and excited states. In this contribution, we extract exchange coupling constants ( J ) for a set of iron (III) binuclear and tetranuclear complexes from all‐electron calculations using non‐collinear spin‐flip time‐dependent density functional theory (NC‐SF‐TDDFT). For 12 binuclear complexes with J ‐values ranging from −6 to −132 cm −1 , our benchmark calculations using the short‐range hybrid ω PBEh functional and 6‐31G(d,p) basis set agree well with the experimentally derived values (mean absolute error of 4.7 cm −1 ). For the tetranuclear SMMs, the computed J constants are within 6 cm −1 from the experimentally derived values. We explore the range of applicability of the Heisenberg model by analyzing bonding patterns in these Fe(III) complexes using natural orbitals (NO), their occupations, and the number of effectively unpaired electrons. The results illustrate the efficiency of the spin‐flip protocol for computing the exchange couplings and the utility of the NO analysis in assessing the validity of effective spin Hamiltonians.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1986

System engineering study of electrodynamic tether as a spaceborne generator and radiator of electromagnetic waves in the ULF/ELF frequency band

The transmission and generation by orbiting tethered satellite systems of information carrying electromagnetic waves in the ULF/ELF frequency band to the Earth at suitably high signal intensities was examined and the system maintaining these intensities in their orbits for long periods of time without excessive onboard power requirements was investigated. The injection quantity power into electromagnetic waves as a function of system parameters such as tether length and orbital height was estimated. The basic equations needed to evaluate alternataing current tethered systems for external energy requirements are presented. The energy equations to tethered systems with various lengths, tether resistances, and radiation resistances, operating at different current values are applied. Radiation resistance as a function of tether length and orbital height is discussed. It is found that ULF/ELF continuously radiating systems could be maintained in orbit with moderate power requirements. The effect of tether length on the power going into electromagnetic waves and whether a single or dual tether system is preferable for the self-driven mode is discussed. It is concluded that the single tether system is preferable over the dual system.

Estes, R. D.↗

DOE OSTI2017

Development of a Dynamic Lidar Uncertainty Framework

As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote-sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote-sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote-sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards for quantifying remote sensing device uncertainty for power performance testing consider uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device and are generally fixed, leading to climatic uncertainty values that apply to the entire measurement campaign. However, real-world experience and a consideration of the fundamentals of the measurement process have shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we describe the development of a new dynamic lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from a field measurement site to assess the ability of the framework to predict errors in lidar-measured wind speed. The results show how uncertainty varies over time and can be used to help select data with different levels of uncertainty for different applications, for example, low uncertainty data for power performance testing versus all data for plant performance monitoring.

17 WIND ENERGY↗

DOE OSTI2017

Contribution of acidic components to the total acid number (TAN) of bio-oil

Bio-oil or pyrolysis oil — a product of thermochemical decomposition of biomass under oxygen-limited conditions — holds great potential to be a substitute for nonrenewable fossil fuels. But, its high acidity, which is primarily due to the degradation of hemicelluloses, limits its applications. For the evaluation of bio-oil production and treatment, it is essential to accurately measure the acidity of bio-oil. The total acid number (TAN), which is defined as the amount of potassium hydroxide needed to titrate one gram of a sample and has been established as an ASTM method to measure the acidity of petroleum products, has been employed to investigate the acidity of bio-oil. The TAN values of different concentrations of bio-oil components such as standard solutions of acetic acid, propionic acid, vanillic acid, hydroxybenzoic acid, syringic acid, hydroxymethylfurfural, and phenol were analyzed according to the ASTM D664 standard method. Our method showed the same linear relationship between the TAN values and the molar concentrations of acetic, propionic, and hydroxybenzoic acids. A different linear relationship was found for vanillic acid, due to the presence of multiple functional groups that can contribute to the TAN value. Furthermore, the influence of the titration solvent on the TAN values has been determined by comparing the TAN values and titration curves obtained from the standard method with results from the TAN analysis in aqueous environment and with equilibrium modeling results. Aqueous bio-oil samples with a known amount of acetic acid added were also analyzed. The additional acetic acid in bio-oil samples caused a proportional increase in the TAN values. These results of this research indicate that the TAN value of a sample with acids acting as monoprotic acids in the titration solvent can be converted to the molar concentration of total acids. For a sample containing acids that act as diprotic and polyprotic acids, however, its TAN value cannot be simply converted to the molar concentration of total acids because these acids have a stronger contribution to the TAN values than the contribution of monoprotic acids.

09 BIOMASS FUELS↗

NASA NTRS2009

Gaussian and Lognormal Models of Hurricane Gust Factors

A document describes a tool that predicts the likelihood of land-falling tropical storms and hurricanes exceeding specified peak speeds, given the mean wind speed at various heights of up to 500 feet (150 meters) above ground level. Empirical models to calculate mean and standard deviation of the gust factor as a function of height and mean wind speed were developed in Excel based on data from previous hurricanes. Separate models were developed for Gaussian and offset lognormal distributions for the gust factor. Rather than forecasting a single, specific peak wind speed, this tool provides a probability of exceeding a specified value. This probability is provided as a function of height, allowing it to be applied at a height appropriate for tall structures. The user inputs the mean wind speed, height, and operational threshold. The tool produces the probability from each model that the given threshold will be exceeded. This application does have its limits. They were tested only in tropical storm conditions associated with the periphery of hurricanes. Winds of similar speed produced by non-tropical system may have different turbulence dynamics and stability, which may change those winds statistical characteristics. These models were developed along the Central Florida seacoast, and their results may not accurately extrapolate to inland areas, or even to coastal sites that are different from those used to build the models. Although this tool cannot be generalized for use in different environments, its methodology could be applied to those locations to develop a similar tool tuned to local conditions.

Merceret, Frank↗

DOE OSTI2023

Kinetic model-based group contribution method for derived cetane number prediction of oxygenated fuel components and blends

A four-step autoignition model was used to derive an expression for the ignition delay (ID) measured in ignition quality testers (IQT) with the derived cetane number (DCN) determined from this ID using the ASTM D6890 standard correlation. The model predicts DCN for individual compounds and blends as a function of each compound's global initiation and net chain branching rate constants. Expressions for these values were determined assuming they could be related to the functional groups present in each compound. Measurement data for 125 compounds and 94 binary and ternary blends (including oxygenates: alcohols, aldehydes, esters, ethers, and ketones), from literature and from measurements performed in our lab, were used to obtain the dependence of the measured ignition delay on each functional group. The new kinetic model-based group contribution method was able to predict the ignition delay of both pure compounds and blends, with an average DCN error of 4.4 (19%) and 2.8 (11%), respectively. Here, the blend model was also used to develop an ID mixing rule by incorporating existing IQT ignition delay data for each compound. Use of the mixing rule gave an average DCN error of 3.6 (16%) for blends. Both the blend model and mixing rule were found to be superior compared to standard linear by volume fraction or mole fraction mixing rules commonly used to estimate the DCN of mixtures.

42 ENGINEERING↗

DOE OSTI2022

Simple Autoignition Model for the Derived Cetane Number of Oxygenated Compounds and Fuel Blends

A simple four-step autoignition model was used to derive an expression for the ignition delay and derived cetane number (DCN) measured by ignition quality testers (IQT) for oxygenated compounds and blends as a function of each compound’s global initiation and net chain branching rate constants. Expressions for these values were determined assuming they could be related to the functional groups present in a given compound. Measurements for 124 compounds and 94 binary and ternary blends, gathered from the literature and performed at UW-Madison, were used to obtain the explicit dependence of the measured ignition delay on each functional group. The new model was able to successfully describe the ignition behavior of both the compounds and blends, with an average DCN error of 5.0 (22%) and 3.5 (15%), respectively. Additionally, the blend model could naturally be modified into a mixing rule by incorporating existing IQT ignition delay data for each compound. Use of the mixing rule was found to offer improvements over the full prediction, with an average DCN error of 3.2 (14%).

09 BIOMASS FUELS↗

DOE OSTI2022

Simple Autoignition Model for the Derived Cetane Number of Oxygenated Compounds and Fuel Blends

A four-step autoignition model was used to derive an expression for the ignition delay and derived cetane number (DCN) measured by ignition quality testers (IQT) for oxygenated compounds and blends as a function of each compound’s global initiation and net chain branching rate constants. Expressions for these values were determined assuming they could be related to the functional groups present in a compound. Measurements for 124 compounds and 94 binary and ternary blends, gathered from the literature and performed at UW-Madison, were used to obtain the dependence of the measured ignition delay on each functional group. The new model was able to describe the ignition behavior of both the compounds and blends, with an average DCN error of 5.0 (22%) and 3.5 (15%), respectively. Additionally, the blend model was transformed into a mixing rule by incorporating existing IQT ignition delay data for each compound. Use of the mixing rule was found to offer improvements over the full prediction, with an average DCN error of 3.2 (14%).

Witkowski, Dustin↗

DOE OSTI2023

IDAES-PSE 2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.0.0 Release Highlights Removal of deprecated features from IDAES v1 Update to Pyomo v6.5 – this required a number of updates to support the new NL solver writer and to address some changes in Pyomo Creation of new testing suite for backward compatibility, model robustness and verification More general implementation of the Helmholtz EoS. This brings some new features like standard property diagrams, choice of mass or mole basis, and new state variable options Standardizing names in Heat Exchanger models (breaking change from v2.0.0a2): Control Volumes named hot_side and cold_side Ports names hot_side_inlet, hot_side_outlet, cold_side_inlet and cold_side_outlet Config Blocks names hot_side_config and cold_side_config Config arguments for user provided names for each side: hot_side_name and cold_side_name. Updating Keras surrogate tool to use v1.1 of OMLT New prototype API for model initialization (idaes.core.initialization) The new API uses "Model Initializer" objects instead of class methods, allowing for the definition of multiple initialization routines for a single model A number of common, model agnostic initialization routines have also been defined, including initialization from data, block-decomposition and a general hierarchical approach equivalent to the existing method for common unit models New metadata for thermophysical properties – valid_range This can be used to record the range of values over which a property value can be trusted, such as the range of experimental data used to regress parameters A number of new utility functions have been added to check for properties with values outside the valid range and to set bounds based on this metadata Updated construction of balance expressions in Control Volumes to remove unneeded terms In the past, unneeded terms were added as a constant 0 term, however they will now be dropped entirely from the expression This was necessary due to more strict unit checking in the new Pyomo solver writer which no longer ignores 0 terms Updates to metadata for thermophysical properties to better define known properties and units of measurement This results in more strict enforcement of standard naming for thermophysical and reaction properties Users can still define custom properties, but these must be done explicitly using the define_custom_properties() method instead of being implicitly created by add_property() Updated convergence tester utility tool to support definition of benchmark files (JSON format) and comparison of performance to benchmarks Set default iteration limit for IPOPT in IDAES config to 200 iterations Update scaling of example models to work with new Pyomo NL solver writer Improve testing of extensions and examples infrastructure to avoid need for downloading files Updated distillation column to centralize common functionality and remove a number of Pyomo warnings

IDAES↗

DOE OSTI2025

Radial Steering Displacement Method to Measure Dispersion

The measurement of the dispersion function in a transfer line plays the crucial role for the development of an accurate optics model for the beam tracking simulation. In this technical note, we describe an experimental method to measure the dispersion value at multi-wire location MW063 in the NASA Space Radiation Laboratory (NSRL) transfer line. Finally, we use our MADX model to calculate the dispersion function at the same multi-wire location. The ultimate goal of this measurement and calculation is to understand the right value of dispersion function and the beam size (emittance) at the entrance of NSRL beam line.

43 PARTICLE ACCELERATORS↗