Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Physics Modeling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Status of the framework for research in nuclear science and engineering - 25460

The Framework for Research in Nuclear Science and Engineering (FRENSIE) is a collection of packages written in C++ that are designed primarily to aid in methods development. New features are continually being added to the various packages and as a result, the Forward Adjoint Continuous Energy Monte Carlo (FACEMC) tool provided by FRENSIE continues to undergo verification and validation. While FRENSIE is not currently open source, the ultimate goal is for it to be released as such. A few of the more recent developments in FRENSIE include the addition of combinatorial geometry capabilities using ROOT, tet and hex meshing, support for thermal neutron S (α; β) data, photon Doppler broadening, electron interactions using basic analog electron physics models as well as an elastic moment-preserving model, and atomic relaxation. Preliminary verification results for much of the newly added physics models show good agreement between FACEMC and MCNP6. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2016

Holodeck Testbed Project

The main objective of the Holodeck Testbed is to create a cost effective, realistic, and highly immersive environment that can be used to train astronauts, carry out engineering analysis, develop procedures, and support various operations tasks. Currently, the Holodeck testbed allows to step into a simulated ISS (International Space Station) and interact with objects; as well as, perform Extra Vehicular Activities (EVA) on the surface of the Moon or Mars. The Holodeck Testbed is using the products being developed in the Hybrid Reality Lab (HRL). The HRL is combining technologies related to merging physical models with photo-realistic visuals to create a realistic and highly immersive environment. The lab also investigates technologies and concepts that are needed to allow it to be integrated with other testbeds; such as, the gravity offload capability provided by the Active Response Gravity Offload System (ARGOS). My main two duties were to develop and animate models for use in the HRL environments and work on a new way to interface with computers using Brain Computer Interface (BCI) technology. On my first task, I was able to create precise computer virtual tool models (accurate down to the thousandths or hundredths of an inch). To make these tools even more realistic, I produced animations for these tools so they would have the same mechanical features as the tools in real life. The computer models were also used to create 3D printed replicas that will be outfitted with tracking sensors. The sensor will allow the 3D printed models to align precisely with the computer models in the physical world and provide people with haptic/tactile feedback while wearing a VR (Virtual Reality) headset and interacting with the tools. Getting close to the end of my internship the lab bought a professional grade 3D Scanner. With this, I was able to replicate more intricate tools at a much more time-effective rate. The second task was to investigate the use of BCI to control objects inside the hybrid reality ISS environment. This task looked at using an Electroencephalogram (EEG) headset to collect brain state data that could be mapped to commands that a computer could execute. On this Task, I had a setback with the hardware, which stopped working and was returned to the vendor for repair. However, I was still able to collect some data, was able to process it, and started to create correlation algorithms between the electrical patterns in the brain and the commands we wanted the computer to carry out. I also carried out a test to investigate the comfort of the headset if it is worn for a long time. The knowledge gained will benefit me in my future career. I learned how to use various modeling and programming tools that included Blender, Maya, Substance Painter, Artec Studio, Github, and Unreal Engine 4. I learned how to use a professional grade 3D scanner and 3D printer. On the BCI Project I learned about data mining and how to create correlation algorithms. I also supported various demos including a live demo of the hybrid reality lab capabilities at ComicPalooza. This internship has given me a good look into engineering at NASA. I developed a more thorough understanding of engineering and my overall confidence has grown. I have also realized that any problem can be fixed, if you try hard enough, and as an engineer it is your job to not only fix problems but to embrace coming up with solutions to those problems.

Arias, Adriel↗
DOE OSTI2017

A search for new phenomena in pp collisions at $\sqrt{s} = 13\,\text {TeV} $ in final states with missing transverse momentum and at least one jet using the $\alpha _{\mathrm {T}}$ variable

A search for new phenomena is performed in final states containing one or more jets and an imbalance in transverse momentum in pp collisions at a centre-of-mass energy of 13 $\,\text {TeV}$ . The analysed data sample, recorded with the CMS detector at the CERN LHC, corresponds to an integrated luminosity of 2.3 $\,\text {fb}^{-1}$ . Several kinematic variables are employed to suppress the dominant background, multijet production, as well as to discriminate between other standard model and new physics processes. The search provides sensitivity to a broad range of new-physics models that yield a stable weakly interacting massive particle. The number of observed candidate events is found to agree with the expected contributions from standard model processes, and the result is interpreted in the mass parameter space of fourteen simplified supersymmetric models that assume the pair production of gluinos or squarks and a range of decay modes. For models that assume gluino pair production, masses up to 1575 and 975 $\,\text {GeV}$ are excluded for gluinos and neutralinos, respectively. For models involving the pair production of top squarks and compressed mass spectra, top squark masses up to 400 $\,\text {GeV}$ are excluded.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1998

Simulation of Ultra-Small MOSFETs Using a 2-D Quantum-Corrected Drift-Diffusion Model

The continued down-scaling of electronic devices, in particular the commercially dominant MOSFET, will force a fundamental change in the process of new electronics technology development in the next five to ten years. The cost of developing new technology generations is soaring along with the price of new fabrication facilities, even as competitive pressure intensifies to bring this new technology to market faster than ever before. To reduce cost and time to market, device simulation must become a more fundamental, indeed dominant, part of the technology development cycle. In order to produce these benefits, simulation accuracy must improve markedly. At the same time, device physics will become more complex, with the rapid increase in various small-geometry and quantum effects. This work describes both an approach to device simulator development and a physical model which advance the effort to meet the tremendous electronic device simulation challenge described above. The device simulation approach is to specify the physical model at a high level to a general-purpose (but highly efficient) partial differential equation solver (in this case PROPHET, developed by Lucent Technologies), which then simulates the model in 1-D, 2-D, or 3-D for a specified device and test regime. This approach allows for the rapid investigation of a wide range of device models and effects, which is certainly essential for device simulation to catch up with, and then stay ahead of, electronic device technology of the present and future. The physical device model used in this work is the density-gradient (DG) quantum correction to the drift-diffusion model [Ancona, Phys. Rev. B 35(5), 7959 (1987)]. This model adds tunneling and quantum smoothing of carrier density profiles to the drift-diffusion model. We used the DG model in 1-D and 2-D (for the first time) to simulate both bipolar and unipolar devices. Simulations of heavily-doped, short-base diodes indicated that the DG quantum corrections do not have a large effect on the IN characteristics of electronic devices without heteroj unction s. On the other hand, ultra-small MOSFETs certainly exhibit important quantum effects that the DG model will include: quantum repulsion of the inversion and gate charges from the oxide interfaces, and quantum tunneling through thin gate oxides. We present initial results of 2-D DG simulations of ultra-small MOSFETs. Subtle but important issues involving the specification of the model, boundary conditions, and interface constraints for DG simulation of MOSFETs will also be illuminated.

Biegal, Bryan A.↗
DOE OSTI2022

Thick Electrode Design for Facile Electron and Ion Transport: Architectures, Advanced Characterization, and Modeling

The demand for lithium ion batteries continues to expand for powering applications such as portable electronics, grid-scale energy storage, and electric vehicles. As the application requirements advance, the innovation of lithium ion batteries toward higher energy density and power output is required. Along with the investigation of new materials, an important strategy for increasing battery energy content is to design electrodes with high areal loading to minimize the fraction of nonactive materials such as current collectors, separators, and packaging components, resulting in significant gains in energy content and the reduction of the system-level cost. However, the adoption of thick high areal loading electrodes has been impeded by sluggish charge transport and mechanical instability. With conventional slurry cast electrodes, battery function significantly deteriorates with increases in electrode thickness due to high cell polarization and the incomplete utilization of active materials. Thus, a consideration of approaches that facilitate an understanding and eventual adoption of high-loading electrodes is warranted to enable the deliberate advancement of next-generation batteries. Herein, this Account considers three aspects critical to the science and technology of thick high-loading electrodes. The first discussion covers recent approaches to the design and fabrication of high-loading electrodes. Ensuring electrical contact throughout the electrode is accomplished through the manipulation of conductive additives or using a conductive scaffold within the electrode. Ion transport can be facilitated through electrode design and fabrication approaches that deliberately control the electrode porosity and tortuosity. Second, advanced characterization methodologies are presented as the ability to determine the origins of transport limitations provide the insight needed to deliberately approach future designs. Spectroscopic and diffraction methods have been used to characterize the 2D and 3D pore structure and composition of the electrodes. Furthermore, operando methods that yield spatially and temporally resolved information regarding the progression of the electrochemical reaction are highlighted. The third aspect considered is the utilization of modeling. Physically based continuum models linked with the results of experimental characterization have been demonstrated and then allow the rapid simulation of a variety of deliberate electrode designs and their impacts on functional electrochemistry. Variables relevant to the designs can be tested by the model under a series of use conditions to identify those of most promise for a specific application. Finally, an outlook on future opportunities for high-loading battery electrode research is provided to inform and entice practitioners in the field to pursue these important directions of inquiry.

25 ENERGY STORAGE↗
DOE OSTI2013

EXTRAPOLATION OF THE SOLAR CORONAL MAGNETIC FIELD FROM SDO/HMI MAGNETOGRAM BY A CESE-MHD-NLFFF CODE

Due to the absence of direct measurement, the magnetic field in the solar corona is usually extrapolated from the photosphere in a numerical way. At the moment, the nonlinear force-free field (NLFFF) model dominates the physical models for field extrapolation in the low corona. Recently, we have developed a new NLFFF model with MHD relaxation to reconstruct the coronal magnetic field. This method is based on CESE-MHD model with the conservation-element/solution-element (CESE) spacetime scheme. In this paper, we report the application of the CESE-MHD-NLFFF code to Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI) data with magnetograms sampled for two active regions (ARs), NOAA AR 11158 and 11283, both of which were very non-potential, producing X-class flares and eruptions. The raw magnetograms are preprocessed to remove the force and then inputted into the extrapolation code. Qualitative comparison of the results with the SDO/AIA images shows that our code can reconstruct magnetic field lines resembling the EUV-observed coronal loops. Most important structures of the ARs are reproduced excellently, like the highly sheared field lines that suspend filaments in AR 11158 and twisted flux rope which corresponds to a sigmoid in AR 11283. Quantitative assessment of the results shows that the force-free constraint is fulfilled very well in the strong-field regions but apparently not that well in the weak-field regions because of data noise and numerical errors in the small currents.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

ExoToComsol v.0.1

SAND2024-08641O ExoToComsol models capability-sharing. For example, the software can develop a multiphysics modeling chain using physics modeling capabilities from different research institutions using either SIERRA or COMSOL. The converter program allows exporting user-defined, region-of-interest from a large FE model, which results in a finer mesh of the submodel that can be used for subsequent analysis. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rahman, Farhan↗
DOE OSTI2018

Extreme-Scale Bayesian Inference for Uncertainty Quantification of Complex Simulations

Uncertainty quantification (UQ)—that is, quantifying uncertainties in complex mathematical models and their large-scale computational implementations—is widely viewed as one of the outstanding challenges facing the field of CS&E over the coming decade. The EUREKA project set to address the most difficult class of UQ problems: those for which both the underlying PDE model as well as the uncertain parameters are of extreme scale. In the project we worked on these extreme-scale challenges in the following four areas: 1. Scalable parallel algorithms for sampling and characterizing the posterior distribution that exploit the structure of the underlying PDEs and parameter-to-observable map. These include structure-exploiting versions of the randomized maximum likelihood method, which aims to overcome the intractability of employing conventional MCMC methods for solving extreme-scale Bayesian inversion problems by appealing to and adapting ideas from large-scale PDE-constrained optimization, which have been very successful at exploring high-dimensional spaces. 2. Scalable parallel algorithms for construction of prior and likelihood functions based on learning methods and non-parametric density estimation. Constructing problem-specific priors remains a critical challenge in Bayesian inference, and more so in high dimensions. Another challenge is construction of likelihood functions that capture unmodeled couplings between observations and parameters. We will create parallel algorithms for non-parametric density estimation using high dimensional N-body methods and combine them with supervised learning techniques for the construction of priors and likelihood functions. 3. Bayesian inadequacy models, which augment physics models with stochastic models that represent their imperfections. The success of the Bayesian inference framework depends on the ability to represent the uncertainty due to imperfections of the mathematical model of the phenomena of interest. This is a central challenge in UQ, especially for large-scale models. We propose to develop the mathematical tools to address these challenges in the context of extreme-scale problems. 4. Parallel scalable algorithms for Bayesian optimal experimental design (OED). Bayesian inversion yields quantified uncertainties in the model parameters, which can be propagated forward through the model to yield uncertainty in outputs of interest. This opens the way for designing new experiments to reduce the uncertainties in the model parameters and model predictions. Such experimental design problems have been intractable for large-scale problems using conventional methods; we will create OED algorithms that exploit the structure of the PDE model and the parameter-to-output map to overcome these challenges. Parallel algorithms for these four problems were created, analyzed, prototyped, implemented, tuned, and scaled up for leading-edge supercomputers, including UT-Austin’s own 10 petaflops Stampede system, ANL’s Mira system, and ORNL’s Titan system. While our focus is on fundamental mathematical/computational methods and algorithms, we will assess our methods on model problems derived from several DOE mission applications, including multiscale mechanics and ice sheet dynamics.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1983

Satellite freeze forecast system: Executive summary

A satellite-based temperature monitoring and prediction system consisting of a computer controlled acquisition, processing, and display system and the ten automated weather stations called by that computer was developed and transferred to the national weather service. This satellite freeze forecasting system (SFFS) acquires satellite data from either one of two sources, surface data from 10 sites, displays the observed data in the form of color-coded thermal maps and in tables of automated weather station temperatures, computes predicted thermal maps when requested and displays such maps either automatically or manually, archives the data acquired, and makes comparisons with historical data. Except for the last function, SFFS handles these tasks in a highly automated fashion if the user so directs. The predicted thermal maps are the result of two models, one a physical energy budget of the soil and atmosphere interface and the other a statistical relationship between the sites at which the physical model predicts temperatures and each of the pixels of the satellite thermal map.

Martsolf, J. D.↗
NASA NTRS2002

Opportunities for Breakthroughs in Large-Scale Computational Simulation and Design

Opportunities for breakthroughs in the large-scale computational simulation and design of aerospace vehicles are presented. Computational fluid dynamics tools to be used within multidisciplinary analysis and design methods are emphasized. The opportunities stem from speedups and robustness improvements in the underlying unit operations associated with simulation (geometry modeling, grid generation, physical modeling, analysis, etc.). Further, an improved programming environment can synergistically integrate these unit operations to leverage the gains. The speedups result from reducing the problem setup time through geometry modeling and grid generation operations, and reducing the solution time through the operation counts associated with solving the discretized equations to a sufficient accuracy. The opportunities are addressed only at a general level here, but an extensive list of references containing further details is included. The opportunities discussed are being addressed through the Fast Adaptive Aerospace Tools (FAAST) element of the Advanced Systems Concept to Test (ASCoT) and the third Generation Reusable Launch Vehicles (RLV) projects at NASA Langley Research Center. The overall goal is to enable greater inroads into the design process with large-scale simulations.

Alexandrov, Natalia↗
NASA NTRS2004

Calibration, Data Acquisition, and Post Analysis of Turbulent Fluid Flow in a Calibration Jet Using Hot-wire Anemometry

The Turbine Branch concentrates on the following areas: Computational Fluid Dynamics (CFD), and implementing experimental procedures to obtain physical modeling data. Hot-wire Anemometry is a valuable tool for obtaining physical modeling data. Hot-wire Anemometry is likely to remain the principal research tool for most turbulent air/gas flow studies. The Hot-wire anemometer consists of a fine wire heated by electric current. When placed in a fluid stream, the hot-wire loses heat to the fluid by forced convection. In forced convection, energy transfer is due to molecular motion imposed by an extraneous force moving fluid parcels. When the hot-wire is in "equilibrium", the rate of heat input to the wire is equal to the rate of heat loss at the wire ends. The equality between heat input and heat loss is the basis for King s equation, which relates the electrical parameters of the hot-wire to the flow parameters of the fluid. Hot-wire anemometry is based on convective heat transfer from a heated wire element placed in a fluid flow. Any change in the fluid flow condition that affects the heat transfer from the heated element will be detected virtually instantaneously by a constant-temperature Hot-wire anemometry system. The system implemented for this research is the IFA 300. The system is a fully-integrated, thermal anemometer-based system that measures mean and fluctuating velocity components in air, water, and other fluids. It also measures turbulence and makes localized temperature measurements. A constant-temperature anemometer is a bridge and amplifier circuit that controls a tiny wire at constant temperature. As a fluid flow passes over the heated sensor, the amplifier senses the bridge off-balance and adjusts the voltage to the top of the bridge, keeping the bridge in balance. The voltage on top of the bridge can then be related to the velocity of the flow. The bridge voltage is sensitive to temperature as well as velocity and so the built-in thermocouple circuit can be attached to a thermocouple that can measure the fluid temperature. Additional information is included in the original extended abstract.

Moreno, Michelle↗
NASA NTRS2017

Improved Intraseasonal Variability in the NASA GEOS AGCM with 2-moment Microphysics and a Shallow Cumulus Parameterization

Weather and climate models have long struggled to realistically simulate the Madden-Julian Oscillation (MJO). Here we present a significant improvement in MJO simulation in NASA's GEOS atmospheric model with the implementation of 2-moment microphysics and the UW shallow cumulus parameterization. Comparing ten-year runs (2007-2016) with the old (1mom) and updated (2mom+shlw) model physics, the updated model has increased intra-seasonal variance with increased coherence. Surface fluxes and OLR are found to vary more realistically with precipitation, and a moisture budget suggests that changes in rain reevaporation and the cloud longwave feedback help support heavy precipitation. Preliminary results also show improved MJO hindcast skill.

Madden-Julian Oscillatio↗
NASA NTRSDate not supplied

Medium-Range River Flood Forecasts Using a Long Short-Term Memory Network

River flooding and the impacts are a concern for decision makers throughout the United States. Accurate medium-range forecasts (~3-7 days) are critical for providing advanced outlooks to emergency management officials. Unfortunately, accurately forecasting rainfall-runoff and the subsequent rise and fall within rivers remain a challenge in hydrological modeling. While complex physical modeling systems are the standard for representing the hydrological processes, they are computationally demanding and can require extensive calibration. Further, uncertainties remain in the model parameters and input data. The use of machine learning can reduce some of the computational demand while maintaining high accuracy. Therefore, this project makes use of a Long Short-Term Memory (LSTM) network which explicitly accounts for the time-dependent nature of rainfall-runoff modeling. The developed LSTM was trained to predict river gauge height, or stage height, based on time-lagged input features which include: gauge height to initialize the model, the NASA Short-term Prediction Research and Transition Center’s instance of the Land Information System (SPoRT-LIS) relative soil moisture to describe the rainfall infiltration rate, and 6-hr Multi-Radar Multi-Sensor quantitative precipitation estimate (MRMS QPE). The developed LSTM based system is then used to produce 7-day forecasts with a 6-hr temporal resolution using three different quantitative precipitation forecasts (QPF) from the NWS’s Weather Prediction Center (WPC), the NCEP Global Forecast System (GFS) model and the National Blend of Models (NBM). This trained modeling system has been implemented as an experimental product at over 100 different rivers in collaboration with at multiple National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC) across the eastern half of the United States. The developed LSTM model achieved average Nash-Sutcliffe efficiency (NSE) 0.89 higher than the equivalent medium-range National Water Model ensemble member forecast over a 7-day forecast. In addition to the initial development and evaluation, this project has continued to expand. While the initial model was developed for precipitation dominated basins, expansion of the project has taken it to basins effected by snow melt. This presentation will provide an overview of the project with focus on recent developments on incorporating snow melt processes into the model.

Andrew T. White↗
DOE OSTI2015

Validation of the National Solar Radiation Database (NSRDB) (2005-2012): Preprint

Publicly accessible, high-quality, long-term, satellite-based solar resource data is foundational and critical to solar technologies to quantify system output predictions and deploy solar energy technologies in grid-tied systems. Solar radiation models have been in development for more than three decades. For many years, the National Renewable Energy Laboratory (NREL) developed and/or updated such models through the National Solar Radiation Data Base (NSRDB). There are two widely used approaches to derive solar resource data from models: (a) an empirical approach that relates ground-based observations to satellite measurements and (b) a physics-based approach that considers the radiation received at the satellite and creates retrievals to estimate clouds and surface radiation. Although empirical methods have been traditionally used for computing surface radiation, the advent of faster computing has made operational physical models viable. The Global Solar Insolation Project (GSIP) is an operational physical model from the National Oceanic and Atmospheric Administration (NOAA) that computes global horizontal irradiance (GHI) using the visible and infrared channel measurements from the Geostationary Operational Environmental Satellites (GOES) system. GSIP uses a two-stage scheme that first retrieves cloud properties and then uses those properties in the Satellite Algorithm for Surface Radiation Budget (SASRAB) model to calculate surface radiation. NREL, the University of Wisconsin, and NOAA have recently collaborated to adapt GSIP to create a high temporal and spatial resolution data set. The product initially generates the cloud properties using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms [3], whereas the GHI is calculated using SASRAB. Then NREL implements accurate and high-resolution input parameters such as aerosol optical depth (AOD) and precipitable water vapor (PWV) to compute direct normal irradiance (DNI) using the DISC model. The AOD and PWV, temperature, and pressure data are also combined with the MMAC model to simulate solar radiation under clear-sky conditions. The current NSRDB update is based on a 4-km x 4-km resolution at a 30-minute time interval, which has a higher temporal and spatial resolution. This paper demonstrates the evaluation of the data set using ground-measured data and detailed evaluation statistics. The result of the comparison shows a good correlation to the NSRDB data set. Further, an outline of the new version of the NSRDB and future plans for enhancement and improvement are provided.

14 SOLAR ENERGY↗
DOE OSTI2022

ML-PSA

The computer code uses a parallel simulated annealing framework with embedded machine learning components to solve multi-constrained optimization problems. The software automatically balances the execution of low and high fidelity physics models within the optimization procedure. The low fidelity model is used to rapidly explore the design space while the high fidelity physics model is executed sparingly to account for complex design constraints that are not resolved by the quickly executing low fidelity model.

Gurecky, William↗
NASA NTRS1993

Aerothermodynamics of a MESUR Martian entry

Computational fluid dynamics models for the thermodynamics and transport properties used in an equilibrium version of the Langley Aerothermodynamics Upwind Relaxation Algorithm (LAURA) for Martian Atmospheric entries are described. In addition, the physical models used in a nonequilibrium version of LAURA for Martian-entry flows are described. Uncertainties exist in defining constants used in the transport properties for the equilibrium model and in many of the physical models for the nonequilibrium version. Solutions from the two codes using the best available constants are examined at the Martian-entry conditions characteristic of the Mars Environmental Survey (MESUR) mission. While the flowfields are near thermal equilibrium, chemical nonequilibrium effects are present in the entry cases examined. Convective heating at the stagnation point for these flows (assuming fully catalytic wall boundary conditions) is approximately 100 W/sq cm. Radiative heating is negligible.

Mitcheltree, R. A.↗
DOE OSTI2024

Designing observables for measurements with deep learning

Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models. When the inference is performed with unfolded cross sections, the observables are designed using physics intuition and heuristics. We propose to design targeted observables with machine learning. Unfolded, differential cross sections in a neural network output contain the most information about parameters of interest and can be well-measured by construction. The networks are trained using a custom loss function that rewards outputs that are sensitive to the parameter(s) of interest while simultaneously penalizing outputs that are different between particle-level and detector-level (to minimize detector distortions). We demonstrate this idea in simulation using two physics models for inclusive measurements in deep inelastic scattering. We find that the new approach is more sensitive than classical observables at distinguishing the two models and also has a reduced unfolding uncertainty due to the reduced detector distortions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗