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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.

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At least 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Microgravity Emissions Laboratory Testing of the Light Microscopy Module Control Box Fan

The Microgravity Emissions Laboratory (MEL) was developed at the NASA Glenn Research Center for the characterization, simulation, and verification of the International Space Station (ISS) microgravity environment. This Glenn lab was developed in support of the Fluids and Combustion Facility (FCF). The MEL is a six-degrees-of-freedom inertial measurement system that can characterize the inertial response forces (emissions) of components, subrack payloads, or rack-level payloads down to 10 7g. The inertial force output data generated from the steady-state or transient operations of the test article are used with finite element analysis, statistical energy analysis, and other analysis tools to predict the on-orbit environment at specific science or rack interface locations. Customers of the MEL have used benefits in isolation performance testing in defining available attenuation during the engineering hardware design phase of their experiment s development. The Light Microscopy Module (LMM) Control Box (LCB) fan was tested in the MEL in June and July of 2002. The LMM is planned as a remotely controllable on-orbit microscope subrack facility that will be accommodated in an FCF Fluids Integrated Rack on the ISS. The disturbances measured in the MEL test resulted from operation of the air-circulation fan within the LCB. The objectives of the testing were (1) to identify an isolator to be added to the LCB fan assembly to reduce fan-speed harmonics and (2) to identify the fan-disturbance forcing functions for use in rack-response analysis of the LMM and Fluids Integrated Rack facility. This report describes the MEL, the testing process, and the results from ground-based MEL LCB fan testing.

McNelis, Anne M.↗
NASA NTRS2018

Research and Development of The Immersive Simulations and Engineering Environment

March of 2018 marked the conclusion of the primary updates to the immersive Simulations and Engineering Environment (iSEE) at Kennedy Space Center (KSC). Many of the problems that had arisen during the previous semester have been addressed and rectified. These included the malfunction to one of the lab's primary routers, the inefficiency of the capture environment, and various interface issues in the analysis software, Jack. This semester was primarily research and development oriented with some focus on implementation of the new hardware and software that was received last semester. The new computers and cameras that arrived sometime during the winter were installed, and the lab received its second operation opportunity. The second operation was a major milestone for the lab, both in terms of what the abilities were and what can be learned from its use. The operation performed was a virtual simulation of a critical task that would occur, if it should be needed, in the Multi Payload Processing Facility. It was done to gather human factors data on its safety and process controls. The technicians were able to come to a number of conclusions about how to perform their task as a result of utilizing iSEE. Another key breakthrough this semester was the introduction to Jack Script, a scripting language built into our analysis software that further extend Jack capabilities. In addition to the aforementioned, many preparations were made for family day, an exposition for KSC families to come out and tour the spaceport. Due to family day being moved to the spring, the video made last fall had to be updated with recent environment changes in preparation for the Family Day demonstrations.

Motion Capture↗
DOE OSTI2022

Outdoor Test Bed Performance of a Power Line Sensor Using a Real-Time Event Simulator

This report summarizes the design and application of an outdoor power line sensor testbed (OPLST) with a real-time simulator and power meter to compare a potential transformer (PT) and current transformer (CT) versus an advanced line-post sensor. The OPLST was created to validate advanced medium-voltage (20/34.5 kV) outdoor power line sensors used in electrical distribution systems. Electrical utilities have installed metering and relay-protection transformers like PTs and CTs for several decades. The PTs/CTs are iron core measurement transformers based on the electromagnetic induction principle and provide reliable data in normal grid operation. However, new outdoor power line sensors (OPLS) using other technologies like voltage divider, Rogowski coil and optical principles have become available and may have favorable performance and costs compared to PTs/CTs. Therefore, the importance of testing these technologies with the PT/CT, to compare the measured phase voltage/current at different power grid scenarios is crucial to understand the performance of these new OPLS. For this study a G&W Electric Model CVS-36-O power line sensor was chosen as the OPLS to test with voltage/current signals. An OPAL-RT Technologies Model OP4510 real-time simulator and Schweitzer Engineering Laboratories Model SEL-735 power meter were installed with the 20/34.5 kV OPLST to compare the measured transient events collected from an advanced OPLS and the PT/CT. This system is installed at the Distributed Energy Communications and Control (DECC) lab, Oak Ridge National Laboratory (ORNL). The simulator generated different power grid scenarios (electrical faults, capacitor bank operation, service restoration, etc.), and its analog-output signals were connected to the voltage/current amplifiers that feed the 20/34.5 kV aerial cable loop through the PT/CT devices. Additional PT/CT devices were also wired with the medium voltage aerial cable loop to measure the phase current/voltage signals and server as references. After each test, common format for transient data exchange (COMTRADE) files were collected from the SEL-735 power meter and used to compare the performance of the OPLS with the PT/CT. The behavior of analog signals, harmonic components, total harmonic distortion and crest factors were assessed and found favorable for the G&W sensor as compared to the reference PT/CT.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A surprising proliferation of detwinning in β -tin at extreme loading rates

Integrating data from dynamic compression experiments of condensed matter across three national laboratories has led to insight and quantitative calibration of materials strength over decades of loading rate. For many materials, a single strength model (such as PTW) is sufficient to capture the flow-stress strain rate relationship which is monotonic. Here, we show here that β -tin, a tetragonal metal, exhibits dramatic deviations from this behavior. Naive fitting to a single PTW model is insufficient to capture the behavior; indeed, such resulting inferred flow stress versus strain exhibits a non-monotonic behavior. We suggest a resolution to this by proposing that in β -tin there are important Bauschinger effects arising from favorable conditions for twinning and detwinning. A simple yield surface model when paired with PTW hardening captures the experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nontrivial critical behavior at magnetic transitions: A case study of Sm 7 ⁢Pd 3

We present a comprehensive analysis of the critical behavior of Sm 7 ⁢Pd 3 in the vicinity of its second-order magnetoelastic transition at 𝑇 c =173 K. The critical exponents (CEs) 𝛽 and 𝛾, determined using both the standard convergence procedure and the average normalized slope (ANS) method, diverge at 𝑇 c –-a characteristic typically associated with first-order transitions. Notably, none of the established universality classes satisfactorily describe the critical behavior of Sm 7 ⁢Pd 3 , and we discuss the possible origins of this deviation in the context of the strong spin-lattice coupling intrinsic to the sample. We emphasize the importance of accurately selecting the critical temperature and magnetic field ranges to ensure robust critical behavior analysis, and propose a quantitative approach to assess the reliability of the extracted CEs. Additionally, we demonstrate that in the ANS method, the critical exponents 𝛽 and 𝛾 should be calculated separately using data for 𝑇 ⩽ 𝑇 c and 𝑇 ⩾ 𝑇 c , respectively. In conclusion, our findings underscore the need for a revised theoretical framework to accurately describe second-order magnetoelastic transitions.

Ferrimagnets↗
DOE OSTI2021

CNN-Encoder-Decoder Model

Code and data for training CNN-Encoder-Decoder model described in the publication 'Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder-decoder models.'

Konstantinova, Tatiana [Brookhaven National Lab. (↗
DOE OSTI2024

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Abstract Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.

25 ENERGY STORAGE↗
DOE OSTI2020

Computational fluid dynamics analysis of char conversion in Sandia’s pressurized entrained flow reactor

Design and analysis of practical reactors utilizing solid feedstocks rely on reaction rate parameters that are typically generated in lab-scale reactors. Evaluation of the reaction rate information often relies on assumptions of uniform temperature, velocity, and species distributions in the reactor, in lieu of detailed measurements that provide local information. This assumption might be a source of substantial error, since reactor designs can impose significant inhomogeneities, leading to data misinterpretation. Spatially resolved reactor simulations help understand the key processes within the reactor and support the identification of severe variations of temperature, velocity, and species distributions. In this work, Sandia’s pressurized entrained flow reactor is modeled to identify inhomogeneities in the reaction zone. Tracer particles are tracked through the reactor to estimate the residence times and burnout ratio of introduced coal char particles in gasifying environments. The results reveal a complex mixing environment for the cool gas and particles entering the reactor along the centerline and the main high-speed hot gas reactor flow. Furthermore, the computational fluid dynamics (CFD) results show that flow asymmetries are introduced through the use of a horizontal gas pre-heating section that connects to the vertical reactor tube. Computed particle temperatures and residence times in the reactor differ substantially from the idealized plug flow conditions typically evoked in interpreting experimental measurements. Furthermore, experimental measurements and CFD analysis of heat flow through porous refractory insulation suggest that for the investigated conditions (1350 °C, <20 atm), the thermal conductivity of the insulation does not increase substantially with increasing pressure.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Perspectives of active Si photonics devices for data communication and optical sensing

Si photonics has made rapid progress in research and commercialization in the past two decades. While it started with electronic–photonic integration on Si to overcome the interconnect bottleneck in data communications, Si photonics has now greatly expanded into optical sensing, light detection and ranging (LiDAR), optical computing, and microwave/RF photonics applications. From an applied physics point of view, this perspective discusses novel materials and integration schemes of active Si photonics devices for a broad range of applications in data communications, spectrally extended complementary metal–oxide–semiconductor (CMOS) image sensing, as well as 3D imaging for LiDAR systems. We also present a brief outlook of future synergy between Si photonic integrated circuits and Si CMOS image sensors toward ultrahigh capacity optical I/O, ultrafast imaging systems, and ultrahigh sensitivity lab-on-chip molecular biosensing.

electronic band structure↗
DOE OSTI2021

Modeling of Glycolate Destruction in the Recycle Collection Tank

The Savannah River Site’s DWPF is being upgraded with the introduction of the NG flowsheet. Glycolic acid has been shown to be a superior alternative to formic acid for sludge processing. The new flowsheet improves or maintains necessary parameters such as 1) reduction of mercury, 2) adjustment of feed rheology, 3) pH stability, and 4) adjustment of melter oxidation/reduction potential. Further, the use of glycolic acid virtually eliminates the potential for catalytic hydrogen generation in DWPF processing DWPF process condensates are collected and returned to the Savannah River Site (SRS) CSTF. The RCT collects off-gas condensate during chemical processing, vitrification, and other unit operations performed in DWPF and is the singular return vessel delivering recycle effluent back to CSTF. Each batch of recycle will have a small amount of glycolate from chemical processing and melter off-gas condensates. To avoid potential flammability issues due to thermolysis of glycolate in the CSTF, Savannah River National Laboratory (SRNL) provided to Savannah River Remediation (SRR) at their request a Task Technical and Quality Assurance Plan (TTQAP) to quantify and mitigate glycolate returns via DWPF’s recycle stream. The request included testing of a process to oxidize glycolate and other organic species that are responsible for hydrogen generation from thermolysis. Following that work SRR provided a Task Technical Request (TTR) that requested process modeling. In 2021 a TTQAP was issued to cover the modeling work. Modeling draws data from laboratory scale studies using chemical simulants and radioactive waste samples. Chemical kinetic modeling was performed to evaluate the feasibility of using sodium permanganate to destroy glycolate in the RCT. The results from the laboratory studies were summarized in a series of reports. Reference 9 is a report of lab scale processing of actual DWPF Slurry Mix Evaporate Condensate Tank (SMECT) and Offgas Condensate Tank (OGCT) samples in the SRNL Shielded Cells. Tests at caustic conditions demonstrated sodium permanganate was effective in converting glycolate to oxalate, and permanganate (Mn 7+ ) is reduced to manganate (Mn 6+ ) with no significant formation of carbon dioxide or carbonate. Equation (1) was found to best describe the observed reaction of glycolate with permanganate under nominal (60 to 145 mg/L in RCT) glycolate entrainment conditions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Properties of planetary fluids at high pressures and temperatures

Observational data obtained by the Voyager space probes to the giant planets Jupiter, Saturn, Uranus, and Neptune have provided valuable information, which is used to refine the picture of the nature of the interiors of these planets. Major results from the Voyager missions include observations of substantial magnetic fields and improved models of internal density distributions. The goal is to obtain equations of state and electrical conductivity data for planetary gases (H2 and He) and the ices (H2O, CH4, and NH3, and their mixtures), which are considered to be the major constituents of the giant planets. These data are needed to test theoretical data bases used to construct models of the chemical composition of planetary interiors, models which are consistent with observables such as mass, diameter, gravitational moments, rotation rate, and magnetic field. The 100 GPa (1 Mbar) pressures and several 1000 K temperatures in the giant planets can be achieved in the lab by the shock compression of liquid specimens. Results are briefly examined.

Nellis, W. J.↗
NASA NTRS2015

Formation Flying for Satellites and Unmanned Aerial Vehicles

The shrinking size of satellites and unmanned aerial vehicles (UAVs) is enabling lower cost missions. As sensors and electronics continue to downsize, the next step is multiple vehicles providing different perspectives or variations for more precise measurements. While flying a single satellite or UAV autonomously is a challenge, flying multiple vehicles in a precise formation is even more challenging. The goal of this project is to develop a scalable mesh network between vehicles (satellites or UAVs) to share real-time position data and maintain formations autonomously. Newly available low-cost, commercial off-the-shelf credit card size computers will be used as the basis for this network. Mesh networking techniques will be used to provide redundant links and a flexible network. The Small Projects Rapid Integration and Test Environment Lab will be used to simulate formation flying of satellites. UAVs built by the Aero-M team will be used to demonstrate the formation flying in the West Test Area. The ability to test in flight on NASA-owned UAVs allows this technology to achieve a high Technology Readiness Level (TRL) (TRL-4 for satellites and TRL-7 for UAVs). The low cost of small UAVs and the availability of a large test range (West Test Area) dramatically reduces the expense of testing. The end goal is for this technology to be ready to use on any multiple satellite or UAV mission.

Merrill, Garrick↗
DOE OSTI2021

Performance Evaluation of Comparative Vacuum Monitoring and Piezoelectric Sensors for Structural Health Monitoring of Rotorcraft Components

The costs associated with the increasing maintenance and surveillance needs of aging structures are rising at an unexpected rate. Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today’s extensive fleet of aging aircraft and space vehicles. Aircraft maintenance and repairs represent about a quarter of a commercial fleet’s operating costs. The application of Structural Health Monitoring (SHM) systems using distributed sensor networks can reduce these costs by facilitating rapid and global assessments of structural integrity. The use of in-situ sensors for real-time health monitoring can overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. The ease of monitoring an entire on-board network of distributed sensors means that structural health assessments can occur more often, allowing operators to be even more vigilant with respect to flaw onset. SHM systems also allow for condition-based maintenance practices to be substituted for the current time-based or cycle-based maintenance approach thus optimizing maintenance labor. The Federal Aviation Administration has conducted a series of SHM validation and certification programs intended to comprehensively support the evolution and adoption of SHM practices into routine aircraft maintenance practices. This report presents one of those programs involving a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Sikorsky, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp., Acellent Technologies Inc., and the Federal Aviation Administration (FAA) carried out a trial validation and certification program to evaluate Comparative Vacuum Monitoring (CVM) and Piezoelectric Transducers (PZT) as a structural health monitoring solution to specific rotorcraft applications. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM and PZT systems into rotorcraft maintenance programs and the document revisions necessary to allow for their routine use as an alternate means of performing periodic structural inspections. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically relevant experimental effort to quantify the reliability of a CVM system applied to Sikorsky S-92 fuselage frame application and a PZT system applied to an S-92 main gearbox mount beam application. The applications included both local and global damage detection assessments. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for SHM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and is acceptable to both the aviation industry and regulatory bodies. The validation work completed in this program demonstrated the ability of both CVM and PZT SHM systems to detect cracks in rotorcraft components. It proved the ability to use final system response parameters to provide a Green Light/Red Light (“GO” – “NO GO”) decision on the presence of damage. In additional to quantifying the performance of each SHM system for the trial applications on the S-92 platform, this study also identified specific methods that can be used to optimize damage detection, guidance on deployment scenarios that can affect performance and considerations that must be made to properly apply CVM and PZT sensors. These results support the main goal of safely integrating SHM sensors into rotorcraft maintenance programs. Additional benefits from deploying rotorcraft Health and Usage Monitoring Systems (HUMS) may be realized when structural assessment data, collected by an SHM system, is also used to detect structural damage to compliment the operational environment monitoring. The use of in-situ sensors for health monitoring of rotorcraft structures can be a viable option for both flaw detection and maintenance planning activities. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM and PZT technology. It will also streamline future regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.

42 ENGINEERING↗
DOE OSTI2025

Scalable Data Center Capacity for DOE's AI Prototype: A Rapidly Available Gigawatt Data Center for DOE

The multilaboratory Gigawatt Data Center working group was commissioned to identify approaches to rapidly establish federal data centers with scalable capacities up to 1,000 MW. These state-of-the-art facilities will serve as hubs for interdisciplinary collaboration, industry partnerships, and transformative applications of artificial intelligence. The proposed strategic shift includes facilitating multilaboratory collaboration, prioritizing operational efficiency, expanding public–private partnerships, optimizing investments, ensuring long-term contractual flexibility, supporting open science and secure data enclaves, and exploiting high-speed national networks. Owing to their extensive experience and best practices, the US Department of Energy national laboratories are uniquely positioned to lead this initiative. We recommend conducting a feasibility analysis to rapidly identify the optimal sites for this initiative, and the effort will likely involve private industry for design, construction, financing, and operational integration. We also propose establishing multiple geographically diverse sites to ensure energy resilience, high operational reliability, and a diverse user base, thereby effectively addressing the nation’s critical needs.

42 ENGINEERING↗
DOE OSTI2025

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗
DOE OSTI2022

Redox chemistry of plutonium and plutonium surrogates in vitrified nuclear wastes

Lanthanide borosilicate glasses containing Pu and the analog element, Ce, were subjects of an X-ray absorption spectroscopy investigation to quantify the +3/+4 ratio of the redox-sensitive element. The data show that the dominant oxidation states are +4 for Pu and +3 for Ce. The data also indicate that the reductive potential of glasses can be quantified, although allowances must be made for glass composition, from a solution chemistry method adapted to glass chemistry. These data can therefore be used to formulate glass compositions and processing schedules that lead to knowledge of the oxidation state of Pu in melts. Furthermore, the data show that Ce is a poor analog for Pu behavior in melt and that the suitability of surrogates can be evaluated by this method. The methods demonstrated in this paper can be used to estimate the oxidation states of a range of multi-valent elements as functions of temperature and composition with data from only a single redox couple.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Strong QCD from Hadron Structure Experiments

The topical workshop Strong QCD from Hadron Structure Experiments took place at Jefferson Lab from November 6–9, 2019. Impressive progress in relating hadron structure observables to the strong QCD mechanisms has been achieved from the ab initio QCD description of hadron structure in a diverse array of methods in order to expose emergent phenomena via quasi-particle formation. The wealth of experimental data and the advances in hadron structure theory make it possible to gain insight into strong interaction dynamics in the regime of large quark–gluon coupling (the strong QCD regime), which will address the most challenging problems of the Standard Model on the nature of the dominant part of hadron mass, quark–gluon confinement, and the emergence of the ground and excited state hadrons, as well as atomic nuclei, from QCD. This workshop aimed to develop plans and to facilitate the future synergistic efforts between experimentalists, phenomenologists, and theorists working on studies of hadron spectroscopy and structure with the goal to connect the properties of hadrons and atomic nuclei available from data to the strong QCD dynamics underlying their emergence from QCD. These results pave the way for a future breakthrough extension in the studies of QCD with an Electron–Ion Collider in the U.S.

Brodsky, S. J.↗