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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Coherent diffractive imaging with twisted X-rays: Principles, applications, and outlook

Recent technological breakthroughs in synchrotron and x-ray free electron laser facilities have revolutionized nanoscale structural and dynamic analyses in condensed matter systems. This review provides a comprehensive overview of the advancements in coherent scattering and diffractive imaging techniques, which are now at the forefront of exploring materials science complexities. These techniques, notably Bragg coherent diffractive imaging and x-ray photon correlation spectroscopy, x-ray magnetic dichroism, and x-ray correlation analysis leverage beam coherence to achieve volumetric three-dimensional imaging at unprecedented sub-nanometer resolutions and explore dynamic phenomena within sub-millisecond timeframes. Such capabilities are critical in understanding and developing advanced materials and technologies. Simultaneously, the emergence of chiral crystals—characterized by their unique absence of standard inversion, mirror, or other roto-inversion symmetries—presents both challenges and opportunities. These materials exhibit distinctive interactions with light, leading to phenomena such as molecular optical activity, chiral photonic waveguides, and valley-specific light emissions, which are pivotal in the burgeoning fields of photonic and spintronic devices. This review elucidates how novel x-ray probes can be leveraged to unravel these properties and their implications for future technological applications. A significant focus of this review is the exploration of new avenues in research, particularly the shift from conventional methods to more innovative approaches in studying these chiral materials. Inspired by structured optical beams, the potential of coherent scattering techniques utilizing twisted x-ray beams is examined. This promising direction not only offers higher spatial resolution but also opens the door to previously unattainable insights in materials science. By contextualizing these advancements within the broader scientific landscape and highlighting their practical applications, this review aims to chart a course for future research in this rapidly evolving field.

Physics↗
NASA NTRS1998

Light Scattering by Nonspherical Particles

Improved understanding of electromagnetic scattering by nonspherical particles is important to many science and engineering disciplines and was the subject of the Conference on Light Scattering by Nonspherical Particles: Theory, Measurements, and Applications. The conference was held 29 September-1 October 1998 at the Goddard Institute for Space Studies in New York City and brought together 115 participants from 18 countries. The main objective of the conference was to highlight and summarize the rapid advancements in the field, including numerical methods for computing the single and multiple scattering of electromagnetic radiation by nonspherical and heterogeneous particles, measurement approaches, knowledge of characteristic features in scattering patterns, retrieval and remote sensing techniques, nonspherical particle sizing, and various practical applications. The conference consisted of twelve oral and one poster sessions. The presentations were loosely grouped based on broad topical categories. In each of these categories invited review talks highlighted and summarized specific active areas of research. To ensure a high-quality conference, all abstracts submitted had been reviewed by members of the Scientific Organizing Committee for technical merit and content. The conference program was published in the June 1998 issue of the Bulletin of the American Meteorological Society and is available on the World Wide Web at http://www.giss.nasa.gov/-crmim/conference/program.html. Authors of accepted papers and review presentations contributed to a volume of preprints published by the American Meteorological Society' and distributed to participants at the conference.

Mishchenko, Michael I.↗
NASA NTRS2012

Numerical Simulation of Rocket Exhaust Interaction with Lunar Soil

This technology development originated from the need to assess the debris threat resulting from soil material erosion induced by landing spacecraft rocket plume impingement on extraterrestrial planetary surfaces. The impact of soil debris was observed to be highly detrimental during NASA s Apollo lunar missions and will pose a threat for any future landings on the Moon, Mars, and other exploration targets. The innovation developed under this program provides a simulation tool that combines modeling of the diverse disciplines of rocket plume impingement gas dynamics, granular soil material liberation, and soil debris particle kinetics into one unified simulation system. The Unified Flow Solver (UFS) developed by CFDRC enabled the efficient, seamless simulation of mixed continuum and rarefied rocket plume flow utilizing a novel direct numerical simulation technique of the Boltzmann gas dynamics equation. The characteristics of the soil granular material response and modeling of the erosion and liberation processes were enabled through novel first principle-based granular mechanics models developed by the University of Florida specifically for the highly irregularly shaped and cohesive lunar regolith material. These tools were integrated into a unique simulation system that accounts for all relevant physics aspects: (1) Modeling of spacecraft rocket plume impingement flow under lunar vacuum environment resulting in a mixed continuum and rarefied flow; (2) Modeling of lunar soil characteristics to capture soil-specific effects of particle size and shape composition, soil layer cohesion and granular flow physics; and (3) Accurate tracking of soil-borne debris particles beginning with aerodynamically driven motion inside the plume to purely ballistic motion in lunar far field conditions. In the earlier project phase of this innovation, the capabilities of the UFS for mixed continuum and rarefied flow situations were validated and demonstrated for lunar lander rocket plume flow impingement under lunar vacuum conditions. Applications and improvements to the granular flow simulation tools contributed by the University of Florida were tested against Earth environment experimental results. Requirements for developing, validating, and demonstrating this solution environment were clearly identified, and an effective second phase execution plan was devised. In this phase, the physics models were refined and fully integrated into a production-oriented simulation tool set. Three-dimensional simulations of Apollo Lunar Excursion Module (LEM) and Altair landers (including full-scale lander geometry) established the practical applicability of the UFS simulation approach and its advanced performance level for large-scale realistic problems.

Liever, Peter↗
DOE OSTI2020

K 2 CuX 3 (X = Cl, Br): All-Inorganic Lead-Free Blue Emitters with Near-Unity Photoluminescence Quantum Yield

Recently, copper(I) halides have been gaining increased attention as highly luminescent nontoxic alternatives to lead halide perovskites for optoelectronic applications. In this paper, we report preparation of blue emitting, lead free, all-inorganic halides K 2 CuX 3 (X = Cl, Br) through five synthetic methods including traditional solid-state and solution methods. The photoluminescence (PL) emission spectra of K 2 CuCl 3 and K 2 CuBr 3 exhibit narrow peaks centered at 392 and 388 nm with full widths at half-maximum (fwhm) values of ~54 nm. The visible bright blue emission is corroborated by the remarkably high photoluminescence quantum yield (PLQY) values up to ~97%. Furthermore, radioluminescence measurements on K 2 CuCl 3 yield a bright peak at 404 nm under irradiation with X-rays at 200 kVp and 20 mA, which is optimal for use with PMTs and Si photomultipliers, suggesting a strong potential of this family for radiation detection applications. Based on our combined experimental and computational investigations, the origin of the efficient luminescence in K 2 CuX 3 is attributed to the high stability self-trapped excitons (STE) formed in the one-dimensional anionic ∞ 1 [CuX 3 ] 2– chains. Advantageously, K 2 CuX 3 demonstrate improved air- and photostability compared to the previously reported copper(I) halides. The discovery of highly efficient and high stability light emitters based on earth-abundant copper(I) halides paves the way for their potential practical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1974

The application of ERTS imagery to monitoring Arctic sea ice

The author has identified the following significant results. Because of the effect of sea ice on the heat balance of the Arctic and because of the expanding economic interest in arctic oil and minerals, extensive monitoring and further study of sea ice is required. The application of ERTS data for mapping ice is evaluated for several arctic areas, including the Bering Sea, the eastern Beaufort Sea, parts of the Canadian Archipelago, and the Greenland Sea. Interpretive techniques are discussed, and the scales and types of ice features that can be detected are described. For the Bering Sea, a sample of ERTS-1 imagery is compared with visual ice reports and aerial photography from the NASA CV-990 aircraft. The results of the investigation demonstrate that ERTS-1 imagery has substantial practical application for monitoring arctic sea ice. Ice features as small as 80-100 m in width can be detected, and the combined use of the visible and near-IR imagery is a powerful tool for identifying ice types. Sequential ERTS-1 observations at high latitudes enable ice deformations and movements to be mapped. Ice conditions in the Bering Sea during early March depicted in ERTS-1 images are in close agreement with aerial ice observations and photographs.

Barnes, J. C.↗
DOE OSTI2025

BESS Digital Assurance, Supply Chain Risk Management, and Emerging Regulation Session Two

The TADA BESS Supply Chain Workshops are designed to equip participants with the knowledge and tools necessary to address the evolving challenges at the intersection of battery energy storage systems (BESS), cybersecurity, and supply chain vulnerabilities. The workshops emphasize the application of Cyber-Informed Engineering (CIE) principles using INL’s procurement guide and the CIE-BAT tool. Attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their BESS projects. Additionally, the program fosters a collaborative network of practitioners and provides guidance on navigating emerging regulatory requirements, including FEOC rules under the OBBB framework, to assess and enhance organizational readiness. A comprehensive exploration of BESS cybersecurity supply chain risks, systematic vendor risk assessment through the BESS Procurement Guide, and practical application of the INL SCRM Chatbot for enhanced supply chain resilience. This is Session 2 of 3. (Full Version)

25 - ENERGY STORAGE↗
DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1974

The application of ERTS imagery to mapping snow cover in the western United States

The author has identified the following significant results. In much of the western United States a large part of the utilized water comes from accumulated mountain snowpacks; thus, accurate measurements of snow distributions are required for input to streamflow prediction models. The application of ERTS-1 imagery for mapping snow has been evaluated for two geographic areas, the Salt-Verde watershed in central Arizona and the southern Sierra Nevada in California. Techniques have been developed to identify snow and to differentiate between snow and cloud. The snow extent for these two drainage areas has been mapped from the MSS-5 (0.6 - 0.7 microns) imagery and compared with aerial survey snow charts, aircraft photography, and ground-based snow measurements. The results indicate that ERTS imagery has substantial practical applications for snow mapping. Snow extent can be mapped from ERTS-1 imagery in more detail than is depicted on aerial survey snow charts. Moreover, in Arizona and southern California cloud obscuration does not appear to be a serious deterrent to the use of satellite data for snow survey. The costs involved in deriving snow maps from ERTS-1 imagery appear to be very reasonable in comparison with existing data collection methods.

Barnes, J. C.↗
DOE OSTI2020

Erratum to: Soft- and reactive landing of ions onto surfaces: concepts and applications

Soft- and reactive landing of mass-selected ions is gaining attention as a promising approach for the precisely-controlled preparation of materials on surfaces that are not amenable to deposition using conventional methods. A broad range of ionization sources and mass filters are available that make ion soft-landing a versatile tool for surface modification using beams of hyperthermal (<100?eV) ions. The ability to select the mass-to-charge ratio of the ion, its kinetic energy and charge state, along with precise control of the size, shape, and position of the ion beam on the deposition target distinguishes ion soft landing from other surface modification techniques. Soft- and reactive landing have been used to prepare interfaces for practical applications as well as precisely-defined model surfaces for fundamental investigations in chemistry, physics, and materials science. For instance, soft- and reactive landing have been applied to study the surface chemistry of ions isolated in the gas-phase, prepare arrays of proteins for high-throughput biological screening, produce novel carbon-based and polymer materials, enrich the secondary structure of peptides and the chirality of organic molecules, immobilize electrochemically-active proteins and organometallics on electrodes, create thin films of complex molecules, and immobilize catalytically active organometallics as well as ligated metal clusters. In addition, soft landing has enabled investigation of the size-dependent behavior of bare metal clusters in the critical subnanometer size regime where chemical and physical properties do not scale predictably with size. The morphology, aggregation, and immobilization of larger bare metal nanoparticles, which are directly relevant to the design of catalysts as well as improved memory and electronic devices, have also been studied using ion soft landing. This review article begins in section 1 with a brief introduction to the existing applications of ion soft- and reactive landing. Section 2 provides an overview of the ionization sources and mass filters that have been used to date for soft landing of mass-selected ions. A discussion of the competing processes that occur during ion deposition as well as the types of ions and surfaces that have been investigated follows in section 3. Section 4 discusses the physical phenomena that occur during and after ion soft landing, including retention and reduction of ionic charge along with factors that impact the efficiency of ion deposition. The influence of soft landing on the secondary structure and biological activity of complex ions is addressed in section 5. Lastly, an overview of the structure and mobility as well as the catalytic, optical, magnetic, and redox properties of bare ionic clusters and nanoparticles deposited onto surfaces is presented in section 6. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:439–479, 2016.

Johnson, Grant E.↗
NASA NTRSDate not supplied

Machine-Learning for Safety Critical Airborne Applications Part II: Case Study

The exceptional progress in the field of Artificial Intelligence (AI) systems, enabled by Machine Learning (ML) technology in recent years provides historic opportunities for the aviation industry. Current certification standards for avionics were developed prior to the ML renaissance and have several fundamental incompatibilities with the ML technology. WG-114 is working hard to release a new standard as soon as possible but for now there is no recognized means of compliance for ML based systems even of low criticality. In this talk, we present the custom ML workflow that can be used comply with all objectives of the current certification standards for a low-criticality (DAL D and C) ML-based system. To illustrate the practical application of the custom ML workflow we present a case study of a system based on a Deep Neural Network (DNN) intended to detect and identify airport runway signs. We present the system design, data generation, training, and verification in detail and describe how the design assurance objectives can be met for a DAL D and DAL C systems.

Johann Schumann↗
DOE OSTI2021

An Algebra of Machine Learners with Applications

Machine learning (ML) methods are increasingly being applied to solve complex, data-driven problems in diverse areas, by exploiting the physical laws derived from first principles such as thermal hydraulics and the abstract laws developed recently for data and computing infrastructures. These physical and abstract laws encapsulate, typically in compact algebraic forms, the critical knowledge that complements data-driven ML models. We present a unified perspective of these laws and ML methods using an abstract algebra (A;⊕,Ⓧ), wherein the performance estimation and classification tasks are characterized by the additive ⊕ operations, and the diagnosis, reconstruction, and optimization tasks are characterized by the difference Ⓧ operations. This abstraction provides ML codes and their performance characterizations that are transferable across different areas. We describe practical applications of these abstract operations using examples of throughput profile estimation tasks in data transport infrastructures, and power-level and sensor error estimation tasks in nuclear reactor systems.

Rao, Nageswara↗
NASA NTRS1991

A proven knowledge-based approach to prioritizing process information

Many space-related processes are highly complex systems subject to sudden, major transients. In any complex process control system, a critical aspect is rapid analysis of the changing process information. During a disturbance, this task can overwhelm humans as well as computers. Humans deal with this by applying heuristics in determining significant information. A simple, knowledge-based approach to prioritizing information is described. The approach models those heuristics that humans would use in similar circumstances. The approach described has received two patents and was implemented in the Alarm Filtering System (AFS) at the Idaho National Engineering Laboratory (INEL). AFS was first developed for application in a nuclear reactor control room. It has since been used in chemical processing applications, where it has had a significant impact on control room environments. The approach uses knowledge-based heuristics to analyze data from process instrumentation and respond to that data according to knowledge encapsulated in objects and rules. While AFS cannot perform the complete diagnosis and control task, it has proven to be extremely effective at filtering and prioritizing information. AFS was used for over two years as a first level of analysis for human diagnosticians. Given the approach's proven track record in a wide variety of practical applications, it should be useful in both ground- and space-based systems.

Corsberg, Daniel R.↗
NASA NTRS2005

X-Aerogels for Structural Components and High Temperature Applications

Future NASA missions and space explorations rely on the use of materials that are strong ultra lightweight and able to withstand extreme temperatures. Aerogels are low density (0.01-0.5 g/cu cm) high porosity materials that contain a glass like structure formed through standard sol-gel chemistry. As a result of these structural properties, aerogels are excellent thermal insulators and are able to withstand temperatures in excess of l,000 C. The open structure of aerogels, however, renders these materials extremely fragile (fracturing at stress forces less than 0.5 N/sq cm). The goal of NASA Glenn Research Center is to increase the strength of these materials by templating polymers and metals onto the surface of an aerogel network facilitating the use of this material for practical applications such as structural components of space vehicles used in exploration. The work this past year focused on two areas; (1) the research and development of new templated aerogels materials and (2) process development for future manufacturing of structural components. Research and development occurred on the production and characterization of new templating materials onto the standard silica aerogel. Materials examined included polymers such as polyimides, fluorinated isocyanates and epoxies, and, metals such as silver, gold and platinum. The final properties indicated that the density of the material formed using an isocyanate is around 0.50 g/cc with a strength greater than that of steel and has low thermal conductivity. The process used to construct these materials is extremely time consuming and labor intensive. One aspect of the project involved investigating the feasibility of shortening the process time by preparing the aerogels in the templating solvent. Traditionally the polymerization used THF as the solvent and after several washes to remove any residual monomers and water, the solvent around the aerogels was changed to acetonitrile for the templating step. This process took a couple of days. It was experimentally determined that the polymerization reaction could be done in acetonitrile instead of THF with no detrimental effects to the properties of the resulting aerogel. Other changes in the time needed to crosslink the gels in the isocyanate solution as well as changes to the subsequent washing procedure could also shorten the processing time with no effect on the properties. Processing methods were also developed that allowed a variety of shapes as well as sizes of these materials to be formed.

Source record↗
DOE OSTI2022

Melt pool temperature measurement and monitoring during laser powder bed fusion based additive manufacturing via single-camera two-wavelength imaging pyrometry (STWIP)

Melt pool (MP) temperature is one of the determining factors and a key signature for evaluating the properties of printed components in metal additive manufacturing (AM). The state-of-the-art measurement systems are hindered, primarily by the large-scale data acquisition and processing demands. In this work, we introduce a novel coaxial, high-speed, single-camera two-wavelength imaging pyrometer (STWIP) system as opposed to the typical utilization of multiple cameras for measuring MP temperature profiles in laser powder bed fusion (LPBF) processes. Developed on a commercial LPBF machine (EOS M290), the STWIP system demonstrated its ability to quantitatively monitor the MP temperature and its variation for 50 layers at high framerates (>30,000 fps) for a real-world application (standard fatigue specimens) print. High performance computing is employed to analyze the acquired big data (MP images), for determining each MP's average temperature and 2D temperature profile. The MP temperature evolution in the gage section of a fatigue specimen is also examined at a temporal resolution of 1 ms, by evaluating the MP temperatures in the samples' first, middle, and last layers. This report is the first of its kind on monitoring MP temperature distribution and evolution at such a large, detailed scale for longer durations in practical applications.

42 ENGINEERING↗
DOE OSTI2023

Operational control with set-points tuning - application to mobile robots

This paper proposes a novel method for the optimal tuning of set points for multiple-layered control system structure widely seen in robotics and other complex industrial processes composed of a number of subsystems. The terminal sliding mode control (SMC) is used as the low-level control strategy to ensure the stability of subsystems. When uncertainties exist, it can be shown that the deteriorated system performance will be improved by the outer loop with set points tuning. For this purpose, the learning of the new set point is designed to compensate for the effects caused by uncertainties during the system operation. At the same time, the system is proven to stay with the original set point when the compensation is introduced. A practical application to a holonomic mobile robot system is given to illustrate the presented method. Desired results have been obtained.

42 ENGINEERING↗
DOE OSTI2022

Colloidal Quantum Dot Solar Cells: Progressive Deposition Techniques and Future Prospects on Large-area Fabrication

Colloidally grown nanosized semiconductors yield extremely high-quality optoelectronic materials. Many examples have pointed to near perfect photoluminescence quantum yields, meaning non-radiative optical processes (losses) can be completely suppressed allowing for technology-leading materials as high purity color centers in display technology and emitters in LEDs. Furthermore, because of high chemical yield, and improved understanding of the surfaces, these materials, particularly colloidal quantum dots (QDs) can also be ideal candidates for other optoelectronic applications. Given the strong drive toward carbon neutrality and limiting climate change, electricity from solar photovoltaics will play a large role in the power generation sector. QDs have been developed and shown dramatic improvements over the past 15 years as photoactive materials in photovoltaics and have unique potential with various innovative deposition properties which could lead to exceptionally low-cost and high-performance devices. Once the key issues related to charge transport in optically thick arrays are addressed, QD-based photovoltaic technology could become a better candidate for practical application. In this article, w e show how the possibilities of different deposition techniques could bring QD-based solar cells to the industrial level and discuss the challenges for perovskite QD solar cells in particular, to achieve large-area fabrication for further advancing technology to solve pivotal energy and environmental issues.

14 SOLAR ENERGY↗
DOE OSTI2026

Universal Relationship between Limiting Current and Electrochemical Transport Properties in Malonate-Based Polymer Electrolytes

There is considerable interest in developing high-performance electrolytes for rechargeable lithium batteries. For practical applications, the electrolyte must support large dc currents. However, the parameters most often reported in the literature, conductivity, κ, and current fraction, ρ + , reflect ion transport in the limit of infinitesimal currents. In this limit, the efficacy of an electrolyte is given by the product κρ + . The limiting current density, i lim , is the maximum current density that can be applied across an electrolyte; the cell voltage diverges if the applied current density exceeds ilim. This parameter reflects ion transport in the limit of large dc currents and is therefore of practical interest. It would therefore be convenient if i lim could be predicted from measurements of κρ + . In order to explore this possibility, we studied six malonate-based polymers and PEO at a fixed salt concentration (r = 0.08) and temperature (90°C) using symmetric cells with planar electrodes. Unfortunately, there is no correlation between ilim and κρ + . When the applied current density, i, is less than ilim, the cell voltage approaches a stable plateau, ϕ plateau . Here, we found a linear dependence between i and thickness-normalized plateau potential, ϕ plateau L –1 , irrespective of the magnitude of the applied current. In all seven polymer electrolytes, we found a linear correlation between ilim and the slopes of these lines, σ. In other words, measurements of σ can be used to predict the limiting current.

Jana, Rounak [Lawrence Berkeley National Laborator↗
DOE OSTI2024

Geant4 Monte-Carlo (GEMC) A database-driven simulation program

GEMC[1] is an application that harnesses the power of databases to execute Geant4 Monte-Carlo simulations. The databases (MYSQL, CSQL, TEXT) define the geometry, materials, digitization algorithms, readout electronics and output formats. Implemented in C++, GEMC also boasts a user-friendly Python API that facilitates detector construction and database population. GEMC can handle real-life scenarios such as geometry variations and the run number-dependent calibration constants and digitization parameters. This abstract provides an overview of GEMC, accompanied by examples that showcase its versatility. We delve into the practical application of GEMC within the the CLAS12 experimental program at Jefferson Lab.

Ungaro, Maurizio↗