Training the Next-Generation of Radiographers Using Time-Gated Optical Imaging at the University of Nevada, Reno’s Zebra Pulsed Power Facility
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A psychologist's view of validating aviation systems
All systems, no matter what they are designed to do, have shortcomings that may make them less productive than was hoped during the initial development. Such shortcomings can arise at any stage of development: from conception to the end of the implementation life cycle. While systems failure and errors of a lesser magnitude can occur as a function of mechanical or software breakdown, the majority of such problems, in aviation are usually laid on the shoulders of the human operator and, to a lesser extent, on human factors. The operator bears the responsibility and blame even though, from a human factors perspective, error may have been designed into the system. Human factors is not a new concept in aviation. The name may be new, but the issues related to operators in the loop date back to the industrial revolution of the nineteenth century and certainly to the aviation build-up for World War I. During this first global confrontation, military services from all sides discovered rather quickly that poor selection and training led to drastically increased personnel losses. While hardware design became an issue later, the early efforts were primarily focused on increased care in pilot selection and on their training. This actually involved early labor-intensive simulation, using such devices as sticks and chairs mounted on rope networks which could be manually moved in response to control input. The use of selection criteria and improved training led to more viable person-machine systems. More pilots survived training and their first ten missions in the air, a rule of thumb arrived at by experience which predicted ultimate survival better than any other. This rule was to hold through World War II. At that time, personnel selection and training became very sophisticated based on previous standards. Also, many psychologists were drafted into Army Air Corps programs which were geared towards refining the human factor. However, despite the talent involved in these programs and the tremendous build-up of aviation during the war, there were still aircraft designs that were man killers (no sexism implied since all combat pilots were men). One classic design error that was identified fifty years ago was the multipointer altimeter, which could easily be misread especially by a pilot under considerable task load. It has led to flying fully operational aircraft into the terrain. The authors of the research which formally identified this problem put 'Human Errors' in quotes to express their dissatisfaction with the traditional approach to accident investigation. It traditionally places the burden of guilt on the operator. Some of these altimeters still exist in older aircraft to this day.
Stein, Earl S.↗
A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems
With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.
24 POWER TRANSMISSION AND DISTRIBUTION↗
A Comparison of Two Control Display Unit Concepts on Flight Management System Training
One of the biggest challenges for a pilot in the transition to a 'glass' cockpit is understanding the flight management system (FMS). Because of both the complex nature of the FMS and the pilot-FMS interface, a large portion of transition training is devoted to the FMS. The current study examined the impact of the primary pilot-FMS interface, the control display unit (CDU), on FMS training. Based on the hypothesis that the interface design could have a significant impact on training, an FMS simulation with two separate interfaces was developed. One interface was similar to a current-generation design, and the other was a multiwindows CDU based on graphical user interface techniques. For both application and evaluation reasons, constraints were applied to the graphical CDU design to maintain as much similarity as possible with the conventional CDU. This preliminary experiment was conducted to evaluate the interface effects on training. Sixteen pilots with no FMS experience were used in a between-subjects test. A time-compressed, airline-type FMS training environment was simulated. The subjects were trained to a fixed-time criterion, and performance was measured in a final, full-mission simulation context. This paper describes the technical approach, simulation implementation, and experimental results of this effort.
Abbott, Terence S.↗
Locating the Isolator Shock-Train Leading Edge with Limited Pressure Information
Real-time detection and control of the isolator shock-train leading edge (STLE) is important to the performance of high-speed air-breathing engines, such as dual-mode scramjets. Typically, the STLE location is determined using wall static-pressure measurements, but there are often restrictions on the placement and overall number of the pressure transducers, reducing the viability and accuracy of such approaches. To address these issues, we introduce the adaptive pressure profile (APP) method for estimating the STLE location. This method does not require extensive prior characterization of the isolator or engine model. Instead, it uses real-time pressure measurements from a small number of transducers to adaptively learn the isolator pressure profile and subsequently uses this deduced profile to estimate the STLE location in a data-driven manner. The APP method works well in situations with sparse transducer placement. It produces accurate estimates when the STLE location is 1) not bounded by two or more transducers or 2) between two transducers that are several isolator duct heights apart. We demonstrate the efficacy of the APP method using simulations and experimental data from direct-connect isolator models. This validation shows that the APP method is accurate and robust for different flow regimes, transducer configurations, and model geometries.
Gregory J. Hunt↗
Human Factors in Training: Space Medical Proficiency Training
The early Constellation space missions are expected to have medical capabilities very similar to those currently on the Space Shuttle and the International Space Station (ISS). For Crew Exploration Vehicle (CEV) missions to the ISS, medical equipment will be located on the ISS, and carried into CEV in the event of an emergency. Flight surgeons (FS) on the ground in Mission Control will be expected to direct the crew medical officer (CMO) during medical situations. If there is a loss of signal and the crew is unable to communicate with the ground, a CMO would be expected to carry out medical procedures without the aid of a FS. In these situations, performance support tools can be used to reduce errors and time to perform emergency medical tasks. The space medical training work is part of the Human Factors in Training Directed Research Project (DRP) of the Space Human Factors Engineering (SHFE) Project under the Space Human Factors and Habitability (SHFH) Element of the Human Research Program (HRP). This is a joint project consisting of human factors team from the Ames Research Center (ARC) with Immanuel Barshi as Principal Investigator and the Johnson Space Center (JSC). Human factors researchers at JSC have recently investigated medical performance support tools for CMOs on-orbit, and FSs on the ground, and researchers at the Ames Research Center performed a literature review on medical errors. Work on medical training has been conducted in collaboration with the Medical Training Group at the Johnson Space Center (JSC) and with Wyle Laboratories that provides medical training to crew members, biomedical engineers (BMEs), and to flight surgeons under the Bioastronautics contract. One area of research building on activities from FY08, involved the feasibility of just-in-time (JIT) training techniques and concepts for real-time medical procedures. A second area of research involves FS performance support tools. Information needed by the FS during the ISS mission support, especially for an emergency situation (e.g., fire onboard ISS), may be located in many different places around the FS s console. A performance support tool prototype is being developed to address this issue by bringing all of the relevant information together in one place. The tool is designed to include procedures and other information needed by a FS during an emergency, as well as procedures and information to be used after the emergency is resolved. Several walkthroughs of the prototype with FSs have been completed within a mockup of an ISS FS console. Feedback on the current tool design as well as recommendations for existing ISS FS displays were captured. The tool could have different uses depending on the situation and the skill of the user. An experienced flight surgeon could use it during an emergency situation as a decision and performance support tool, whereas a new flight surgeon could use it as JITT, or part of his/her regular training. The work proposed for FY10 continues to build on this strong collaboration with the Space Medical Training Group and previous research.
Byrne, Vicky E.↗
Research and Technology Challenges for Human Data Analysts in Future Safety Management Systems
Enabling new and novel concepts of operations for Advanced Air Mobility poses an important need to evolve current safety management systems (SMS) and is posited to be realized through advances in Machine Learning (ML) Data Sciences and Artificial Intelligence. The “In-time Aviation Safety Management System” (IASMS) concept of operations supports the need to evolve today’s SMS to become more tailorable, scalable, and interoperable in response to forecasted changes expected for the future airspace system. Key to IASMS is integration of proactive and predictive ML algorithms trained to provide “in time” detection and mitigation of hazards and emergent risks through new methods and novel data types. IASMS research and technology development includes human factors design considerations for these systems to include human-system teaming, innovations in human interfaces and management of complex digital data information, human-system interaction/model-based system engineering, and verification and validation for data assurance and trust.
Chad L Stephens↗
Generation of ultrafast electron bunch trains via trapping into multiple periods of plasma wakefields
We demonstrate a novel approach to the generation of femtosecond electron bunch trains via laser-driven wakefield acceleration. We use two independent high-intensity laser pulses, a drive, and an injector, each creating their own plasma wakes. The interaction of the laser pulses and their wakes results in a periodic injection of free electrons in the drive plasma wake via several mechanisms, including ponderomotive drift, wake-wake interference, and pre-acceleration of electrons directly by strong laser fields. Electron trains were generated with up to four quasi-monoenergetic bunches, each separated in time by a plasma period. In conclusion, the time profile of the generated trains is deduced from an analysis of beam loading and confirmed using 2D particle-in-cell simulations
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
An Alternative Approach to Human Servicing of Crewed Earth Orbiting Spacecraft
As crewed spacecraft have grown larger and more complex, they have come to rely on spacewalks, or Extravehicular Activities (EVA), for assembly and to assure mission success. Typically, these spacecraft maintain all of the hardware and trained personnel needed to perform an EVA on-board at all times. Maintaining this capability requires up-mass, volume for storage of EVA hardware, crew time for ground and on-orbit training, and on-orbit maintenance of EVA hardware. This paper proposes an alternative methodology, utilizing either launch-on-need hardware and crew or regularly scheduled missions to provide EVA capability for space stations in low Earth orbit after assembly complete. Much the same way that one would call a repairman to fix something at their home these EVAs are dedicated to maintenance and upgrades of the orbiting station. For crew safety contingencies it is assumed the station would be designed such the crew could either solve those issues from inside the spacecraft or use the docked Earth to Orbit vehicles as a return lifeboat, in the same manner as the International Space Station (ISS) which does not rely on EVA for crew safety related contingencies. This approach would reduce ground training requirements for long duration crews, save Intravehicular Activity (IVA) crew time in the form of EVA hardware maintenance and on-orbit training, and lead to more efficient EVAs because they would be performed by specialists with detailed knowledge and training stemming from their direct involvement in the development of the EVA. The on-orbit crew would then be available to focus on the immediate response to any failures such as IVA systems reconfiguration or jumper installation as well as the day-to-day operations of the spacecraft and payloads. This paper will look at how current unplanned EVAs are conducted on ISS, including the time required for preparation, and offer an alternative for future spacecraft. As this methodology relies on the on-time and on-need launch of spacecraft, any space station that utilized this approach would need a robust transportation system, possibly including more than one launch vehicle capable of carrying crew. In addition, the fault tolerance of the future space station would be an important consideration in how much time was available for EVA preparation after the failure. Ideally the fault tolerance of the station would allow for the maintenance tasks to be grouped such that they could be handled by regularly scheduled maintenance visits and not contingency launches. Each future program would have to weigh the risk of on-time launch against the increase in available crew time for the main objective of the spacecraft. This is only one of several ideas that could be used to reduce or eliminate a station's reliance on rapid turnaround EVAs using on-board crew. Others could include having shirt-sleeve access to critical systems or utilizing low pressure temporarily pressurized equipment bays.
Mularski, John R.↗
An Onboard ISS Virtual Reality Trainer
Prior to the retirement of the Space Shuttle, many exterior repairs on the International Space Station (ISS) were carried out by shuttle astronauts, trained on the ground and flown to the Station to perform these specific repairs. With the retirement of the shuttle, this is no longer an available option. As such, the need for ISS crew members to review scenarios while on flight, either for tasks they already trained for on the ground or for contingency operations has become a very critical issue. NASA astronauts prepare for Extra-Vehicular Activities (EVA) or Spacewalks through numerous training media, such as: self-study, part task training, underwater training in the Neutral Buoyancy Laboratory (NBL), hands-on hardware reviews and training at the Virtual Reality Laboratory (VRLab). In many situations, the time between the last session of a training and an EVA task might be 6 to 8 months. EVA tasks are critical for a mission and as time passes the crew members may lose proficiency on previously trained tasks and their options to refresh or learn a new skill while on flight are limited to reading training materials and watching videos. In addition, there is an increased need for unplanned contingency repairs to fix problems arising as the Station ages. In order to help the ISS crew members maintain EVA proficiency or train for contingency repairs during their mission, the Johnson Space Center's VRLab designed an immersive ISS Virtual Reality Trainer (VRT). The VRT incorporates a unique optical system that makes use of the already successful Dynamic On-board Ubiquitous Graphics (DOUG) software to assist crew members with procedure reviews and contingency EVAs while on board the Station. The need to train and re-train crew members for EVAs and contingency scenarios is crucial and extremely demanding. ISS crew members are now asked to perform EVA tasks for which they have not been trained and potentially have never seen before. The Virtual Reality Trainer (VRT) provides an immersive 3D environment similar to the one experienced at the VRLab crew training facility at the NASA Johnson Space Center. VRT bridges the gap by allowing crew members to experience an interactive, 3D environment to reinforce skills already learned and to explore new work sites and repair procedures outside the Station.
Miralles, Evelyn↗
Human Factors in Training - Space Medicine Proficiency Training
The early Constellation space missions are expected to have medical capabilities very similar to those currently on the Space Shuttle and International Space Station (ISS). For Crew Exploration Vehicle (CEV) missions to ISS, medical equipment will be located on ISS, and carried into CEV in the event of an emergency. Flight Surgeons (FS) on the ground in Mission Control will be expected to direct the Crew Medical Officer (CMO) during medical situations. If there is a loss of signal and the crew is unable to communicate with the ground, a CMO would be expected to carry out medical procedures without the aid of a FS. In these situations, performance support tools can be used to reduce errors and time to perform emergency medical tasks. Work on medical training has been conducted in collaboration with the Medical Training Group at the Space Life Sciences Directorate and with Wyle Lab which provides medical training to crew members, Biomedical Engineers (BMEs), and to flight surgeons under the JSC Space Life Sciences Directorate s Bioastronautics contract. The space medical training work is part of the Human Factors in Training Directed Research Project (DRP) of the Space Human Factors Engineering (SHFE) Project under the Space Human Factors and Habitability (SHFH) Element of the Human Research Program (HRP). Human factors researchers at Johnson Space Center have recently investigated medical performance support tools for CMOs on-orbit, and FSs on the ground, and researchers at the Ames Research Center performed a literature review on medical errors. The work proposed for FY10 continues to build on this strong collaboration with the Space Medical Training Group and previous research. This abstract focuses on two areas of work involving Performance Support Tools for Space Medical Operations. One area of research building on activities from FY08, involved the feasibility of just-in-time (JIT) training techniques and concepts for real-time medical procedures. In Phase 1, preliminary feasibility data was gathered for two types of prototype display technologies: a hand-held PDA, and a Head Mounted Display (HMD). The PDA and HMD were compared while performing a simulated medical procedure using ISS flight-like medical equipment. Based on the outcome of Phase 1, including data on user preferences, further testing was completed using the PDA only. Phase 2 explored a wrist-mounted PDA, and compared it to a paper cue card. For each phase, time to complete procedures, errors, and user satisfaction ratings were captured.
Connell, Erin↗
A High-Voltage High-Reliability Scalable Architecture for Electric Vehicle Power Electronics (Final Report)
This project developed and demonstrated new composite converter technologies that lead to high power density (> 20 kW/L) at power levels of 10s of kW, 100s of kW, or possibly higher, with fundamental advances in converter efficiency and Q that lead to substantial increases in mean time to failure (MTTF). These advantages were realized through development of new composite converter topologies that perform buck, boost, or other conversion functions and that are scalable to higher voltage and power levels through sharing of voltage and current stresses among multiple dissimilar partial-power converter modules. The project led to experimental demonstration of a125 kW multifunction electric vehicle power conversion system having in-creased dc bus voltage (950 V nominal, 1200 V peak) that interfaces a 200 V to 400 V battery pack, and that includes integrated level 2 wired charging and wireless charging functions. The project incorporated SiC MOSFET modules having switching frequencies in excess of 100 kHz, planar magnetics, a hierarchical control architecture that enables scaling to higher voltages and powers with additional converter modules, and a high-power density in excess of 20 kW/L. The research demonstrated how a more complex converter approach can increase mean-time-to-failure, even though the number of elements is increased. This is achieved through significant reduction of temperature rise through fundamentally superior converter circuit topologies. The research also demonstrated new high power planar magnetics that increase power density. The technology is appropriate to a variety of applications including EV power trains, EV charging, PV inverters, battery storage, and similar areas. These systems potentially can be manufactured in the U.S.
33 ADVANCED PROPULSION SYSTEMS↗
Augmented Reality Data Generation for Training Deep Learning Neural Network
One of the major challenges in deep learning is retrieving sufficiently large labeled training datasets, which can become expensive and time consuming to collect. A unique approach to training segmentation is to use Deep Neural Network (DNN) models with a minimal amount of initial labeled training samples. The procedure involves creating synthetic data and using image registration to calculate affine transformations to apply to the synthetic data. The method takes a small dataset and generates a highquality augmented reality synthetic dataset with strong variance while maintaining consistency with real cases. Results illustrate segmentation improvements in various target features and increased average target confidence.
Torres, Gil↗
Shared automated vehicle fleet operations for first-mile last-mile transit connections with dynamic pooling
Shared automated vehicles (SAVs) have the potential to promote transit ridership by providing efficient first-mile last-mile (FMLM) connections through reduced operational costs to fleet providers and lower out-of-pocket costs to riders. To help plan for a future of integrated mobility, this paper investigates the impacts of SAVs serving FMLM connections, as a mode that provides flexibility in access/egress decisions and is well coordinated with train station schedules. To achieve this objective, a novel dynamic pooling algorithm was introduced to match SAVs with riders while coordinating the riders' arrival times at the light-rail station to a known train schedule. Microsimulations of SAVs and travelers throughout two central Austin neighborhoods show how larger service areas, higher levels of SAV demand, and longer arrival times between successive trains require larger SAV fleet sizes and higher SAV utilization rates to deliver close traveler wait times. Four-person SAVs appear to perform similar to 6-seat SAVs but will cost less to provide. Using a dynamic pooling algorithm tightly coordinated with train arrivals (every 15 min) delivers 87% of travelers to their stations in time to catch the next train, whereas uncoordinated assignments deliver just 58% of travelers in time.
33 ADVANCED PROPULSION SYSTEMS↗
Iterative sampling of expensive simulations for faster deep surrogate training
Deep neural network (DNN) surrogates of expensive physics simulations are enabling a rapid change in the way that common experimental design and analysis tasks are approached. Surrogate models allow simulations to be performed in parallel and separately from downstream tasks, thereby enabling analyses that would be impossible with the simulation in-the-loop; surrogates based on DNNs can effectively emulate diverse non-scalar data of the types collected in fusion and laboratory-astrophysics experiments. The challenge is in training the surrogate model, for which large ensembles of physics simulations must be run, preferably without wasting computational effort on uninteresting simulations. Here, in this paper, we present an iterative sampling scheme that can preferentially propose simulations in interesting regions of parameter space without neglecting unexplored regions, allowing high-quality and wide-ranging surrogate models to be trained using 2–3 times fewer simulations compare to space-filling designs. Our approach uses an explicit importance function defined on the simulation output space, balanced against a measure of simulation density which serves as a proxy for surrogate accuracy. It is easy to implement and can be tuned to find interesting simulations early in the study, allowing surrogates to be trained quickly and refined as new simulations become available; this represents an important step towards the routine generation of deep surrogate models quickly enough to be truly relevant to experimental work.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
FORCE Update 2024
The Framework for Optimization of Resources and Economics (FORCE) tool suite is the U.S. Department of Energy’s Nuclear Integrated Energy Systems (IES) Program flagship tool suite for technoeconomic IES analysis of IES. This tool suite is useful for analysis designed to evaluate and improve the technoeconomics of energy production systems, particularly for systems including nuclear technology. In this report, we document the development activity for the FORCE tool suite to extend its capabilities as performed during fiscal year 2024. In addition to reliability and accessibility, capability is one of the three standards guiding the development of the FORCE tool suite and the software codes that are its constituent parts. Extending the capabilities of the FORCE tool suite allows analysis both within the IES program as well as industry, university, and laboratory partners to perform analysis with more accuracy, insight, and impactful narrative. Four areas of capability development were the focus of activity this year: economic parameter uncertainty quantification, multiresolution analysis, components-to-optimization workflow automation, and statespace construction workflows for real-time optimal control. In economic parameter uncertainty quantification, the ability of HERON to capture risk due to scenarios (weather and energy demand uncertainty) was expanded to also include uncertainties in financial parameters such as capital cost or operation and maintenance costs. By including these sources of uncertainty, which are sometimes very large compared with scenario uncertainty, HERON is better able to capture the risk posed by investment in various IES technology. Because of this, analysts can also consider the reduction in risks that can be realized by choice of some technologies. In multiresolution analysis, development activity extended on work completed previously. In fiscal year 2023, methods for decomposing time series signals, such as demand, solar and wind availability, and price profiles, were analyzed and down-selected to those most effective at splitting signals into different resolutions. These resolutions allow considering the influence of different energy demand and supply behaviors across different time scales. For example, energy demand might be divided into seasonal, weekly, and hourly profiles. In fiscal year 2024, this preliminary work was extended and implemented within the Risk Analysis Virtual Environment (RAVEN) risk and uncertainty analysis platform, which is used throughout the FORCE framework. This development of the “multi-resolution time series analysis” (MR-TSA) module in RAVEN allows training synthetic history generators on complex time series. These synthetic history generators can then be used in HERON for generating scenarios that represent possible market and weather scenarios that can be analyzed on different time scales. We envision completing this work in the future, implementing multiresolution dispatch optimization strategies that can make the most beneficial use of these stratified time histories. In components-to-optimization workflow development, workflows for translating user inputs of components into algorithms for algebraic optimization were selected and implemented. Similar algorithms within the Holistic Energy Resource Optimization Network (HERON) were separated from the main code base of HERON and gathered with the components-to-optimization workflows in the new Dispatch Optimization Variable Engine (DOVE) software library. This modularization allows FORCE users to analyze dispatch optimization and energy system duty cycles independently of HERON, which previously was a burdensome task. Additionally, these dispatch optimization algorithms, set up in an independent library, can now be used across all software applications within FORCE, especially including the real-time optimal control software Optimization of Real-time Capacity Allocation (ORCA). Allowing FORCE software to share dispatch optimization algorithms within a single library allows for improved software maintenance and reliability. In statespace characterization workflow development, alternative workflows for optimizing dispatch with additional technical accuracy was the focus, particularly to improve the real-time optimization decision making in ORCA. Using algorithms and workflows initially developed for the Feasible Actuator Range Modifier (FARM), workflows for determining the statespace representation of IES were identified and demonstrated. The resulting dispatch optimization required a more robust optimization algorithm than that originally used in HERON (and moved to DOVE), which required adding an alternate workflow to DOVE that can more accurately match the behavior of physical systems using a partial differential equation representation. In conclusion, capability developments in the FORCE tool suite in fiscal year 2024 have improved the ability of the FORCE tool suite to perform
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Statistical methods and neural network approaches for classification of data from multiple sources
Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.
Benediktsson, Jon Atli↗
Generation of multiple attosecond sub-bursts in femtosecond pulse trains by long-wavelength driving lasers
High-order harmonics generated by a long intense femtosecond laser are known experimentally to create attosecond pulse trains (APTs). In the time domain, an APT consists of a sequence of sharp attosecond bursts that are equally separated by each half optical cycle. Here, in this work, we show that such well-known features can be modified when a longer wavelength driving laser is used. From our simulations, we show that multiple shorter attosecond sub-bursts exist in the femtosecond pulse train within each half optical cycle and the duration of each sub-burst scales approximately as $λ^{–2}_0$ with the driving laser wavelength λ 0 . We show that such sub-bursts can be found using quantitative rescattering model for harmonics generated from a single atom, and their origin is due to the interference of the quantum orbits from first two returns of the recombining electron. We further show that such sub-bursts can be phase matched under proper laser focusing condition and the position of the gas cell, thus, such new features should be observable experimentally.
47 OTHER INSTRUMENTATION↗