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NASA NTRSDate not supplied

Design Optimization Study of Fault Tolerant and Redundant Motor Drivetrains for Urban Air Mobility Vehicles

Fully electric and hybrid electric aircraft will require extremely lightweight and reliable electric motor drivetrains to meet performance and safety goals. It is likely that to meet reliability targets some form of fault tolerance and/or redundancy will need to be used in the electric motor drivetrain. The use of either redundancy or fault tolerance will result in a reduction in drivetrain performance. In this paper, an example design study is carried out comparing redundant and fault tolerant drivetrains based on an example fault tolerant motor topology for a tilt rotor UAM application. Results show a minimal weight penalty for the incorporation of fault tolerance into drivetrains with the example motor explored here.

Thomas Francis Tallerico↗

NASA NTRSDate not supplied

Design Optimization Study of Fault Tolerant and Redundant Motor Drivetrains for Urban Air Mobility Vehicles

Thomas Tallerico↗

NASA NTRS2022

Balancing Predictive and Reactive Science Planning for Mars 2020 Perseverance

The design of the science planning process for a space science mission needs to find a balance between operational and resource constraints and scientific decision-making. Science planning has previously been characterized as either predictive or reactive. Predictive science planning is needed when constraints drive science activities to be planned far in advance. For example, a combination of long one-way light time plus high-stakes science decisions drove the Cassini-Huygens mission to Saturn to have an extremely predictive planning process. On the other extreme, reactive science planning is needed when constraints drive science activities to be planned based on the results of the previous plan. For example, the Mars Exploration Rover mission interacted with the surface of Mars, and so the planning team needed to know the state of the rover at the end of each planning cycle before starting the next cycle. Operational and resource constraints that require management on intermediate timescales has led to the development of a science planning process between these two extremes. For example, the Mars Science Laboratory is a technically complex rover and has a parallel predictive process that allows the operations team to manage engineering constraints several days in advance while maintaining the reactive tactical planning process similar to that of MER. The Mars 2020 Perseverance rover is a technically complex rover in the MSL style, but has an added layer of science complexity: it is tasked with collecting a returnable cache of scientifically valuable samples of Mars within prime mission. Thus, the science planning process also needs to accommodate high-stakes longer-term science decisions in the style of Cassini. In order to balance the push-pull of these constraints, we have developed a science campaign-focused operational paradigm for Mars 2020 Perseverance that allows for both predictive planning to accommodate technological complexity and high-stakes science decisions as well as reactive planning to accommodate the realities of interacting with the martian surface. This paradigm influenced the design of operational processes and operational tools.

Spanovich, Nicole↗

NASA NTRSDate not supplied

New Astrophysical Discovery with Ground and Space-Based Observatories

Astronomy is robust with discovery thanks to a wide range of international observing facilities in space and on the ground. For example, space telescopes such as Webb and Hubble are now detecting the most distant galaxies in the universe, as well as the cosmological effects of dark matter and dark energy. Ground based radio telescope arrays are peering into nurseries of star-formation, finding regions of infant stars and also their circumstellar disks in which planetary systems are forming. Even black holes have now been imaged. This overview talk will highlight recent advances in observational astrophysics, especially the scientifically powerful examples where multiple facilities are used in complement. Examples include the study of solar system bodies, the characterization of exoplanet atmospheres, and detections of electromagnetic radiation associated with cosmic sources of gravitational waves.

Jennifer J Wiseman↗

NASA NTRSDate not supplied

Overlapping Conditions in IMPACT

Introduction The IMPACT 1.0 model assumes independence of 119 different medical conditions. That is, it is assumed that one medical event maps to a single medical condition. However, it is often the case that a single medical event may lead to several different concurrent conditions (e.g., a single accident resulting in multiple fractures, chest/abdominal trauma, and sepsis). There is a significant overlap between many of the conditions in ICL 1.0. For example, depression and anxiety are two separate conditions but often co-exist. This overlap may lead to an overestimation in the incidence calculation. In future iterations of the model, we plan to address progression of medical conditions. Methods There are two possible methods to approach progression of a medical condition. The first is to take any medical condition in ICL, calculate its incidence and determine its outcomes no matter what sequelae occur. For example, calculate the incidence of prostatitis and determine its metrics (e.g., loss of crew life, risk of medical evacuation or task time affected) no matter what the sequelae might be (sepsis, hydronephrosis, renal failure, etc.). The second approach is to take any medical condition in the ICL and calculate the outcomes based on transitions to any other condition. For example, calculate the incidence of nephrolithiasis and determine the incidence of each possible transition (e.g., hydronephrosis, renal failure, UTI, pyelonephritis, sepsis) and then determine its metrics. With perfectly informative evidence, the answers should be the same using either method. The question is which approach best reduces the opportunity for overlapping. Results The team identified 31 conditions that were the most likely to transition to a secondary condition such as sepsis and respiratory failure. It was felt that the first approach, as described above, would be the easiest to implement and would have the greatest reduction in overlapping conditions. The identified conditions were primarily infectious conditions, like UTI and pneumonia (which could lead to both respiratory failure and sepsis), toxic inhalations, cardiac arrest, seizures, and trauma. Once these conditions were identified, the conditions were divided between the four clinicians on the team to identify those that were not consistent with the others in way they were approached. There were 13 conditions that were found to be problematic. Some condition definitions needed to be changed to eliminate references to transitions or to update them in cases where the definitions were out of date. Some conditions required new evidence to correct the loss of crew life, and one condition was felt to have so much overlap that it was recommended for elimination. A second effort required modification of the sepsis incidence calculation to prevent double counting of the conditions, such as prostatitis, that could progress to sepsis. Conclusion The team made significant progress in clarifying the method for dealing with transitions from one medical condition to another. In addition, errors were found and corrected in definitions, incidence, and loss of crew life as well as a condition in which reference was made to a CLiFF that was no longer included. These modifications will provide greater fidelity in the IMPACT model and reduce overlap.

Arian Anderson↗

NASA NTRS2023

2023 Astrophotonics Roadmap: Pathways to Realizing Multi-Functional Integrated Astrophotonic Instruments

Photonic technologies offer numerous functionalities that can be used to realize astrophotonic instruments. The most spectacular example to date is the ESO Gravity instrument at the Very Large Telescope in Chile that combines the light-gathering power of four 8 m telescopes through a complex photonic interferometer. Fully integrated astrophotonic devices stand to offer critical advantages for instrument development, including extreme miniaturization when operating at the diffraction-limit, as well as integration, superior thermal and mechanical stabilization owing to the small footprint, and high replicability offering significant cost savings. Numerous astrophotonic technologies have been developed to address shortcomings of conventional instruments to date, including for example the development of photonic lanterns to convert from multimode inputs to single mode outputs, complex aperiodic fiber Bragg gratings to filter OH emission from the atmosphere, complex beam combiners to enable long baseline interferometry with for example, ESO Gravity, and laser frequency combs for high precision spectral calibration of spectrometers. Despite these successes, the facility implementation of photonic solutions in astronomical instrumentation is currently limited because of (1) low throughputs from coupling to fibers, coupling fibers to chips, propagation and bend losses, device losses, etc, (2) difficulties with scaling to large channel count devices needed for large bandwidths and high resolutions, and (3) efficient integration of photonics with detectors, to name a few. In this roadmap, we identify 24 key areas that need further development. We outline the challenges and advances needed across those areas covering design tools, simulation capabilities, fabrication processes, the need for entirely new components, integration and hybridization and the characterization of devices. To realize these advances the astrophotonics community will have to work cooperatively with industrial partners who have more advanced manufacturing capabilities. With the advances described herein, multi-functional integrated instruments will be realized leading to novel observing capabilities for both ground and space based platforms, enabling new scientific studies and discoveries.

astrophotonics↗

NASA NTRSDate not supplied

GEER (Glenn Extreme Environments Rig): An Introduction

Our solar system contains many examples of what may be called extreme environments. These can be examples of high temperature and pressure environments in places like the deep atmospheres of the gas giants or on the surface of Venus. The permanently shadowed regions of the moon are examples of extreme cold conditions, a more common phenomena in space. Other extreme environments may be driven by high radiation conditions, or perhaps reactive atmospheric chemistry, which happens to be another feature of Venus. The extreme environments in the solar system pose interesting challenges to missions and technologists as they plan approaches to exploring and understanding our solar system.

GEER↗

NASA NTRSDate not supplied

Analysis of Launch Vehicle Liftoff Debris: Historical Perspective from Space Shuttle and Application to Artemis I

Human exploration-class launch vehicles are inherently prone to debris due to the extreme environments generated during pre-launch operations, liftoff, and flight. The use of cryogenic propellants often requires thermal protection system (TPS) coatings, typically foam, to maintain the propellant conditions in the tank and prevent an accumulation ice on the external surface of the vehicle. Some ice growth is to be expected at umbilical interfaces, vents, flanges, or brackets where it is difficult to apply TPS. This ice may come loose at any time due to wind on the launch pad, structural vibration and acoustics after rocket ignition, or aerodynamic forces during flight. This phenomena is especially apparent on vehicles with no TPS, such as the Saturn V rockets used in the Apollo Program, see Figure 1. During propellant tanking, the thermal contraction of the underlying substrate may generate cracks in the TPS (Figure 1). Chunks of TPS can release due to the expansion of ingested gas from cryopumping or from aerodynamic forces if the crack creates an offset surface. Most foams will also have a certain amount of “popcorning” where small pieces of foam will pop off during flight because of the differential between the static surface pressure and the pressure of the gas trapped in the foam cell structure. There are a number of other coating or closeout materials that may be shed from the vehicle and become debris. During pre-launch operations and liftoff, the vehicle may also be exposed to debris originating from the launch pad or ground support equipment. This debris is separate from foreign object debris, or FOD, which is not intended to be present and is strictly controlled through operations and maintenance procedures. In this case, debris is generated from hardware and materials that are necessary for launch and are subject to the intense vibration, acoustics, and direct plume impingement of the launch environment. Examples include ice from umbilicals, tape and tie wraps that protect cables, and rust or corrosion from the launch platform. While NASA has historically been aware of debris as a potential issue that could cause a failure resulting in loss of mission, loss of vehicle, or loss of crew, the likelihood and severity of that risk was not always well understood or given sufficient weight in program and flight decisions. After the Space Shuttle Columbia accident (STS-107), the investigation found that foam TPS debris shed from the external tank was the proximate cause of the damage to the orbiter wing. Six previous observations of debris released from the foam ramp that covered the bipod connecting the forward end of the orbiter to the external tank resulted in minor changes or were determined to be accepted flight risks. Two occurrences of bipod ramp foam loss were not identified until the STS-107 investigation. Despite the damage inflicted by these debris strikes, the Shuttle Program Requirements Control Board deemed the vehicle safe to fly. During the Return to Flight effort following the Columbia disaster, NASA Engineering developed a process for the assessment of debris transport, impact, and damage tolerance to support independent assessments of risk by NASA Safety and Mission Assurance (S&MA). Under this system, each element (vehicle or ground system) defines a catalog of all expected debris based on launch history, component testing, or analysis. Debris transport analysis (DTA) is conducted using the debris catalog characteristics and potential flow transport mechanisms (e.g., vehicle aerodynamics, gravity, wind, plume-driven). The predicted debris impact locations and velocities are provided to the hardware owners, who use available test data and analysis to determine whether each component can withstand the impacts. In cases where the element hardware may be severely damaged or fail, the options are to mitigate the debris source through some change in design or operation, or to work with S&MA to try to characterize the probability of the impact and damage for program risk acceptance. Because of the differences in debris characteristics and transport, the DTA has been divided between the Liftoff and Ascent regimes. The development and application of Liftoff DTA methodology from the Shuttle Program to the current Artemis Program is the subject of this paper. Liftoff DTA covers the time from the start of pre-launch operations at the launch pad, up until the vehicle clears the launch tower and there is no longer any interaction with ground systems. Debris transport during this period is broadly classified as either gravity, wind, and plume-entrained (GWPE) or plume driven (PD). GWPE debris is generally lower speed, travelling in a forward-to-aft direction. PD transport includes flow features from the rocket ignition transient, as well as plume impingement and recirculation that occur as the vehicle lifts off the launch platform. In these cases, the debris typically moves in an aft-to-forward direction at higher speeds. The applicable transport mechanisms must be considered for each piece of debris depending on the material, and release location and time. For example, rust or metallic debris from the tower could fall (GWPE) and impact the vehicle before landing on the launch platform deck where it could be also be transported by plume impingement (PD). However, falling ice (GWPE) from an umbilical is unlikely to survive impact with the vehicle or launch platform and be available for PD transport. Modeling of debris transport is accomplished using a set of DTA tools which simulate debris trajectories subject to a reference frame acceleration (i.e., gravity) and aerodynamic drag. Where the trajectory encounters a solid surface, the debris is allowed to rebound with a specified coefficient of restitution. The drag is calculated by interpolating the fluid state at each point in the debris trajectory from high-fidelity computational fluid dynamics (CFD) simulations of the launch vehicle and pad. The CFD data may either be static (steady state or time averaged), typically for GWPE transport, or dynamic (time-accurate) for PD flow features like the ignition transient. Examples of the CFD flow field solutions for the Space Launch System (SLS) rocket and launch pad are shown in Figure 2. Typical SLS debris trajectory predictions from DTA are illustrated in Figure 3. The final version of this paper will include a more detailed examination of the Liftoff DTA process developed during the Shuttle Program, and how it has been augmented and applied to the SLS rocket under the Artemis Program. Comparisons with debris observations from the Artemis I launch will demonstrate validation of the tools and methodology.

Debris↗

NASA NTRSDate not supplied

The Role of Isotopes in Understanding of the Formation and Evolution of the Solar System

Comparative planetology via isotope geochemistry has played a critical role in advancing our understanding of the inventories of both volatiles and organics in the atmospheres of solar system bodies. For example, the 12 C/ 13 C measured from atmospheric methane in Titan provided upper limits on the amount of time that methane has been present in the atmosphere, demonstrating that methane was only present in Titan’s atmosphere during at most the last 1 billion years (Gyr). As another example, measurements of 14 N/ 15 N in atmospheric HCN and N 2 provide direct evidence of how photochemistry influences stable isotopes. These observations were used to determine that Titan’s nitrogen originated as NH 3 in the protosolar nebula, thus constraining the origin and delivery of volatiles to the Saturnian moon Titan. These measurements of 12 C/ 13 C and 14 N/ 15 N in Titan’s atmosphere were obtained with remote and in situ observations from the Cassini spacecraft in Saturn orbit. However, combining spacecraft observations like these throughout the solar system with Earth-based telescope observations, including both ground-based and from orbiting telescopes, produces far more powerful results. For example, the lower limit obtained for 14 N/ 15 N in HCN in Pluto’s atmosphere by ALMA combined with New Horizons observations of the atmospheric composition provides a valuable tool for determining the origin of nitrogen for Pluto if the influences of condensation and aerosol trapping on isotopes can be constrained. Isotopes measured at Jupiter and Saturn and their moons, combined with composition measurements from comets provide information on giant planet formation processes and primordial conditions when the solar system was forming. In all of these studies, measurements of isotope ratios in comets have served as a reference for processes in the protosolar nebula.

Kathleen E. Mandt↗

NASA NTRSDate not supplied

A Machine Learning Approach to Improve Air Traffic Management Initiatives

Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.

Farzan Masrour Shalmani↗

NASA NTRSDate not supplied

Discrete Rotational Energy for Polyatomic Molecules in Direct Simulation Monte Carlo

The environments experienced by vehicles upon entry into planetary atmospheres generally result in severe aerothermodynamic loading. These flows are characterized by the formation of strong shock waves, behind which high temperatures and non-equilibrium environments are generated, and the accurate prediction of internal energy relaxation and chemical kinetics becomes important. This manuscript focuses on the internal relaxation of the rotational energy of polyatomic molecules. Previous work has described the implementation of a discrete rotational energy model for diatomic molecules (linear rotators). Later, Gimelshein et. al extended this model to include polyatomic molecular internal energies. However, they did not include details on how to generate the particles at equilibrium conditions and centrifugal distortion was not included in their model. The purpose of the present study is to extend the afore-mentioned models to the treatment of discrete rotational energy in polyatomic molecules with the addition of centrifugal distortion. An overview of the background theory needed for the implementation of the model will be given followed by the model itself and verification of the model. For example, sampled versus Boltzmann distributions are compared in Figure 1 for CH3 (oblate rotator – two quantum numbers) and examples of thermal relaxation behavior for linear and non-linear rotators are presented in Figure 2. In addition, comparisons of thermophysical properties will be presented, as well as examples of adiabatic relaxation and application to hypersonic flow.

Rarefied Gas Dynamics↗

DOE OSTI2020

High temperature synthesis for power production and storage

Thermochemical energy storage (TCES) for concentrating solar power (CSP) systems provides higher energy density than sensible energy storage systems. An ammonia-based TCES system dissociates endothermically into hydrogen and nitrogen. The stored energy is released when supercritical hydrogen and nitrogen react exothermically to synthesize ammonia. Prior ammonia synthesis systems are unable to produce temperatures consistent with modern power blocks requiring a working fluid, for example steam or carbon dioxide, to be heated to greater than 600° C., for example about 650° C. An ammonia synthesis system heats steam from, for example 350° C. to 650° C. under pressure of about 26 MPa. The hydrogen and nitrogen are preheated with a flow of supercritical fluid prior to the synthesis step to provide reaction rates sufficient to heat power block working fluid to the desired temperature.

14 SOLAR ENERGY↗

DOE OSTI2020

Leakage detection using smart field technology

Various examples are provided for gas leakage detection from geologic storage sites. In one example, a system for detection of gas leakage from a geologic storage site includes permanent down-hole gauges (PDGs) disposed at different depths within a well and an evaluation system that can determine a three-dimensional (3D) location of the gas leakage from the geologic storage site and a leakage indicator. The 3D location and leakage indicator can be determined using pressure data provided by the PDGs. The geologic storage site can store, e.g., CO2, natural gas or other type of gas. In another example, a method for detecting gas leakage from a geologic storage site includes receiving pressure data provided by PDGs disposed within one or more wells associated with the geologic storage site, determining key performance indicators using the pressure data and determining a 3D location and a leakage indicator using the key performance indicators.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Fault-tolerant controller for modular multi-level converters

Various examples are provided related to fault-tolerant controller architectures for multi-level converters. In one example, a multi-level converter includes an array of power modules. The power modules can include a controller communicatively coupled to controllers of adjacent power modules in the array of power modules. Circuitry of the controllers can receive operational data from the adjacent power modules; identify a fault condition in an adjacent power module using the operational data; and initiate reconfiguration of the array of power modules in response to an indication of the fault condition, where the reconfiguration bypasses the adjacent power module. In another example, a method includes identifying a fault condition in an adjacent power module in an array of power modules based upon operational data from one or more adjacent power modules of the array; and initiating reconfiguration of the array of power modules in response to an indication of the fault condition.

42 ENGINEERING↗

DOE OSTI2020

Stable bromine charge storage in porous carbon electrodes using tetraalkylammonium bromides for reversible solid complexation

Electrolytes for use in electric double-layer capacitors (EDLCs; often referred as supercapacitors or ultracapacitors) are disclosed. In one example, the electrolyte comprises viologen in both the anolyte and the catholyte (with bromide). In another example, the electrolyte comprises viologen (in the anolyte) and tetraalkylammonium with bromide (in the catholyte), wherein the tetraalkylammonium is used to achieve solid complexation of bromine in the activated carbon of the cathode. In a third example, a zinc bromine/tetraalkylammonium supercapacitor/battery hybrid is disclosed. Also disclosed is a corrosion resistant bipolar pouch cell that can be used with the electrolyte embodiments described herein.

25 ENERGY STORAGE↗

DOE OSTI2020

Scanning tunneling thermometer

Various examples are provided related to scanning tunneling thermometers and scanning tunneling microscopy (STM) techniques. In one example, a method includes simultaneously measuring conductance and thermopower of a nanostructure by toggling between: applying a time modulated voltage to a nanostructure disposed on an interconnect structure, the time modulated voltage applied at a probe tip positioned over the nanostructure, while measuring a resulting current at a contact of the interconnect structure; and applying a time modulated temperature signal to the nanostructure at the probe tip, while measuring current through a calibrated thermoresistor in series with the probe tip. In another example, a device includes an interconnect structure with connections to a first reservoir and a second reservoir; and a scanning tunneling probe in contact with a probe reservoir. Electrical measurements are simultaneously obtained for temperature and voltage applied to a nanostructure between the reservoirs.

Chinivaranahalli Shastry, Abhay Shankar↗

DOE OSTI2021

Methods for aligning a spectrometer

An example method for aligning a spectrometer is described herein. The spectrometer includes a radiation source, a crystal analyzer, and a detector that are all positioned on an instrument plane. The method includes rotating the crystal analyzer about an axis that is within the instrument plane and perpendicular to a rotation plane such that (i) a reciprocal lattice vector of the crystal analyzer is within the instrument plane or (ii) a component of the reciprocal lattice vector within the rotation plane is perpendicular to the instrument plane. An origin of the reciprocal lattice vector is located on the axis. The method further includes tilting the crystal analyzer or translating the detector such that the reciprocal lattice vector bisects a line segment that is bounded by the detector and the radiation source. Example spectrometers related to the example method are also disclosed.

Mortensen, Devon R.↗

DOE OSTI2021

Flow cell systems, flow cell batteries, and hydrogen production processes

Flow cell systems are provided. Example flow cell systems can include an H + /H 2 half-cell and a counterpart Fe 3+ /Fe 2+ or V 5+ /V 4+ half-cell. Flow cell systems can also include a half-cell in fluid communication with an electrolyte regeneration chamber. Embodiments of these flow cells systems can be configured to produce hydrogen through electrolysis. Flow cell battery systems are also disclosed. Example flow cell battery systems can include an H + /H 2 analyte; and a counterpart Fe 3+ /Fe 2+ or V 5+ /V 4+ catholyte. Processes for generating hydrogen are also disclosed. Example processes can include generating protons from a Fe 3+ /Fe 2+ or V 5+ /V 4+ electrolyte solution; and reacting the protons with H 2 O to form H 2 .

Wang, Wei↗