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NASA NTRS2004

Teleoperated Modular Robots for Lunar Operations

Solar system exploration is currently carried out by special purpose robots exquisitely designed for the anticipated tasks. However, all contingencies for in situ resource utilization (ISRU), human habitat preparation, and exploration will be difficult to anticipate. Furthermore, developing the necessary special purpose mechanisms for deployment and other capabilities is difficult and error prone. For example, the Galileo high gain antenna never opened, severely restricting the quantity of data returned by the spacecraft. Also, deployment hardware is used only once. To address these problems, we are developing teleoperated modular robots for lunar missions, including operations in transit from Earth. Teleoperation of lunar systems from Earth involves a three second speed-of-light delay, but experiment suggests that interactive operations are feasible.' Modular robots typically consist of many identical modules that pass power and data between them and can be reconfigured for different tasks providing great flexibility, inherent redundancy and graceful degradation as modules fail. Our design features a number of different hub, link, and joint modules to simplify the individual modules, lower structure cost, and provide specialized capabilities. Modular robots are well suited for space applications because of their extreme flexibility, inherent redundancy, high-density packing, and opportunities for mass production. Simple structural modules can be manufactured from lunar regolith in situ using molds or directed solar sintering. Software to direct and control modular robots is difficult to develop. We have used genetic algorithms to evolve both the morphology and control system for walking modular robots3 We are currently using evolvable system technology to evolve controllers for modular robots in the ISS glove box. Development of lunar modular robots will require software and physical simulators, including regolith simulation, to enable design and test of robot software and hardware, particularly automation software. Ready access to these simulators could provide opportunities for contest-driven development ala RoboCup (http://www.robocup.org/). Licensing of module designs could provide opportunities in the toy market and for spin-off applications.

Globus, Al↗

NASA NTRS2017

Characterization of a Pressure-Fed LOX/LCH4 Reaction Control System Under Simulated Altitude and Thermal Vacuum Conditions

A liquid oxygen, liquid methane (LOX/LCH4) reaction control system (RCS) was tested at NASA Glenn Research Center's Plum Brook Station in the Spacecraft Propulsion Research Facility (B-2) under simulated altitude and thermal vacuum conditions. The RCS is a subsystem of the Integrated Cryogenic Propulsion Test Article (ICPTA) and was initially developed under Project Morpheus. Composed of two 28 lbf-thrust and two 7 lbf-thrust engines, the RCS is fed in parallel with the ICPTA main engine from four propellant tanks. 40 tests consisting of 1,010 individual thruster pulses were performed across 6 different test days. Major test objectives were focused on system dynamics, and included characterization of fluid transients, manifold priming, manifold thermal conditioning, thermodynamic vent system (TVS) performance, and main engine/RCS interaction. Peak surge pressures from valve opening and closing events were examined. It was determined that these events were impacted significantly by vapor cavity formation and collapse. In most cases the valve opening transient was more severe than the valve closing. Under thermal vacuum conditions it was shown that TVS operation is unnecessary to maintain liquid conditions at the thruster inlets. However, under higher heat leak environments the RCS can still be operated in a self-conditioning mode without overboard TVS venting, contingent upon the engines managing a range of potentially severe thermal transients. Lastly, during testing under cold thermal conditions the engines experienced significant ignition problems. Only after warming the thruster bodies with a gaseous nitrogen purge to an intermediate temperature was successful ignition demonstrated.

Atwell, Matthew J.↗

NASA NTRS2012

An Examination of Coarse Sun Sensor Contingencies in Attitude Determination and the Sun Vector Calculation

Satellite pointing is vital to the success of a mission. One element of that entails describing the position of the sun relative to the frame of the satellite. Coarse Sun Sensors (CSS) are typically used to provide the information to calculate the sun's position in Safe Modes or contingency operations. In the OCO-2 configuration there are 13 CSS total, which provide redundant 4 celestial coverage. Failures of the individual CSS elements can introduce holes in the celestial coverage resulting in potential loss of sun knowledge. These failures must be analyzed to determine if the contingency plan is sufficient to assure mission success. First the static case was looked at and determined that at a maximum, 3 CSS failures can be sustained on the body and 1 on the array without causing coverage holes. Also array sensors are more important to mission success. The Sun Vector calculation has been transcribed to MATLAB code and failure scenarios are being examined to determine the maximum error given a set of failure scenarios. This activity indicated that if there is a loss of the sun, the sun-searching algorithm could be modified to use XZ rotation as that is guaranteed to find it whereas the design using the YZ rotation misses the sun if it is at the + or - Y orientation.

celestial bodies↗

NASA NTRS2015

Evaluation of the Oxygen Concentrator Prototypes: Pressure Swing Adsorption Prototype and Electrochemical Prototype

An oxygen concentrator is needed to provide enriched oxygen in support of medical contingency operations for future exploration human spaceflight programs. It would provide continuous oxygen to an ill or injured crew member in a closed cabin environment. Oxygen concentration technology is being pursued to concentrate oxygen from the ambient environment so oxygen as a consumable resource can be reduced. Because oxygen is a critical resource in manned spaceflight, using an oxygen concentrator to pull oxygen out of the ambient environment instead of using compressed oxygen can provide better optimization of resources. The overall goal of this project is to develop an oxygen concentrator module that minimizes the hardware mass, volume, and power footprint while still performing at the required clinical capabilities. Should a medical event occur that requires patient oxygenation, the release of 100 percent oxygen into a small closed cabin environment can rapidly raise oxygen levels to the vehicles fire limit. The use of an oxygen concentrator to enrich oxygen from the ambient air and concentrate it to the point where it can be used for medical purposes means no oxygen is needed from the ultra-high purity (99.5+% O2) oxygen reserve tanks. By not adding oxygen from compressed tanks to the cabin environment, oxygen levels can be kept below the vehicle fire limit thereby extending the duration of care provided to an oxygenated patient without environmental control system intervention to keep the cabin oxygen levels below the fire limits. The oxygen concentrator will be a Food and Drug Administration (FDA) clearable device. A demonstration unit for the International Space Station (ISS) is planned to verify the technology and provide oxygen capability. For the ISS, the demonstration unit should not exceed 10 kg (approximately 22 lb), which is the soft stowage mass limit for launch on resupply vehicles for the ISS. The unit's size should allow for transport within the spacecraft to an ill crewmember. The user interface needs to be designed for ease of use by the local care provider and with consideration to the limited amount of training available to the astronaut corps for medical equipment and procedures.

Gilkey, Kelly M.↗

NASA NTRS2000

Orbiter Capability for Providing Water to the International Space Station according to the Most Probable Flight Attitudes

Water to be generated by, delivered to, and processed by the International Space Station (ISS) is a critical Environmental Control and Life Support (ECLS) element, especially for the early ISS missions. A significant portion of the water required by the ISS shall be provided by the Shuttle Transportation System (STS) Orbiter. The balance of water generated by the Orbiter Fuel Cells (FC), minus that water consumed by the Orbiter Flash Evaporator System (FES) and crew, is available for transfer to the ISS. During later missions, crew respired and perspired water, as well as effluent water from the Orbiter LiOH canisters, will be collected as condensate and available for transfer to the ISS. Orbiter radiator performance provides the most variance in determining the amount of net Orbiter water available for transfer to the ISS. As radiator performance decreases, the dependence upon the FES (and FC water) increases for rejecting Orbiter waste heat. Generally, radiator performance decreases as the ISS assembly size increases (especially as solar arrays are added), and also as beta angle increases. ISS solar array deployment necessitates the use of models with articulating solar arrays (for Earth local-vertical attitudes), as array position dramatically affects Orbiter radiator performance. Recent developments in the relaxation of beta angle limitations have also increased the complexity and difficulty of providing water to the ISS. Other factors that may hinder the ability to transfer water are the number of empty Contingency Water Containers (CWCs) available, duration of open-hatch time, crew activity timeline, and full CWC storage capability. A parametric study has been accomplished that provides a quick-reference table for determining expected water generation rates for ISS missions 2A.2 through 7A.1. An hourly Orbiter water generation rate is reported according to a matrix that consists of: (1) (six) significant changes in ISS assembly configuration; (2) (four) beta angles (0 deg. , +37 deg., +53 deg. , and +75 deg.); (3) the (three) most representative ISS attitudes (XPOP-O, XPOP-180 and +XVV); (4) (four) Orbiter radiator configurations (both stowed, starboard deployed, port deployed, and both deployed) and (5) the (two) conditions (radiator inlet temperatures and fuel cell power) most consistent with sleep and wake periods. Those permutations of higher probability of occurrence than others have been identified. Another parametric study has been accomplished that provides a quick-reference table for determining expected water generation rates for ISS assembly complete missions. An hourly Orbiter water generation rate is reported according to a matrix that consists of: (1) (seven) beta angles (-75 deg., -60 deg., -30 deg., 0 deg., +30 deg., +60 deg., and +75 deg.); (2) the (nine) PYR angles that define the corners of the envelope; (3) (four) Orbiter radiator configurations (both stowed, starboard deployed, port deployed, and both deployed) and (4) the (two) conditions (radiator inlet temperatures and fuel cell power) most consistent with sleep and wake periods.

Dunaway, Brian↗

NASA NTRS2011

Crew Health and Performance Improvements with Reduced Carbon Dioxide Levels and the Resource Impact to Accomplish Those Reductions

Carbon dioxide (CO2) removal is one of the primary functions of the International Space Station (ISS) atmosphere revitalization systems. Primary CO2 removal is via the ISS s two Carbon Dioxide Removal Assemblies (CDRAs) and the Russian carbon dioxide removal assembly (Vozdukh); both of these systems are regenerable, meaning that their CO2 removal capacity theoretically remains constant as long as the system is operating. Contingency CO2 removal capability is provided by lithium hydroxide (LiOH) canisters, which are consumable, meaning that their CO2 removal capability disappears once the resource is used. With the advent of 6 crew ISS operations, experience showing that CDRA failures are not uncommon, and anecdotal association of crew symptoms with CO2 values just above 4 mmHg, the question arises: How much lower do we keep CO2 levels to minimize the risk to crew health and performance, and what will the operational cost to the CDRAs be to do it? The primary crew health concerns center on the interaction of increased intracranial pressure from fluid shifts and the increased intracranial blood flow induced by CO2. Typical acute symptoms include headache, minor visual disturbances, and subtle behavioral changes. The historical database of CO2 exposures since the beginning of ISS operations has been compared to the incidence of crew symptoms reported in private medical conferences. We have used this database in an attempt to establish an association between the CO2 levels and the risk of crew symptoms. This comparison will answer the question of the level needed to protect the crew from acute effects. As for the second part of the question, operation of the ISS s regenerable CO2 removal capability reduces the limited life of constituent parts. It also consumes limited electrical power and thermal control resources. Operation of consumable CO2 removal capability (LiOH) uses finite consumable materials, which must be replenished in the long term. Therefore, increased CO2 removal means increased resource use, with increased logistical capability to maintain necessary resources on board ISS. We must strike a balance between sufficiently low CO2 levels to maintain crew health and CO2 levels which are operationally feasible for the ISS program

James, John T.↗

NASA NTRS2013

First Results from NASA's Lunar Atmosphere and Dust Environment Explorer (LADEE)

As of early August, 2013, the Lunar Atmosphere and Dust Environment Explorer (LADEE) mission is scheduled for launch on a Minotaur V rocket from Wallops Flight Facility during a five-day launch period that opens on Sept. 6, 2013 (early Sept. 7 UTC). LADEE will address 40 year-old mysteries of the lunar atmosphere and the question of levitated lunar dust. It will also pioneer the next generation of optical space communications. LADEE will assess the composition of the lunar atmosphere and investigate the processes that control its distribution and variability, including sources, sinks, and surface interactions. LADEE will also determine whether dust is present in the lunar exosphere, and reveal its sources and variability. These investigations are relevant to our understanding of surface boundary exospheres and dust processes occurring at many objects throughout the solar system, address questions regarding the origin and evolution of lunar volatiles, and have potential implications for future exploration activities. Following a successful launch, LADEE will enter a series of phasing orbits, which allows the spacecraft to arrive at the Moon at the proper time and phase. This approach accommodates any dispersion in the Minotaur V launch injection. LADEE's arrival at the moon in early October. The spacecraft will approach the moon from its leading edge, travel behind the Moon out of sight of the Earth, and then re-emerge and execute a three-minute Lunar Orbit Insertion maneuver. This will place LADEE in an elliptical retrograde equatorial orbit with an orbital period of approximately 24 hours. A series of maneuvers is then performed to reduce the orbit to become nearly circular with a 156-mile (250- kilometer) altitude. Spacecraft checkout and science instrument commissioning will commence in early-October and will nominally span 30 days but can be extended for an additional 30 days in the event of contingencies. Following commissioning, the 100-day Science Phase is performed at an orbit with periapsis between 20-60 km. This orbit must be constantly managed due to the Moon's highly inhomogeneous gravity field. During the Science Phase, the moon will rotate more than three times underneath the LADEE orbit. LADEE employs a high heritage instrument payload: a Neutral Mass Spectrometer (NMS) from Goddard Space Flight Center, an Ultraviolet/Visible Spectrometer (UVS) from Ames Research Center, and a dust detection experiment (LDEX) from the University of Colorado/LASP. It will also carry the Lunar Laser Communications Demonstration (LLCD) as a technology demonstration. The LLCD is funded by the Human Exploration Operations Mission Directorate (HEOMD), managed by GSFC, and built by the MIT Lincoln Lab. Contingent upon LADEE's successful lunar orbit insertion and checkout, we will report the early results from the science investigations.

Moon↗

NASA NTRS2007

Automated Target Planning for FUSE Using the SOVA Algorithm

The SOVA algorithm was originally developed under the Resilient Systems and Operations Project of the Engineering for Complex Systems Program from NASA s Aerospace Technology Enterprise as a conceptual framework to support real-time autonomous system mission and contingency management. The algorithm and its software implementation were formulated for generic application to autonomous flight vehicle systems, and its efficacy was demonstrated by simulation within the problem domain of Unmanned Aerial Vehicle autonomous flight management. The approach itself is based upon the precept that autonomous decision making for a very complex system can be made tractable by distillation of the system state to a manageable set of strategic objectives (e.g. maintain power margin, maintain mission timeline, and et cetera), which if attended to, will result in a favorable outcome. From any given starting point, the attainability of the end-states resulting from a set of candidate decisions is assessed by propagating a system model forward in time while qualitatively mapping simulated states into margins on strategic objectives using fuzzy inference systems. The expected return value of each candidate decision is evaluated as the product of the assigned value of the end-state with the assessed attainability of the end-state. The candidate decision yielding the highest expected return value is selected for implementation; thus, the approach provides a software framework for intelligent autonomous risk management. The name adopted for the technique incorporates its essential elements: Strategic Objective Valuation and Attainability (SOVA). Maximum value of the approach is realized for systems where human intervention is unavailable in the timeframe within which critical control decisions must be made. The Far Ultraviolet Spectroscopic Explorer (FUSE) satellite, launched in 1999, has been collecting science data for eight years.[1] At its beginning of life, FUSE had six gyros in two IRUs and four reaction wheels. Over time through various failures, the satellite has been left with one reaction wheel on the vehicle skew axis and two gyros. To remain operational, a control scheme has been implemented using the magnetic torque rods and the remaining momentum wheel.[2] As a consequence, there are attitude regions where there is insufficient torque authority to overcome environmental disturbances (e.g. gravity gradient torques). The situation is further complicated by the fact that these attitude regions shift inertially with time as the spacecraft moves through earth s magnetic field during the course of its orbit. Under these conditions, the burden of planning targets and target-to-target slew maneuvers has increased significantly since the beginning of the mission.[3] Individual targets must be selected so that the magnetic field remains roughly aligned with the skew wheel axis to provide enough control authority to the other two orthogonal axes. If the field moves too far away from the skew axis, the lack of control authority allows environmental torques to pull the satellite away from the target and can potentially cause it to tumble. Slew maneuver planning must factor the stability of targets at the beginning and end, and the torque authority at all points along the slew. Due to the time varying magnetic field geometry relative to any two inertial targets, small modifications in slew maneuver timing can make large differences in the achievability of a maneuver.

Heatwole, Scott↗

DOE OSTI2020

Real Time Applications Using Linear State Estimation Technology (RTA/LSE)

Electric Power Group, LLC (EPG) was awarded DOE OE0000849 to design, develop, and demonstrate three real-time applications for monitoring power system stability. The applications use phasor measurements and processing by a linear state estimator (LSE) to improve system visibility and accuracy. These applications leverage the large and expanding phasor measurement systems being deployed by utilities throughout the world, and specifically in the United States. Phasor measurements provide a much more detailed view of the power systems than traditional SCADA systems, so can provide better visibility of events and an early warning of developing problems. These three applications are real-time contingency analysis (RTCA), area angle monitoring (AAM), and a voltage stability index (VSI). The project was fully completed as proposed. All three applications were successfully implemented and demonstrated. There were challenges the caused project delays, however. While the RTCA is based on well-known concepts and readily available tools, its deployment using phasor measurements meant it needed to be adapted to sometimes sparse measurements and limited coverage areas. EPG met these challenges with extensive modeling and testing. The AAM is based on unproven research, so EPG had to deal with a lot of unknowns and develop additional methods for practical implementation. These activities took more time than anticipated, so required extra time for development. In addition, there was difficulty getting adequate measurement locations for successful deployment and utility installation delays. Consequently, the project required a time extension to complete. These three real-time applications, RTCA, AAM, and VSI, use only phasor measurement input. Since they do not require the EMS or other measurement systems, they can serve as a backup and security check for the EMS. Phasor measurements are validated and extended by an LSE. The LSE will always produce an output even when some inputs are impaired, so the RT applications will operate even when the EMS state estimator does not converge and the applications it supports cannot function. Consequently, these RT applications can provide valuable additional reliability for control center operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2016

Concept of Operations for a Prospective "Proving Ground" in the Lunar Vicinity

NASA is studying conceptual architectures for a "Proving Ground" near the Moon or in high lunar orbit to conduct human space exploration missions that bridge the gap between today's operations with the International Space Station (ISS) and future human exploration of Mars beginning in the 2030s. This paper describes the framework of a concept of operations ("Conops") for candidate activities in the Proving Ground. The Conops discusses broad goals that the Proving Ground might address, such as participation from commercial entities, support for human landings on the Moon, use of mature technologies, and growth of capability through a steady cadence of increasingly ambitious piloted missions. Additional Proving Ground objectives are outlined in a companion paper. Key elements in the Conops include the Orion spacecraft (with mission kits for docking and other specialized operations) and the Space Launch System (SLS) heavy-lift rocket. Potential additions include a new space suit, commercial launch vehicles and logistics carriers, Solar Electric Propulsion (SEP) stages to move elements between different orbits and eventually take them on excursions to deep space, a core module with multiple docking ports, a habitation block, and robotic and piloted lunar landers. The landers might include reusable ascent modules which could remain docked to in-space elements between lunar sorties. A module providing advanced regenerative life support functions could launch to the ISS, and later move to the Proving Ground. The architecture will include infrastructure for launch preparation, communication, mission control, and range safety. The Conops describes notional missions chosen to guide the design of the architecture and its elements. One such mission might be the delivery of a approximately 10-t Transit Habitat element, comanifested with Orion on a Block 1B SLS launcher, to the Proving Ground. In another mission, the architecture might participate in direct human exploration of an asteroidal boulder brought to high lunar orbit by the Asteroid Redirect Mission. The Proving Ground stack could serve as a staging point and tele-operation center for robotic and piloted Moon landings. With the addition of a SEP stage, the architecture could support months-long excursions within and beyond the Earth's sphere of influence, possibly culminating in a year-long mission to land humans on a near-Earth asteroid. In the last case, after returning to near-lunar space, two of the asteroid explorers could join two crewmembers freshly arrived from Earth for a Moon landing, helping to quantify the risk of landing deconditioned crews on Mars. In a conceptual mission particularly stressing to system design, Proving Ground elements could transit to Mars orbit. Other possible design-driving operations include relocation of the stack with no crew on board, the unpiloted journey of the advanced life support module from ISS to the lunar vicinity, excursions to other destinations in near-Earth space, and additional support for Mars exploration in conjunction with the Evolvable Mars Campaign. The Proving Ground Conops concludes with a discussion of aborts and contingency operations

Love, Stanley G.↗

DOE OSTI2020

GIS-Based Modeling of Contaminated Soil Volumes at Multiple Sites in the Formerly Utilized Sites Remedial Action Program - 20149

The remediation of hazardous, toxic, and radioactive waste (HTRW) sites produces cost-related risks associated with the estimation of contaminated soil or debris volumes. Historical risk-management techniques include cost contingencies to cover volume uncertainties that affect project budgeting and decision-making. The Buffalo District teamed with project partners to lessen volume uncertainty and reduce project risks at multiple HTRW sites managed under the Formerly Utilized Sites Remedial Action Program (FUSRAP). Historical remedial investigations under FUSRAP commonly identified the presence of radiological material in site media, the associated human health risk, and then areas of remediation. To manage remedial execution and reduce risk, pre-design or remediation-phase sampling essentially 'chased' contamination, which was not conducive to efficient predictive budgeting derived from Feasibility Study (FS) cost analyses. The Buffalo District first optimized their approach to better understand volume uncertainty by utilizing the Argonne National Laboratory's Bayesian Approaches for Adaptive Spatial Sampling (BAASS) software [1]. BAASS processed soft data (e.g., gamma walk-over data) and spatial sampling data to estimate the lateral extent of contaminated soil irrespective of depth (i.e., gross contamination extent) and define areas of contaminant uncertainty. The software performed a binary transformation of contaminant concentrations at all sampling points based upon remedial action goals or a sum of ratios approach (i.e., clean, impacted, or range of impacts in soil). The model produced two-dimensional (horizontal) contaminant probability contours and statistical uncertainty in the sampling coverage and resulting contaminant extents. This method was translated vertically by partitioning the sampling data into depth brackets that produced a stacked representation of contaminant extents and uncertainty in the subsurface (i.e., similar to construction lifts). The results commonly led to a better understanding of project uncertainty and the need for sampling strategies that produce high-confidence soil volumes, which control costs. The BAASS-based delineations were eventually replaced by Empirical Bayesian Kriging (EBK) methods available in ArcGIS Spatial or 3D Analysts [2]. The EBK method calculates contaminant probability zones derived from user-controlled semivariograms of the spatial datasets. The resulting probability zones (e.g., 50% or 80% of contaminant probability) represent the two-dimensional surface delineation of the overall horizontal remedial area, similarly to BAASS. However, unlike BAASS, the vertical sampling data within these probability zones became vertical control points to contour a subterranean surface that connects subsurface points to the land-surface delineations of contamination. The resulting representation of horizontal and vertical impacts within an enclosed envelop (volume) of soil included uncertainty distributions that are used to plan uncertainty-reduction sampling. These data-driven and math-based models of three-dimensional sampling results produced well-bounded remedial volumes for project planning and better uncertainty predictions during project budgeting. The EBK method was applied to several FUSRAP sites managed by the Buffalo District and compared to less rigorously modeled sites previously remediated by the District. The comparison of modeled to actual remediated volumes provide a basis for validating the volume-estimation method. This comparison is important to ensure modeled volumes match physical boundaries of site remediation. FUSRAP sites with denser investigative sampling and lesser volume uncertainty proved useful in remedial planning and contracting. The Buffalo District noted that historical sites with sparser sampling arrays had greater disparity between estimated volumes and final remedial volumes. The benefit achieved over the cost of detailed soil sampling appears positive for FUSRAP projects, especially where impacts vary widely and appear unbounded by investigation-phase sampling. The subsequent Empirical Bayesian Kriging of contamination coupled with vertical contouring for soil estimations reduces uncertainty in soil volumes or indicates where sampling is required to reduce uncertainty, which together optimize remedial planning and budgeting. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2008

Scripting Module for the Satellite Orbit Analysis Program (SOAP)

This add-on module to the SOAP software can perform changes to simulation objects based on the occurrence of specific conditions. This allows the software to encompass simulation response of scheduled or physical events. Users can manipulate objects in the simulation environment under programmatic control. Inputs to the scripting module are Actions, Conditions, and the Script. Actions are arbitrary modifications to constructs such as Platform Objects (i.e. satellites), Sensor Objects (representing instruments or communication links), or Analysis Objects (user-defined logical or numeric variables). Examples of actions include changes to a satellite orbit ( v), changing a sensor-pointing direction, and the manipulation of a numerical expression. Conditions represent the circumstances under which Actions are performed and can be couched in If-Then-Else logic, like performing v at specific times or adding to the spacecraft power only when it is being illuminated by the Sun. The SOAP script represents the entire set of conditions being considered over a specific time interval. The output of the scripting module is a series of events, which are changes to objects at specific times. As the SOAP simulation clock runs forward, the scheduled events are performed. If the user sets the clock back in time, the events within that interval are automatically undone. This script offers an interface for defining scripts where the user does not have to remember the vocabulary of various keywords. Actions can be captured by employing the same user interface that is used to define the objects themselves. Conditions can be set to invoke Actions by selecting them from pull-down lists. Users define the script by selecting from the pool of defined conditions. Many space systems have to react to arbitrary events that can occur from scheduling or from the environment. For example, an instrument may cease to draw power when the area that it is tasked to observe is not in view. The contingency of the planetary body blocking the line of sight is a condition upon which the power being drawn is set to zero. It remains at zero until the observation objective is again in view. Computing the total power drawn by the instrument over a period of days or weeks can now take such factors into consideration. What makes the architecture especially powerful is that the scripting module can look ahead and behind in simulation time, and this temporal versatility can be leveraged in displays such as x-y plots. For example, a plot of a satellite s altitude as a function of time can take changes to the orbit into account.

Carnright, Robert↗

DOE OSTI2021

The phylogenetic roots of addiction: compulsive drug seeking, natural and drug-sensitive reward, and the acquisition of learned habits

Our rational faculties permit us humans to maximize the utility of our actions. We perform a fundamental type of cost-benefit analysis in which we frame a problem, assign values to the different paths, and then choose from among a set of available options, the course that promises the most favorable outcome. So why then does addiction appear to be so impervious to the associated costs, so unaffected by undesired consequences, and ultimately so resistant to cognitive oversight? The answer may likely be found in the fact that the drivers for compulsive drug seeking and drug taking are located in affective brain circuits, circuits that are structured and patterned by learning with repeated activation. These deep processes exhibit significant resistance to control by our cognitive faculties. The general consensus is that addiction arises from mechanisms that overvalue the magnitude of reward, discount the associated risks, and thereby bias individuals towards compulsive pursuit of addictive drugs. Behavioral disruption and dependence appear to arise at the intersection of a number of connected but separate phenomena: expectations for the occurrence of specific events, behaviors that seek encounters with them, the ability to notice and learn nonrandom, co-occurring conditions, the prediction and valuation of consequences, the forming of enduring memories, and the drivers of focused behavior through compulsion, habits, and acquired routines. It is important to recognize that each one of these individual faculties are present and well developed across the entire phylogenetic tree of bilateral metazoans. The goal of this special volume is dedicated to exploring the degree to which inherent elements can account for addiction and addiction-associated phenomena. In much of the literature on addiction, the underlying processes are often viewed as distinctly mammalian, arising, in part, from the strong cognitive capacities of this taxon. Some phenomena may even be regarded to exist only in primates, or even solely in humans. This supposition arises from the fact that studies are conducted almost exclusively in mammals and primates, while evolutionary antecedents of the behavior are rarely considered. A more comprehensive perspective that examines drug reward and reinforcement in a wider range of organisms demonstrates that many of the component traits are actually well developed across the greater metazoan lineage, and they may well predate the emergence of a mammalian clade by a wide margin. The collection of papers assembled here supports the notion that the capacity to associate cues and quences has not arisen in mammals de novo. Rather, the neural mechanisms for detecting contingencies and for predicting future outcomes are very deeply rooted across broad phylogenetic divisions. Our understanding of an ability to associate paired events has been enriched by work in invertebrate preparations in both classical and operant conditioning scenarios [Cook and Carew, 1986, 1989a, 1989b]. The ability to learn allows us to connect cues and behavioral actions to their associated consequences. Pavlovian conditioning enriches surrounding cues with predictive value. Outcomes with positive valence generate appetitive responses and approach to the associated cues, while those perceived as aversive bring cue avoidance and withdrawal. Humans are not the only life forms capable of such short- and long-term modulations of behavior

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Systems Analysis of Biomass and Coal Co-firing Power Plants with Deep Carbon Capture Toward Net-zero Emissions

Achieving a net-zero emission economy in the United States requires integrating diverse low-carbon and negative-emission technologies into the existing fossil fuel-dominant power fleet. Potential technologies from the low-carbon portfolio include renewable power, fossil power with carbon capture and storage (CCS), bioenergy with CCS (BECCS), and direct air capture (DAC). Renewable power is a clean energy source but has to pair with costly battery storage to provide dispatchable electricity. Fossil power with CCS offers dispatchable electricity yet still relies on DAC to offset residual emissions, even when deploying deep CCS with more than 90% CO2 capture. Coal-biomass co-firing with CCS, a subset of BECCS, is a reliable energy production technology that can be retrofitted from existing electricity generation units (EGUs). Power plant retrofit maximizes the use of the current U.S. coal power fleet without the need for large-scale deployment of new renewable power, battery storage, or DAC. Retrofitting coal-biomass co-firing with deep CCS in EGUs is a promising option, but not a universal solution. Biomass co-firing at a power plant introduces economic challenges and indirectly poses pressure on land and water resources. Meanwhile, retrofitting deep CCS affects plant efficiency and raises electricity generation costs. Overall, the technical feasibility and economic viability of plant retrofits vary across EGUs, as they are contingent upon the regional availability of biomass, unit-specific characteristics, site-specific fuel supply costs, and adjacent CO2 storage potential. Government incentives like 45Q can improve the retrofit viability, though the impact requires further quantification. A comprehensive analysis at the unit level is essential to address the question regarding the fate of the U.S. coal-fired electricity generation fleet toward the net-zero emission goal. This study conducts a systematic techno-economic-environmental assessment of EGUs to identify the viability of biomass co-firing and deep CCS retrofits in the U.S. coal-fired power fleet. Specifically, it characterizes the techno-economic performance of deep carbon capture, estimates life cycle greenhouse gas (GHG) emissions, and conducts a fleet-level assessment on retrofit viability. The key objectives are (1) to estimate the unit-specific performance and retrofitted cost under various biomass co-firing levels and CO2 capture rates; (2) to determine the possibility of reaching net-zero emission at the fleet level; (3) to quantify the cumulative capacities that are suitable for plant retrofits under current and future biomass supply scenarios; and (4) to improve the understanding of policy impacts on such retrofits to help the power sector’s transition to a net-zero economy. Techno-economic Model of Deep Carbon Capture. This study develops the performance and economic models for Monoethanolamine-based post-combustion CO2 capture at 95–99% capture rates. The process is simulated in Aspen Plus, analyzing the performance of carbon capture technology by varying the plant sizes, solvent lean loading, CO2 concentrations, and flue gas inlet temperature. Based on the key inputs and output parameters of CO2 capture, a reduced-order performance model of deep carbon capture is formulated. In addition, an engineering-economic model integrating the performance metrics is developed to estimate the capital as well as operation and maintenance (O&M) costs. Capital cost estimations follow the framework of the Integrated Environmental Control Model (IECM) and incorporate data regressions from three technical reports by IECM, the National Energy Technology Laboratory (NETL), and the National Renewable Energy Laboratory. The O&M cost estimation utilizes the actual inventory consumption rate and labor requirements. Both performance and cost models are embedded into IECM v13.0-beta, a fossil-fuel power plant modeling tool. Life Cycle Assessment of Power Plants. This study estimates the GHG emissions of power plants through life cycle assessment (LCA). The LCA scope includes fuel supply, combustion-based power generation, and CO2 transport and storage. The fuel-based life cycle module is designed following the framework of the NETL Unit Process Library and CO2U LCA Guidance Toolkit. The module is then incorporated into IECM v13.0-beta. The process-based LCA is applied to estimate the GHG emissions of coal and biomass supply, coal- and coal-biomass co-firing power plant operation, as well as CO2 pipeline transport and geographical sequestration. An uncertainty analysis is conducted to quantify the variability and uncertainty associated with the LCA using the Latin Hypercube Sampling (LHS) method. Fleet-level Assessment. This study evaluates the technical and economic feasibility of selected coal-fired EGUs, examines the role of tax credits in retrofit viability, and assesses the competitiveness of retrofitted units against other low-carbon options. Unit screening identifies EGUs for the study, focusing on new, efficient baseload units with air pollution controls. The power plant databases are then established to organize unit-specific information on performance and operating conditions from the relevant public databases. Biomass for co-firing retrofits is selected based on home and neighboring county availability, ensuring sustained operation with at least a 5% co-firing level. The CO2 storage site is determined by state-level storage potential, with ArcGIS Pro and NETL CO2 Saline Storage Cost Model used to identify the optimal balance between the nearest transport distances and affordable storage costs. The latest IECM v13.0-beta is then employed to configure and evaluate the eligible EGUs with or without the deployment of deep CCS and biomass co-firing. A supply curve is established to illustrate the cumulative installed capacity suitable for retrofits at different cost levels. A sensitivity analysis on tax credits for carbon sequestration is performed. Finally, a unit-level cost comparison is conducted among retrofitted plants, renewable power with battery storage, and abated fossil fuels with DAC. Expected Results. This study evaluates the technical, economic, and environmental metrics of each EGU across an array of CO2 capture rates and biomass co-firing level scenarios. Unit-level comparisons will identify critical factors influencing technical performance. The supply curves with and without tax incentives will provide insights into the impact of tax credits on biomass co-firing and CCS deployment. The cost comparisons with renewables and DAC-retrofit will assess the competitiveness of the retrofitted units. Life cycle emissions from each unit will be assessed to identify the scenarios under which net-zero emissions can be achieved. These analyses are expected to determine the total coal-fired capacity suitable for serving as a low-carbon energy source with or without tax incentives. The study results are novel in identifying optimal unit-specific strategies for producing carbon-neutral power, whether through retrofitting EGUs with deep CCS, biomass co-firing, DAC, or installing renewable power with battery. The findings will provide insight into nationwide efforts to ensure reliable, affordable, and low-carbon electricity. It also will inform investment decisions and policies in the deployment of deep carbon capture and negative emission technologies for a net-zero energy future.

Biomass Co-firing↗

DOE OSTI2021

Influence of microstructure on replacement and porosity generation during experimental dolomitization of limestones

Replacement reactions commonly alter the multiscale pore structures of rocks during fluid-rock interactions. Analysis of these processes in various model fluid-rock systems during controlled laboratory experiments provides insights into the origins of microstructures found in natural materials. This study focused on understanding the effects of initial starting material permeability and resultant differences in transport pathways on porosity and mineralogical changes during limestone dolomitization.A series of replacement experiments (32–317 days in duration) have been conducted in which 1.59 cm (5/8 in.) diameter cores of two different limestones were reacted with saturated MgCl2 solutions at 200 °C. The Texas Cream (Austin Chalk) is a high-porosity, high-permeability limestone, whereas both the porosity and permeability of the Carthage Marble (Burlington Limestone) are relatively low. Altered limestones were imaged using scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), Time-of-Flight Secondary Ion Mass Spectrometry (ToF–SIMS) and electron microprobe analysis (EMPA). A representative grain boundary of the low-porosity limestone was targeted for a focused ion beam (FIB) lift-out and characterized using transmission electron microscopy (TEM). These results were coupled with analyses of radial changes in the porosity distribution of the core derived from X-ray and neutron small- and ultra-small angle scattering ((U)SANS/(U)SAXS).The high-porosity/permeability limestone showed a four times faster bulk replacement rate than the lower-porosity/permeability material, and a different mechanism of porosity development. For the low-porosity limestone, a two-stage replacement occurred, with the reacted region of the core consisting of an inner rim in which the limestone was replaced by two calcite-dolomite solid solutions, and an outer rim in which the dolomite was replaced by magnesite. Elongated pores formed along grain boundaries at the initial limestone/dolomite reaction interface, and additional nanometer-scale porosity was formed at the secondary magnesite replacement rim. Grain boundaries were identified as preferential pathways for transport leading to dolomitization and a grain boundary diffusion rate was calculated based on microstructural characterization. In contrast, replacement in the high-porosity limestone was accompanied by porosity generation through replacement of individual grains by dolomite throughout the sample and, in longer runs, magnesite in outer parts of the core. These observations emphasize that both the mechanisms of the replacement reaction and the microstructure and chemistry of the replaced product are contingent on the initial structure of the starting material.

36 MATERIALS SCIENCE↗

DOE OSTI2024

PV Generation and Load Forecasting for Adjuntas PR Community Microgrids

Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.

Sundararajan, Aditya [Oak Ridge National Laborator↗