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All sources NASA NTRS DOE OSTI

NASA NTRS2013

Managing the Risk of Command File Errors

Command File Error (CFE), as defined by the Jet Propulsion Laboratory's (JPL) Mission Operations Assurance (MOA) is, regardless of the consequence on the spacecraft, either: an error in a command file sent to the spacecraft, an error in the process for developing and delivering a command file to the spacecraft, or the omission of a command file that should have been sent to the spacecraft. The risk consequence of a CFE can be mission ending and thus a concern to space exploration projects during their mission operations. A CFE during space mission operations is often the symptom of some kind of imbalance or inadequacy within the system that comprises the hardware & software used for command generation and the human experts involved in this endeavour. As we move into an era of enhanced collaboration with other NASA centers and commercial partners, these systems become more and more complex and hence it is all the more important to formally model and analyze CFEs in order to manage the risk of CFEs. Here we will provide a summary of the ongoing efforts at JPL in this area and also explain some more recent developments in the area of developing quantitative models for the purpose of managing CFE's.

Bayesian Belief Networks↗

NASA NTRS2013

Modeling to Improve the Risk Reduction Process for Command File Errors

The Jet Propulsion Laboratory has learned that even innocuous errors in the spacecraft command process can have significantly detrimental effects on a space mission. Consequently, such Command File Errors (CFE), regardless of their effect on the spacecraft, are treated as significant events for which a root cause is identified and corrected. A CFE during space mission operations is often the symptom of imbalance or inadequacy within the system that encompasses the hardware and software used for command generation as well as the human experts and processes involved in this endeavor. As we move into an era of increased collaboration with other NASA centers and commercial partners, these systems become more and more complex. Consequently, the ability to thoroughly model and analyze CFEs formally in order to reduce the risk they pose is increasingly important. In this paper, we summarize the results of applying modeling techniques previously developed to the DAWN flight project. The original models were built with the input of subject matter experts from several flight projects. We have now customized these models to address specific questions for the DAWN flight project and formulating use cases to address their unique mission needs. The goal of this effort is to enhance the project's ability to meet commanding reliability requirements for operations and to assist them in managing their Command File Errors.

spacecraft↗

DOE OSTI2022

Incorporate day-ahead robustness and real-time incentives for electricity market design

In this paper, we propose a two-stage electricity market framework to explore the participation of distributed energy resources (DERs) in a day-ahead (DA) market and a real-time (RT) market. The objective is to determine the optimal bidding strategies of the aggregated DERs in the DA market and generate online incentive signals for DER-owners to optimize the social-welfare taking into account network operational constraints. Distributionally robust optimization is used to explicitly incorporate data-based statistical information of renewable forecasts into the supply/demand decisions in the DA market. We evaluate the conservativeness of bidding strategies distinguished by different risk aversion settings. In the RT market, a bi-level time-varying optimization problem is proposed to design the online incentive signals to tradeoff the RT imbalance penalty for distribution system operators (DSOs) and the costs of individual DER-owners. This enables tracking their optimal dispatch to provide fast balancing services, in the presence of time-varying network states while satisfying the voltage regulation requirement. Simulation results on both DA wholesale market and RT balancing market demonstrate the necessity of this two-stage design, and its robustness to uncertainties, the performance of convergence, the tracking ability and the feasibility of the resulting network operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Using Machine Learning to Understand Electric and Hybrid Vehicles Ownership in Burdened and Nonburdened Communities

Transitioning to electric and hybrid vehicles (EHVs) for all communities is a pivotal step toward sustainable transportation and environmental conservation. This paper aims to understand the adoption of EHVs, focusing on burdened communities (BCs) in the United States. The EHV ownership-based analysis combines two datasets—behavioral data from the Puget Sound Regional Travel Survey integrated with BCs (Justice40) data covering transportation insecurity, environmental burden, social vulnerability, health vulnerability, and climate and disaster risk burden. After creating this unique database, descriptive analysis and modeling are used to analyze the data and predict EHV ownership in the future. Specifically, we use a new method that combines particle swarm optimization (PSO) with a stacking model named PSO-Stacking, which incorporates heterogeneous base learners of machine learning and deep learning. PSO applies a customized objective function to select the optimal hyperparameters for heterogeneous learners within the stacking model, effectively addressing challenges such as multicollinearity, data imbalance, nonlinearity, and overfitting. The proposed solution covers more accurate results than standard benchmark models for EHV ownership in BCs and non-BCs. In addition, the results of the PSO-Stacking method are explained using the local interpretable model-agnostic explanations technique. Results show a negative correlation between the BCs indicators, that is, higher transportation insecurity associated with lower EHV ownership. Furthermore, BCs have higher future climate risk scores, diesel particulate matter levels, and PM2.5 in the air than non-BCs because of higher conventional vehicle ownership. These communities are at higher risk and can benefit from electrification, EV infrastructure, and EV policies to address environmental challenges.

Aslam, Zeeshan [ORNL]↗

DOE OSTI2025

Proactively Addressing Employee Well-Being to Foster Workplace Safety in a U.S. National Laboratory

The daily conduct of high-risk, high-consequence work, by its very nature, can be mentally demanding. Research demonstrates that failure to manage or mitigate these demands can degrade psychological well-being and mental health. Degradations in employee well-being impact individual performance, jeopardizing safety and mission success in the workplace. This Commentary describes efforts taken at Sandia National Laboratories over the past eight years to evaluate, understand, learn from, and mitigate factors such as stress, burnout, work-life imbalances, and disengagement in the workplace that can degrade employee well-being. After evaluating these four facets of employee well-being, Sandia National Laboratories initiated several programs to address observations and outcomes, including the Thrive program, the Take 10 Initiative, work-life balance resources, employee resource groups such as the Sandia Parents Group, and Workplace Improvement Networks. Collectively, the intent of these programs is to ensure employee mental readiness to conduct hazardous high-risk work effectively and safely. Preliminary data suggest that these programs are succeeding. In conclusion, other national laboratories and organizations, regardless of size, may wish to apply similar approaches to improve employee well-being and thereby increase the likelihood of mission success.

Absorption↗

NASA NTRS1991

System reliability and risk assessment task goals and status

The major focus for continued development of the Numerical Evaluation of Stochastic Structures Under Stress (NESSUS) codes is in support of system testing and certification of advanced propulsion systems. Propulsion system testing has evolved over the years from tests designed to show success, to tests designed to reveal reliability issues before service use. Such test conditions as performance envelope corners, high rotor imbalance, power dwells, and overspeed tests are designed to shake out problems that can be associated with low and high cycle fatigue, creep, and stress rupture, bearing durability, and the like. Subsystem testing supports system certification by standing as an early evaluation of the same durability and reliability concerns as for the entire system. The NESSUS software system is being further developed to support the definition of rigorous subsystem and system test definition and reliability certification. The principal technical issues are outlined which are related to system reliability, including key technology issues such as failure mode synergism, sequential failure mechanisms, and fault tree definition.

Cruse, T. A.↗

NASA NTRS2014

A Computational Model for Simulating Spaceflight Induced Bone Remodeling

An overview of an initial development of a model of bone loss due to skeletal unloading in weight bearing sites is presented. The skeletal site chosen for the initial application of the model is the femoral neck region because hip fractures can be debilitating to the overall performance health of astronauts. The paper begins with the motivation for developing such a model of the time course of change in bone in order to understand the mechanism of bone demineralization experienced by astronauts in microgravity, to quantify the health risk, and to establish countermeasures. Following this, a general description of a mathematical formulation of the process of bone remodeling is discussed. Equations governing the rate of change of mineralized bone volume fraction and active osteoclast and osteoblast are illustrated. Some of the physiology of bone remodeling, the theory of how imbalance in remodeling can cause bone loss, and how the model attempts to capture this is discussed. The results of a preliminary validation analysis that was carried out are presented. The analysis compares a set of simulation results against bone loss data from control subjects who participated in two different bed rest studies. Finally, the paper concludes with outlining the current limitations and caveats of the model, and planned future work to enhance the state of the model.

Pennline, James A.↗

NASA NTRSDate not supplied

Exploring Flooded Fraction Prediction through Machine Learning Models Focusing on Medical Infrastructure in the Southeast U.S. Coastal Areas

Rising sea levels due to climate change increasingly threaten medical infrastructure through flooding. This study develops machine learning models to predict flood exposure for 11,508 medical facilities in the southeastern coastal regions of the United States by integrating datasets including meteorological, hydrological, topographic, and geological data, the Natural Risk Index, and historical flood records from NASA, HIFLD, and FEMA. Six regression models, namely Linear Regression, Support Vector Regression, Random Forest, k-Nearest Neighbors, XGBoost, and Artificial Neural Networks, are trained using 16 explanatory variables identified through literature review and correlation analysis. Data preprocessing employs the SMOGN for class imbalance and Winsorization for outliers. Model performance is evaluated using MAE, MSE, and RMSE, with Random Forest and XGBoost models achieving the highest performance (MSE of 2.58e-5 and 3.69e-5, respectively). This multifactorial approach allows the models to capture complex flood-influencing relationships, enhancing adaptability and performance across geographic regions. Future work focuses on expanding across the U.S. and developing a near real-time flood monitoring system.

Jihoon Chung↗

DOE OSTI2026

Laser polarization effects on crossed-beam energy transfer in inertial confinement fusion

Crossed-beam energy transfer (CBET) is a consequential process in inertial confinement fusion (ICF) experiments. CBET depends on the polarization state of the interacting lasers and can, in turn, alter their polarization. Here, we derive analytical expressions for two-beam CBET with linearly and circularly polarized light, and present simulations of CBET that include polarization effects for ICF conditions relevant to experiments on the National Ignition Facility. The results show that CBET mixes the polarization of the beams as they propagate through the target and leads to azimuthal power imbalances between beams within a given cone (i.e., at the same polar angle in the target chamber). These variations are shown to correlate with variations in measured backscatter between beams of a same quadruplet. Using circularly polarized, instead of linearly polarized, light produces similar overall levels of CBET within a cone of beams but tends to reduce the azimuthal variations. This is expected to slightly improve irradiation symmetry and reduce the risk of backscatter from stimulated Brillouin scattering. Circular polarization may therefore be beneficial for the next generation of laser fusion drivers.

Physics - Plasma physics↗

DOE OSTI2026

Geothermal district energy systems coupled with seasonal underground thermal energy storage: a U.S. techno-economic screening by climate and geology

In the United States, cooling-dominated commercial building loads can cause geothermal heat pump-based district energy systems to accumulate a long-term subsurface thermal imbalance, motivating the incorporation of seasonal underground thermal energy storage. We developed a transferable workflow to evaluate geothermal district systems that pair ground heat exchangers with seasonal underground thermal energy storage. Using standardized hourly loads for seven commercial buildings and a uniform cost framework, we simulated ten U.S. cities with a physics-based ground heat exchanger model, subsurface storage simulations, and economic assessment to isolate the roles of climate and hydrogeology. In cooling-dominated cities, underground thermal energy storage supplied the majority of annual cooling, cutting electricity use and summer peaks substantially while achieving levelized costs comparable to or below conventional chiller-boiler plants. In cooler climates, the storage share shrunk, required borefield size and costs rose, and levelized cost of energy increased nearly linearly with declining underground thermal energy storage fraction, indicating storage fraction as the primary economic lever. Sensitivity analysis showed capital risk dominated by borefield drilling and surface heating, ventilation, and air-conditioning and piping, with underground thermal energy storage costs secondary. This workflow provides a transparent foundation for site-specific design and screening of next-generation geothermal district energy systems.

25 ENERGY STORAGE↗

NASA NTRS2016

Novel Radiomitigator for Radiation-Induced Bone Loss

Radiation-induced bone loss can occur with radiotherapy patients, accidental radiation exposure and during long-term spaceflight. Bone loss due to radiation is due to an early increase in oxidative stress, inflammation and bone resorption, resulting in an imbalance in bone remodeling. Furthermore, exposure to high-Linear Energy Transfer (LET) radiation will impair the bone forming progenitors and reduce bone formation. Radiation can be classified as high-LET or low-LET based on the amount of energy released. Dried Plum (DP) diet prevents bone loss in mice exposed to total body irradiation with both low-LET and high-LET radiation. DP prevents the early radiation-induced bone resorption, but furthermore, we show that DP protects the bone forming osteoblast progenitors from high-LET radiation. These results provide insight that DP re-balances the bone remodeling by preventing resorption and protecting the bone formation capacity. This data is important considering that most of the current osteoporosis treatments only block the bone resorption but do not protect bone formation. In addition, DP seems to act on both the oxidative stress and inflammation pathways. Finally, we have preliminary data showing the potential of DP to be radio-protective at a systemic effect and could possible protect other tissues at risk of total body-irradiation such as skin, brain and heart.

countermeasure↗

NASA NTRSDate not supplied

Demand Capacity Balancing at Vertiports for Initial Strategic Conflict Management of Urban Air Mobility Operations

Urban Air Mobility (UAM) is a new transportation concept that enables highly automated, cooperative, passenger or cargo-carrying air transportation services in and around urban areas. To achieve the high level of operational density and complexity desired by the UAM community, an airspace system that allows UAM operators to readily access and operate safely and efficiently in the airspace is needed. This airspace system will require air traffic management designed to reduce the risk of conflicts and loss of separation between UAM flights. In general, strategic conflict management is considered as the first layer of conflict management for safe flight operations to condition the traffic to reduce the need for airborne separation provision, the second layer of conflict management. Demand Capacity Balancing (DCB) is one of the concept components to achieve strategic conflict management. DCB strategically evaluates traffic demand and resource capacities to allow UAM operators to determine when, where and how they operate, while mitigating conflicting needs for airspace and vertiport capacity. DCB can be applied whenever UAM demand exceeds the capacity in airspace or at vertiports. As the UAM ecosystem evolves with advanced technologies and matured operational procedures, more complicated conflict management will likely be needed. In the current UAM ‘Concept of Operation (ConOps) 1.0’ operational stage defined by FAA, however, it will be meaningful to explore the demand capacity balancing at vertiports only, as an initial strategic conflict management approach for UAM operations because vertiport capacity seems to be a bottleneck of UAM traffic. For this research, we developed a demand-capacity imbalance detection and resolution service for UAM. This DCB service identifies the demand from operators and compares the demand to a given capacity at the shared resources (i.e., vertiports) over the upcoming time horizon which is divided into time bins having a constant interval. When a new flight plan is submitted, the algorithm embedded in the DCB service checks the available time bins based on the desired departure time and estimated arrival time at origin and destination vertiports, respectively. If the time bins for the originally desired times are already occupied by other flights (i.e., demand is at or above capacity), the algorithm finds the next available time bins for takeoff and landing and shifts the conflicting departure time to the earliest time that satisfies the capacity constraints at both origin and destination vertiports. The details of the algorithm will be described in the final manuscript. Figure 1 shows that the proposed DCB algorithm works well for a sample traffic scenario. In this example, a total of 144 flights, split between two operators, are planned over 2 hours, traveling 10 routes between five vertiports. In the heatmaps, the horizontal axis shows 12 time bins where each bin represents a 12-minute interval, and the vertical axis shows five vertiports. The number in each cell shows the number of operations, counting both departures and arrivals, at a specific vertiport in each time bin. For the given capacity of 2 operations/vertiport/bin, Figure 1 shows that the original demand sometimes exceeds the capacity, but the modified demand is reduced to the given capacity after resolving demand-capacity imbalances. When UAM flights are operated, it is expected that many practical issues would arise in the federated system architecture with multiple operators. UAM operators may experience a time synchronization issue due to communication delay between operator and vehicle. UAM vehicles would fly at different flight speeds, depending on vehicle models. Actual departure and arrival times can have large variations, compared to the schedule. The lead time from flight plan submission to desired departure time can vary by service type (e.g., regular shuttle service vs. on-demand service). Using the proposed DCB algorithm, we also investigated how the actual flight schedule and DCB performance are affected by these uncertainties such as unsynchronized times between operators, flight speed differences, lead time differences, and departure time errors. The final manuscript will include the background of this research work, the description of the DCB algorithm and its use cases with traffic scenarios. It will also provide the analytical results about the impact of various uncertainties that can occur in actual UAM operations on the DCB at vertiports, in terms of demand distribution changes, number of simultaneous operations, and delay propagation.

Urban Air Mobility↗