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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

La Hague Legacy Waste Recovery Program: Scope, Progress and Issues -12080

A significant inventory of process waste of varying natures and quantities has been generated during the thirty years of operation of UP2 400 facility on the site of La Hague, France. The retrieval, packaging and final storage of such an inventory has never been achieved before in France and thus requires the design and qualification of new processes, equipment, and waste packages. Following AREVA strategic decisions and French safety authority requirements, the legacy waste program has begun around the year 2000 and is scheduled to be completed around the year 2025. It is under the responsibility of AREVA Site Value Development Project teams. For each category of waste to be recovered, AREVA teams conducted detailed investigations, defined recovery modes, treatment processes, as well as final waste package forms, which they subsequently submitted to French safety and waste management authorities. A Task force initiative was subsequently launched to optimize the program cost and scenario, and lead to an optimization of about 15% of the entire program. The qualification of processes and waste packages required a significant amount of research and development which is now well under way for processes, and scheduled to be completed in 2015. Preparation work has begun on several installations to clear space for the construction of future retrieval facilities, scheduled to begin in the coming three years. La Hague Legacy waste retrieval program represents a significant challenge in the sense that it covers a significant variety and quantity of waste needing recovery and reconditioning, with tight financial objectives and a binding recovery schedule. During the past five years, AREVA SVD successfully conducted design, research, development, and qualification activities which lead to the definition of qualified processes and waste packages for each retrieval program. Preparation work and supplier consultations are now on-going, in order to meet our objectives of beginning retrieval operations in compliance with our commitments to the safety authorities, in 2015 and 2016. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Charging Decision Making for Automated Electric Vehicles

Abstract: Overview/Significance With the emerging of autonomous driving and its application in electric vehicles, the charging decision making should be taken over by the vehicles. Autonomous electric vehicles will require the capability to make charging decisions according to the battery energy state, the travel demand and also the available charging infrastructure. The introduction of autonomous driving technology would remove the challenge of co-locating charging infrastructure with driver destinations and presents a driver-free method for EVs to reach nearby charging stations. This will significantly change the charging behavior of electric vehicles. EV driver will no longer need to be present at charging stations for charging actions. Automated EVs can drive to nearby charging stations to perform charging actions by themselves when necessary. Meanwhile, connected vehicles technology makes electric vehicles have the capability to sense and obtain pertinent information from nearby charging station networks and then calculate the corresponding costs and availability for charging. All these promising capabilities bring new opportunities to construct an automatic charging decision making system and improve energy efficiency of charging actions for automatic electric vehicles. Abstract: Methodology This research introduces optimization tools and case studies on charging decision making for automated electric vehicles under personal usage scenario. The designed optimization tools aim to provide charging strategies, i.e. the optimal choice of charging station outside home and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. Abundant real world EV data is utilized to construct stochastic energy consumption prediction model under realistic driving conditions. Advanced dynamic programming technology is utilized to model and solve the potential multi-stage decision making process by considering realistic uncertainties of EV energy cost and dynamics of itinerary information. Large-scale simulation studies using Chicago itinerary data are demonstrated to illustrate energy benefits from automatic charging decision making for future autonomous EVs. Abstract: Results/Conclusions Optimal automatic charging decision making and its energy benefits have been studied for personal automated electric vehicles. A data-driven method based on large amount of real-world data and the corresponding real-time update algorithm have been proposed to construct multi-channel stochastic energy consumption prediction model. Based on the energy cost prediction, dynamic multi-stage charging decision-making models are established for optimal strategies during a daily itinerary. Derived charging strategies can minimize the monetary and energy cost of charging actions for autonomous vehicles. Simulations and case studies show the potential ability of autonomous electric vehicles to reduce the range anxiety and charging infrastructure dependency. Automatic charging decision making can mitigate the pain from electric vehicle charging necessity outside home. In conclusion, automated electric vehicles equipped with an appropriate automatic charging decision making system will help EV driver to relieve from heavy time cost of recharging, reduce the energy cost of performing charging actions and improve the energy efficiency in future electrified transportation system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2010

Pitch Guidance Optimization for the Orion Abort Flight Tests

The National Aeronautics and Space Administration created the Constellation program to develop the next generation of manned space vehicles and launch vehicles. The Orion abort system is initiated in the event of an unsafe condition during launch. The system has a controller gains schedule that can be tuned to reduce the attitude errors between the simulated Orion abort trajectories and the guidance trajectory. A program was created that uses the method of steepest descent to tune the pitch gains schedule by an automated procedure. The gains schedule optimization was applied to three potential abort scenarios; each scenario tested using the optimized gains schedule resulted in reduced attitude errors when compared to the Orion production gains schedule.

Stillwater, Ryan Allanque↗
DOE OSTI2019

Approximate Latent Factor Algorithm for Scenario Selection and Weighting in Transmission Expansion Planning

One major difficulty in transmission expansion planning is selecting the representative scenarios to use to evaluate candidate transmission networks. The variability in demand and renewable generation makes the inclusion of several scenarios critical when calculating reliability and cost, but including too many scenarios in an optimization is computationally intractable. To reduce the number of representative operating conditions needed to obtain an accurate approximation of the system, we propose a method rooted in multivariate statistics that exploits the latent correlative structure between different scenarios and network configurations. The proposed algorithm includes an objective and rigorous way to select a subset of scenarios that provide as much information about the system as possible, and a method to accurately approximate the system cost from that scenario subset. The result is a set of scenarios and weights that are easily incorporated into traditional transmission expansion planning formulations. We apply this to a 312-bus WECC model with 8,736 distinct operating conditions. The transmission plans found with the proposed method are more reliable and have a lower total cost than those from other scenario reduction techniques, as well as a smaller error between the expected system performance from the optimization objective and the actual system performance.

42 ENGINEERING↗
DOE OSTI2025

Optimizing Power Line Undergrounding Decisions under Varying Wildfire Risk and Weather Scenarios

Abstract—The threat of wildfire ignitions from electric power equipment has led utilities to increasingly turn to preemptive power shutoffs, which, while effective in reducing grid-induced wildfire risk, can cause significant load loss. Undergrounding power lines is an alternative strategy for preventing grid-induced wildfires. However, undergrounding lines is costly, so an efficient undergrounding plan must balance reductions in wildfire risk and load loss with the cost of undergrounding lines. We propose a robust optimization model to identify which power lines to underground to maximize load served while limiting wildfire risk across a range of wildfire risk and weather scenarios. Since solving this problem may be computationally heavy for large power grids and many operating scenarios, we present a delayed constraint generation algorithm to iteratively add scenarios until an optimal solution is found. We evaluate the performance of this framework on the RTS-GMLC with scenarios representing a year of operating conditions and compare it with a stochastic programming formulation. Our results indicate that our undergrounding model is successful in reducing load shed and risk compared to baseline cases in which no mitigation action is taken and only power shutoffs are implemented (no undergrounding). The robust formulation also reduces more load shed than the stochastic formulation in the most extreme scenarios. Index Terms—grid resilience, optimization, transmission systems, underground power lines, wildfire risk.

Taylor, S. [Department of Electrical and Computer ↗
DOE OSTI2025

Recovery Simulator and Analysis Formulation: Mathematical Framework for Enhanced Resilience and Resource Allocation

This report introduces recovery simulator and analysis (RSA), a framework aimed at enhancing the resilience of electrical grids post-disruption. The RSA model leverages an optimization problem formulation that focuses on maximizing the load served (or optionally customers served) through a coordinated and cooptimized recovery of non-black start generation, transmission lines, feeders and substations subject to labor budget constraints. By integrating advanced linear programming techniques, the simulator selects efficient reocovery pathways, optimizing both short-term and long-term grid recovery strategies. The mathematical framework guides decision-making through a comprehensive evaluation of potential recovery actions, factoring in the trade-offs between labor constraints and load (or optionally customer) restoration efficacy. This enables grid operators to simulate diverse outage scenarios and delineate optimal recovery pathways, thereby prioritizing critical repair tasks and ensuring resource allocation is both economical and effective. The intended use case of RSA is to allow planners to explore many recovery scenarios quickly and determine assets most critical across a wide range of scenarios, and therefore strong candidates for hardening or additional investment. RSA might also be used in an operational setting, following a single event, for exploring efficient recovery pathways.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Surface Qualification Toolpath Optimization for Hybrid Manufacturing

Hybrid manufacturing machine tools have great potential to revolutionize manufacturing by combining both additive manufacturing (AM) and subtractive manufacturing (SM) processes on the same machine tool. A prominent issue that can occur when going from AM to SM is that the SM process toolpath does not account for geometric discrepancies caused by the previous AM step, which leads to increased production times and tool wear, particularly when wire-based directed energy deposition (DED) is used as the AM process. This work discusses a methodology for approximating a part’s surface topology using on-machine contact probing and formulating an optimized SM toolpath using the surface topology approximation. Three different geometric surface approximations were used: triangular, trapezoidal, and a hybrid of both. SM toolpaths were created using each geometric approximation and assessed according to three objectives: reducing total machining time, reducing surface roughness, and reducing cutting force. Different prioritization scenarios of the optimization goals were also investigated. The optimal surface approximation that yielded the most improvement in the optimization was determined to be the hybrid surface topology approximation. Furthermore, it was shown that when the machining time or cutting force optimization goals were prioritized, there was little improvement in the other optimization goals.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Optimizing Nuclear Material Accounting and Measurement Systems

The ability to create nuclear weapons from 235 U and 239 Pu makes it imperative to closely account for these materials as they progress through a nuclear fuel cycle. Improved measurement systems provide more accurate estimates of material quantities and material unaccounted for (MUF). This work provides examples of how two safeguards computational toolboxes can optimize and analyze hypothetical nuclear fuel cycle scenarios. The NUclear Measurement System Optimization (NUMSO) toolbox uses operations research techniques to find optimal solutions to safeguards measurement problems based on minimizing the variance of the estimated MUF. The SafeGuards Analysis (SGA) toolbox employs Monte Carlo techniques to analyze a given configuration of measurement methods and material flows to determine the probabilities of Type I (false detection) and Type II (missed detection) errors. Applying these toolboxes to a realistic fuel cycle scenario demonstrates the capability of NUMSO and SGA to address nuclear safeguards problems. Working in tandem, both toolboxes are able to determine how to quickly improve upon an existing safeguards measurement system and to calculate the resulting improvement in the error probabilities of the system. This information shows engineers not only how to develop new measurement systems but also how to improve existing systems in the most efficient manner.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Risk-averse optimization for resilience enhancement of complex engineering systems under uncertainties

With the growth of complexity and extent, large scale interconnected network systems, e.g., transportation networks or infrastructure networks, become more vulnerable to external disturbances. Hence, managing potential disruptive events during the design, operating, and recovery phase of an engineered system and therefore improving the system’s resilience is an important yet challenging task. Here, to ensure system resilience after the occurrence of failure events, this study proposes a mixed-integer linear programming (MILP) based restoration framework using heterogeneous dispatchable agents. The scenario-based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from nature. Moreover, different from conventional SO using deterministic equivalent formulations, the CVaR risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves a large-scale MILP problem and thus an adequate decomposition technique i.e. modified Lagrangian dual decomposition, is also employed to achieve tractable computational complexity. Case study results based on the IEEE 37-bus test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations.

42 ENGINEERING↗
DOE OSTI2017

Socially optimal replacement of conventional with electric vehicles for the US household fleet

In this study, a framework is proposed for minimizing the societal cost of replacing gas-powered household passenger cars with battery electric ones (BEVs). The societal cost consists of operational costs of heterogeneous driving patterns' cars, the government investments for charging deployment, and monetized environmental externalities. The optimization framework determines the timeframe needed for conventional vehicles to be replaced with BEVs. It also determines the BEVs driving range during the planning timeframe, as well as the density of public chargers deployed on a linear transportation network over time. We leverage datasets that represent U.S. household driving patterns, as well as the automobile and the energy markets, to apply the model. Results indicate that it takes 8 years for 80% of our conventional vehicle sample to be replaced with electric vehicles, under the base case scenario. The socially optimal all-electric driving range is 204 miles, with chargers placed every 172 miles on a linear corridor. All of the public chargers should be deployed at the beginning of the planning horizon to achieve greater savings over the years. Sensitivity analysis reveals that the timeframe for the socially optimal conversion of 80% of the sample varies from 6 to 12 years. The optimal decision variables are sensitive to battery pack and vehicle body cost, gasoline cost, the discount rate, and conventional vehicles' fuel economy. In conclusion, faster conventional vehicle replacement is achieved when the gasoline cost increases, electricity cost decreases, and battery packs become cheaper over the years.

25 ENERGY STORAGE↗
NASA NTRS1990

Lunar orbiting microwave beam power system

A microwave beam power system using lunar orbiting solar powered satellite(s) and surface rectenna(s) was investigated as a possible energy source for the Moon's surface. The concept has the potential of reduced system mass by placing the power source in orbit. This can greatly reduce and/or eliminate the 14 day energy storage requirement of a lunar surface solar system. Also propellants required to de-orbit to the surface are greatly reduced. To determine the practicality of the concept and the most important factors, a zero-th order feasibility analysis was performed. Three different operational scenarios employing state of the art technology and forecasts for two different sets of advanced technologies were investigated. To reduce the complexity of the problem, satellite(s) were assumed in circular equatorial orbits around the Moon, supplying continuous power to a single equatorial base through a fixed horizontal rectenna on the surface. State of the art technology yielded specific masses greater than 2500 kg/kw, well above projections for surface systems. Using advanced technologies the specific masses are on the order of 100 kg/kw which is within the range of projections for surface nuclear (20 kg/kw) and solar systems (500 kg/kw). Further studies examining optimization of the scenarios, other technologies such as lasers transmitters and nuclear sources, and operational issues such as logistics, maintenance and support are being carried out to support the Space Exploration Initiative (SEI) to the Moon and Mars.

Fay, Edgar H.↗
NASA NTRS1990

Lunar orbiting microwave beam power system

A microwave-beam power system using lunar orbiting solar-powered satellite(s) and surface rectenna(s) is investigated as a possible energy source for the moon's surface. The concept has the potential of achieving reduced system mass by placing the power source in orbit. This can greatly reduce and/or eliminate the 14-d energy storage requirement of a lunar surface solar system. Also, propellants required to de-orbit to the surface are greatly reduced. To determine the practicality of the concept and the most important factors, a zeroth-order feasibility analysis is performed. Three different operation scenarios employing state of the art technology and forecasts for two different sets of advanced technologies are investigated. To reduce the complexity of the problem, satellite(s) are assumed to be in circular equatorial orbits around the moon, supplying continuous power to a single equatorial base through a fixed horizontal rectenna on the surface. State of the art technology yields specific masses greater than 2500 kg/kw, well above projections for surface systems. The specific masses are on the order of 100 kg/kW using advanced technologies, which is within the range of projections for surface nuclear (20 kg/kW) and solar systems (500 kg/kW). Further studies examining optimization of the scenarios, other technologies, such as laser transmitters and nuclear sources, and operational issues, such as logistics, maintenance, and support, are being carried out to support the Space Exploration Initiative (SEI) to the moon and Mars.

Fay, Edgar H.↗
NASA NTRS1994

Optimal control theory determination of feasible return-to-launch-site aborts for the HL-20 Personnel Launch System vehicle

The personnel launch system (PLS) being studied by NASA is a system to complement the space shuttle and provide alternative access to space. The PLS consists of a manned spacecraft launched by an expendable launch vehicle (ELV). A candidate for the manned spacecraft is the HL-20 lifting body. In the event of an ELV malfunction during the initial portion of the ascent trajectory, the HL-20 will separate from the rocket and perform an unpowered return to launch site (RTLS) abort. This work details an investigation, using optimal control theory, of the RTLS abort scenario. The objective of the optimization was to maximize final altitude. With final altitude as the cost function, the feasibility of an RTLS abort at different times during the ascent was determined. The method of differential inclusions was used to determine the optimal state trajectories, and the optimal controls were then calculated from the optimal states and state rates.

Dutton, Kevin E.↗
DOE OSTI2022

Building energy efficiency and load flexibility optimization using phase change materials under futuristic grid scenario

This work investigates the energy savings and load flexibility capacity of phase change material (PCM) integrated into building envelope under a future energy generation scenario, where 80% of the energy load comes from renewable sources. Using community-scale modeling, we first determine the net thermal storage required to fully manage the demand variability at a utility grid, and then using whole-building simulations we optimize operating parameters - such as PCM thickness, latent heat, interior setpoint profile, PCM distribution, and heat transfer coefficient - to determine the optimal conditions required for maximum energy reduction and load flexibility without compromising occupants' thermal comfort. The optimal PCM-integrated envelope proposed in this study can provide annual load flexibility up to 33.6% and annual energy savings up to 10.8% in a lightweight residential building located in Baltimore, MD. This study is relevant given the increasing contributions of renewable energy in the total energy generation mix, leading to a significant time-imbalance between peak energy demand and peak energy production. While thermal energy storage using PCM is a recognized technique, there is no known prior study dedicated to examining the thermal performance of PCM-integrated envelope under future energy generation scenarios. This study bridges the research gap by investigating a practical way to implement PCMs in the buildings and maximize the energy efficiency as well as load flexibility related benefits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

SU-G-TeP3-13: The Role of Nanoscale Energy Deposition in the Development of Gold Nanoparticle-Enhanced Radiotherapy

Purpose: Gold nanoparticles (GNPs) can enhance radiotherapy effects. The high photoelectric cross section of gold relative to tissue, particularly at lower energies, leads to localized dose enhancement. However in a clinical context, photon energies must also be sufficient to reach a target volume at a given depth. These properties must be balanced to optimize such a therapy. Given that nanoscale energy deposition patterns around GNPs play a role in determining biological outcomes, in this work we seek to establish their role in this optimization process. Methods: The PENELOPE Monte Carlo code was used to generate spherical dose deposition kernels in 1000 nm diameter spheres around 50 nm diameter GNPs in response to monoenergetic photons incident on the GNP. Induced “lesions” were estimated by either a local effect model (LEM) or a mean dose model (MDM). The ratio of these estimates was examined for a range of photon energies (10 keV to 2 MeV), for three sets of linear-quadratic parameters. Results: The models produce distinct differences in expected lesion values, the lower the alpha-beta ratio, the greater the difference. The ratio of expected lesion values remained constant within 5% for energies of 40 keV and above across all parameter sets and rose to a difference of 35% for lower energies only for the lowest alpha-beta ratio. Conclusion: Consistent with other work, these calculations suggest nanoscale energy deposition patterns matter in predicting biological response to GNP-enhanced radiotherapy. However the ratio of expected lesions between the different models is largely independent of energy, indicating that GNP-enhanced radiotherapy scenarios can be optimized in photon energy without consideration of the nanoscale patterns. Special attention may be warranted for energies of 20 keV or below and low alpha-beta ratios.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Revisiting the definition of field capacity as a functional parameter in a layered agronomic soil profile beneath irrigated maize

The soil water content at the condition of field capacity (θ FC ) is a key parameter in irrigation scheduling and has been suggested to be determined by running a synthetic drainage experiment until the flux rate (q) at the bottom of the soil profile achieves a predefined negligible value (q FC ). We question the impact of q FC on the assessment of field capacity. Moreover, calculating θ FC as the integral mean of the water content profile when q is equal to q FC is strictly valid only for uniform soil profiles. By contrast, this practice is ambiguous and biased for stratified soil profiles due to the soil water content discontinuity at the layer interfaces. In this study, the concept of field capacity was revisited and adapted to practical agronomic heuristics. By resorting to the assessment of root-zone water storage capacity (W), we envision field capacity as a functional hydraulic parameter derived from synthetic irrigation scheduling scenarios to minimize drought stress, drainage, and nitrate leachate below the root zone. A functional analysis was carried out on a 135-cm-thick layered soil profile beneath maize in eastern Nebraska. On-farm irrigation scheduling applications and agricultural practices were recorded for 20 years (2001–2020) at a daily time step. Hydrus-1D was calibrated and validated with direct measurements of the soil water retention curve and soil water content data, respectively, in each soil layer. A set of functional field capacity values was derived from 24 irrigation scheduling scenarios, and the optimal water storage capacity at field capacity (W FC ) was approximately 50 cm (corresponding to about 80% saturation in the soil profile). An average irrigation amount of 217.5 mm distributed over 21 events was obtained by using optimal irrigation scheduling, which was initiated when the matric pressure head took on a value of –700 cm and the irrigation rate was set at 1.0 cm d –1 . This irrigation practice ensured water storage at approximately the same level (ideally at W FC ) by sustaining only evapotranspiration fluxes in the uppermost portion of the root zone and by limiting excessive drainage. This protocol can be transferred to other agricultural fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Mitigating life cycle GHG emissions of roads to be built through 2030: Case study of a Chinese province

We report the expansion of road networks in emerging economies such as China causes significant greenhouse gas (GHG) emissions. This development is conflicting with China's commitment to achieve carbon neutrality. Thus, there is a need to better understand life cycle emissions of road infrastructure and opportunities to mitigate these emissions. Existing impact studies of roads in developing countries do not address recycled materials, improved pavement maintenance, or pavement-vehicle interaction and electric vehicle (EV) adoption. Combining firsthand information from Chinese road construction engineers with publicly available data, this paper estimates a comprehensive account of GHG emissions of the road pavement network to be constructed in the next ten years in the Shandong province in Northern China. Further, we estimate the potential of GHG emission reductions achievable under three scenario sets: maintenance optimization, alternative pavement material replacement, and EV adoption. Results show that the life cycle GHG emissions of highways and Class 1–4 roads to be constructed in the next 10 years amount to 147 Mt CO2-eq. Considering the use phase in our model reveals that it is the dominant stage in terms of emissions, largely due to pavement-vehicle interaction. Vehicle electrification can only moderately mitigate these emissions. Other stages, such as materials production and road maintenance and rehabilitation, contribute substantially to GHG emissions as well, highlighting the importance of optimizing the management of these stages. Surprisingly, longer, not shorter maintenance intervals, yield significant emission reductions. Another counter-intuitive finding is that thicker and more material-intensive pavement surfaces cause lower emissions overall. Taken together, optimal maintenance and rehabilitation schedules, alternative material use, and vehicle electrification provide GHG reduction potentials of 11%, 4%–16% and 2%–6%, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Understanding the electromagnetic topology during the ohmic breakdown in tokamaks considering self-generated electric fields

We report that the physical mechanisms of the ohmic breakdown in a tokamak have been understood based on the classical Townsend avalanche theory. However, a new systematic theory (Yoo et al 2018 Nat. Commun.9 3523) recently demonstrated that electron avalanches during the ohmic breakdown are completely different from the Townsend avalanche due to strong self-generated electric fields. In this study, we elucidate the multi-dimensional effects of the self-generated electric field on plasma dynamics during the ohmic breakdown. We also propose a novel electromagnetic topology analysis method that can easily predict the overall plasma behavior and where the main plasma is generated. The topology analysis method is validated by a state-of-art particle simulation for various magnetic configurations. New physical insights into the complex electromagnetic topology would facilitate designing more reliable and optimized ohmic breakdown scenarios in future tokamaks, such as ITER and beyond.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗