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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Reliability-informed end-of-use decision making for product sustainability using two-stage stochastic optimization

The concept of circular economy has been diffused in recent decades to promote economic growth that does not add to the burden on natural resource extraction. Re-X options (e.g., reuse, repair, refurbish, remanufacture, recycle) have been gradually adopted in the product development process and optimized to reduce or eliminate waste and pollution. Although manufacturing incorporating Re-X options can be more environmentally friendly, it involves more sources of uncertainty than traditional manufacturing since the end-of-use products can be collected from multiple origins with various quantities and qualities, and the market demand for both new and remanufactured products cannot be forecasted perfectly. Thus, there is a need to optimize the Re-X policy to alleviate the negative impacts of the higher uncertainty. One option is using the reliability information of new products to estimate the end-of-use conditions and applying multi-stage stochastic optimization to capture multiple demand scenarios. This paper develops a two-stage stochastic optimization model to optimize the quality thresholds for reuse, recycling, and remanufacturing options. Our objective is to minimize the total cost, energy consumption, and environmental impact of producing and providing warranty service for a product family. The model employs reliability information of product components to estimate the warranty service cost and the end-of-use conditions of the returned resources. A case study on a general product family is implemented to illustrate the efficacy of the optimization model. Finally, results show that the two-stage optimization can achieve cost and environmental impact reduction for a hybrid manufacturing and remanufacturing process.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Optimizing Medical Kits for Space Flight

The Integrated Medical Model (IMM) uses Monte Carlo methodologies to predict the occurrence of medical events, their mitigation, and the resources required during space flight. The model includes two modules that utilize output from a single model simulation to identify an optimized medical kit for a specified mission scenario. This poster describes two flexible optimization routines built into SAS 9.1. The first routine utilizes a systematic process of elimination to maximize (or minimize) outcomes subject to attribute constraints. The second routine uses a search and mutate approach to minimize medical kit attributes given a set of outcome constraints. There are currently 273 unique resources identified that are used to treat at least one of 83 medical conditions currently in the model.

Minard, Charles G.↗
DOE OSTI2011

A quantitative comparison of the cost of employing EOR-coupled CSS supplemented with secondary DSF storage for two large CO2 point sources

This paper explores the impact of the temporally dynamic demand for CO2 for enhanced hydrocarbon recovery with CO2 storage. Previous evaluations of economy-wide CO2 capture and geologic storage (CCS) deployment have typically applied a simplifying assumption that 100% of the potential storage capacity for a given formation is available on the first day of the analysis, and that the injection rate impacts only the number of wells required to inject a given volume of fluid per year, making it a cost driver rather than a technical one. However, as discussed by Dahowski and Bachu [1], storing CO2 in a field undergoing CO2 flooding for enhanced oil recovery (EOR) is subject to a set of constraints to which storage in DSFs is not, and these constraints combined with variable demand for CO2 may strongly influence the ability of an EOR field to serve as a baseload storage formation for commercial scale CCS projects undertaken as a means of addressing climate change mitigation targets. This analysis assumes that CCS is being undertaken in order to reduce CO2 emissions from the industrial sources evaluated and that there is enough of a disincentive associated with venting CO2 to the atmosphere that any CO2 not used within the EOR field will be stored in a suitable nearby deep saline formation (DSF). The authors have applied a CO2 demand profile to two cases chosen to illustrate the differences in cost impacts of employing EOR-based CCS as a part of a given source’s CCS portfolio. The first scenario is a less-than-ideal case in which a single EOR field is used for storage and all CO2 not demanded by the EOR project is stored in a DSF; the second scenario is designed to optimize costs by minimizing storage in the DSF and maximizing lower-cost EOR-based storage. Both scenarios are evaluated for two facilities emitting 3 and 6 MtCO2/y, corresponding to a natural gas processing facility and an IGCC electric power plant, respectively. Annual and lifetime average CO2 transport and storage costs are presented, and the impact of added capture and compression costs on overall project economics is examined.

02 PETROLEUM↗
DOE OSTI2014

SU-E-T-54: Benefits of Biological Cost Functions

Purpose: To verify the benefits of the biological cost functions. Methods: TG166 patients were used for the test case scenarios. Patients were planned using Monaco V5.0 (CMS/Elekta, St.Louis, MO) Monaco has 3 biological and 8 physical CFs. In this study the plans were optimized using 3 different scenarios. 1- Biological CFs only 2-Physical CFs only 3- Combination of Physical and Biological CFsMonaco has 3 biological CFs. Target EUD used for the targets, derived from the poisson cell kill model, has an α value that controls the cold spots inside the target. α values used in the optimization were 0.5 and 0.8. if cold spots needs to be penalized α value increased. Serial CF: it's called serial to mimic the behaviour of the serial organs, if a high k value like 12 or 14 is used it controls the maximum dose. Serial CF has a k parameter that is used to shape the whole dvh curve. K value ranges between 1–20. k:1 is used to control the mean dose, lower k value controls the mean dose, higher k value controls the higher dose, using 2 serial CFs with different k values controls the whole DVH. Paralel CF controls the percentage of the volume that tolerates higher doses than the reference dose to mimic the behaviour of the paralel organs. Results: It was possible to achive clinically accepted plans in all 3 scenarios. The benefit of the biological cost functions were to control the mean dose for target and OAR, to shape the DVH curve using one EUD value and one k value simplifies the optimization process. Using the biological CFs alone, it was hard to control the dose at a point. Conclusion: Biological CFs in Monaco doesn't require the ntcp/tcp values from the labs and useful to shape the whole dvh curve. I work as an applications support specialist for Elekta and I am a Ph.D. Student in Istanbul University for radiation therapy physics.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1996

Identifying yield-optimizing environments for two cowpea breeding lines by manipulating photoperiod and harvest scenario

Photoperiod and harvest scenario of cowpea (Vigna unguiculata L. Walp) canopies were manipulated to optimize productivity for use in future controlled ecological life-support systems. Productivity was measured by edible yield rate (EYR:g m-2 day-1), shoot harvest index (SHI: g edible biomass [g total shoot dry weight]), and yield-efficiency rate (YER:g edible biomass m-2 day-1 per[g nonedible shoot dry weight]). Breeding lines 'IT84S-2246' (S-2246) and "IT82D-889' (D-889) were grown in a greenhouse under 8-, 12-, or 24-h photoperiods. S-2246 was short-day and D-889 was day-neutral for flowering. Under each photoperiod, cowpeas were harvested either for leaves only, seeds only, or leaves plus seeds (mixed harvest). Photoperiod did not affect EYR of either breeding line for any harvest scenario tested. Averaged over both breeding lines, seed harvest gave the highest EYR at 6.7 g m-2 day-1. The highest SHI (65%) and YER (94 mg m-2 day-1 g-1) were achieved for leaf-only harvest of D-889 under an 8-h photoperiod. For leaf-only harvest of S-2246, both SHI and YER increased with increasing photoperiod, but declined for seed-only and mixed harvests. However, photoperiod had no effect on SHI or YER for D-889 for any harvest scenario. A second experiment utilized the short-day cowpea breeding line 'IT89KD-288' (D-288) and the day-neutral breeding line 'IT87D-941-1' (D-941) to compare yield parameters using photoperiod extension under differing lamp types. This experiment confirmed the photoperiod responses of D-889 and S-2246 to a mixed-harvest scenario and indicated that daylength extension with higher irradiance from high pressure sodium lamps further suppressed EYR, SHI, and YER of the short-day breeding line D-288.

Non-NASA Center↗
DOE OSTI2022

Hydrogen Energy Storage Integrated with a Combined Cycle Plant

A project is being developed that will build upon the existing infrastructure and resources at the Intermountain Power Project (IPP) site to provide reliable, dispatchable energy and to support the transmission of renewable energy resources while transitioning to an economical green energy future. The concept study depicted in this report outlines a techno-economic optimization to fulfill the demand for 30% vol hydrogen co-firing in the IPP 840 MW advanced class combined cycle power plant. In an initial step, a site assessment concluded the site has sufficient land available to co-locate a hydrogen production and storage facility. The team evaluated and defined a scalable concept that considered technology characteristics, including input and output models to be used for optimization purposes. The concept for the hydrogen production and storage system integrates multiple technologies, to determine system size and scalable approach, for each of the technologies evaluated, the team defined component and subcomponent sizes, minimum and maximum capacity, modularity, component utility consumption (electric, water), component flexibility and servicing, layout, and technology status, as well as technology alternatives. For hydrogen generation, the project considers Siemens Energy’s Silyzer-300 (S300) technology, a 17.5 MW modular Proton Exchange Membrane (PEM) electrolyzer. For the S300 configuration, the team determined that three S300 arrays, or approximately 1,000 kg/hr, per block would yield a compact block design. This configuration results in a fairly wide and flexible arrangement that fits well into the spaces available at the site. Therefore, the overall design approach is based on multiple identical blocks of 3 arrays to minimize engineering cost and optimize constructability. In parallel, a transmission screening study was conducted to determine any potential transmission constraints from the energy sources that could feed the hydrogen production equipment. The study results show that minimum transmission constraints would be encountered to deliver 400 MW renewable generation from southern California, or south-central Wyoming. At last, the techno-economic analysis concluded that a scenario that uses solar and wind power yields the lowest levelized cost of hydrogen (LCOH 2 ) production and the lowest cost per tonne of CO 2 reduced. In this optimized scenario, the hydrogen production plant was determined as 6,201 kg/hr and the hydrogen storage (underground cavern) was determined as 4,600 tonnes. The resulting capacity factor for the hydrogen production plant was 66.33% with 8,745 operating hours in one year. This techno-economic analysis provided various options for integrating hydrogen storage at the Intermountain Power Plant site to co-fire the CCPP units. The results provide insightful data about the magnitude of capacity needed and the economics of producing hydrogen and reducing CO 2 emissions.

08 HYDROGEN↗
DOE OSTI2014

Operation of Concentrating Solar Power Plants in the Western Wind and Solar Integration Phase 2 Study

The Western Wind and Solar Integration Study (WWSIS) explores various aspects of the challenges and impacts of integrating large amounts of wind and solar energy into the electric power system of the West. The phase 2 study (WWSIS-2) is one of the first to include dispatchable concentrating solar power (CSP) with thermal energy storage (TES) in multiple scenarios of renewable penetration and mix. As a result, it provides unique insights into CSP plant operation, grid benefits, and how CSP operation and configuration may need to change under scenarios of increased renewable penetration. Examination of the WWSIS-2 results indicates that in all scenarios, CSP plants with TES provides firm system capacity, reducing the net demand and the need for conventional thermal capacity. The plants also reduced demand during periods of short-duration, high ramping requirements that often require use of lower efficiency peaking units. Changes in CSP operation are driven largely by the presence of other solar generation, particularly PV. Use of storage by the CSP plants increases in the higher solar scenarios, with operation of the plant often shifted to later in the day. CSP operation also becomes more variable, including more frequent starts. Finally, CSP output is often very low during the day in scenarios with significant PV, which helps decrease overall renewable curtailment (over-generation). However, the configuration studied is likely not optimal for High Solar Scenario implying further analysis of CSP plant configuration is needed to understand its role in enabling high renewable scenarios in the Western United States.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Clinical Decision Support Software: Modeling and Capabilities

With its distance from Earth and communication delays, exploration space flight will place new demands for crew autonomy. Crewmembers operating during such missions require a dedicated Clinical Decision Support System (CDSS) that enhances their earth independence by augmenting their knowledge, skills, and abilities in different medical scenarios. A CDSS is a software application that must function optimally in diverse and varied scenarios (while interfacing with and providing actionable information to appropriate vehicle systems) to augment crew performance by enhancing or adding knowledge, skills, and abilities that preserve health and wellness and ultimately helping to ensure mission success. In addition, the software tool should provide various functions and computational models to meet the demands of astronauts beyond low earth orbit.

B Russell↗
DOE OSTI2012

Climate Change Impacts to Hydropower Generation in Pacific Northwest River Basins (Final Report)

This is the final technical report describing the research findings for the project, “Climate change impacts to hydropower generation in Pacific Northwest river basins”. The overall objective of the research was to develop a new approach to assess which types of climate changes would adversely affect hydropower production. This research had three major components. First, we conducted a geospatial analysis of the region to identify potential focus basins. We compiled geospatial data on hydropower dam locations, contributing areas to hydropower dams, and climate characteristics in contributing areas. For the potential focus basins identified, we classified these basins by hydrologic regime as snow-dominated, rain-dominated, or mixed. Rain-dominated basins were primarily found in Oregon, with some in western Washington. Most of the basins draining west out of the Cascades in Washington were mixed, whereas most of the potential focus basins in Idaho were snowmelt-dominated. Next, we developed a stochastic conceptual modeling approach in which we stochastically generate a wide range of climate scenarios and use these scenarios to generate time series of reservoir inflows. We developed and applied this modeling approach to the North Fork of the Clearwater River in Idaho. Results showed that increases in temperature in the basin led to decreases in streamflow, even in cases where precipitation increased. Finally, we used the reservoir inflow scenarios from the stochastic conceptual model to force a reservoir operations model for Dworshak reservoir. The wide range of climate scenarios generated by the hydrologic model allowed us to test the sensitivity of reservoir operations objectives to shifts in both temperature and precipitation. Results showed that scenarios with lower inflow had less hydropower generation, but changes in hydropower generation were not always proportional to changes in inflow. Current reservoir operations were better suited to maximize hydropower generation under climate change scenarios with no shift in inflow timing but did not optimize power generation under warming scenarios with earlier snowmelt runoff.

13 HYDRO ENERGY↗
DOE OSTI2018

Fuel-Optimal Periodic Control of Passenger Cars in Cruise Based on Pontryagin's Minimum Principle

This paper aims to exploit the fuel saving potential of a passenger car in cruising scenarios by deriving an optimal periodic control strategy based on Pontyagin’s Minimum Principle. Gear shifting and coupling between the engine and the driveline are explicitly included in the optimal control problem, and this leads to a hybrid system. By investigating the analytically derived optimal trajectory in the state space, we confirm the optimal periodic control. Considerable advantages in fuel economy up to 4.8% compared to conventional steady control are demonstrated.

automotive control↗
DOE OSTI2022

Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation

In this paper, we present mixed integer linear programming (MILP) formulations to obtain optimal sizing for a battery energy storage system (BESS) and solar generation system in an extreme fast charging station (XFCS) to reduce the annualized total cost. The proposed model characterizes a typical year with eight representative scenarios and obtains the optimal energy management for the station and BESS operation to exploit the energy arbitrage for each scenario. Contrasting extant literature, this paper proposes a constant power constant voltage (CPCV) based improved probabilistic approach to model the XFCS charging demand for weekdays and weekends. This paper also accounts for the monthly and annual demand charges based on realistic utility tariffs. Furthermore, BESS life degradation is considered in the model to ensure no replacement is needed during the considered planning horizon. Different from the literature, this paper offers pragmatic MILP formulations to tally BESS charge/discharge cycles using the cumulative charge/discharge energy concept. McCormick relaxations and the Big-M method are utilized to relax the bi-linear terms in the BESS operational constraints. Finally, a robust optimization-based MILP model is proposed and leveraged to account for uncertainties in electricity price, solar generation, and XFCS demand. Case studies were performed to signify the efficacy of the proposed formulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Modern Radiation Therapy for Hodgkin Lymphoma: Field and Dose Guidelines From the International Lymphoma Radiation Oncology Group (ILROG)

Radiation therapy (RT) is the most effective single modality for local control of Hodgkin lymphoma (HL) and an important component of therapy for many patients. These guidelines have been developed to address the use of RT in HL in the modern era of combined modality treatment. The role of reduced volumes and doses is addressed, integrating modern imaging with 3-dimensional (3D) planning and advanced techniques of treatment delivery. The previously applied extended field (EF) and original involved field (IF) techniques, which treated larger volumes based on nodal stations, have now been replaced by the use of limited volumes, based solely on detectable nodal (and extranodal extension) involvement at presentation, using contrast-enhanced computed tomography, positron emission tomography/computed tomography, magnetic resonance imaging, or a combination of these techniques. The International Commission on Radiation Units and Measurements concepts of gross tumor volume, clinical target volume, internal target volume, and planning target volume are used for defining the targeted volumes. Newer treatment techniques, including intensity modulated radiation therapy, breath-hold, image guided radiation therapy, and 4-dimensional imaging, should be implemented when their use is expected to decrease significantly the risk for normal tissue damage while still achieving the primary goal of local tumor control. The highly conformal involved node radiation therapy (INRT), recently introduced for patients for whom optimal imaging is available, is explained. A new concept, involved site radiation therapy (ISRT), is introduced as the standard conformal therapy for the scenario, commonly encountered, wherein optimal imaging is not available. There is increasing evidence that RT doses used in the past are higher than necessary for disease control in this era of combined modality therapy. The use of INRT and of lower doses in early-stage HL is supported by available data. Although the use of ISRT has not yet been validated in a formal study, it is more conservative than INRT, accounting for suboptimal information and appropriately designed for safe local disease control. The goal of modern smaller field radiation therapy is to reduce both treatment volume and treatment dose while maintaining efficacy and minimizing acute and late sequelae. This review is a consensus of the International Lymphoma Radiation Oncology Group (ILROG) Steering Committee regarding the modern approach to RT in the treatment of HL, outlining a new concept of ISRT in which reduced treatment volumes are planned for the effective control of involved sites of HL. Nodal and extranodal non-Hodgkin lymphomas (NHL) are covered separately by ILROG guidelines.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

A Data-Driven Multi-Period Importance Sampling Strategy for Stochastic Economic Dispatch

Power systems with high penetrations of renewable energy (e.g., wind power) require sophisticated approaches to optimize system performance due to uncertainty in short-term system generation capacity. In this paper, we combine a data-driven analog scenario selection method with importance sampling to create a novel scenario construction approach for two-stage stochastic economic dispatch problems with a large number of wind farms on a network. The proposed method produces scenarios with realistic physics by finding high-fidelity analogs that can describe future states of the system. We show how to extend this method to multi-period operations and demonstrate the effectiveness of this technique by simulating economic dispatch operations on a synthetic test system over the course of a week.

data-driven forecasting↗
DOE OSTI2017

Incorporating Resilience into Transportation Planning

To aid decision making for developing transportation infrastructure, the National Renewable Energy Laboratory has developed the Scenario Evaluation, Regionalization and Analysis (SERA) model. The SERA model is a geospatially and temporally oriented model that has been applied to determine optimal production and delivery scenarios for hydrogen, given resource availability and technology cost and performance, for use in fuel cell vehicles. In addition, the SERA model has been applied to plug-in electric vehicles.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Market optimization and technoeconomic analysis of hydrogen-electricity coproduction systems

Decarbonization efforts across North America, Europe, and beyond rely on variable renewable energy sources such as wind and solar, as well as alternative fuels, such as hydrogen, to support the sustainable energy transition. These advancements have prompted a need for more flexibility in the electric grid to complement non-dispatchable energy sources and increased demand from electrification. Integrated energy systems are well suited to provide this flexibility, but conventional technoeconomic modeling paradigms neglect the time-varying dynamic nature of the grid and thus undervalue resource flexibility. In this work, we develop a computational optimization framework for dynamic market-based technoeconomic comparison of integrated energy systems that coproduce low-carbon electricity and hydrogen (e.g., solid oxide fuel cells, solid oxide electrolysis) against technologies that only produce electricity (e.g., natural gas combined cycle with carbon capture) or only produce hydrogen. Our framework starts with rigorous physics-based process models, built in the open-source Institute for the Design of Advanced Energy Systems (IDAES) modeling and optimization platform, for six energy process concepts. Using these rigorous models and a workflow to optimally design each technology, the framework is shown to be capable of evaluating new and emerging technologies in varying energy markets under a plethora of future scenarios (i.e., renewables penetration, carbon tax, etc.). Ultimately, our framework finds that solid oxide fuel cell-based coproduction systems achieve positive profits for 85% of the analyzed market scenarios. From these market optimization results, we use multivariate linear regression (R 2 values up to 0.99) to determine which electricity price statistics are most significant to predict the optimized annual profit of each system. The proposed framework provides a powerful tool for directly comparing flexible, multi-product energy process concepts to help discern optimal technology and integration options.

08 HYDROGEN↗
DOE OSTI2024

Assessing the design of integrated methane sensing networks

Abstract While methane is the second largest contributor to global warming after carbon dioxide, it has a larger warming effect over a much shorter lifetime. Despite accelerated technological efforts to radically reduce global carbon dioxide emissions, rapid reductions in methane emissions are needed to limit near-term warming. Being primarily emitted as a byproduct from agricultural activities and energy extraction, methane is currently monitored via bottom–up (i.e. activity level) or top–down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected and emission rates are challenging to characterize with current monitoring frameworks. In this paper, we study the design of a layered monitoring approach that combines bottom–up and top–down approaches as an integrated sensing network. By recognizing that varying meteorological conditions and emission rates impact the efficacy of bottom–up monitoring, we develop a probabilistic approach to optimal sensor placement in its bottom–up network. Subsequently, we derive an inverse Bayesian framework to quantify the improvement that a design-optimized integrated framework has on emission-rate quantifications and their uncertainties. We find that under realistic meteorological conditions, the overall error in estimating the true emission rates is approximately 1.3 times higher, with their uncertainties being approximately 2.4 times higher, when using a randomized network over an optimized network, highlighting the importance of optimizing the design of integrated methane sensing networks. Further, we find that optimized networks can improve scenario coverage fractions by more than a factor of 2 over experimentally-studied networks, and identify a budget threshold beyond which the rate of optimized-network coverage improvement exhibits diminishing returns, suggesting that strategic sensor placement is also crucial for maximizing network efficiency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty

Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO2 emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly infrastructure modifications, inefficient pipeline routing, and economic shortfalls. To address this challenge, we propose a novel optimization workflow that is based on mixed-integer linear programming and explicitly integrates probabilistic modeling of storage uncertainty into CCS infrastructure design. This workflow generates multiple infrastructure scenarios by sampling storage capacity distributions, optimally solving each scenario using a mixed-integer linear programming model, and aggregating results into a heatmap to identify core infrastructure components that have a low likelihood of underperforming. A risk index parameter is introduced to balance trade-offs between cost, CO2 processing capacity, and risk of underperformance, allowing stakeholders to quantify and mitigate uncertainty in CCS planning. Applying this workflow to a CCS dataset from the US Department of Energy’s Carbon Utilization and Storage Partnership project reveals key insights into infrastructure resilience. Reducing the risk index from 15% to 0% is observed to lead to an 83.7% reduction in CO2 processing capacity and a 77.1% decrease in project profit, quantifying the trade-off between risk tolerance and project performance. Furthermore, our results highlight critical breakpoints, where small adjustments in the risk index produce disproportionate shifts in infrastructure performance, providing actionable guidance for decision-makers. Unlike prior approaches that aimed to cheaply repair underperforming infrastructure, our workflow constructs robust CCS networks from the ground up, ensuring cost-effective infrastructure under storage uncertainty. These findings demonstrate the practical relevance of incorporating uncertainty-aware optimization into CCS planning, equipping decision-makers with a tool to make informed project planning decisions.

Olson, Daniel↗