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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Cosmic microwave background and inflation in multi-fractional spacetimes

We use FIRAS and Planck 2015 data to place observational bounds on inflationary scenarios in multi-fractional spacetimes with q-derivatives. While a power-law expansion in the geometric time coordinate is subject to the usual constraints from the tensor-to-scalar ratio, model-independent best fits of the black-body and scalar spectra yield upper limits on the free parameters of the multi-fractal measure of the theory. When the measure describing the fractal spacetime geometry is non-oscillating, information on the CMB black-body spectrum places constraints on the theory independent from but weaker than those obtained from the Standard Model, astrophysical gravitational waves and gamma-ray bursts (GRBs). When log oscillations are included and the measure describes a discrete fractal spacetime at microscopic scales, we obtain the first observational constraints on the amplitudes of such oscillations and find, in general, strong constraints on the multi-scale geometry and on the dimension of space. These results complete the scan and reduction of the parameter space of the theory. Black-body bounds are obtained also for the theory with weighted derivatives.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1984

Administrative automation in a scientific environment

Although the scientific personnel at GSFC were advanced in the development and use of hardware and software for scientific applications, resistance to the use of automation or purchase of terminals, software and services, specifically for administrative functions was widespread. The approach used to address problems and constraints and plans for administrative automation within the Space and Earth Sciences Directorate are delineated. Accomplishments thus far include reduction of paperwork and manual efforts; improved communications through telemail and committees; additional support staff; increased awareness at all levels on ergonomic concerns and the need for training; better equipment; improved ADP skills through experience; management commitment; and an overall strategy for automating.

Jarrett, J. R.↗
NASA NTRS1992

Life support systems for Mars transit

The structural elements of life-support systems are reviewed in order to assess the suitability of specific features for use during a Mars mission. Life-support requirements are estimated by means of an approximate input/output analysis, and the advantages are listed relating to the use of recycling and regeneration techniques. The technological options for regeneration are presented in categories such as CO2 reduction, organics removal, polishing, food production, and organics oxidation. These data form the basis of proposed mission requirements and constraints as well as the definition of what constitutes an adequate reserve. Regenerative physical/chemical life-support systems are championed based exclusively on the mass savings inherent in the technology. The resiliency and 'soft' failure modes of bioregenerative life-support systems are identified as areas of investigation.

Macelroy, R. D.↗
NASA NTRS1998

A modernized approach to meet diversified earth observing system (EOS) AM-1 mission requirements

From a flight dynamics perspective, the EOS AM-1 mission design and maneuver operations present a number of interesting challenges. The mission design itself is relatively complex for a low Earth mission, requiring a frozen, Sun-synchronous, polar orbit with a repeating ground track. Beyond the need to design an orbit that meets these requirements, the recent focus on low-cost, 'lights out' operations has encouraged a shift to more automated ground support. Flight dynamics activities previously performed in special facilities created solely for that purpose and staffed by personnel with years of design experience are now being shifted to the mission operations centers (MOCs) staffed by flight operations team (FOT) operators. These operators' responsibilities include flight dynamics as a small subset of their work; therefore, FOT personnel often do not have the experience to make critical maneuver design decisions. Thus, streamlining the analysis and planning work required for such a complicated orbit design and preparing FOT personnel to take on the routine operation of such a spacecraft both necessitated increasing the automation level of the flight dynamics functionality. The FreeFlyer(TM) software developed by AI Solutions provides a means to achieve both of these goals. The graphic interface enables users to interactively perform analyses that previously required many parametric studies and much data reduction to achieve the same result In addition, the fuzzy logic engine enables the simultaneous evaluation of multiple conflicting constraints, removing the analyst from the loop and allowing the FOT to perform more of the operations without much background in orbit design. Modernized techniques were implemented for EOS AM-1 flight dynamics support in several areas, including launch window determination, orbit maintenance maneuver control strategies, and maneuver design and calibration automation. The benefits of implementing these techniques include increased fuel available for on-orbit maneuvering, a simplified orbit maintenance process to minimize science data downtime, and an automated routine maneuver planning process. This paper provides an examination of the modernized techniques implemented for EOS AM-1 to achieve these benefits.

Newman, Lauri Kraft↗
NASA NTRS1998

A Modernized Approach to Meet Diversified Earth Observing System (EOS) AM-1 Mission Requirements

From a flight dynamics perspective, the EOS AM-1 mission design and maneuver operations present a number of interesting challenges. The mission design itself is relatively complex for a low Earth mission, requiring a frozen, Sun-synchronous, polar orbit with a repeating ground track. Beyond the need to design an orbit that meets these requirements, the recent focus on low-cost, "lights out" operations has encouraged a shift to more automated ground support. Flight dynamics activities previously performed in special facilities created solely for that purpose and staffed by personnel with years of design experience are now being shifted to the mission operations centers (MOCs) staffed by flight operations team (FOT) operators. These operators' responsibilities include flight dynamics as a small subset of their work; therefore, FOT personnel often do not have the experience to make critical maneuver design decisions. Thus, streamlining the analysis and planning work required for such a complicated orbit design and preparing FOT personnel to take on the routine operation of such a spacecraft both necessitated increasing the automation level of the flight dynamics functionality. The FreeFlyer(trademark) software developed by AI Solutions provides a means to achieve both of these goals. The graphic interface enables users to interactively perform analyses that previously required many parametric studies and much data reduction to achieve the same result. In addition, the fuzzy logic engine .enables the simultaneous evaluation of multiple conflicting constraints, removing the analyst from the loop and allowing the FOT to perform more of the operations without much background in orbit design. Modernized techniques were implemented for EOS AM-1 flight dynamics support in several areas, including launch window determination, orbit maintenance maneuver control strategies, and maneuver design and calibration automation. The benefits of implementing these techniques include increased fuel available for on-orbit maneuvering, a simplified orbit maintenance process to minimize science data downtime, and an automated routine maneuver planning process. This paper provides an examination of the modernized techniques implemented for EOS AM-1 to achieve these benefits.

Newman, Lauri Kraft↗
NASA NTRSDate not supplied

Analysis and Optimization of Test Plans for Advanced Exploration Systems Reliability and Supportability

Future crewed exploration missions beyond Low Earth Orbit (LEO) will operate farther from Earth and be logistically isolated for longer than any previous human spaceflight mission. Under these conditions, supportability and reliability willbestronger drivers of mission mass and risk than they have been in the past. Items with high failure rates, or uncertain failure rates, can result in high spares mass requirements and/or high risk on deep space missions. Testing is a critical element of system development which provides the opportunity to identify and resolve design issues, defects, or other failure modes before they cause problems during a mission. Reliability growth programs can reduce failure rates by identifying and remove failure modes via design changes, and long-duration life testing can provide valuable data to reduce failure rate estimate uncertainty and verify (to some level of confidence) that components are as reliable as expected. Testing activities take time and resources, however, and must be incorporated into program plans in order to be fully effective. This paper presents an integrated reliability test plan analysis and optimization methodology, which has been used to inform Advanced Exploration Systems (AES) Life Support Systems (LSS) ground test planning for future missions. The methodology determines the optimal number of test units to purchase and allocation of test time –split between reliability growth and uncertainty reduction testing –across a given set of items in order to minimize spares mass for a given mission under constraints on total test cost and schedule. Model outputs also include expected spares mass after testing and the expected number of modifications or refurbishments during testing, both of which can inform program planning. Discussion of the model, conclusions, and future work are also presented.

Testing↗
NASA NTRSDate not supplied

Analysis and Optimization of Test Plans for Advanced Exploration Systems Reliability and Supportability

Future crewed exploration missions beyond Low Earth Orbit (LEO) will operate farther from Earth and be logistically isolated for longer than any previous human spaceflight mission. Under these conditions, supportability and reliability willbestronger drivers of mission mass and risk than they have been in the past. Items with high failure rates, or uncertain failure rates, can result in high spares mass requirements and/or high risk on deep space missions. Testing is a critical element of system development which provides the opportunity to identify and resolve design issues, defects, or other failure modes before they cause problems during a mission. Reliability growth programs can reduce failure rates by identifying and remove failure modes via design changes, and long-duration life testing can provide valuable data to reduce failure rate estimate uncertainty and verify (to some level of confidence) that components are as reliable as expected. Testing activities take time and resources, however, and must be incorporated into program plans in order to be fully effective. This paper presents an integrated reliability test plan analysis and optimization methodology, which has been used to inform Advanced Exploration Systems (AES) Life Support Systems (LSS) ground test planning for future missions. The methodology determines the optimal number of test units to purchase and allocation of test time –split between reliability growth and uncertainty reduction testing –across a given set of items in order to minimize spares mass for a given mission under constraints on total test cost and schedule. Model outputs also include expected spares mass after testing and the expected number of modifications or refurbishments during testing, both of which can inform program planning. Discussion of the model, conclusions, and future work are also presented.

Testing↗
NASA NTRSDate not supplied

CHP-PRA: Sensorimotor Countermeasures Proof of Concept

The capabilities included in Crew Health and Performance (CHP) systems are designed to keep the crew healthy, happy, and productive. In doing so, the CHP system allows reductions in human related risks to be realized. All human missions, though particularly future Artemis and Mars missions, have constraints on the mass and volume allocated to the CHP system. Thus, trades must be made on how to best buy down risk while still meeting other requirements. Understanding how different CHP capabilities influence medical, performance, and long-term health risks is key to making informed choices among these trades. To address this, an integrated CHP Probabilistic Risk Assessment (PRA) model is being developed. Much like how IMPACT is designed to allow medical resource trades informed by medical risks, CHP PRA will enable analogous trades in human system risks across all CHP functions and capabilities. There are 29 Human System Risk Board (HSRB) risk areas to consider for model implementation. This effort focused solely on the implementation of countermeasures associated with sensorimotor risk. The framework and lessons learned from this proof of concept will benefit the implementation of other risks and countermeasures. Astronauts experience sensorimotor changes when entering or exiting a microgravity environment. While most sensorimotor issues are resolved within a few days, they pose a serious risk to crew health and performance during the adaptation period. This adaptation period aligns with gravity transitions and key mission phases where the crew members may be required to complete challenging tasks ideally with an undisturbed sensorimotor system. Countermeasures such as training or pharmaceuticals can be used to mitigate risk. This effort began by identifying physiological changes to the sensorimotor system that could lead to functional performance decrements or medical conditions and potential countermeasures, so that the overall effect of sensorimotor disturbance on human risk could be captured. We then selected example countermeasures, mapped relationships between countermeasures and outcomes, and used results from existing sensorimotor investigations to define the relationship between a sensorimotor countermeasure and subsequent performance of a sensorimotor-affected task. Specifically, using shuttle landing performance data, we compared the use or absence of inflight training capabilities to yield a relative performance change that can be applied to the prediction of the risk associated with manual control performance in future missions, as depicted in the figure. This sensorimotor proof of concept demonstrates how HRP research information can be transformed into quantifiable relationships to describe changes in HSRB medical and performance risks.

Caroline R Austin↗
DOE OSTI2019

Modeling anaerobic soil organic carbon decomposition in Arctic polygon tundra: insights into soil geochemical influences on carbon mineralization

Abstract. Rapid warming of Arctic ecosystems exposes soil organic matter (SOM) to accelerated microbial decomposition, potentially leading to increased emissions of carbon dioxide ( CO 2 ) and methane ( CH 4 ) that have a positive feedback on global warming. Current estimates of the magnitude and form of carbon emissions from Earth system models include significant uncertainties, partially due to the oversimplified representation of geochemical constraints on microbial decomposition. Here, we coupled modeling principles developed in different disciplines, including a thermodynamically based microbial growth model for methanogenesis and iron reduction, a pool-based model to represent upstream carbon transformations, and a humic ion-binding model for dynamic pH simulation to build a more versatile carbon decomposition model framework that can be applied to soils under varying redox conditions. This new model framework was parameterized and validated using synthesized anaerobic incubation data from permafrost-affected soils along a gradient of fine-scale thermal and hydrological variabilities across Arctic polygonal tundra. The model accurately simulated anaerobic CO 2 production and its temperature sensitivity using data on labile carbon pools and fermentation rates as model constraints. CH 4 production is strongly influenced by water content, pH, methanogen biomass, and presence of competing electron acceptors, resulting in high variability in its temperature sensitivity. This work provides new insights into the interactions of SOM pools, temperature increase, soil geochemical feedbacks, and resulting CO 2 and CH 4 production. The proposed anaerobic carbon decomposition framework presented here builds a mechanistic link between soil geochemistry and carbon mineralization, making it applicable over a wide range of soils under different environmental settings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-E-T-417: The Impact of Normal Tissue Constraints On PTV Dose Homogeneity for Intensity Modulated Radiotherapy (IMRT), Volume Modulated Arc Therapy (VMAT) and Tomotherapy

Purpose: Complex intensity modulated arc therapy tends to spread low dose to normal tissue(NT)regions to obtain improved target conformity and homogeneity and OAR sparing.This work evaluates the trade-offs between PTV homogeneity and reduction of the maximum dose(Dmax)spread to NT while planning of IMRT,VMAT and Tomotherapy. Methods: Ten prostate patients,previously planned with step-and-shoot IMRT,were selected.To fairly evaluate how PTV homogeneity was affected by NT Dmax constraints,original IMRT DVH objectives for PTV and OARs(femoral heads,and rectal and bladder wall)applied to 2 VMAT plans in Pinnacle(V9.0), and Tomotherapy(V4.2).The only constraint difference was the NT which was defined as body contours excluding targets,OARs and dose rings.NT Dmax constraint for 1st VMAT was set to the prescription dose(Dp).For 2nd VMAT(VMAT-NT)and Tomotherapy,it was set to the Dmax achieved in IMRT(~70-80% of Dp).All NT constraints were set to the lowest priority.Three common homogeneity indices(HI),RTOG-HI=Dmax/Dp,moderated-HI=D95%/D5% and complex-HI=(D2%-D98%)/Dp*100 were calculated. Results: All modalities with similar dosimetric endpoints for PTV and OARs.The complex-HI shows the most variability of indices,with average values of 5.9,4.9,9.3 and 6.1 for IMRT,VMAT,VMAT-NT and Tomotherapy,respectively.VMAT provided the best PTV homogeneity without compromising any OAR/NT sparing.Both VMAT-NT and Tomotherapy,planned with more restrictive NT constraints,showed reduced homogeneity,with VMAT-NT showing the worst homogeneity(P<0.0001)for all HI.Tomotherapy gave the lowest NT Dmax,with slightly decreased homogeneity compared to VMAT. Finally, there was no significant difference in NT Dmax or Dmean between VMAT and VMAT-NT. Conclusion: PTV HI is highly dependent on permitted NT constraints. Results demonstrated that VMAT-NT with more restrictive NT constraints does not reduce Dmax NT,but significantly receives higher Dmax and worse target homogeneity.Therefore, it is critical that planners do not use too restrictive NT constraints during VMAT optimization.Tomotherapy plan was not as sensitive to NT constraints,however,care shall be taken to ensure NT is not pushed too hard.These results are relevant for clinical practice.The biological effect of higher Dmax and increased target heterogeneity needs further study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Hydropower Operations in the Colorado River Basin: Institutional Analysis of Opportunities and Constraints

The Colorado River Basin is facing an unprecedented drought. In ongoing drought management efforts, limited attention has been paid to hydropower generation. While some studies do exist on hydropower, they are quantitative in nature and focus on calculating the reduction in megawatts generated at dams in the Basin with declining water availability. These studies simplify the complex process of hydropower generation; water availability is but one factor that impacts hydropower generation. At a more fundamental level, formal institutional arrangements, that is, laws, policies, and rules create the framework within which dams are operated and hydropower is generated. This paper conducts a comparative institutional analysis of water, environment, and energy laws and policies and changes therein to understand the constraints and opportunities faced by hydropower generation in the Colorado River Basin. To tease out the nuances in how institutional arrangements affect dam operations and hydropower generation, the comparative analysis focuses on the two largest and strategically important dams in the Basin: Hoover and Glen Canyon. This paper uses Elinor Ostrom’s Institutional Analysis and Development Framework to analyze laws and policies at three levels: constitutional-choice, collective-choice, and operational levels. Constitutional-choice level laws and policies apply to the entire Basin, whereas collective-choice level and operational level laws and policies are dam specific. Hoover and Glen Canyon Dams face similar biophysical challenges by the virtue of their location in the same river basin. Yet, despite the similarity in the biophysical setting, the analysis in this study finds that the differences in the applicability of constitutional-choice level laws along with the differences in dam specific collective-choice and operational level institutional arrangements produce a distinct set of constraints for hydropower generation at Hoover and Glen Canyon Dams. Even without a drought, water and environmental laws at both the constitutionalchoice and collective-choice levels as well as power contracts constrain hydropower generation and limit the flexibility with which Glen Canyon Dam can be operated. Water and environmental laws also impose specific water release requirements that, at times, require off-peak power generation at Glen Canyon Dam. On the other hand, even with a drought, Hoover Dam faces limited hydropower generation constraints and can operate flexibly. This is because constitutional-choice level laws and dam-specific collective-choice and operational level laws pose limited constraints for flexible daily operations at Hoover. The result is that Hoover Dam can generate hydropower at the same level as it did three decades ago and operate flexibly to provide ancillary services and peaking generation. While water and environmental laws and policies pose constraints for hydropower generation, the analysis in this study further finds that specific historic provisions within energyrelated institutional arrangements and recent changes within power contracts have maintained and even enhanced the value of hydropower to power customers. Historic institutional provisions ensure that hydropower is sold ‘at cost’ making this resource economically competitive with wholesale electricity market rates. Recent power contract modifications have resulted in the amendment of an older resale prohibition clause to expand the flexibility available to power customers in using their capacity and/or energy allocation in RTOs, ISOs, and bulk power markets. This amendment has opened up an opportunity for customers, especially Hoover power customers, to use flexible generation and ancillary services in a market environment. In addition, the extension of power contract duration to the legally maximum term has enhanced the reliability and stability of this resource for customers. In the Colorado River Basin, despite the enduring economic responsibility of power customers—where laws require customers to pay for a large portion of construction and O&M costs whether or not they actually receive hydropower— the persistent threat of a drought-induced water shortage, and constraints imposed by water and environmental laws and policies, power customers continue to invest in this resource as energy-related institutional arrangements and power contract provisions protect the reasons why they value hydropower. Lastly, the analysis in this study finds that the consequences of changes in hydropower generation for energy users, irrigators, and environmental programs in the Basin depend on how specific institutional arrangements tie electricity revenues to irrigation aid and environmental programs, and how the power contracts themselves are set up. Collective-choice level institutional arrangements create a higher level of financial dependency of irrigation aid and environmental programs on electricity revenues in the Upper Basin—the legal subdivision of Colorado River where Glen Canyon Dam is located—compared to the Lower Basin—the legal subdivision of Colorado River where Hoover Dam is located. Therefore, changes in hydropower generation or the way its revenue is collected and used will have far reacting detrimental consequences for the Upper Basin. Likewise, differences in the nature of power contracts for Glen Canyon and Hoover Dams also creates differences in the financial impact incurred by energy users when there is a reduction in hydropower generation. While this study identifies the types of impacts on resource users as a result of specific institutional arrangements, the calculation of extent of impact warrants further attention. Hydropower in the United States is in a unique position today. The strategic importance of this resource for the nation’s electricity sector is rapidly growing even as its contribution to overall electricity generation remains fairly small. This strategic importance, however, is built hydropower’s ability to operate flexibly in order to support the integration of intermittent renewable generating sources and the expansion of electricity markets. As this study shows, such flexibility may not be available at certain plants not due to the lack of water availability but because of institutional constraints. Institutional arrangements may also require dam operators to first consider high priority water uses (such as irrigation or environmental needs), which in turn may limit the ability to generate hydropower when it is most valuable or useful. Engineering and quantitative models, such as production cost models, recognize policy constraints for hydropower operations but often inadequately capture or assume away such constraints in the models. A failure to account for policy constraints in these models runs the risk of inaccurate representation of the operational flexibility and capacity available at specific hydropower plants, which can result in over/underestimation of hydropower’s ability to support the integration of variable renewable resources and address grid reliability concerns. Against this background, this paper and the analysis herein serves as an example of how we can systematically identify institutional constraints (and opportunities) that influence the flexibility in not only generating electricity at specific dams but also using this hydropower once it is generated.

13 HYDRO ENERGY↗
DOE OSTI2026

Hydrogen applications in airport operations: a review using the Port Authority of New York and New Jersey as an illustrative airport system

Airports combine aircraft propulsion, ground operations, stationary power systems, and fuel logistics in ways that make emissions reduction technically and operationally complex. Existing studies often assess hydrogen applications in these areas separately, limiting understanding of the shared infrastructure, safety, and operational constraints that shape airport deployment. This review evaluates hydrogen across three airport-relevant operational domains: aviation propulsion, ground support equipment and vehicles, and stationary power systems. Within aviation propulsion, the review examines sustainable aviation fuel production and hydrogen-powered aircraft as two distinct hydrogen-relevant pathways. The Port Authority of New York and New Jersey is used as an illustrative airport system to relate the literature to a real operating context. Drawing on peer-reviewed studies, technical reports, demonstration projects, and public operational information, the review also includes screening-level calculations of hydrogen demand and potential CO 2 e reductions for selected applications. The findings show that hydrogen's role is highly application-specific. Near-term opportunities are strongest where hydrogen serves as a low-carbon process input, supports selected high-utilization ground equipment, or contributes to resilient stationary power-system configurations. Hydrogen-powered aircraft remain a longer-term option because storage, fueling infrastructure, certification, cost, and NO x management continue to constrain deployment. Across all domains, infrastructure readiness, fuel logistics, safety requirements, and leakage management emerge as recurring determinants of viability. Future research should focus on cross-domain infrastructure planning, comparative assessment of hydrogen against alternative pathways, improved treatment of leakage and non-CO 2 effects, and clearer safety and regulatory frameworks for airport deployment.

08 HYDROGEN↗
NASA NTRSDate not supplied

Low-Noise Propeller Design with the Vortex Lattice Method and Gradient-Based Optimization

This work aims to combine an aerodynamic model based on the unsteady vortex lattice method with an acoustic model provided by Farassat’s formulation 1A to perform gradient-based propeller optimizations with aerodynamic and acoustic constraints. Two optimization cases are attempted and successfully converged: an isolated propeller and a propeller-wing configuration, both operating in a 0.11 Mach freestream, representative of a cruise condition. The results show that simultaneously decreasing the RPM and increasing the collective angle of a propeller is an effective noise reduction technique, as expected, and little difference is observed between the two cases. A vortex dissipation model was found to be needed to stabilize the derivatives of the vortex lattice outputs, and a constraint on the propeller blade’s chord distribution concavity was needed to produce realistic-looking blade chord distributions.

Propellers↗
DOE OSTI2014

A Multi-Objective, Hub-and-Spoke Supply Chain Design Model For Densified Biomass

In this paper we propose a model to design the supply chain for densified biomass. Rail is typically used for long-haul, high-volume shipment of densified biomass. This is the reason why a hub-and-spoke network structure is used to model this supply chain. The model is formulated as a multi-objective, mixed-integer programing problem under economic, environmental, and social criteria. The goal is to identify the feasibility of meeting the Renewable Fuel Standard (RFS) by using biomass for production of cellulosic ethanol. The focus in not just on the costs associated with meeting these standards, but also exploring the social and environmental benefits that biomass production and processing offers by creating new jobs and reducing greenhouse gas (GHG) emissions. We develop an augmented ?-constraint method to find the exact Pareto solution to this optimization problem. We develop a case study using data from the Mid-West. The model identifies the number, capacity and location of biorefineries needed to make use of the biomass available in the region. The model estimates the delivery cost of cellulosic ethanol under different scenario, the number new jobs created and the GHG emission reductions in the supply chain.

09 BIOMASS FUELS↗
DOE OSTI2024

Recovered supernova Ia rate from simulated LSST images

Aims.TheVera C. RubinObservatory’s Legacy Survey of Space and Time (LSST) will revolutionize time-domain astronomy by detecting millions of different transients. In particular, it is expected to increase the number of known type Ia supernovae (SN Ia) by a factor of 100 compared to existing samples up to redshift ∼1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis of the properties and rates of these objects. However, the impact of all other sources of uncertainty on the measurement of the SN Ia rate must still be evaluated. The comprehension and reduction of such uncertainties will be fundamental both for cosmology and stellar evolution studies, as measuring the SN Ia rate can put constraints on the evolutionary scenarios of different SN Ia progenitors. Methods.We used simulated data from the Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) and LSST Data Preview 0 to measure the SN Ia rate on a 15 deg 2 region of the “wide-fast-deep” area. We selected a sample of SN candidates detected in difference images, associated them to the host galaxy with a specially developed algorithm, and retrieved their photometric redshifts. We then tested different light-curve classification methods, with and without redshift priors (albeit ignoring contamination from other transients, as DC2 contains only SN Ia). We discuss how the distribution in redshift measured for the SN candidates changes according to the selected host galaxy and redshift estimate. Results.We measured the SN Ia rate, analyzing the impact of uncertainties due to photometric redshift, host-galaxy association and classification on the distribution in redshift of the starting sample. We find that we are missing 17% of the SN Ia, on average, with respect to the simulated sample. As 10% of the mismatch is due to the uncertainty on the photometric redshift alone (which also affects classification when used as a prior), we conclude that this parameter is the major source of uncertainty. We discuss possible reduction of the errors in the measurement of the SN Ia rate, including synergies with other surveys, which may help us to use the rate to discriminate different progenitor models.

Astronomy & Astrophysics↗
DOE OSTI2025

Advancing Grid Resilience through Smart Charge Management: Findings from Maryland’s Pilot

This report presents research findings from a four-year Smart Charge Management (SCM) pilot program conducted by Maryland’s largest electric utilities—Baltimore Gas and Electric (BGE), Potomac Electric Power Company (Pepco), and Delmarva Power & Light (DPL)—to evaluate strategies for optimizing electric vehicle (EV) charging loads and enhancing grid stability. Supported by the U.S. Department of Energy (DOE), Argonne National Laboratory collaborated with all project partners and examined the effectiveness of Time-of-Use (TOU) and Load Balancing (LB) strategies in managing peak demand, deferring costly infrastructure upgrades, and reducing grid constraints at the feeder level. Using charging data from over 4,600 EV drivers, the study analyzed SCM’s impact on the distribution systems of BGE and Pepco, which consists of over 2000 feeders. Unlike prior research that focused on system-wide trends or synthetic feeders, this analysis offers granular, feeder-level insights based on real-world operational data. It highlights how transformer density, load profiles, and infrastructure constraints influence smart charging performance. Results show feeder-level conditions play a crucial role in SCM effectiveness, with most feeders benefiting more from LB, while TOU-based SCM may be sufficient for others. By 2035, LB reduced peak charging loads by 27% on average, compared to 23% under TOU-based SCM, though some feeders saw reductions exceeding 35%, while others experienced minimal impact. Feeders with higher transformer utilization and limited capacity benefited more from LB, which more effectively distributed charging demand during off-peak hours. Beyond reducing grid constraints, SCM offers long-term operational and financial benefits. By shifting EV charging demand strategically, utilities can optimize asset utilization, delay infrastructure investments, and enhance grid performance. In terms of infrastructure upgrade deferrals, at the feeder level, LB consistently reduced peak charging loads and resulting infrastructure upgrade costs, particularly in high EV enrollment areas, decreasing the number of overloaded transformers by up to 35%, while TOU-based SCM achieved 20-30% reductions depending on feeder characteristics. At the system level, LB has the potential to defer total upgrade costs by $\$$186 million for BGE, compared to $\$$159 million under TOU-based SCM. For Pepco, TOU-based SCM performed slightly better, deferring upgrade costs by $\$$30 million, compared to $\$$29 million under LB. Section 4.5 reviews some of the system differences between BGE and Pepco. However, as EV adoption scales, TOU-based SCM will introduce secondary peak charging loads, reinforcing the need for more advanced, adaptive SCM approaches to prevent new grid challenges. As EV adoption continues to grow, feeder-level managed charging strategies will be essential for mitigating grid stress, improving infrastructure efficiency, and maintaining energy affordability for consumers. This report provides critical insights for utilities, Public Utility Commissions (PUCs), and state agencies on the role of feeder-specific smart charging in infrastructure planning, policy development, and grid modernization. The findings underscore the importance of tailored, data-driven SCM solutions that align with local grid conditions, ensuring a resilient, cost-effective transition to increasing EV adoption while safeguarding distribution system performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

SU-F-BRF-11: Dose Rearrangement in High Dose Locally Advanced Lung Patients Based On Perfusion Imaging

Purpose: The use of mean lung dose (MLD) limits allows individualization of lung patient tumor doses at safe levels. However, MLD does not account for local lung function differences between patients, leading to toxicity variability at the same MLD. We investigated dose rearrangement to minimize dose to functional lung, as measured by perfusion SPECT, while maintaining target coverage and conventional MLD limits. Methods: Retrospective plans were optimized for 15 locally advanced NSCLC patients enrolled in a prospective imaging trial. A priority-based optimization system was used. The baseline priorities were (1) meet OAR dose constraints, (2) maximize target gEUD, and (3) minimize physical MLD. As a final step, normal tissue doses were minimized. To determine the benefit of rearranging dose using perfusion SPECT, plans were reoptimized to minimize functional lung gEUD as the 4th priority. Results: When only minimizing physical MLD, the functional lung gEUD was 10.8+/−5.0 Gy (4.3–19.8 Gy). Only 3/15 cases showed a decrease in functional lung gEUD of ≥4% when rearranging dose to minimize functional gEUD in the cost function (10.5+/−5.0 Gy range 4.3−19.7). Although OAR constraints were respected, the dose rearrangement resulted in ≥10% increases in gEUD to an OAR in 4/15 cases. Only slight reductions in functional lung gEUD were noted when omitting the minimization of physical MLD, suggesting that constraining the target gEUD minimizes the potential to redistribute dose. Conclusion: Prioritydriven optimization permits the generation of plans that respect traditional OAR limits and target coverage, but with the ability to rearrange dose based on functional imaging. The latter appears to be limited due to the decreased solution space when constraining target coverage. Since dose rearrangement may increase dose to other OARs, it is also worthwhile to investigate global biomarkers of lung toxicity to further individualize treatment in this population. Partially supported by R01-CA142840 and P01-CA59827.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Mixed Integer Nonlinear Programming Approaches to Enhance Resiliency and Response Strategies in Critical Infrastructure

Motivation. Critical infrastructures are large, complex engineered systems that must be operated robustly under abnormal conditions resulting from natural hazards or intentional acts. For example, electric power systems must be robust to line faults, water utilities must rapidly mitigate contamination incidents, and computing networks must adapt to adversarial intrusions to protect critical information. Problem. It is difficult for decision-makers within resiliency analysis in critical infrastructure to optimize designs and develop effective response strategies that can account for uncertainties. Facing incomplete information and the sheer scope that a natural hazard or attack vector may incorporate, response can be ineffective without reliable, scalable decision support tools. These problems are intrinsically nonlinear and involve discrete decisions, and unfortunately, existing off-the-shelf mathematical programming methods cannot support optimization-based decision-making of these nonlinear at scale. Method/Approach and Results. This project emphasized development of fundamental optimization strategies that supported real- time mitigation and response for critical infrastructures. In particular, the project developed multi- tree approaches based on piecewise outer-approximations for solution of mixed-integer nonlinear programming (MINLP) problems. These techniques alternate between an MILP or MISOCP relaxation to obtain a lower bound and candidate discrete solutions and an NLP subproblem to obtain upper bounds. Using tailored relaxations based on problem structure, these methods were used to solve several key applications in resilience and response of critical infrastructure. This work resulted in two open-source, copyrighted software packages: CORAMIN (https://github.com/Coramin/Coramin) -- an object-oriented mathematical programming framework that supports tailored multi-tree algorithms for solution of large- scale mixed-integer nonlinear programming; and EGRET (Electrical Grid Research and Engineering Toolkit) (https://github.com/grid- parity-exchange/Egret) -- a declarative mathematical programming framework built upon CORAMIN and Pyomo for formulation and solution of resilience and operations problems in power grid systems. Furthermore, these tools resulted in several important published results, including the following: The first known global optimization approach that could solve the unit-commitment problem with nonlinear power flow constraints on medium-sized test problems; Improved parallel optimization-based bounds tightening and strengthening of relaxations of AC power flow constraints; and, Optimization-based approaches for improved grid resilience and use of demand response to improve grid resilience with reduction in capital requirements. Result Implications. This project developed first-of-a-kind algorithms for decision-making in critical infrastructure resilience operations and planning, as well as a next-generation toolkit for MINLP researchers. These approaches leveraged high-performance computing architectures to solve some of the largest, most challenging nonlinear discrete optimization problems to global optimality, and these successes were captured in open-source software to enable optimization-based decision-making, and efficient solution of MINLP formulations for electric power transmission grids.

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