Search NASA⌕ Search

SEARCH · Search NASA

Results for “scenario decomposition”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Scenario Grouping and Decomposition Algorithms for Chance-Constrained Programs

A lower bound for a finite-scenario-based chance-constrained program is the quantile value corresponding to the sorted optimal objective values of scenario subproblems. This quantile bound can be improved by grouping subsets of scenarios at the expense of solving larger subproblems. The quality of the bound depends on how the scenarios are grouped. In this paper, we formulate a mixed-integer bilevel program that optimally groups scenarios to tighten the quantile bounds. For general chance-constrained programs, we propose a branch-and-cut algorithm to optimize the bilevel program, and for chance-constrained linear programs, a mixed-integer linear-programming reformulation is derived. Here, we also propose several heuristics for grouping similar or dissimilar scenarios. Our computational results demonstrate that optimal grouping bounds are much tighter than heuristic bounds, resulting in smaller root-node gaps and better performance of scenario decomposition for solving chance-constrained 0-1 programs. Also, the optimal grouping bounds can be greatly strengthened using larger group size.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Stochastic pre-event preparation for enhancing resilience of distribution systems

Extreme weather events are the common causes for power supply interruptions and power outages in electrical distribution systems. Improving the distribution system and enhancing its resilience is becoming crucial due to the increased frequency of extreme weather events. Preparation and allocation of multiple flexible resources, such as mobile resources, fuel resources, and labor resources before extreme weather events can mitigate the effects of extreme weather events and enhance the resilience of power distribution systems. Here, in this paper, a two-stage stochastic mixed-integer linear programming (SMILP) is proposed to optimize the preparation and resource allocation process for upcoming extreme weather events, which leads to faster and more efficient post-event restoration. The objective of the proposed two-stage SMILP is to maximize the served load and minimize the operating cost of flexible resources. The first stage in the optimization problem selects the amounts and locations of different resources. The second stage considers the operational constraints of the distribution system and repair crew scheduling constraints. The proposed stochastic pre-event preparation model is solved by a scenario decomposition method, Progressive Hedging (PH), to ease the computational complexity introduced by a large number of scenarios. Furthermore, to show the impact of solar photovoltaic (PV) generation on system resilience, three types of PV systems are considered during a power outage and the resilience improvements with different PV penetration levels are compared. Numerical results from simulations on a large-scale (more than 10,000 nodes) distribution feeder have been used to validate the effectiveness and scalability of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1992

Scare Tactics: Evaluating Problem Decompositions Using Failure Scenarios

Our interest is in the design of multi-agent problem-solving systems, which we refer to as composite systems. We have proposed an approach to composite system design by decomposition of problem statements. An automated assistant called Critter provides a library of reusable design transformations which allow a human analyst to search the space of decompositions for a problem. In this paper we describe a method for evaluating and critiquing problem decompositions generated by this search process. The method uses knowledge stored in the form of failure decompositions attached to design transformations. We suggest the benefits of our critiquing method by showing how it could re-derive steps of a published development example. We then identify several open issues for the method.

Helm, B. Robert↗
DOE OSTI2021

Proactive Operations and Investment Planning via Stochastic Optimization to Enhance Power Systems’ Extreme Weather Resilience

We present scalable stochastic optimization approaches for improving power systems’ resilience to extreme weather events. We consider both proactive redispatch and transmission line hardening as alternatives for mitigating expected load shed due to extreme weather, resulting in large-scale stochastic linear programs (LPs) and mixed-integer linear programs (MILPs). We solve these stochastic optimization problems with progressive hedging (PH), a parallel, scenario-based decomposition algorithm. Our computational experiments indicate that our proposed method for enhancing power system resilience can provide high-quality solutions efficiently. With up to 128 scenarios on a 2,000-bus network, the operations (redispatch) and investment (hardening) resilience problems can be solved in approximately 6 min and 2 h of wall-clock time, respectively. Additionally, we solve the investment problems with up to 512 scenarios, demonstrating that the approach scales very well with the number of scenarios. Moreover, the method produces high quality solutions that result in statistically significant reductions in expected load shed. Our proposed approach can be augmented to incorporate a variety of other operational and investment resilience strategies, or a combination of such strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Increasing temperatures reduce invertebrate abundance and slow decomposition

Decomposition is an essential ecosystem service driven by interacting biotic and abiotic factors. Increasing temperatures due to climate change can affect soil moisture, soil fauna, and subsequently, decomposition. Understanding how projected climate change scenarios will affect decomposition is of vital importance for predicting nutrient cycling and ecosystem health. In this study, we experimentally addressed the question of how the early stages of decomposition would vary along a gradient of projected climate change scenarios. Given the importance of biodiversity for ecosystem service provisioning, we measured the effect of invertebrate exclusion on red maple ( Acer rubrum ) leaf litter breakdown along a temperature gradient using litterbags in warming chambers over a period of five weeks. Leaf litter decomposed more slowly in the warmer chambers and in the litterbag treatment that minimized invertebrate access. Moreover, increasing air temperature reduced invertebrate abundance and richness, and altered the community composition, independent of exclusion treatment. Using structural equation models, we were able to disentangle the effects of average air temperature on leaf litter loss, finding a direct negative effect of warming on the early stages of decomposition, independent of invertebrate abundance. This result indicates that not only can climate change affect the invertebrate community, but may also directly influence how the remaining organisms interact with their environment and their effectiveness at provisioning ecosystem services. Overall, our study highlights the role of biodiversity in maintaining ecosystem services and contributes to our understanding of how climate change could disrupt nutrient cycling.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Origins of Magnetite Nanocrystals in Martian Meteorite ALH84001

The Martian meteorite ALH84001 preserves evidence of interaction with aqueous fluids while on Mars in the form of microscopic carbonate disks. These carbonate disks are believed to have precipitated 3.9 Ga ago at beginning of the Noachian epoch on Mars during which both the oldest extant Martian surfaces were formed, and perhaps the earliest global oceans. Intimately associated within and throughout these carbonate disks are nanocrystal magnetites (Fe3O4) with unusual chemical and physical properties, whose origins have become the source of considerable debate. One group of hypotheses argues that these magnetites are the product of partial thermal decomposition of the host carbonate. Alternatively, the origins of mag- netite and carbonate may be unrelated; that is, from the perspective of the carbonate the magnetite is allochthonous. For example, the magnetites might have already been present in the aqueous fluids from which the carbonates were believed to have been deposited. We have sought to resolve between these hypotheses through the detailed characterized of the compo- sitional and structural relationships of the carbonate disks and associated magnetites with the orthopyroxene matrix in which they are embedded. Extensive use of focused ion beam milling techniques has been utilized for sample preparation. We then compared our observations with those from experimental thermal decomposition studies of sideritic carbonates under a range of plausible geological heating scenarios. We conclude that the vast majority of the nanocrystal magnetites present in the car- bonate disks could not have formed by any of the currently proposed thermal decomposition scenarios. Instead, we find there is considerable evidence in support of an alternative allochthonous origin for the magnetite unrelated to any shock or thermal processing of the carbonates.

Thomas-Keprta, Kathie L.↗
DOE OSTI2025

A Fundamentally New Coupled Approach to Contact Mechanics via the Dirichlet‐Neumann Schwarz Alternating Method

Contact phenomena are crucial for understanding the behavior of mechanical systems. However, existing computational approaches for simulating mechanical contact often face numerical challenges, such as inaccurate physical predictions, energy conservation errors, and unwanted oscillations. Here, we introduce an alternative technique for simulating dynamic contact based on the non‐overlapping Schwarz alternating method, originally developed for domain decomposition. In multibody contact scenarios, this method treats each body as a separate, non‐overlapping domain and prevents interpenetration using an alternating Dirichlet–Neumann iterative process. This approach has a strong theoretical foundation, eliminates the need for contact constraints, and offers flexibility, making it ideal for multiscale and multiphysics applications. We conducted a numerical comparison between the Schwarz method and traditional methods, such as the Lagrange multiplier and penalty methods, focusing on a benchmark impact problem. Our results indicate that the Schwarz alternating method outperforms traditional methods in several key areas: it provides more accurate predictions for various measurable quantities and demonstrates exceptional energy conservation capabilities. To address unwanted oscillations in contact velocities and forces, we explored various algorithms and stabilization techniques, ultimately opting for the naïve‐stabilized Newmark scheme for its simplicity and effectiveness. Additionally, we validated the efficiency of the Schwarz method in a three‐dimensional impact problem, highlighting its inherent capacity to accommodate different mesh topologies, time‐integration schemes, and time steps for each interacting body.

Schwarz alternating method↗
DOE OSTI2026

SPAROW: Stochastic Programming and Related Optimization Workflows

SAND2026-16703O SPAROW: Stochastic Programming and Related Optimization Workflows is a Python library tool that facilitates the development and solution of stochastic programming problems. It provides a user-friendly class structure for defining stochastic programs through scenario-based representations of uncertainties. SPAROW incorporates multiple optimization strategies, including integer programming with all scenarios, progressive hedging, Benders decomposition, and Snoglode, a novel technique developed by Carnegie Mellon University. It also features interfaces to external solvers and functions that are commonly used in analysis workflows, making it applicable to a wide range of scientific and engineering design challenges, particularly in power grid planning. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hart, William [Sandia National Lab. (SNL-NM), Albu↗
DOE OSTI2021

MOFLUX Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: Modeling Archive

This Modeling Archive is in support the publication “Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: A Mechanistic Modeling Analysis” (Liang et al., 2021). Here we provide model code, inputs, outputs and evaluation datasets for the Microbial ENzyme Decomposition (MEND) model for the Missouri Ozarks AmeriFlux eddy covariance measurement site (MOFLUX) near Ashland, Missouri USA. The MEND model was developed with explicit representation of microbial and enzyme pools to mechanistically simulate the role of microbial organisms and extracellular enzymes in soil organic carbon (SOC) decomposition. Long-term SOC dynamics under intensified moisture extremes are studied using the MEND model that is parameterized with 11 years of measurements from the MOFLUX forest. The model explicitly represents microbial dormancy and resuscitation, different types of SOC-degrading enzymes, and how they vary with changes in soil moisture (Wang et al. 2015, 2019). A combination of two levels of frequency and severity of soil moisture, as well as a control with normal interannual variability, are used to simulate a range of moisture scenarios over 100 years. The code of Microbial-ENzyme Decomposition (MEND) as well as the input and output data are included in the archive. A user’s manual (MEND_Readme.pdf) is included with instructions for compiling and running the model to simulate soil organic carbon decomposition under various moisture scenarios. This dataset contains the modelling archive contained within a compressed (*.zip) file, a file-level metadata file in comma separate (*.csv) format, and two instructional files in PDF (*.pdf) format.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

New Insights into the Origin of Magnetite Crystals in ALH84001 Carbonate Disks

Martian meteorite ALH84001 preserves evidence of interaction with aqueous fluids while on Mars in the form of microscopic carbonate disks believed to have formed approx.3.9 Ga ago at beginning of the Noachian epoch. Intimately associated within and throughout these carbonate disks are nanocrystal magnetites (Fe3O4) with unusual chemical and physical properties, whose ori gins have become the source of considerable debate. One group of hypotheses argues that these magnetites are the product of partial thermal decomposition of the host carbonate. Alternatively, the origins of magnetite and carbonate may be unrelated: that is, from the perspective of the carbonate the magnetite is allochthonous. We have sought to resolve between these hypotheses through the detailed characterized of the compositional and structural relationships between the carbonate disks, their associated magnetites and the orthopyroxene matrix in which they are embedded [1]. Comparison of these results with experimental thermal decomposition studies of sideritic carbonates conducted under a range of heating scenarios suggests that the magnetite nanocrystals in the ALH84001 carbonate disks are not the products of thermal decomposition.

Thomas-Keptra, Katie L.↗
NASA NTRS2012

The Origin of Magnetite Crystals in ALH84001 Carbonate Disks

Martian meteorite ALH84001 preserves evidence of interaction with aqueous fluids while on Mars in the form of microscopic carbonate disks believed to have formed approx 3.9 Ga ago at beginning of the Noachian epoch. Intimately associated within and throughout these carbonate disks are nanocrystal magnetites (Fe3O4) with unusual chemical and physical properties, whose origins have become the source of considerable debate. One group of hypotheses argues that these magnetites are the product of partial thermal decomposition of the host carbonate. Alternatively, the origins of magnetite and carbonate may be unrelated; that is, from the perspective of the carbonate the magnetite is allochthonous. We have sought to resolve between these hypotheses through the detailed characterized of the compositional and structural relationships between the carbonate disks, their associated magnetites and the orthopyroxene matrix in which they are embedded. Comparison of these results with experimental thermal decomposition studies of sideritic carbonates conducted under a range of heating scenarios suggests that the magnetite nanocrystals in the ALH84001 carbonate disks are not the products of thermal decomposition.

Thomas-Keprta, K. L.↗
NASA NTRS2020

Adaptation in U.S. Corn Belt Increases Resistance to Soil Carbon Loss With Climate Change

Increasing the amount of soil organic carbon (SOC) has agronomic benefits and the potential to mitigate climate change. Previous regional predictions of SOC trends under climate change often ignore or do not explicitly consider the effect of crop adaptation (i.e., changing planting dates and varieties). We used the DayCent biogeochemical model to examine the effect of adaptation on SOC for corn and soybean production in the U.S. Corn Belt using climate data from three models. Without adaptation, yields of both corn and soybean tended to decrease and the decomposition of SOC tended to increase leading to a loss of SOC with climate change compared to a baseline scenario with no climate change. With adaptation, the model predicted a substantially higher crop yield. The increase in yields and associated carbon input to the SOC pool counteracted the increased decomposition in the adaptation scenarios, leading to similar SOC stocks under different climate change scenarios. Consequently, we found that crop management adaptation to changing climatic conditions strengthen agroecosystem resistance to SOC loss. However, there are differences spatially in SOC trends. The northern part of the region is likely to gain SOC while the southern part of the region is predicted to lose SOC.

Soil Organic Carbon (SOC)↗
DOE OSTI2026

Dynamic analysis of fully constrained Cable-Driven Parallel Robots for automated prefabricated component installation

This paper presents a dynamic analysis and validation framework to assess a fully constrained six-anchor Cable-Driven Parallel Robot (CDPR) for automated installation of prefabricated facade components. Compared with conventional eight-anchor systems, the six-anchor configuration simplifies setup and reduces cost, but it also reduces control authority, shrinks the wrench-feasible workspace, and tightens orientation limits. Consequently, it is unclear a priori whether dynamically feasible trajectories exist to move the end effector from pickup to the facade. A constrained trajectory optimization is formulated to enforce the system dynamics, cable-tension bounds, and pose/velocity limits, and the framework is evaluated in simulation at three levels: (i) an idealized reference model, (ii) a lab-scale prototype model incorporating measured anchor misalignments and identified damping, and (iii) a full-scale three-story building model with load decomposition for structural feasibility checks. Across these scenarios, the analysis shows that optimal, constraint-satisfying trajectories exist that move the end effector from pickup to installation while maintaining a near-plumb, level orientation at the final pose. Collectively, this multi-scale dynamic analysis and validation framework supports the deployment readiness of the six-anchor CDPR and provides a prototype-based sensitivity case study of how measured anchor placement deviations affect feasibility.

CDPR↗
DOE OSTI2023

Identifying human failure events (HFEs) for external hazard probabilistic risk assessment

In recent years, several advancements in nuclear power plant (NPP) probabilistic risk assessment (PRA) have been driven by increased understanding of external hazards, plant response, and uncertainties. However, major sources of uncertainty associated with external hazard PRA remain. One important source is how risk-significant human actions that are carried out to enable plant response and recovery from natural hazards cause the close coupling of physical impacts on plants and overall plant risk during these hazard events. This makes human reliability and human-plant interactions important elements to consider in resolving PRA gaps in external hazards. One of the challenges in considering human response in external hazard probabilistic risk assessment (XHPRA) is that most existing human reliability analysis (HRA) models were not developed for assessing actions outside the control room (termed ex-control room actions) and hazard response. To support this new scope, HRA models will need to be developed or modified to support identification of human activities, causal factors, and uncertainties inherent in external hazard response, thereby providing insights regarding event timing and physical event conditions as they relate to human performance. In this study, there are two main objectives: (1) evaluate the applicability of an existing cognitive-based HRA method, Phoenix, to ex-control room actions, and (2) identify sources of uncertainty to be characterized or reduced in order to make this method suitable for XHPRA. The first step of such work is performed by assessing the suitability of existing HRA methods to support identifying human failure events (HFEs) for human response to flooding hazards. These HFEs are human actions or inactions that are involved in human responses to flooding hazards and could contribute to the loss of a critical function for the plant in the scenario being examined. Here, in this work, decomposition analyses using the cognitive-based Phoenix HRA model are used to identify HFEs. The Phoenix method was found to be suitable for analyzing ex-control room actions as well as identifying specific HFEs and underlying crew failure modes (CFMs). However, the method's suitability for use in ex-control room actions would benefit from expanding the available CFMs to accommodate a larger variety of physical and communication tasks.

42 ENGINEERING↗
NASA NTRS1990

Army-NASA aircrew/aircraft integration program (A3I) software detailed design document, phase 3

The capabilities and design approach of the MIDAS (Man-machine Integration Design and Analysis System) computer-aided engineering (CAE) workstation under development by the Army-NASA Aircrew/Aircraft Integration Program is detailed. This workstation uses graphic, symbolic, and numeric prototyping tools and human performance models as part of an integrated design/analysis environment for crewstation human engineering. Developed incrementally, the requirements and design for Phase 3 (Dec. 1987 to Jun. 1989) are described. Software tools/models developed or significantly modified during this phase included: an interactive 3-D graphic cockpit design editor; multiple-perspective graphic views to observe simulation scenarios; symbolic methods to model the mission decomposition, equipment functions, pilot tasking and loading, as well as control the simulation; a 3-D dynamic anthropometric model; an intermachine communications package; and a training assessment component. These components were successfully used during Phase 3 to demonstrate the complex interactions and human engineering findings involved with a proposed cockpit communications design change in a simulated AH-64A Apache helicopter/mission that maps to empirical data from a similar study and AH-1 Cobra flight test.

Banda, Carolyn↗
DOE OSTI2024

Long duration battery sizing, siting, and operation under wildfire risk using progressive hedging

Battery sizing and siting problems are computationally challenging due to the need to make long-term planning decisions that are cognizant of short-term operational decisions. This paper considers sizing, siting, and operating batteries in a power grid to maximize their benefits, including price arbitrage and load shed mitigation, during both normal operations and periods with high wildfire ignition risk. Here we formulate a multi-scenario optimization problem for long duration battery storage while considering the possibility of load shedding during Public Safety Power Shutoff (PSPS) events that de-energize lines to mitigate severe wildfire ignition risk. To enable a computationally scalable solution of this problem with many scenarios of wildfire risk and power injection variability, we develop a customized temporal decomposition method based on a progressive hedging framework. Extending traditional progressive hedging techniques, we consider coupling in both placement variables across all scenarios and state-of-charge variables at temporal boundaries. This enforces consistency across scenarios while enabling parallel computations despite both spatial and temporal coupling. The proposed decomposition facilitates efficient and scalable modeling of a full year of hourly operational decisions to inform the sizing and siting of batteries. With this decomposition, we model a year of hourly operational decisions to inform optimal battery placement for a 240-bus WECC model in under 70 min of wall-clock time.

25 ENERGY STORAGE↗
DOE OSTI2021

Intensified Soil Moisture Extremes Decrease Soil Organic Carbon Decomposition: A Mechanistic Modeling Analysis

Earth system models have predicted that there will be more frequent and severe precipitation and drought events in terrestrial ecosystems. Microbially mediated decomposition of soil organic carbon (SOC) tends to increase as soils wet and decrease as soils dry. However, the long-term SOC change under intensified moisture extremes remains poorly known as it depends on the frequency and intensity of soil drying and wetting. In this study, we explored long-term SOC dynamics under scenarios of alternating drying-wetting cycles using the Microbial-ENzyme Decomposition model, a mechanistic microbial model. The model was parameterized with 11 years of observations from a temperate deciduous broadleaf forest site, showing satisfactory model performance in both model calibration (R 2 = 0.67) and validation (R 2 = 0.69) against heterotrophic respiration. We then used the model to simulate the long-term SOC dynamics under five scenarios of alternating drying-wetting cycles with different frequencies and severities over a period of 100 years. Results showed that the changes in active microbial biomass C and the corresponding turnover rates of SOC pools were more sensitive to soil drying than soil wetting. As a result, the cumulative soil carbon emission from microbial respiration decreased by 433.7 g C m -2 after the 100-year simulation in the highest frequency and intensity moisture scenario, but was not significantly affected by the lowest frequency and intensity scenario. This study emphasizes the nonlinear response of SOC decomposition to soil moisture changes, which causes decreased decomposition by microbes under drying that is, not compensated by increased decomposition under wetting conditions.

58 GEOSCIENCES↗
DOE OSTI2022

Stochastic scheduling of generating units with weekly energy storage: A hybrid decomposition approach

We propose a solution method for the large-scale stochastic unit commitment (SUC) problem with weekly-dispatched energy storage and significant weather-dependent stochastic generating capacity. Weekly storage facilities that mostly charge during weekends and discharge during weekdays require a weekly scheduling of generating units, which result in a large-scale optimization problem. This SUC problem is formulated as a two-stage stochastic model and we use the conditional value-at-risk as a risk measure. Using a Benders framework, the proposed solution method decomposes the problem into a mixed-integer linear master problem and linear and continuous subproblems. The master problem corresponds to the first-stage decisions throughout the week and includes all the commitment (binary) variables and their corresponding constraints. The subproblems correspond to the actual dispatch of the generating units on a weekly basis. Based on the success of column-and-constraint generation algorithms to solve robust optimization problems, we improve the low communication between the master problem and the subproblems in the standard Benders decomposition by adding primal variables and constraints from the subproblems to the master problem, which provides a better approximation of the recourse function. Furthermore, our computational experiments demonstrate the effectiveness of the proposed decomposition method using an instance of the South Carolina synthetic system with 90 generating units under 40 scenarios.

25 ENERGY STORAGE↗