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DOE OSTI2021

Brightest cluster galaxies are statistically special from z = 0.3 to z = 1

ABSTRACT We study brightest cluster galaxies (BCGs) in ∼5000 galaxy clusters from the Hyper Suprime-Cam (HSC) Subaru Strategic Program. The sample is selected over an area of 830 deg2 and is uniformly distributed in redshift over the range of z = 0.3−1.0. The clusters have stellar masses in the range of 1011.8−1012.9M⊙. We compare the stellar mass of the BCGs in each cluster to what we would expect if their masses were drawn from the mass distribution of the other member galaxies of the clusters. The BCGs are found to be ‘special’, in the sense that they are not consistent with being a statistical extreme of the mass distribution of other cluster galaxies. This result is robust over the full range of cluster stellar masses and redshifts in the sample, indicating that BCGs are special up to a redshift of z = 1.0. However, BCGs with a large separation from the centre of the cluster are found to be consistent with being statistical extremes of the cluster member mass distribution. We discuss the implications of these findings for BCG formation scenarios.

Dalal, Roohi (ORCID:0000000279989899)↗

DOE OSTI2025

Sustainable forest management for carbon, wood and biodiversity must consider natural disturbance regimes

Abstract Forests provide multiple benefits that include sequestering carbon, supporting biodiversity, and providing wood products. Managing temperate forests to achieve these benefits sustainably is challenging given the legacy of past human impacts, the complexity of forest development, and increasing but unpredictable disturbances. These intricacies prompted debate about the most effective management of temperate forests to achieve climate mitigation while sustaining forest productivity, regeneration, and diversity. Most research focused on management impacts under relatively stable environmental conditions, whereas forests are facing increasingly frequent and more severe climatic and biotic disturbances. Here, we used a regionally calibrated forest growth model to simulate the effects of forest management on carbon, biodiversity, and wood production under prevalent natural disturbances in the northeastern US. We developed scenarios combining forest management and disturbance regimes based on assumptions of species-specific tree regeneration and mortality rates. We compared passive management with no cutting that prioritizes maintaining existing forest trees with two common regional harvest practices: exploitative timber harvest that prioritizes short-term wood production and regenerative timber harvest that prioritizes regenerating biodiverse native forest tree seedlings. In the absence of natural disturbance, no cutting provides 41% higher forest carbon storage and 50% higher tree structural diversity after 100 years than exploitative or regenerative practices. The presence of drought, windstorm, pest, and disease disturbances altered these outcomes. Regenerative forest management provides 25% higher forest carbon storage and 26% higher tree structural diversity after 100 years than no cutting. Our results demonstrate that wood products and their carbon storage and fossil emission avoidance are important for determining the total climate benefits of forest management under natural disturbances. Our findings highlight that natural disturbance regimes should be considered when selecting management to promote multiple forest benefits, suggesting the need for tailored as opposed to one-size-fits-all policies to sustainably manage temperate forests worldwide.

Liu, Weier (ORCID:0000000265914239)↗

DOE OSTI2020

Selective acoustic control of photon-mediated qubit-qubit interactions

Quantum technologies such as quantum sensing, quantum imaging, quantum communications, and quantum computing rely on the ability to actively manipulate the quantum state of light and matter. Quantum emitters, such as color centers trapped in solids, are a useful platform for the realization of elementary building blocks (qubits) of quantum information systems. In particular, the modular nature of such solid-state devices opens up the possibility to connect them into quantum networks and create nonclassical states of light shared among many qubits. The function of a quantum network relies on efficient and controllable interactions among individual qubits. In this context, we present a scheme where optically active qubits of differing excitation energies are mutually coupled via a dispersive interaction with a shared mode of an optical cavity. This generally off-resonant interaction is prohibitive of direct exchange of information among the qubits. However, we propose a scheme in which by acoustically modulating the qubit excitation energies it is, in fact, possible to tune to resonance a preselected pair of qubits and thus open a communication channel between them. This method potentially enables fast (approximately nanoseconds) and parallelizable on-demand control of a large number of physical qubits. In this work, we develop an analytical and a numerical theoretical model demonstrating this principle and suggest feasible experimental scenarios to test the theoretical predictions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2012

Preparation for the Proof of Concept Flight of the Veggie Plant Growth Chamber

Veggie is a small plant growth chamber designed and built by ORBITEC that will fly to the International Space Station on SpaceX-3, scheduled for the summer of 2013. Ultimately Veggie will be used for research, education and outreach, and crew recreation. We want to demonstrate the functionality of this hardware by testing a scenario that could allow the crew to grow and consume fresh vegetables. Veggie will be collapsed and transported flat in a cargo transfer bag, and deployed on orbit, where it will be installed in an EXPRESS rack. The chamber consists of three subsystems: an LED light cap, a transparent bellows, and a root mat reservoir assembly. The bellows and flexible support arms allow the distance between plants and light cap to be adjusted for different ages and types of plants. Researchers at Kennedy Space Center and ORBITEC have been working to develop the plant growth interfaces for the proof of concept flight. We have developed a rooting pillow, consisting of a small bag containing media, time release fertilizer, seeds, and a wicking surface to conduct water from the root mat reservoir. Prototype pillows have been tested and results have influenced the design of flight pillows, which will be modified for microgravity from flight-approved materials. Several studies have been conducted selecting species and comparing media types in analog systems. Water content seems to be the most important factor differentiating media types in these small growth volumes (100 mL). Media type also influenced microbial levels on plants. Since produce sanitizing agents are not currently approved for growing food crops on orbit, plants and media types having very low microbial levels are being selected. Lettuce, mizuna, and other salad greens typically have microbial counts less than 10(exp 4) colony forming units and thus are good candidates for spaceflight. As we approach flight verification testing, we will finalize species, media selection, harvesting, and microbial sampling procedures. Next steps include testing of Veggie flight and ground hardware and associated equipment. This research was funded by NASA.

Massa, Gioia↗

DOE OSTI2022

A MeerKAT-meets-LOFAR study of MS 1455.0 + 2232: a 590 kiloparsec ‘mini’-halo in a sloshing cool-core cluster

ABSTRACT Radio mini-haloes are poorly understood, moderately extended diffuse radio sources that trace the presence of magnetic fields and relativistic electrons on scales of hundreds of kiloparsecs, predominantly in relaxed clusters. With relatively few confirmed detections to-date, many questions remain unanswered. This paper presents new radio observations of the galaxy cluster MS 1455.0+2232 performed with MeerKAT (covering the frequency range 872−1712 MHz) and LOFAR (covering 120−168 MHz), the first results from a homogeneously selected mini-halo census. We find that this mini-halo extends for ∼590 kpc at 1283 MHz, significantly larger than previously believed, and has a flatter spectral index (α = −0.97 ± 0.05) than typically expected. Our X-ray analysis clearly reveals a large-scale (254 kpc) sloshing spiral in the intracluster medium. We perform a point-to-point analysis, finding a tight single correlation between radio and X-ray surface brightness with a super-linear slope of $b_{\rm 1283\, MHz} = 1.16^{+0.06}_{-0.07}$ and $b_{\rm 145\, MHz} = 1.15^{+0.09}_{-0.08}$; this indicates a strong link between the thermal and non-thermal components of the intracluster medium. Conversely, in the spectral index/X-ray surface brightness plane, we find that regions inside and outside the sloshing spiral follow different correlations. We find compelling evidence for multiple sub-components in this mini-halo for the first time. While both the turbulent (re-)acceleration and hadronic scenarios are able to explain some observed properties of the mini-halo in MS 1455.0+2232, neither scenario is able to account for all the evidence presented by our analysis.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Fast Iterative Multi-site Hosting Capacity Analysis for Distribution Systems With Search Space Pruning

Interconnection studies for distributed energy resources (DERs) is a time-intensive process, primarily due to the necessity of solving large number of power flow scenarios. Hosting capacity analysis (HCA) is a time-consuming aspect of interconnection studies that is divided into single-site HCA (SHCA) and multi-site HCA (MHCA). From a computational and understandable standpoint, the industry seeks iteration-based solutions for SHCA, although it doesn't maximize the total DER hosting capacity (DERHC) of the grid, as MHCA does. While non-iterative solutions are available for MHCA, they involve a trade-off between the modeling accuracy of the distribution system, solution quality, and ease of understanding. In this work, we present a fast iterative solution for MHCA, reducing computational complexity by eliminating the need to solve power flows for a large amount of search space, thus making iterative solutions feasible. This iterative approach guarantees both a global optimal solution with sufficient time and a fast, close-to-optimal solution through efficient search space pruning. It also easily integrates with existing utility HCA tools. The results are demonstrated on select locations in the IEEE-123 bus system for community-scale interconnection studies. We highlight the benefits of skipping the need to solve millions of power flows, all while maximizing the grid's total DERHC.

Guddanti, Kishan Prudhvi↗

DOE OSTI2022

Platinum Group Metal Catalysts: Supply Chain Deep Dive Assessment

The report “America’s Strategy to Secure the Supply Chain for a Robust Clean Energy Transition” lays out the challenges and opportunities faced by the United States in the energy supply chain as well as the federal government plans to address these challenges and opportunities. It is accompanied by several issue-specific deep dive assessments, including this one, in response to Executive Order 14017 “America’s Supply Chains,” which directs the Secretary of Energy to submit a report on supply chains for the energy sector industrial base. The Executive Order is helping the federal government to build more secure and diverse U.S. supply chains, including energy supply chains. This report focuses on the supply chain for catalysts, specifically platinum group metal (PGM) catalysts, used for decarbonizing energy technologies. Catalysts are substances that increase the rate, conversion, and selectivity of chemical reactions and are used in a variety of applications such as chemical manufacturing, petroleum refining, and catalytic converters. Catalysts containing PGMs (“PGM catalysts”) are particularly useful in widespread industrial applications, including the production of high-volume chemicals such as ammonia, acetic acid, nitric acid, and the refining of crude oil into petroleum products. The PGM metals possess extraordinary properties such as being active oxidation and hydrogenation catalysts; excellent electrical conductors and electrodes; and outstanding adsorbers of oxygen and hydrogen. Within the energy industrial base, PGM catalysts improve the energy and materials efficiency of petroleum refining and chemical industry processes and reduce energy consumption in manufacturing. In addition to their use in catalytic converters, PGM catalysts are important to maximizing the efficiency of emerging decarbonization technologies, specifically in proton exchange membrane (PEM) electrolyzers for green hydrogen production from water and PEM fuel cells for transportation and stationary energy storage. Green hydrogen is expected to play a significant role in decarbonization scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Optimizing Cornell’s future geothermal district heating performance through systems engineering and simulation

Cornell University’s intention to lower its carbon footprint has motivated this engineering evaluation of using low-temperature geothermal energy to supply heat to the campus district energy system. Optimal selection and operation of heat pumps can significantly improve system performance. To support this analysis, we model the quantitative relationships between heat pump configurations and source flow, supply and distribution temperatures, facility heating design options, output heat generation, and carbon abatement outcomes. A systems approach is used for analysis, troubleshooting, and improvement of the model. The result of this effort is a dynamic tool that appropriately responds to hourly thermal demand and communicates energy response for techno-economic analysis. Simulations indicate that a single well-pair in the local Basement Contact Zone reservoir can satisfy almost 68% of annual thermal demand at nearly 20 MWth average capacity while remaining financially competitive compared to conventional heating with levelized cost of heating (LCOH) as low as $4.55/MMBTU ($15.53/MWh th ). A direct-use scheme with heat pump augmentation provided more carbon abatement and 50 to 150% more annual heat supply than one without augmentation. Exploration of three and four well-pair scenarios prove the capability to achieve 50 MW th baseload heating capacity with competitive LCOH and significant carbon reductions.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Novel plant–microbe interactions: Rapid evolution of a legume–rhizobium mutualism in restored prairies

Abstract When plants colonize new habitats, the novel interactions they form with new mutualists or enemies can immediately affect plant performance. These novel interactions also may provoke rapid evolutionary responses and can be ideal scenarios for investigating how species interactions influence plant evolution. To explore how mutualists influence the evolution of colonizing plant populations, we capitalized on an experiment in which two former agricultural fields were seeded with identical prairie seed mixes in 2010. Six years later, we compared how populations of the legume Chamaecrista fasciculata from these sites and their original (shared) source population responded to nitrogen‐fixing rhizobia from the restoration sites in a greenhouse reciprocal cross‐inoculation experiment. We found that the two populations differed both from their original source population and from each other in the benefits they derive from rhizobia, and that one population has evolved reduced allocation to rhizobia (i.e. forms fewer rhizobium‐housing nodules). Synthesis . Our results suggest that these plant populations have evolved different ways of interacting with rhizobia, potentially in response to differences in rhizobium quality between sites. Our study illustrates how microbial mutualists may shape plant evolution in new environments and highlights how variation in microbial mutualists potentially may select for different evolutionary strategies in plant hosts.

Magnoli, Susan M.↗

NASA NTRS2003

Mars Mission Optimization Based on Collocation of Resources

This paper presents a powerful approach for analyzing Martian data and for optimizing mission site selection based on resource collocation. This approach is implemented in a program called PROMT (Planetary Resource Optimization and Mapping Tool), which provides a wide range of analysis and display functions that can be applied to raw data or imagery. Thresholds, contours, custom algorithms, and graphical editing are some of the various methods that can be used to process data. Output maps can be created to identify surface regions on Mars that meet any specific criteria. The use of this tool for analyzing data, generating maps, and collocating features is demonstrated using data from the Mars Global Surveyor and the Odyssey spacecraft. The overall mission design objective is to maximize a combination of scientific return and self-sufficiency based on utilization of local materials. Landing site optimization involves maximizing accessibility to collocated science and resource features within a given mission radius. Mission types are categorized according to duration, energy resources, and in-situ resource utilization. Optimization results are shown for a number of mission scenarios.

G E Chamitoff↗

NASA NTRS2005

Validation Of The Airspace Concept Evaluation System Using Real World Data

This paper discusses the process of performing a validation of the Airspace Concept Evaluation System (ACES) using real world historical flight operational data. ACES inputs are generated from select real world data and processed to create a realistic reproduction of a single day of operations within the National Airspace System (NAS). ACES outputs are then compared to real world operational metrics and delay statistics for the reproduced day. Preliminary results indicate that ACES produces delays and airport operational metrics similar to the real world with minor variations of delay by phase of flight. ACES is a nation-wide fast-time simulation tool developed at NASA Ames Research Center. ACES models and simulates the NAS using interacting agents representing center control, terminal flow management, airports, individual flights, and other NAS elements. These agents pass messages between one another similar to real world communications. This distributed agent based system is designed to emulate the highly unpredictable nature of the NAS, making it a suitable tool to evaluate current and envisioned airspace concepts. To ensure that ACES produces the most realistic results, the system must be validated. There is no way to validate future concepts scenarios using real world historical data, but current day scenario validations increase confidence in the validity of future scenario results. Each operational day has unique weather and traffic demand schedules. The more a simulation utilizes the unique characteristic of a specific day, the more realistic the results should be. ACES is able to simulate the full scale demand traffic necessary to perform a validation using real world data. Through direct comparison with the real world, models may continuee to be improved and unusual trends and biases may be filtered out of the system or used to normalize the results of future concept simulations.

Zelinski, Shannon↗

DOE OSTI2021

Transformational challenge reactor analysis to inform preconceptual core design decisions: Sensitivity study of transient analysis in a hydride-moderated microreactor

The Transformational Challenge Reactor (TCR) program aims to demonstrate a revolutionary design approach enabled by advanced manufacturing and data analytics in the design of nuclear reactors. This article discusses scoping analyses of preconceptual designs to inform TCR design decisions and the evaluation of sensitivities and uncertainties on postulated transient scenarios. The applicability of the systems codes TRACE and RELAP5-3D to TCR transient analysis are examined, and RELAP5-3D models are used to examine the transient response of two candidate core designs at multiple power levels. Then, the uncertainty quantification code RAVEN is used to quantify the effect of several design parameters on reactivity-initiated accident (RIA) progression at hot zero power (HZP) and hot full power (HFP) as well as to assess the impact of uncertainties in transient parameters for the pressurized loss of forced cooling (PLOFC).When results were compared, TRACE and RELAP5-3D showed good agreement in their ability to predict system behavior, but RELAP5-3D calculations were closer to analytical predictions for the RIA. Furthermore, models for a PLOFC accident in two designs (a UO2 and TRISO core) at multiple power levels showed greater temperature margins for the TRISO core at all power levels. Using this information, along with other scoping analyses and constraints, the TCR design team selected a power level of 3 MWth and a TRISO-based core design. For this design RAVEN was applied to vary RIA parameters in RELAP5-3D models at HZP and HFP to understand the effect on figures of merit such as peak power, fuel and coolant temperature, and energy deposition. This sensitivity study found that the inserted reactivity worth was the most important parameter controlling all figures of merit, but for insertion up to 1.5$ no failure of TCR fuel is anticipated. For constant reactivity insertions, the magnitude of the fuel temperature coefficient was found to have the greatest effect on all figures of merit under most circumstances. These results not only demonstrate the anticipated robust safety of the proposed TCR fuel form but also provide a reference for future metal-hydride moderated systems to understand RIA behavior. In the PLOFC, the impact of heat transfer enhancement due to wavy flow channel effects dominated the variance in peak temperatures, and variations in heat exchanger elevation provided the greatest control on natural circulation flow rate. Additionally, no fuel particle failure is anticipated in the PLOFC.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Green Methanol via an Integrated Direct Air Capture, CO 2 Electrolyzer, and Hydrogenation Reactor

This project pioneered a groundbreaking reactor design to produce green methanol by harnessing the electrochemical CO 2 reduction reaction (eCO 2 RR), a cornerstone of power-to-fuels technology. The effort integrated three innovative technologies to achieve carbon-neutral methanol production at a target cost of under $\$$800/ton: 1. Direct Air Capture (DAC): Using a cutting-edge sorbent material developed at Holocene, scalable models were developed to integrate captured atmospheric CO₂ into the reactor system. 2. Intermediate-Temperature CO 2 Electrolyzer: Developed by the University of Tennessee (UTK), this electrolyzer utilizes a cost-effective, proton-conducting solid acid electrolyte (CsH 2 PO 4 , CDP) and a mixed-metal oxide cathode. It achieves high faradaic efficiencies (>98%) by effectively suppressing hydrogen evolution at high current densities, converting CO 2 to CO with remarkable selectivity. 3. Catalysis and Reactor Engineering: Oak Ridge National Laboratory (ORNL) contributed world-class expertise in heterogeneous catalysis and reactor design. Their advanced ASPEN modeling drove systems integration and supported techno-economic and life cycle analyses. This effort was further bolstered by partnerships with industry leaders Air Company and Plug Power, who provided critical guidance on scaling, systems engineering, and the integration of water electrolyzers into large-scale operations. During Phase 1, the team focused on modeling and validating a lab-scale reactor demonstrating the feasibility of the integrated approach. Key accomplishments include a 52% increase in current density at 0.8 V while maintaining >98% CO faradaic efficiency, successful 10× scale-up of the electrolyzer with performance within 5% of coin-cell results, best-in-class durability (168-hour test at 0.6 V with 0.14 mA/cm 2 -h degradation), validated TEA confirming the $\$$800/ton methanol target, and completed preliminary LCA showing potential for net-negative GHG emissions under renewable energy scenarios.

10 SYNTHETIC FUELS↗

DOE OSTI2020

Quantitative assessment of eddy viscosity rans models for turbulent mixed convection in a differentially heated plane channel

Turbulent mixed convection between two vertical, infinite parallel plates at different temperatures is studied using various two-equation turbulence models. The numerical simulations are performed at a turbulent Reynolds number of Re τ = 150 and a Grashof number of Gr = 9.6 × 10 5 . Comparisons are made against the highly trusted DNS results. Consistent with the DNS approach, the current simulations are performed using constant properties and the Boussinesq approximation to predict the influence of buoyancy. Previous studies have provided assessments of two-equation turbulence models for various scenarios, but often rely on a qualitative “eye” test in order to determine the most appropriate model to predict a given flow. This study aims to provide a new form of quantitative assessment that accounts for both the physics captured by the turbulence model as well as the magnitude of the system response quantities (SRQ) using a modified symmetric mean absolute percent error (SMAPE) method. This method is designed to be approachable to researchers at any level and can be applied to system response quantities from multiple research fields. Uncertainty quantification is also performed to determine the discretization error for each turbulence model. Recommendations are made as to which turbulence models best capture the physics – hydrodynamically and thermally – using both local and global validation metrics. Lastly, a sensitivity analysis is performed on the damping functions used in the most accurate models. This underpins the potential of model developments and adjustments most worth pursuing for buoyant flows. Finally, this framework provides a more physics-based comparative analysis of the selected turbulence models.

42 ENGINEERING↗

NASA NTRSDate not supplied

Runway Configuration Management with Offline Reinforcement Learning

Runway configuration management (RCM) is a challenging task, and it affects the efficiency of the National Airspace System (NAS) and airport surface operations significantly. Each airport, depending on the geometry, capacity, local climate patterns, etc. has multiple configurations for the runway usage for arriving and departing flights. Many factors such as the incoming/outgoing traffic load, wind direction and speed, convective weather, cloud ceiling and other environmental factors might affect the choice of a runway configuration at any point in time. However, other factors such as safety measures and regulations, noise abatement, capacity of each configuration, and preference of the air traffic controllers (ATCs) can also play a significant role in selecting the configuration. A sub-optimal selection of the runway configuration, or delay in making configuration changes might result in significant increase in taxi times for aircraft on the surface of the airport, fuel and energy use of the aircraft, and maintenance costs. It can also lead to safety concerns, such as an aircraft performing one or more go-arounds before being able to land. All these factors make RCM an extremely important and challenging decision-making process for the ATCs. The current state of practice sets the runway configuration by the ATCs based on relevant information available at the time including weather, traffic, noise abatement, safety bounds, etc. This makes the decision-making process subjective based on the accuracy of the available information and the bias in human decision making. Unfortunately, this approach yields poor results (e.g., significant delays) if the predicted outcomes are uncertain and their relative impact is not well understood. This is especially evident when the uncertainty increases the size of possible predicted outcomes (combinatorial explosion in possible scenarios) that cannot be handled by human reasoning. On the other hand, an automated approach based on machine intelligence can make use of historical data and search through all (or significant amount of) possible scenarios under uncertainty and make well-informed decisions.

Milad Memarzadeh↗

NASA NTRS2012

VERITAS Deep Observations of the Dwarf Spheroidal Galaxy Segue 1

The VERITAS array of Cherenkov telescopes has carried out a deep observational program on the nearby dwarf spheroidal galaxy Segue 1. We report on the results of nearly 48 hours of good quality selected data, taken between January 2010 and May 2011. No significant gamma-ray emission is detected at the nominal position of Segue 1, and upper limits on the integrated flux are derived. According to recent studies, Segue 1 is the most dark matter-dominated dwarf spheroidal galaxy currently known. We derive stringent bounds on various annihilating and decaying dark matter particle models. The upper limits on the velocity-weighted annihilation cross-section are (sigma nu)(sup 95% CL) approx < 10(exp - 23) cubic cm/s, improving our limits from previous observations of dwarf spheroidal galaxies by at least a factor of 2 for dark matter particle masses m(sub x) approx > 300 GeV. The lower limits on the decay lifetime are at the level of tau(sup 95% CL) approx > 10(exp 24) s. Finally, we address the interpretation of the cosmic ray lepton anomalies measured by ATIC and PAMELA in terms of dark matter annihilation, and show that the VERITAS observations of Segue 1 disfavor such a scenario.

Perkins, J. S.↗

NASA NTRS2021

Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC)

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especiallywhen the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the Optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rdparty .NET package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail. GenETOC was developed in C# using the .NET framework with Windows Presentation Foundation (WPF) serving as the framework from which to create the graphical user interface (GUI). GenETOC user inputs can be categorized into three major data components: definition of the problem (areas of interest, satellite decision parameters, and sensor configurations), definition of performance objectives, and specification of the genetic algorithm (GA) parameters. In the problem definition component, the user is prompted to define the areas of interest against which the performance metrics will be computed, define the sensor parameters and attach them to specific spacecraft, select which satellite orbital parameters will be added to the decision space of the GA, and specify the range of desired values for each optimization parameter. For performance objectives, the user is presented with a list of available coverage and revisit performance based calculation options from which two metrics are chosen to serve as the objective functions that the GA will use to evaluate solutions during the optimization process. Finally, the definition of the GA parameters provides user control over the number of generations (number of optimization iterations), the population size (number of candidate constellations created in each generation), and the adaptive mutation and crossover threshold values (control parameters for how frequently each process occurs during the optimization). GenETOC has been extensively tested to verify the individual components of the optimization process. The GA has been tested against a suite of GA test problems to confirm convergence to the known two and three-dimensional Pareto fronts. The coverage and revisit performance metrics obtained in GenETOC are compared with STK desktop scenarios, confirming the constellations are being appropriately modeled within GenETOC simulations. A walkthrough of a simple, example problem is provided to illustrate the workings of GenETOC and to demonstrate the output available to the mission designer.

mission design↗

DOE OSTI2025

Evaluating the Effectiveness of Soil Profile Rehabilitation for Pluvial Flood Mitigation Through Two-Dimensional Hydrodynamic Modeling

Pluvial flooding, driven by increasingly impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure are exacerbating flooding impacts, resulting in significant socio-economic consequences. This study evaluated the effectiveness of a soil profile rehabilitation scenario using a 2D hydrodynamic modeling approach for the Tiffany Run watershed, Baltimore City. This study utilized different extreme storm events, a high-resolution (1 m) LiDAR Digital Terrain Model (DTM), building footprints, and hydrological soil data. These datasets were integrated into a fully coupled 2D hydrodynamic model, the City Catchment Analysis Tool (CityCAT), to simulate urban flood dynamics. The pre-soil rehabilitation simulation revealed a maximum water depth of 3.00 m in most areas, with hydrologic soil groups C and D, especially downstream of the study area. The post-soil rehabilitation simulation was targeted at vacant lots and public parcels, accounting for 33.20% of the total area of the watershed. This resulted in a reduced water depth of 2.50 m. Additionally, the baseline runoff coefficient of 0.49 decreased to 0.47 following the rehabilitation, and the model consistently recorded a peak runoff reduction rate of 4.10 across varying rainfall intensities. The validation using a contingency matrix demonstrated true-positive rates of 0.75, 0.50, 0.64, and 0 for the selected events, confirming the model’s capability at capturing real-world flood occurrences.

Baltimore City↗