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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Monthly averages of ED2 model simulations initialized with airborne lidar structure, Jan 1981-Dec 2018, Brazilian Amazon

Deforestation and forest degradation (selective logging, fires, fragmentation) have impacted nearly 40% of the original extent of the Brazilian Amazon, and have markedly impacted forest structure across the region. To date, few studies analysed how shifts in forest structure from degradation influence the forest sensitivity to climate extremes, because of the complex interactions between forest structure and micro-environmental conditions. To address this knowledge gap, we carried out a series of simulations across the Brazilian Amazon using the Ecosystem Demography Model (ED2), using observed forest structure derived from 541 airborne lidar transects (375 ha each) and two scenarios representing forest recovery and expansion of degradation to investigate how shifts in forest structure impact ecosystem function under near-average and extreme climate conditions, as part of the manuscript Longo et al 2025 "Degradation and Deforestation Increase the Sensitivity of the Amazon Forest to Climate Extremes". This dataset provides the output results from the ED2 model simulations for the three simulations at monthly time scales, in NetCDF format. For all simulations, we used bias-corrected hourly reanalyses (WFDE5) for most meteorological drivers, except for precipitation, which was obtained from CHIRPS. The meteorological drivers used in the study span 38 years (Jan 1981–Dec 2018). The output results correspond to the last 38 years of simulation (one full cycle of meteorological drivers), in which ED2 simulations used static stand structure (i.e., the forest structure was held constant). The following files are provided:ED2_emean_Global_R004_BrAmaz_s1c0t0l0f0.nc. This corresponds to the Control simulation. The forest structure was obtained from the airborne lidar.ED2_emean_Global_R005_BrAmaz_s1c0t1l1f0.nc. This corresponds to the Degraded simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that expanded deforestation and selective logging across the Amazon.ED2_emean_Global_R006_BrAmaz_s1c0t1l0f0.nc. This corresponds to the Recovery simulation. The forest structure was obtained from a spin-up simulation initialized with airborne lidar and a scenario that completely halted deforestation and degradation, allowing degraded forests to recover for 38 years.We also provide file ED2_zones_R004_BrAmaz_s1c0t0l0f0.nc, which classifies each grid cell into zones used in the reference manuscript: 1: Southeast. 2: South. 3: West. 4: Central. 5: Northeast. 6: North. 7: Northwest". Index 0 corresponds to grid cells excluded from sub-region analyses because they were dominated by flooded forests, deforestation, and naturally non-forest vegetation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Zero-Trust Architecture for Autonomous Edge Computing

We are at the apex of an aviation revolution where autonomy will play a central role in enabling complex, multi-agent systems to communicate, interact, and collaborate on a myriad of applications spanning autonomous swarms to wild-fire management. Autonomy is not an absolute but rather a spectrum ranging from a system requiring significant human intervention to one requiring little to none [1]. For example, the extreme, in the case of an autonomous aircraft, is one that operates independently in the airspace interacting with all other elements (air traffic controllers, other pilots) as if it were a human pilot. Critical to this vision is an architecture that enables autonomous agents to interact with minimal latency. Edge computing is an emerging architecture where compute and storage is pushed to the ‘edge’ of the network in order to minimize the round-trip time from agent to resource thereby mitigating the latency associated with cloud-only based approaches. Additionally, services can generate massive amounts of data (e.g., video feeds), which may require analysis in near real-time. Moving this data to the cloud for further processing may not be feasible due to latency, bandwidth, and cost. Privacy, security, and reliability can also be improved by edge computing architectures. However, this geo-distributed and dynamic* architecture complicates the establishment of unambiguous network security boundaries and can lead to vulnerabilities including man in the middle attacks, replay attacks, physical security breaches of edge nodes, signal interception, etc. This motivates the need for zero-trust architectures [2–4] which de-emphasize the notion of static network perimeters and, as the name implies, do not instill any innate trust in any particular agent. It is required that all agents must be authorized and approved in every transaction. In this paper, we present a zero-trust architecture suitable for edge-computing applications that demand significant low-latency, security, privacy, and reliability.

zero trust↗
NASA NTRS1993

Unsteady Flow Simulations in Support of the SSME HEX Turning Vane Cracking Investigation with the ATD HPOTP

Unsteady flow computations are being performed with the P&W (ATD) and the Rocketdyne baseline configurations of the SSME LO2 turbine turnaround duct (TAD) and heat exchanger (HEX). The work is in support of the HEX inner turning vane cracking investigation. Fatigue cracking has occurred during hot firings with the P&W configuration on the HEX inner vane, and it appears the fix will involve changes to the TAD splitter vane position and to the TAD inner wall curvature to reduce the dynamic loading on the inner vane. Unsteady flow computations on the P&W baseline and fix and on the Rocketdyne baseline reference follow steady-flow screening computations done by MSFC/ED32 on several trial configurations arriving at the fix. The P&W TAD inlet velocity profile has a strong radial velocity component that directs the flow toward the inner wall and raises the local velocity a factor of two and the dynamic pressure a factor, of four. The fix is intended to redistribute the flow more evenly across the HEX inner and outer vanes like the Rocketdyne baseline reference. Vane buffeting at frequencies around 4,000 Hz is the leading suspected cause of the problem. Our simulations (work in progress) are being done with the USA 2D axisymmetric code approximating the flow as axisymmetric u+v 2D (axial, u, and radial, v, components only). The HEX coils are included in the model to make sure the fix does not adversely affect the HEX environment. Turbulent kinetic energy, k, levels where k = 1/2 v' rms2 are locally as high as 10,000 ft2/sec2 for the P&W baseline at the engine interface (between the TAD and HEX) at the HEX inner vane location. However, k is less than 8,000 on the HEX outer vane and only about 4,500 on the HEX inner vane for the Rocketdyne baseline. Unsteady turbulence intensity, v'rms/v, and pressure, p', are being computed in the present computations to compare with steady-flow Reynolds-averaged computations where p'rms = const (pk) for overall rms random turbulence from 0.1 to 12,000 Hz frequency. Random overall static, p'rms fluctuations as large as 1.7 psi are estimated from k on the HEX inner vane for the P&W baseline configuration but only about 0.7 psi for the Rocketdyne configuration.

Dougherty, N. S.↗