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DOE OSTI2025

Bridging the Gap on Data, Metrics, and Analyses for Grid Resilience to Weather Events: Information that utilities can provide regulators, state energy offices, and other stakeholders

A growing number of states require regulated utilities to file resilience plans to improve the electric grid’s ability to anticipate, withstand, adapt to and recover from increasingly severe weather events. This report aims to help state regulators identify and request data, metrics, and analyses from utilities and use it in decisions on utility resilience plans and investments. The report reviews state requirements and utility plans focused on overall grid resilience, climate change resilience and vulnerabilities, infrastructure modernization, storm protection, and wildfire mitigation. It details types of data, metrics, and analyses across five categories--and provides examples of each from the utility plans. The first category is vulnerability assessments, or evaluations of the susceptibility of systems, communities, or assets to potential harm from identified hazards. The second is data on hazards and the exposure of utility assets and customers to these hazards. The third is attribute metrics, or system characteristics that contribute to or describe the resilience of a system. The fourth is performance metrics, which are impacts of resilience investments on system performance--typically a reduction of negative impacts from hazard events. Finally, evaluation and prioritization are analyses that utilities conduct to estimate impacts from resilience measures (evaluation) and prioritize measures based on costs and estimated impacts (prioritization). The report concludes with examples of key trends and emerging best practices for states and utilities, and identifies areas for further research.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2005

Robotic and Human-Tended Collaborative Drilling Automation for Subsurface Exploration

Future in-situ lunar/martian resource utilization and characterization, as well as the scientific search for life on Mars, will require access to the subsurface and hence drilling. Drilling on Earth is hard - an art form more than an engineering discipline. Human operators listen and feel drill string vibrations coming from kilometers underground. Abundant mass and energy make it possible for terrestrial drilling to employ brute-force approaches to failure recovery and system performance issues. Space drilling will require intelligent and autonomous systems for robotic exploration and to support human exploration. Eventual in-situ resource utilization will require deep drilling with probable human-tended operation of large-bore drills, but initial lunar subsurface exploration and near-term ISRU will be accomplished with lightweight, rover-deployable or standalone drills capable of penetrating a few tens of meters in depth. These lightweight exploration drills have a direct counterpart in terrestrial prospecting and ore-body location, and will be designed to operate either human-tended or automated. NASA and industry now are acquiring experience in developing and building low-mass automated planetary prototype drills to design and build a pre-flight lunar prototype targeted for 2011-12 flight opportunities. A successful system will include development of drilling hardware, and automated control software to operate it safely and effectively. This includes control of the drilling hardware, state estimation of both the hardware and the lithography being drilled and state of the hole, and potentially planning and scheduling software suitable for uncertain situations such as drilling. Given that Humans on the Moon or Mars are unlikely to be able to spend protracted EVA periods at a drill site, both human-tended and robotic access to planetary subsurfaces will require some degree of standalone, autonomous drilling capability. Human-robotic coordination will be important, either between a robotic drill and humans on Earth, or a human-tended drill and its visiting crew. The Mars Analog Rio Tinto Experiment (MARTE) is a current project that studies and simulates the remote science operations between an automated drill in Spain and a distant, distributed human science team. The Drilling Automation for Mars Exploration (DAME) project, by contrast: is developing and testing standalone automation at a lunar/martian impact crater analog site in Arctic Canada. The drill hardware in both projects is a hardened, evolved version of the Advanced Deep Drill (ADD) developed by Honeybee Robotics for the Mars Subsurface Program. The current ADD is capable of 20m, and the DAME project is developing diagnostic and executive software for hands-off surface operations of the evolved version of this drill. The current drill automation architecture being developed by NASA and tested in 2004-06 at analog sites in the Arctic and Spain will add downhole diagnosis of different strata, bit wear detection, and dynamic replanning capabilities when unexpected failures or drilling conditions are discovered in conjunction with simulated mission operations and remote science planning. The most important determinant of future 1unar and martian drilling automation and staffing requirements will be the actual performance of automated prototype drilling hardware systems in field trials in simulated mission operations. It is difficult to accurately predict the level of automation and human interaction that will be needed for a lunar-deployed drill without first having extensive experience with the robotic control of prototype drill systems under realistic analog field conditions. Drill-specific failure modes and software design flaws will become most apparent at this stage. DAME will develop and test drill automation software and hardware under stressful operating conditions during several planned field campaigns. Initial results from summer 2004 tests show seven identifi distinct failure modes of the drill: cuttings-removal issues with low-power drilling into permafrost, and successful steps at executive control and initial automation.

Glass, Brian↗

DOE OSTI2020

Determination of Stress-Free Lattice Spacing (D₀) for Residual Stress Relaxation Measurement in Ni-based Superalloys by Neutron Diffraction

The residual stress and its relaxation in critical engineering components are key to structural materials reliability. The determination of residual stress in bulk engineering structures like Ni-based superalloys can be conducted by measuring the lattice strain with penetrating neutron diffraction. The state-of-the-art engineering diffraction beamline VULCAN at the Oak Ridge National Laboratory (ORNL) Spallation Neutron Source (SNS) provides the critical capability to evaluate the residual strain/stress relaxation in engineering component thanks to the high flux and event data features. By the means of residual stress/strain calculation, it is critical to accurately determine the stress-free lattice spacing d₀, which can be altered by the change of chemistry, unrelieved stress, and measurement scheme. Here we reported d₀ determination for determining residual stress relaxation and distribution in Alloy 718 superalloys after different quenching treatments. Selection of locations for d₀ measurement was discussed by considering the neutron path, attenuation and sample alignment. A dynamic d₀ that resulted from atom diffusion and chemistry change was estimated as function of time for the in-situ relaxation characterization. It demonstrated how the dynamic d₀ values may influence on the strain calculation in different thermally treated samples. The results highlight the importance of dynamic d₀ for in-situ relaxation involving chemical changes and provide guidance to the dynamic d₀ measurement and simulation.

36 MATERIALS SCIENCE↗

NASA NTRS2014

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

Modal Filtering↗

NASA NTRS2016

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

Modal Filtering↗

DOE OSTI2021

Online PMU Missing Value Replacement Via Event-Participation Decomposition

We introduce a new method for online Phasor Measurement Unit (PMU) missing value replacement. Our approach allows us to decompose PMU event responses into a non-dynamic component (denoted the participation factor) that can be inferred directly from the past and a dynamic component that can be inferred directly from all other PMUs (denoted the event strength). When missing values occur, we can use these two components, which do not rely on the missing index, to estimate the correct value. The method is extremely fast and can easily be used for online applications. Furthermore, extensive testing on real power system event data reveals that our approach achieves state-of-the-art performance in terms of Mean Absolute Percent Errors (MAPEs) for PMU data dropped during event periods. Here, the method also yields an interpretable and simplified view of events for further analysis and applications. The method relies only on PMU data and does not take outside information such as network topology.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration

This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. Furthermore, to facilitate Bayesian inference with intractable likelihoods, we propose to utilize optimization iterates for maximum a posteriori estimation to build a Gaussian process surrogate model for the unnormalized log-posterior density. We provide bounds for the Hellinger distance between the true and the approximate posterior distributions in terms of the number of design points. We demonstrate the effectiveness of our Bayesian optimization algorithms in nonconvex benchmark objective functions, in a machine learning hyperparameter tuning problem, and in a black-box engineering design problem. The effectiveness of our posterior approximation approach is demonstrated in two Bayesian inference problems for parameters of dynamical systems.

Bayesian inference↗

DOE OSTI2020

On the Solution of ℓ 0 -Constrained Sparse Inverse Covariance Estimation Problems

The sparse inverse covariance matrix is used to model conditional dependencies between variables in a graphical model to fit a multivariate Gaussian distribution. Estimating the matrix from data are well known to be computationally expensive for large-scale problems. Sparsity is employed to handle noise in the data and to promote interpretability of a learning model. Although the use of a convex ℓ 1 regularizer to encourage sparsity is common practice, the combinatorial ℓ 0 penalty often has more favorable statistical properties. In this paper, we directly constrain sparsity by specifying a maximally allowable number of nonzeros, in other words, by imposing an ℓ 0 constraint. Here, we introduce an efficient approximate Newton algorithm using warm starts for solving the nonconvex ℓ 0 -constrained inverse covariance learning problem. Numerical experiments on standard data sets show that the performance of the proposed algorithm is competitive with state-of-the-art methods.

$\ell_0$-Constrained↗

NASA NTRS2011

Determination of Non-Thermal Velocity Distributions from SERTS Linewidth Observations

Non-thermal velocities obtained from the measurement of coronal Extreme Ultraviolet (EUV) linewidths have been consistently observed in solar EUV spectral observations and have been theorized to result fro m many plausible scenarios including wave motions, turbulence, or magnetic reconnection. Constraining these velocities can provide a physical limit for the available energy resulting from unresolved motions in the corona. We statistically determine a series of non-thermal velocity distributions from linewidth measurements of 390 emission lines from a wide array of elements and ionization states observed during the Solar Extreme Ultraviolet Research Telescope and Spectrograph 1991-1997 flights covering the spectral range 174-418 A and a temperature range from 80,000 K to 12.6 MK. This sample includes 248 lines from active regions, 101 lines from quiet-Sun regions, and 41 lines were observed from plasma off the solar limb. We find a strongly peaked distribution corresponding to a non-thermal velocity of 19-22 km/s in all three of the quiet-Sun, active region, and off-limb distributions. For the possibility of Alfven wave resonance heating, we find thai velocities in the core of these distributions do not provide sufficient energy, given typical densities and magnetic field strengths for the coronal plasma, to overcome the estimated coronal energy losses required to maintain the corona at the typical temperatures working as the sole mechanism. We find that at perfect efficiency 50%-60% of the needed energy flux can be produced from the non-thermal velocities measured.

Coyner, Aaron J.↗

NASA NTRS2013

Impact of Surface Roughness and Soil Texture on Mineral Dust Emission Fluxes Modeling

Dust production models (DPM) used to estimate vertical fluxes of mineral dust aerosols over arid regions need accurate data on soil and surface properties. The Laboratoire Inter-Universitaire des Systemes Atmospheriques (LISA) data set was developed for Northern Africa, the Middle East, and East Asia. This regional data set was built through dedicated field campaigns and include, among others, the aerodynamic roughness length, the smooth roughness length of the erodible fraction of the surface, and the dry (undisturbed) soil size distribution. Recently, satellite-derived roughness length and high-resolution soil texture data sets at the global scale have emerged and provide the opportunity for the use of advanced schemes in global models. This paper analyzes the behavior of the ERS satellite-derived global roughness length and the State Soil Geographic data base-Food and Agriculture Organization of the United Nations (STATSGO-FAO) soil texture data set (based on wet techniques) using an advanced DPM in comparison to the LISA data set over Northern Africa and the Middle East. We explore the sensitivity of the drag partition scheme (a critical component of the DPM) and of the dust vertical fluxes (intensity and spatial patterns) to the roughness length and soil texture data sets. We also compare the use of the drag partition scheme to a widely used preferential source approach in global models. Idealized experiments with prescribed wind speeds show that the ERS and STATSGO-FAO data sets provide realistic spatial patterns of dust emission and friction velocity thresholds in the region. Finally, we evaluate a dust transport model for the period of March to July 2011 with observed aerosol optical depths from Aerosol Robotic Network sites. Results show that ERS and STATSGO-FAO provide realistic simulations in the region.

textures↗

NASA NTRS2018

Integrating Cloud-Based Workflows in Continental-Scale Cropland Extent Classification

Accurate information on cropland spatial distribution is required for global-scale assessments and agricultural land use policies. Cloud computing platforms such as Google Earth Engine (GEE) provide unprecedented opportunities for large-scale classifications of Landsat data. We developed a novel method to fuse pixel-based random forest classification of continental-scale Landsat data on GEE and an object-based segmentation approach known as recursive hierarchical segmentation (RHSeg). Using our fusion method, we produced a continental-scale cropland extent map for North America at 30m spatial resolution for the nominal year 2010. The total cropland area for North America was estimated at 275.18 million hectares (Mha). The overall accuracies of the map are>90% across the continent. This map also compares well with the United States Department of Agriculture (USDA) cropland data layer (CDL), Agriculture and Agri-food Canada (AAFC) annual crop inventory (ACI), and the Mexican government agency Servicio de Informacion Agroalimentaria y Pesquera (SIAP)'s agricultural boundaries. Furthermore, our map compared well with sub-country statistics including state-wise and county-wise cropland statistics in regression models resulting in R2 > 0.84. This key contribution paves the way for more detailed products such as crop intensity, crop type, and crop irrigation, and provides a method for creating high-resolution cropland extent maps for other countries where spatial information about croplands are not as prevalent.

Massey, Richard↗

NASA NTRS2020

Dust and gas absorption in the High Mass X-ray Binary IGR J16318−4848

Context. With an absorption column density on the order of 10(exp 24)per sq. cm, IGR J16318-4848 is one of the most extreme cases of a highly obscured High Mass X-ray Binary. Besides the overall continuum absorption, the source spectrum exhibits a strong iron and nickel fluorescence line complex at 6.4 keV. Previous empirical modeling of these features and comparison with radiative transfer simulations raised questions about the structure and covering fraction of the absorber and the profile of the fluorescence lines. Aims. We aim at a self-consistent description of the continuum absorption, the absorption edges and the fluorescent lines to constrain properties of the absorbing material, such as ionization structure and geometry. We further investigate the effects of dust absorption on the observed spectra and the possibility of fluorescent emission from dust grains. Methods. We use XMM-Newton and NuSTAR spectra to first constrain empirically the incident continuum and fluorescence lines. Next, we use XSTAR to construct a customized photoionization model where we vary the ionization parameter, column density, and covering fraction. In a third step, we model the absorption and fluorescence in a dusty olivine absorber and employ both, a simple analytical model for the fluorescent line emission and Monte Carlo radiative transfer, spectral shapes and line fluxes that are very close to the data are generated. Results. Our empirical spectral modeling is in agreement with previous works. Our second model, the single gas absorber does not describe the observational data. In particular, irrespective of the ionization state or column density of the absorber, a much higher covering fraction than previously estimated is needed to produce the strong fluorescence lines and the large continuum absorption. A dusty, spherical absorber (modeled as consisting of olivine dust, although the nature of dust cannot be constrained) is well able to produce the observed continuum absorption and edges. Conclusions. A dense, dusty absorber in the direct vicinity of the source consisting of dust offers a consistent description of both the strong continuum absorption and the strong emission features in the X-ray spectrum of IGR J16318􀀀4848. In particular, for low optical depth of individual grains the dust will contribute significantly to the fluorescent emission, which is the case for typical densities and grain size distribution models.

Ralf Ballhausen↗

DOE OSTI2026

Active operator learning with predictive uncertainty quantification for partial differential equations

With the increased prevalence of neural operators being used to provide rapid solutions to partial differential equations (PDEs), understanding the accuracy of model predictions and the associated error levels is necessary for deploying reliable surrogate models in scientific applications. Existing uncertainty quantification (UQ) frameworks employ ensembles or Bayesian methods, which can incur substantial computational costs during both training and inference. Here, we propose a lightweight predictive UQ method tailored for Deep operator networks (DeepONets) that also generalizes to other operator networks. Numerical experiments on linear and nonlinear PDEs demonstrate that the framework’s uncertainty estimates are unbiased and provide accurate out-of-distribution uncertainty predictions with a sufficiently large training dataset. Our framework provides fast inference and uncertainty estimates that can efficiently drive outer-loop analyses that would be prohibitively expensive with conventional solvers. We demonstrate how predictive uncertainties can be used in the context of Bayesian optimization and active learning problems to yield improvements in accuracy and data-efficiency for outer-loop optimization procedures. In the active learning setup, we extend the framework to Fourier Neural Operators (FNO) and describe a generalized method for other operator networks. To enable real-time deployment, we introduce an inference strategy based on precomputed trunk outputs and a sparse placement matrix, reducing evaluation time by more than a factor of five. Our method provides a practical route to uncertainty-aware operator learning in time-sensitive settings.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Remote Sensing Improves Multi‐Hazard Flooding and Extreme Heat Detection by Fivefold Over Current Estimates

The co‐occurrence of multiple hazards is of growing concern globally as the frequency and magnitude of extreme climate events increases. Despite studies examining the spatial distribution of such events, there has been little work in examining if all relevant life threatening and damaging hazards are captured in existing hazard databases and by common hazard metrics. For example, local/regional flash flooding events are seldom captured by optical satellite instruments and are subsequently excluded from global hazard databases. Similarly, the heat hazard definitions most frequently used in multi‐hazard studies inherently fail to capture events that are life‐threatening but climatologically within an expected range. Our goal is to determine the potential for increasing multi‐hazard event detection capabilities by inferring additional hazard footprints from widely accessible satellite data. We use daily precipitation and temperature satellite data to develop an open‐source framework that infers additional hazard footprints that are not included in traditional methods. With the state of Texas as our study area, we detected 2.5 times as many flood hazards, equivalent to $320 million in property and crop damages. Furthermore, our expanded heat hazard definition increases the impacted area by 56.6%, equivalent to 91.5 million km 2 over an 18 year period. Increasing hazard detection capabilities and expanding existing definitions of hazards using daily satellite data increases the temporal and spatial resolutions at which multi‐hazard events are detected. Having more complete data sets of all relevant hazard extents improves our ability to track global trends and more accurately determine the magnitude of hazard exposure inequities.

equity↗

DOE OSTI2025

An update to the Sandia method for creating Typical Meteorological Years from a limited pool of calendar years

Typical Meteorological Years (TMYs) are essential for the efficient evaluation of energy system performance. Ideally, 30 years of weather data are required to generate TMYs, but significantly fewer years are typically available due to practical limitations. To address this issue, an update to the Sandia method was developed, referred to as the Argonne method, to create TMYs from a limited number of years. Furthermore, this method enhances candidate diversity by systematically shifting original candidate months forward or backward by specific days, creating an expanded pool of candidates. The effectiveness of the Argonne method was validated through statistical testing, comparison of monthly average weather parameters, and numerical simulations. The results demonstrate a high probability of identifying at least one shifted month whose cumulative distribution functions of weather parameters closely align with long-term distributions. In 67 % of all comparisons, the monthly average weather parameters in TMYs generated using the Argonne method exhibit better agreement with long-term averages than TMY3. Moreover, in 74 % of the 318 building simulation cases, the Argonne method outperforms TMY3 in estimating long-term average building heating and cooling demands. Therefore, the Argonne method effectively diversifies the candidate pool and produces typical years that provide more accurate estimations of long-term averages compared to TMY3 when only a limited pool of calendar years (10 years or fewer) is available.

Building energy modeling↗

DOE OSTI2023

Forest reorganisation effects on fuel moisture content can exceed changes due to climate warming in wet temperate forests

Abstract The distributions of vegetation and fire activity are changing rapidly in response to climate warming. In many regions, climate effects on dead fuel moisture content (FMC) are expected to increase future wildfire activity. However, forest FMC is largely driven by microclimate conditions, which are moderated from open weather by vegetation canopies. As shifts in vegetation increase under climate warming, the extent to which future fire activity will be driven by climate directly or associated vegetation shifts remains unresolved. Here, we present a study aimed at quantifying the relative magnitudes of (i) direct climate warming, and (ii) vegetation change, on FMC. Field sites to evaluate these effects were established in a natural laboratory of altered forest states to mature wet temperate forest in south‐eastern Australia. FMC was estimated using a process‐based model and 48 years of reconstructed climate data. Canopy effects on microclimate were captured by transferring inputs from climate to microclimate using models parameterised with field observations. To evaluate the relative magnitude of climate and vegetation effects, we calculated the maximum difference in mean annual FMC across annual climate replicates and compared this to FMC differences across reorganising forest sites. Our results show vegetation effects on FMC can exceed those related to expected climate change. Changes to forest structure and composition increased (+15.7%) and decreased (−12.3%) mean annual FMC, with a larger negative effect when forest cover was completely removed (−18.5%). In contrast, the largest climate effect on FMC was −6.6% across 48‐years of data. Our study demonstrates that the magnitude of vegetation effects on FMC can exceed expected climate change effects. Models of future fire activity that do not account for changing vegetation effects on microclimate are omitting a key biophysical control on FMC and therefore may not be accurately predicting future fire activity.

Brown, Tegan P.↗

NASA NTRS2001

Intercomparison of Satellite Derived Gravity Time Series with Inferred Gravity Time Series from TOPEX/POSEIDON Sea Surface Heights and Climatological Model Output

The upcoming GRACE mission promises to open a window on details of the global mass budget that will have remarkable clarity, but it will not directly answer the question of what the state of the Earth's mass budget is over the critical last quarter of the 20th century. To address that problem we must draw upon existing technologies such as SLR, DORIS, and GPS, and climate modeling runs in order to improve our understanding. Analysis of long-period geopotential changes based on SLR and DORIS tracking has shown that addition of post 1996 satellite tracking data has a significant impact on the recovered zonal rates and long-period tides. Interannual effects such as those causing the post 1996 anomalies must be better characterized before refined estimates of the decadal period changes in the geopotential can be derived from the historical database of satellite tracking. A possible cause of this anomaly is variations in ocean mass distribution, perhaps associated with the recent large El Nino/La Nina. In this study, a low-degree spherical harmonic gravity time series derived from satellite tracking is compared with a TOPEX/POSEIDON-derived sea surface height time series. Corrections for atmospheric mass effects, continental hydrology, snowfall accumulation, and ocean steric model predictions will be considered.

Cox, C.↗

NASA NTRS2012

Self-Nulling Lock-in Detection Electronics for Capacitance Probe Electrometer

A multi-channel electrometer voltmeter that employs self-nulling lock-in detection electronics in conjunction with a mechanical resonator with noncontact voltage sensing electrodes has been developed for space-based measurement of an Internal Electrostatic Discharge Monitor (IESDM). The IESDM is new sensor technology targeted for integration into a Space Environmental Monitor (SEM) subsystem used for the characterization and monitoring of deep dielectric charging on spacecraft. Use of an AC-coupled lock-in amplifier with closed-loop sense-signal nulling via generation of an active guard-driving feedback voltage provides the resolution, accuracy, linearity and stability needed for long-term space-based measurement of the IESDM. This implementation relies on adjusting the feedback voltage to drive the sense current received from the resonator s variable-capacitance-probe voltage transducer to approximately zero, as limited by the signal-to-noise performance of the loop electronics. The magnitude of the sense current is proportional to the difference between the input voltage being measured and the feedback voltage, which matches the input voltage when the sense current is zero. High signal-to-noise-ratio (SNR) is achieved by synchronous detection of the sense signal using the correlated reference signal derived from the oscillator circuit that drives the mechanical resonator. The magnitude of the feedback voltage, while the loop is in a settled state with essentially zero sense current, is an accurate estimate of the input voltage being measured. This technique has many beneficial attributes including immunity to drift, high linearity, high SNR from synchronous detection of a single-frequency carrier selected to avoid potentially noisy 1/f low-frequency spectrum of the signal-chain electronics, and high accuracy provided through the benefits of a driven shield encasing the capacitance- probe transducer and guarded input triaxial lead-in. Measurements obtained from a 2- channel prototype electrometer have demonstrated good accuracy (|error| < 0.2 V) and high stability. Twenty-four-hour tests have been performed with virtually no drift. Additionally, 5,500 repeated one-second measurements of 100 V input were shown to be approximately normally distributed with a standard deviation of 140 mV.

Blaes, Brent R.↗