Architects and planners have been at the forefront of envisioning a future built environment for millennia. However, fragmental views that emphasize one facet of the built environment, such as energy, environment, or groundbreaking technologies, often do not achieve expected outcomes. Buildings are responsible for approximately one-third of worldwide carbon emissions and account for about 40% of primary energy consumption in the U.S. In addition to achieving the very ambitious goal of reducing building-associated greenhouse gas emissions by 75% by 2050, buildings must improve their functionality and performance to meet current and future human, societal, and environmental needs in a changing world. In this article, we introduce a new framework to guide potential evolution of the building stock in the next century, based on greenhouse gas emissions as the common thread to investigate the potential implications of new design paradigms, innovative operational strategies, and disruptive technologies. This framework emphasizes integration of multidisciplinary knowledge, scalability for mainstream buildings, and proactive approaches considering constraints and unknowns. The framework integrates the interrelated aspects of the built environment through a series of quantitative metrics that aim to improve environmental outcomes while optimizing building performance to achieve healthy, adaptive, and productive buildings.

1 aWang, Na1 aPhelan, Patrick, E.1 aGonzalez, Jorge1 aHarris, Chioke, B.1 aHenze, Gregor, P.1 aHutchinson, Robert1 aLangevin, Jared1 aLazarus, Mary, Ann1 aNelson, Brent1 aPyke, Christopher1 aRoth, Kurt1 aRouse, David1 aSawyer, Karma1 aSelkowitz, Stephen, E. uhttps://linkinghub.elsevier.com/retrieve/pii/S036013231730157900520nas a2200133 4500008004100000245010800041210006900149300001200218490000800230100002200238700002600260700002400286856007600310 2006 eng d00aBrownian Motion Based Convective- Conductive Model for the Effective Thermal Conductivity of Nanofluids0 aBrownian Motion Based Convective Conductive Model for the Effect a588-5950 v1281 aPrasher, Ravi, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://buildings.lbl.gov/publications/brownian-motion-based-convective02024nas a2200265 4500008004100000245010800041210006900149260001200218300001400230490000700244520118600251653003801437653002501475653002401500100002601524700001301550700001601563700001301579700001601592700002401608700002201632700002001654700001301674856007101687 2006 eng d00aCharacterization of the Temperature Oscillation Technique to Measure the Thermal Conductivity of Fluids0 aCharacterization of the Temperature Oscillation Technique to Mea c08/2006 a2950-29560 v493 aThe temperature oscillation technique to measure the thermal diffusivity of a fluid consists of filling a cylindrical volume with the fluid, applying an oscillating temperature boundary condition at the two ends of the cylinder, measuring the amplitude and phase of the temperature oscillation at any point inside the cylinder, and finally calculating the fluid thermal diffusivity from the amplitude and phase values of the temperature oscillations at the ends and at the point inside the cylinder. Although this experimental technique was introduced by Santucci and co-workers nearly two decades ago, its application is still limited, perhaps because of the perceived difficulties in obtaining accurate results. Here, we attempt to clarify this approach by first estimating the maximum size of the liquid’s cylindrical volume, performing a systematic series of experiments to find the allowable amplitude and frequency of the imposed temperature oscillations, and then validating our experimental setup and the characterization method by measuring the thermal conductivity of pure water at different temperatures and comparing our results with previously published work.

10aTemperature oscillation technique10aThermal conductivity10athermal diffusivity1 aBhattacharya, Prajesh1 aNara, S.1 aVijayan, P.1 aTang, T.1 aLai, W., Y.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, J. uhttp://www.sciencedirect.com/science/article/pii/S001793100600144X00524nas a2200133 4500008004100000245010900041210006900150300001400219490000600233100002200239700002600261700002400287856007900311 2006 eng d00aEffect of Aggregation Kinetics on the Thermal Conductivity of Nanoscale Colloidal Solutions (Nanofluids)0 aEffect of Aggregation Kinetics on the Thermal Conductivity of Na a1529-15340 v61 aPrasher, Ravi, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://buildings.lbl.gov/publications/effect-aggregation-kinetics-thermal00481nas a2200121 4500008004100000245007500041210006900116260002500185100002200210700002600232700002400258856007700282 2006 eng d00aEffect of Coloidal Chemistry on the Thermal Conductivity of Nanofluids0 aEffect of Coloidal Chemistry on the Thermal Conductivity of Nano aChicago, ILc11/20061 aPrasher, Ravi, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://buildings.lbl.gov/publications/effect-coloidal-chemistry-thermal01256nas a2200169 4500008004100000245004200041210004200083260001200125300001200137490000600149520075900155100002200914700002600936700002400962700002200986856007801008 2006 eng d00aEnhanced Mass Transport in Nanofluids0 aEnhanced Mass Transport in Nanofluids c03/2006 a419-4230 v63 aThermal conductivity enhancement in nanofluids, which are liquids containing suspended nanoparticles, has been attributed to localized convection arising from the nanoparticles' Brownian motion. Because convection and mass transfer are similar processes, the objective here is to visualize dye diffusion in nanofluids. It is observed that dye diffuses faster in nanofluids compared to that in water, with a peak enhancement at a nanoparticle volume fraction, *φ*, of 0.5%. A possible change in the slope of thermal conductivity enhancement at that same *φ* signifies that convection becomes less important at higher *φ*. The enhanced mass transfer in nanofluids can be utilized to improve diffusion in microfluidic devices.

The heat transfer abilities of fluids can be improved by adding small particles of sizes of the order of nanometers. Recently a lot of research has been done in evaluating the thermal conductivity of nanofluids using various nanoparticles. In our present work we address this issue by conducting a series of experiments to determine the effective thermal conductivity of alumina-nanofluids by varying the base fluid with water and antifreeze liquids like ethylene glycol and propylene glycol. Temperature oscillation method is used to find the thermal conductivity of the nanofluid. The results show the thermal conductivity enhancement of nanofluids depends on viscosity of the base fluid. Finally the results are validated with a recently proposed theoretical model.

1 aNara, S.1 aBhattacharya, Prajesh1 aVijayan, P.1 aLai, W., Y.1 aRosenthal, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, Jinlin uhttps://buildings.lbl.gov/publications/experimental-determination-effect00456nas a2200121 4500008004100000245007100041210006900112490000700181100002200188700002600210700002400236856007400260 2005 eng d00aThermal Conductivity of Nanoscale Colloidal Solutions (Nanofluids)0 aThermal Conductivity of Nanoscale Colloidal Solutions Nanofluids0 v941 aPrasher, Ravi, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://buildings.lbl.gov/publications/thermal-conductivity-nanoscale01564nas a2200217 4500008004100000245009500041210006900136260001200205300001600217490000700233520084100240653001901081653002101100653004601121100002601167700001501193700002001208700002401228700002201252856007201274 2004 eng d00aBrownian Dynamics Simulation to Determine the Effective Thermal Conductivity of Nanofluids0 aBrownian Dynamics Simulation to Determine the Effective Thermal c06/2004 a6492–64940 v953 aA nanofluid is a fluid containing suspended solid particles, with sizes on the order of nanometers. Normally, nanofluids have higher thermal conductivities than their base fluids. Therefore, it is of interest to predict the effective thermal conductivity of such a nanofluid under different conditions, especially since only limited experimental data are available. We have developed a technique to compute the effective thermal conductivity of a nanofluid using Brownian dynamics simulation, which has the advantage of being computationally less expensive than molecular dynamics, and have coupled that with the equilibrium Green-Kubo method. By comparing the results of our calculation with the available experimental data, we show that our technique predicts the thermal conductivity of nanofluids to a good level of accuracy.

10acomplex fluids10aDisperse systems10aThermal conduction in nonmetallic liquids1 aBhattacharya, Prajesh1 aSaha, S.K.1 aYadav, Ajay, K.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://buildings.lbl.gov/publications/brownian-dynamics-simulation00495nas a2200121 4500008004100000245009000041210006900131260002600200100002600226700002400252700002200276856007500298 2004 eng d00aDetermining the Effective Viscosity of a Nanofluid Using Brownian Dynamics Simulation0 aDetermining the Effective Viscosity of a Nanofluid Using Brownia aHonolulu, HIc03/20041 aBhattacharya, Prajesh1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://buildings.lbl.gov/publications/determining-effective-viscosity00736nas a2200181 4500008004100000245019400041210006900235260002500304100002600329700001600355700001300371700001300384700002400397700002200421700001300443700002000456856007800476 2004 eng d00aEvaluation of the Temperature Oscillation Technique to Calculate Thermal Conductivity of Water and Systematic Measurement of the Thermal Conductivity of Aluminum Oxide – Water Nanofluiids0 aEvaluation of the Temperature Oscillation Technique to Calculate aAnaheim, CAc11/20041 aBhattacharya, Prajesh1 aVijayan, P.1 aTang, T.1 aNara, S.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aWang, J.1 aSong, David, W. uhttps://buildings.lbl.gov/publications/evaluation-temperature-oscillation00452nas a2200133 4500008004100000245005300041210005200094260001200146100001600158700002400174700002000198700002600218856007400244 2004 eng d00aNumerical Tools For Particle- Fluid Interactions0 aNumerical Tools For Particle Fluid Interactions c02/20041 aCalhoun, R.1 aPhelan, Patrick, E.1 aYadav, Ajay, K.1 aBhattacharya, Prajesh uhttps://buildings.lbl.gov/publications/numerical-tools-particle-fluid00564nas a2200145 4500008004100000245010100041210006900142260002700211100002600238700001500264700002000279700002400299700002200323856007300345 2003 eng d00aDetermining the Effective Thermal Conductivity of a Nanofluid Using Brownian Dynamics Simulation0 aDetermining the Effective Thermal Conductivity of a Nanofluid Us aLas Vegas, NVc07/20031 aBhattacharya, Prajesh1 aSaha, S.K.1 aYadav, Ajay, K.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://buildings.lbl.gov/publications/determining-effective-thermal00463nas a2200109 4500008004100000245009400041210006900135260002800204100002600232700002400258856007100282 2002 eng d00aModeling the Behavior of F1-ATPase Biomolecular Motors Using Brownian Dynamics Simulation0 aModeling the Behavior of F1ATPase Biomolecular Motors Using Brow aScottsdale, AZc09/20021 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://buildings.lbl.gov/publications/modeling-behavior-f1-atpase00473nas a2200109 4500008004100000245010100041210006900142260002600211100002600237700002400263856007600287 2002 eng d00aUnderstanding the Behavior of an F1-ATPase Biomolecular Motor Using Brownian Dynamics Simulation0 aUnderstanding the Behavior of an F1ATPase Biomolecular Motor Usi aBerkeley, CAc06/20021 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://buildings.lbl.gov/publications/understanding-behavior-f1-atpase00439nas a2200133 4500008004100000245004600041210004600087300001200133490000700145100002400152700002200176700002600198856008100224 1995 eng d00aNanofluids for Heat Transfer Applications0 aNanofluids for Heat Transfer Applications a255-2750 v141 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aBhattacharya, Prajesh uhttps://buildings.lbl.gov/publications/nanofluids-heat-transfer-applications